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Primer on artificial intelligence and robotics

This article provides an introduction to artificial intelligence, robotics, and research streams that examine the economic and organizational consequences of these and related technologies. We describe the nascent research on artificial intelligence and robotics in the economics and management literature and summarize the dominant approaches taken by scholars in this area.

This article is a primer on artificial intelligence, robotics, and automation. To begin, we provide definitions of the constructs and describe the key questions that have been addressed so far. We also describe ways in which organizational scholars have been using artificial intelligence tools as part of their research methodology.

Studies of artificial intelligence and robotics base their theory and analysis on constructs of automation, robotics, artificial intelligence and machine learning, and automation. It is important that organizational scholars carefully define any such constructs in their studies and to avoid confusing these related but distinct constructs.

Automation is not a new concept, as innovations such as the steam engine or the cotton gin can be viewed as automating previously manual tasks.
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While artificial intelligence, robotics, and automation are all related concepts, it is important to be aware of the distinctions between each of these constructs. Robotics is largely focused on technologies that could be classified as “manipulators” as per the IFR definition, and accordingly, more directly relates to the automation of physical tasks. On the other hand, artificial intelligence does not require physical manipulation, but rather computer-based learning. The distinction between the two technologies can become fuzzier as applications of artificial intelligence may involve robotics or vice versa.

In many cases, a computer or robot may be able to complete relatively low-value tasks, freeing up the human to focus efforts instead on high-value tasks.

Similarly, artificial intelligence and robotics technology have the capacity to reshape firms and change the structure of organizations dramatically. As discussed above, the adoption of artificial intelligence and robotics technologies will likely alter the bundle of skills and tasks that many occupations are comprised of. By that aspect alone, these technologies will reshape organizations and force firms to restructure themselves to account for these changes. In addition, the composition of the labor force may change to adopt to the new set of skills that are most valued.
Artificial Intelligence artificial intelligence robot

There are a variety of other questions surrounding artificial intelligence and robotics that we encourage organizational scholars to turn to. One topic that has yet to be explored in much detail surrounds the establishment and firm-level consequences for adoption of artificial intelligence and robotics technology. Research could examine performance consequences as well as outcomes related to firm organization and strategy. Scholars can study in what circumstances and in what kinds of firms such adoption has the greatest impact. The adoption of the technology itself can be viewed as an outcome, and scholars can examine what circumstances and factors encourage or discourage the use of these technologies. Certain industries, management styles, or organizational forms may be particularly quick to adopt, and market level forces may also impact the adoption decision. Industry and organizational factors may play a role as well as the backgrounds of individuals and managers within organizations.

There will be a need to evaluate what skills and tasks are still valuable in the labor market compared to skills and tasks that can now be fully automated.

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The Impact of Artificial intelligence and Robotics on the Future Employment Opportunities

The widespread human-robot interaction is increasing progressively as robots have made the life of everyone easy-going and comfortable. In this work, we have analysed the behaviour and characteristics of various types of robots. We have also studied the outgrowing relation between robotics and humans. In our analysis, we also have a selection of aspects of this field, which are done by the numerous technologists as well as scientists. We are interested in exploring the functioning of the human brain by generating a functioning system that resolves problems and gives satisfactory results. Artificial intelligence is a vast field that is also pushing its way in the domain of healthcare, business and quality assurance. Various researches disclose that the corporate sector is joining artificial intelligence to estimate the supply-demand concept and automate human resource systems. The public sector is also developing different intelligent machines for security surveillance and malfunction detection of critical systems like nuclear reactors. Artificial intelligence and robotics are also phenomenal to implement the law and order enforcement without any danger. As artificial intelligence is growing, employment in this domain is also increasing due to the high demand of intelligent machines in each sector worldwide.

We have done a systematic analysis of various kinds of robots by utilizing the comparison parameters to demonstrate the fundamental objective of the development of the robots. The main objective of our research is to expose the consequences of the robotics on human employment opportunities in all the areas.

• While making crucial decisions, intelligent systems can be governed by unprejudiced standards so that decisions can be made practically, based on facts and data. Productivity expansions have so far always led to an upgrading of living circumstances for everybody.

• The significant advantage for employees is that the burden of labor-intensive may reduce for them; tedious, dull work can be done via self-ruling frameworks.

Robots cannot perform a task unless we direct them to do so.

Self-ruling robots are confronting an assortment of open situations, and differing qualities of assignments are incapable of depending on the primary leadership abilities of a human designer. There is a need for showing the complexity of thinking capacities required to comprehend their surroundings and present surroundings and to perform deliberately. In the paper, we have alluded to such thinking abilities as pondering capacities, firmly consistent inside a mind-boggling design. We have introduced an outline of the best in class for some of them. Be that as it may, let us demand once more: the fringe between them is not fresh.
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Artificial intelligence, robotics and eye surgery: are we overfitted?

Historically, the first in-human–robot-assisted retinal surgery occurred nearly 30 years after the first experimental papers on the subject. Similarly, artificial intelligence emerged decades ago and it is only now being more fully realized in ophthalmology. The delay between conception and application has in part been due to the necessary technological advances required to implement new processing strategies. Chief among these has been the better matched processing power of specialty graphics processing units for machine learning. Transcending the classic concept of robots performing repetitive tasks, artificial intelligence and machine learning are related concepts that has proven their abilities to design concepts and solve problems. The implication of such abilities being that future machines may further intrude on the domain of heretofore “human-reserved” tasks. Although the potential of artificial intelligence/machine learning is profound, present marketing promises and hype exceeds its stage of development, analogous to the seventieth century mathematical “boom” with algebra. Nevertheless robotic systems augmented by machine learning may eventually improve robot-assisted retinal surgery and could potentially transform the discipline.

In conclusion, neither artificial intelligence nor robotics is a novel concept, until artificial intelligence is strategically incorporated into robotic systems. Many obstacles exist to human end user adoption of robotics including but not limited to cost, size, functional limits, accuracy, human acceptance and importantly, clearly superior outcomes and safety. In retinal procedures, robotic platforms show a promising role and first human studies are encouraging. That artificial intelligence might enhance these systems is logical, the form that such augmentation takes is only now emerging. What the ultimate form will be is anyone’s guess, as is the eventual role of humans in microsurgery.

The History of Artificial Intelligence

Breaching the initial fog of AI revealed a mountain of obstacles. The biggest was the lack of computational power to do anything substantial: computers simply couldn’t store enough information or process it fast enough. In order to communicate, for example, one needs to know the meanings of many words and understand them in many combinations. Hans Moravec, a doctoral student of McCarthy at the time, stated that “computers were still millions of times too weak to exhibit intelligence.

Ironically, in the absence of government funding and public hype, AI thrived. During the 1990s and 2000s, many of the landmark goals of artificial intelligence had been achieved. In 1997, reigning world chess champion and grand master Gary Kasparov was defeated by IBM’s Deep Blue, a chess playing computer program. In the same year, speech recognition software, developed by Dragon Systems, was implemented on Windows. This was another great step forward but in the direction of the spoken language interpretation endeavor. It seemed that there wasn’t a problem machines couldn’t handle.
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The application of artificial intelligence in this regard has already been quite fruitful in several industries such as technology, banking, marketing, and entertainment. We’ve seen that even if algorithms don’t improve much, big data and massive computing simply allow artificial intelligence to learn through brute force. There may be evidence that Moore’s law is slowing down a tad, but the increase in data certainly hasn’t lost any momentum.

Trust Toward Robots and Artificial Intelligence: An Experimental Approach to Human–Technology Interactions Online

This article reports the results based on a trust game experiment involving robots and AI.

Visual anonymity in the experimental context might also have an impact on behavior. In this type of experiment, players might consider the situation such that they would not ever meet the opponent again.

Our study is based on a minimal condition, giving few cues about the nature of robots and AI. Such minimal conditions are important, especially when analyzing trust and behavior online, where various cues are left out. However, this decision is also a limitation of the study, as we cannot be sure that all participants interpreted the control group opponents as humans.

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Future studies could, however, describe one of the experimental groups explicitly as humans. It would also be good to use female names, as the male name used in our study was considered less trustworthy than the nickname. It might also be possible to conduct an experiment with various types of robots and AI avatars using trust game settings. In addition, more studies on individual factors, such as personality, would be needed, as our results showed that they impacted trust.

AO contributed to the conceptualization, data collection, investigation, methodology, formal analysis, writing original draft, supervision, and funding acquisition. NS contributed to the conceptualization, data collection, investigation, methodology, reviewing and editing the manuscript, and visualization. RL contributed to the conceptualization, investigation, reviewing and editing the manuscript. AK contributed to the methodology, investigation, and review and editing the manuscript.

Artificial Intelligence: Does Consciousness Matter?

Humanoid Robot Head Design Based on Uncanny Valley and FACS

Although the second peak is higher, there is a far greater risk of falling into the uncanny valley. We predict that it is possible to produce a safe familiarity by a non-human-like design.

As shown in Figures 2 and 3, we devise the control architecture of robot head, use computer vision and sensors to get information from human, and send it to three databases.

Robots obtain and analyze information, make comprehensive decisions, and finally realize their inner emotional states through the head expression, such as showing a smiling, upset, happy, or scared expression.

For example, when people expressed surprise, the eyebrows lift and bend higher and eyebrow skin will be stretched. When eyes are wide open, upper eyelid will be pushed up, but when the chin of the face falls, mouth will open. If the head of the emotional robot design is consistent with this, you will get a surprised face. We design the whole framework of robot head, which gives us a blueprint of robot head, so we can make it step by step.

Mount head shell: although we have made the “bones” of the robot head, we still cannot install facial skin directly.

Install facial skin: we often feel bad about the head of facial damaged skin; most of the time ordinary people feel disgusted about the bare skull. I would not be surprised that you are afraid of the robot head in Figure 4.

As shown in Figure 5, first factor in red line is similarity from 1 to 10, and 10 is the most similar.

In order to assess the effect of artificial expression, we invited 100 students and 50 teachers to appraise our robot head and score from 0 to 10.

Students and teachers who took the test did not feel horrible, and the most interesting part is eye.

The 2014 Survey: Impacts of AI and robotics by 2025

Among the key themes emerging from 1,896 respondents’ answers were: – Advances in technology may displace certain types of work, but historically they have been a net creator of jobs. – We will adapt to these changes by inventing entirely new types of work, and by taking advantage of uniquely human capabilities. – Technology will free us from day-to-day drudgery, and allow us to define our relationship with “work” in a more positive and socially beneficial way. – Ultimately, we as a society control our own destiny through the choices we make. – Automation has thus far impacted mostly blue-collar employment; the coming wave of innovation threatens to upend white-collar work as well.

These two groups also share certain hopes and concerns about the impact of technology on employment.

AI and robotics in the European restaurant sector: Assessing potentials for process innovation in a high-contact service industry

The paper is structured as follows: The foundation part provides related research and describes the potential of AI and robotics in service processes. It also contains an overview of relevant research in the restaurant sector. After describing the methodology in section 3, the fourth section describes key results of the empirical analysis. Finally, a reference process is proposed to guide future decision processes of service managers.

The following section provides an overview of the methodology and presents the data collection and data coding process. Current AI and robotics service solutions in the European restaurant market are analyzed that take advantage of AI and robotics technologies.

The political choreography of the Sophia robot: beyond robot rights and citizenship to political performances for the social robotics market

This playful dialogue took place between David Hanson and his designed robot at a robotics trade show in Austin, Texas in March 2016. David Hanson, founder of Hanson Robotics, launched the Sophia robot by ‘chatting’ with it. A video released by CNBC about Sophia quickly garnered millions of views. The world’s leading newspapers including The New York Times, The Guardian, The China Daily, The Times of India and The Sydney Morning Herald published stories about Sophia.

What do we mean by ‘political choreography’? The conceptualisation of choreography provides methodological tools to analyse more systemically underlying political and economic interests behind the Sophia project. We do not use the notion of choreography with reference to dancing.

According to Goertzel, the worldwide media attention the Sophia robot garnered starting in 2017 was not a planned publicity stunt by the company.

The traditional media have played a pivotal role in giving publicity to Hanson Robotics to advance its technological utopia about the future of humanoid robots. The robot evolved into an iconic figure in a fairly short time promoting the idea of the robot as an almost living being. Hanson Robotics has closely monitored Sophia’s public image by preventing the media and journalists from asking Sophia questions that are too difficult or politically sensitive.

While we should recognize the joint human/nonhuman agency in these performances, we should also ask who choreographs us.

It looks like that the embodiment as the special ability offered by social robots is also a stumbling block for designers and social robot business. Desktop assistants, such as Amazon Echo and Google Home, have provided many features at a much lower cost than social robots. Due to the high price, most social robots are mainly marketed for use by companies and public organizations under the headings of care robots or educational robotics. However, it is highly questionable how beneficial the technologies have actually been in these contexts.

The FII investment forum in Riyadh, Saudi Arabia in 2017 was preceded by the media spectacle in which the Sophia robot was granted Saudi citizenship. What that citizenship meant in practice was not specified in detail by the Saudi authorities or Hanson Robotics. However, the granting of citizenship can be seen as a kind of the culmination point in the political choreography of the Sophia robot.

It should be noted that the FII event was launched and hosted by the Crown Prince of Saudi Arabia, Mohammad bin Salman. His policy was widely condemned in the West after the assassination of journalist Jamal Khashogg by a 15-member squad of Saudi assassins. The extensive arrangements for the 2018 FII Economic Forum were largely cancelled when many invited speakers, companies and media houses refused bin Salman’s invitation to come to Riyadh. Although the FII 2018 Economic Forum eventually failed for Saudi Arabia, the 2017 Forum appears to be a successful media performance from the perspective of both Hanson Robotics and bin Salman.

AI and Law What should a robot be allowed to do?

On the other hand, there is the question of who should benefit when AI produces intellectual property. The works they created, however, were mostly based on random algorithms that cannot be compared in any way with human intelligence. In the past ten years, however, AI seems to have “reached a new level of development”, as the BMWi acknowledged in its paper. Today robots write entire film scripts and compose pieces of music. It can hardly be compared with the randomised doodles from back then. So can a robot become a creator – an originator? Lawyers like to refer to a precedent from the animal world. Slater gave his camera to a macaque called Naruto, who snapped a “monkey selfie” that went viral three years later and spread around the world. The animal rights organisation, Peta, tried to sue, on behalf of Naruto, for the proceeds from the photo. This was followed by a lawsuit lasting several years, which was fought in the United States. In 2017, Slater agreed to an out-of-court settlement and pledged to donate a quarter of the future proceeds from the Naturo selfie to Peta. The San Francisco Court of Appeal, however, did not accept the settlement. The lawsuit was dismissed on the grounds that Naturo itself had no say in the settlement and the aim all along had been to set a precedent. In addition, Peta had to pay the photographer’s legal fees. He later sued the German punk band, Terrorgruppe, for using the photo on a record cover without his authorisation. The US Copyright Office stated that copyrights can only be granted to humans and therefore not to animals – or robots. Currently, courts and governments do not absolve people of their responsibility for the AI they have developed, even if their inventions become inventors themselves. The rights and obligations remain with the users of the AI or with those who operate it. The British Copyright Designs and Patent Act came to this decision back in 1988 when the first home computers raised questions similar to those posed by the “learning robot” today. The EU Commission also seems to be sympathetic to this idea.

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Opinion: Robots and AI could soon have feelings, hopes and rights … we must prepare for the reckoning

For some, the question of whether or not the human race will live to see a 22nd century turns upon this latter consideration. While forecasting the imminence of an AI-centric future remains a matter of intense debate, we will need to come to terms with it.

It is clear, however, that the European Parliament is making inroads towards taking an AI-centric future seriously. Included in this draft proposal is preliminary guidance on what it calls “electronic personhood” that would ensure corresponding rights and obligations for the most sophisticated AI.

Yes, driverless cars are problematic, but only in a world where traditional cars exist.

It goes without saying that the very notion of making separate, transferable, editable copies of human beings embodied in robotic form poses both conceptual and practical legal challenges.

Artificial Intelligence

Imagine a robot that has wheels and can pick up objects and put them down. It has sensing capabilities so that it can recognize the objects that it must manipulate and can avoid obstacles. It can be given orders in natural language and obey them, making reasonable choices about what to do when its goals conflict. Such a robot could be used in an office environment to deliver packages, mail, and/or coffee, or it could be embedded in a wheelchair to help disabled people.

The robot can only push doors, and the directions of the doors in the diagram reflect the directions in which the robot can travel. Rooms require keys, and those keys can be obtained from various sources. The robot must deliver parcels, beverages, and dishes from room to room.

Artificial Intelligence

Its observation at time t only depends on the state at time t. The robot’s location at time t+1 depends on its location at time t and its action at time t.

A robot is a container for AI,

There is one class of “robots” that does not move, and does not even have physical presence; bot programs, like chatbots, that operate inside systems. I do not consider them robots, because they are not physical devices operating in the real world.

So, technically you can create a robot that doesn’t require any kind of complex algorithms to take decisions. Some other examples of robots are, a robotic arm, automated control systems in industries, etc.

Some examples of AI are speech recognition, face recognition, natural language processing, etc.

Predicted Influences of Artificial Intelligence on the Domains of Nursing: Scoping Review

In addition to the electronic database searches, a targeted website search was performed to access relevant gray literature. Abstracts and full-text studies were independently screened by 2 reviewers using prespecified inclusion and exclusion criteria. Included articles focused on nursing and digital health technologies that incorporate AI.

Furthermore, to ensure that the nursing-AI relationship promotes person-centered compassionate care, it will be important to understand how nurses may contribute to the co-design of AIHTs.

As mentioned previously, there is a dearth of literature on the topic of compassionate care, nursing, and AI.

Strong and proactive nursing leadership in all roles, sectors, and domains will be required to effectively implement these technologies in ways that preserve person-centered compassionate care.

The paucity of literature has shown that the study of AI and compassionate care is still in its infancy, and more research on this topic is required.

AI has already begun to shape nursing roles, workflows, and the nurse-patient relationship.

The Computer Revolution/Artificial Intelligence/Robotics

Robotics is the science or technology of designing, building and using robots. Robotics is the use of computer-controlled robots to perform manual tasks. Robots are commonly used by the military and businesses to complete tasks that are dangerous for people, such as defusing bombs, exploring shipwrecks, and mines. They are also used to perform monotonous jobs, such as on an assembly line. There are personal or service robots to assist with personal tasks. Robotics research is continuing to make smarter and more capable robots. NASA researchers have developed a way to make a crew of robots work together to grasp, lift, and more heavy loads across rough, varied terrain. The software allows the robots to “share a brain” so that each robot knows what the rest are doing.

Recently, in a partnership with a company called Boston Dynamics, they have created a four legged robot that is able to sprint up to speeds of 28. This robot is also able to regain it’s balance if it is facing a dynamic terrain or is pushed from the side.

Our service men and women often carry heavy combat loads which increases the potential for injuries. Lockheed Martin has come up with this new technology called HULC exoskeleton back in 2010. This design was much heavier and the battery power would die down after about an hour. When carrying such heavy loads this weight is transferred to the ground through powered titanium legs without loss of mobility. The HULC is a completely un-tethered, hydraulic-powered anthropomorphic exoskeleton. This can provide users with the ability to carry loads of up to 200 pounds for longer periods of time and over all types of terrain. This newer design can go up to 8 hours and lasts for days on a single charge if you are just standing guard. It is flexible enough for deep squats, crawls and upper-body lifting. There is a micro-computer attached within the suit that moves with the individual. Lockheed Martin is also exploring exoskeleton designs for industrial use and a wider variety of military mission specific applications. The HULC is now being revamped to be smaller, lighter, and more energy-efficient, including an unloaded machine gun on a pivoting mechanical arm. HULC adds an artificial external spine, hips, and legs to a soldier’s flesh and bones. They are also working on a fuel cell type which would last about 72 hours in the harshest conditions.

Google’s new project is working on cars that use artificial intelligence to drive themselves without the need of any human intervention. These vehicles can sense anything near the vehicle, they mimic the decision a driver makes, and they are programmed with road maps and speed limit information. Not only can the AI do everything that a human can, it can do it better. Robot drivers react faster than humans, have 360-degree perception, and do not get distracted, sleepy or intoxicated. These new vehicles could make driving safer and also be better for the environment. They can optimize the amount of fuel used and if accidents are no longer a concern, they could be built lighter thus requiring even less fuel. If this sounds too farfetched, consider the fact that Google has already drove the AI vehicles on the road and through city traffic.

Robots can be used to search for hazardous materials or gas leaks, all of which could potentially be dangerous for humans. Industries that use robots for these dangerous tasks include the coal mining industry. These robots can be used to mine coal or even to search for people in collapsed mines. They can also be used in assembly lines to speed up production and ensure that the product is consistently up to standards. Another area where robots are frequently used is in the medical field. A doctor can control the robot to make his rounds, called “virtual rounds”, without ever leaving his office. In these robot-assisted surgeries, surgeons control the actions of the robot, allowing for more accurate results, and a steadier hand. They can be used to help navigate through caves, trails, buildings and other places to see if they are safe enough for the soldiers to enter into. They are also useful in locating and disposing of bombs, landmines, and other kinds of explosive devices.Right now the military robots are controlled by the soldiers but researchers are currently working on autonomous robots that will be able to navigate by themselves.

Although there are many benefits in creating robots to benefit society, some people may have concerns with the idea of having robots around. Society has many concerns with the implications robots may cause, some may think that robots may come to close to realistic that may potentially harm humanity. Other concerns that may arise with the issue of having robots take over human jobs. Another disadvantage of having robots is that they may be expensive to build and maintain. Robots also have limited duties, so they are only able to perform specific task and are not able to think for themselves.

Artificial Intelligence: The New Frontier in Surgery

Artificial intelligence now well established in several industries has now begun to make a change with significant improvements in the practice of medicine.

A transition from traditional laparoscopic surgery to robotic surgery has already taken place.

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Advances in computing capability, machine engineering and robotics and the ever improving development of smart algorithms is allowing growth of the application of AI at a rapid pace.

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The political choreography of the Sophia robot: beyond robot rights and citizenship to political performances for the social robotics market

This playful dialogue took place between David Hanson and his designed robot at a robotics trade show in Austin, Texas in March 2016. David Hanson, founder of Hanson Robotics, launched the Sophia Artificial Intelligence Female robot 2021 by ‘chatting’ with it. A video released by CNBC about Sophia quickly garnered millions of views. The world’s leading newspapers including The New York Times, The Guardian, The China Daily, The Times of India and The Sydney Morning Herald published stories about Sophia.

5 Wonderful Humanoid Robots With Emotions & Artificial Intelligence

According to Goertzel, the worldwide media attention the Sophia robot garnered starting in 2017 was not a planned publicity stunt by the company.
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The traditional media have played a pivotal role in giving publicity to Hanson Robotics to advance its technological utopia about the future of humanoid robots. The robot evolved into an iconic figure in a fairly short time promoting the idea of the robot as an almost living being.

Hanson Robotics has closely monitored Sophia’s public image by preventing the media and journalists from asking Sophia questions that are too difficult or politically sensitive.

While we should recognize the joint human/nonhuman agency in these performances, we should also ask who choreographs us.
Artificial Intelligence artificial intelligence female robot 2021

It looks like that the embodiment as the special ability offered by social robots is also a stumbling block for designers and social robot business. Desktop assistants, such as Amazon Echo and Google Home, have provided many features at a much lower cost than social robots. Due to the high price, most social robots are mainly marketed for use by companies and public organizations under the headings of care robots or educational robotics. However, it is highly questionable how beneficial the technologies have actually been in these contexts.

The FII investment forum in Riyadh, Saudi Arabia in 2017 was preceded by the media spectacle in which the Sophia robot was granted Saudi citizenship. What that citizenship meant in practice was not specified in detail by the Saudi authorities or Hanson Robotics. However, the granting of citizenship can be seen as a kind of the culmination point in the political choreography of the Sophia robot.

Accordingly, in this paper, we argue for an improved assessment of the perceived threats of AI and propose a survey scale to measure these threat perceptions. First, a broadly usable measurement would need to address perceived threats of AI as a precondition to any actual fear experienced. This conceptual difference is subsequently based on the literature on fear and fear appeals. Second, the perceived threat of AI would need to take into account the context-dependency of respective fears as most real-world applications of AI are highly domain-specific.

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The collected data supports the factorial structure of the proposed TAI scale. Still, such distinct perceptions do also differ between the domains tested. Contrarily, autonomous decision-making in which an AI unilaterally decides on the proscribed treatment was met with relatively bigger apprehension.

While item 3 broadly queries the fear of AI in general, item 2 specifically inquiries about its specific impacts on the economic sector. Items 1 and 4 query a specific functionality of AI, with item 4 focusing on the human-machine connection. Thus, the items are mixed in their expressiveness and aim at different aspects of AI’s impact.

As a consequence, we decided to focus our measurement solely on AI as it depicts the core issue of the nascent technology, i.

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Comparison Between the Facial Flow Lines of Androids and Humans

Although the above comparative analyses seem a promising approach for evaluating androids, they only focused on a limited number of typical emotional expressions. These typical expressions are only a part of the rich and various patterns of facial motions that are a complex combination of several independent motions.

Artificial Intelligence artificial intelligence female robot 2021

Figure 1 shows the marker locations on the neutral faces of the female android, Affetto, and one of the adult males. In total, 120 and 116 markers were attached to the faces of the female android and Affetto, respectively, at intervals of approximately 10 mm. We attached 125, 103, and 117 markers to each of the three adult males.

The nine DUs of the female android and 16 DUs of Affetto were measured one by one. First, the positional command was set to 0 for one of the actuators so that the initial marker positions could be measured. It was then changed to 255 so that the final marker positions could be measured.

The facial movements for the 44 AUs of the human participants were also measured one by one. First, the participants practiced one of the AUs by watching their facial movements in a mirror. Next, they presented their neutral faces so that the initial marker positions could be measured. They then showed the AU so that the final marker positions could be measured.

A DU is an exact unit of artificial facial motion produced by a single actuator of the android. On the other hand, an AU is a superficial unit of human facial action subdivided and extracted from complex facial expressions. This means that an AU can be replicated more precisely by any DU combinations than a single DU.

A coordinate system was defined based on the initial positions of several reference markers. The top of the chin was set as the origin, and the y-axis was defined as the direction from the origin to the nose root. The direction perpendicular to the y-axis from the nose top was defined as the direction of the z-axis.

In this section, we introduce the representative distributions of the displacement vectors in the eye, forehead, and mouth areas. Nine types of DUs and AUs were chosen to compare the flow lines and surface undulations of the androids and adult males.

Figure 3 compares the distributions of the displacement vectors for three types of facial motions around the eyes on the x–y plane. The subfigures of the left, middle, and right columns correspond to the female android, Affetto, and the average of the three adult males, respectively. Each row shows one of the DUs and the closest motion corresponding to an AU. The motions to lower the upper eyelid, raise the lower eyelid, and look up are depicted in the top, middle, and bottom rows, respectively. The orientations and lengths of the black arrows represent the orientations and amplitudes of the displacement vectors at each point. The heat maps represent the z component of the displacement vectors. Blue regions indicate depressed areas, whereas yellow and red regions indicate elevated areas.
Artificial Intelligence artificial intelligence female robot 2021

Figure 4 compares the distributions of the displacement vectors for two types of facial motions around the forehead on the x–y plane. Because Affetto had only one actuator for the eyebrows, the same DU was adopted for this comparison.

Figures 5 and 6 compare the distributions of the displacement vectors for four types of facial motions around the mouth on the x–y plane. For the former motion, the skin was depressed around the mouth while it was elevated around the cheek for both the androids and adult males.

Figures 8 and 9 compare the complexities of the upper and lower face motions, respectively, for the androids and adult males. The vertical axis indicates the complexity Cr, while the horizontal axis indicates the radius r of the target area.

The androids and adult males showed a noticeable difference in the complexity of the upper face motions. The complexity was greater for the adult males when the radius was above 20 mm. In contrast, the androids and adult males showed similar levels of complexity for the lower face motions.

Thus, humans’ facial flow lines were more complex than androids’ in the upper face areas. Although AUs and DUs are not precisely compatible, as noted in section 2. This is because the precise replication of humans’ curved AUs can not be expected with a single unit of androids’ straight DUs. One possible solution for this mismatch is the adoption of combinations of DUs to replicate a single AU. Comparison of flow lines between AUs and combinations of several DUs is one of the future issues. Another solution is redesigning a face mechanism for a problematic DU so that the flow lines would match an AU.

Additionally, the displacement distributions of the adult males were quite similar for AU1, AU2, and AU7, as shown in Figures 3 and 4. In other words, the actual degrees of freedom available for the human face to show AUs are fewer than those defined in the FACS. This suggests that a “perfect” facial mechanism that can differentiate all AUs is unnecessary when replicating an average person’s features in an android robot.

The second difference between the androids and adult males was found in the skin surface undulation patterns around the upper face, especially the forehead motions shown in Figure 4. The skin tended to be depressed in the upstream areas of the flow lines and elevated in the downstream areas for the androids, as shown in Figure 10A.

The above surface undulations in the adult males are quite challenging to replicate in androids. This is because the designers need to control the flow lines on the x–y plane and the undulations simultaneously. Flow line control can be achieved relatively easily by tuning the motion trajectories of the internal mechanisms, their combinations, and the stiffness of the skin materials. However, undulation control requires additional mechanisms to elevate and depress the skin surface at several areas unless muscle-like actuators are embedded in the skin.

Because only three Japanese young adult males participated in this study, it is difficult to conclude that the identified features above are common in humans. There should be non-negligible differences in the faces of people when considering facial deformation mechanisms. For example, skin material properties such as the stiffness and surface tension change with age and physique. The power and controllability of facial muscles can also change with age and should be different between males and females or depending on one’s occupation and culture.

Therefore, there should not be only one set of motion characteristics for humans. Instead, there should be acceptable ranges of displacement distributions and motion characteristics for human facial motions, and their subtle differences should express different personalities.

In summary, the human facial motions were more complex than those of the androids. Innovative composite motion mechanisms to control both the flow lines and surface undulations are required to design advanced androids capable of exhibiting more realistic facial expressions.

This work was partially supported by PRESTO JST, Grant Number JPMJPR1652 and A-Lab Co. The authors declare that this study received funding from A-Lab, Co.

We also thank Takeru Misu for helping with the data acquisition and the staff of A-lab Co.

Robotic Arm Control System Based on AI Wearable Acceleration Sensor

The position of mechanical arm in people’s life is getting higher and higher. It replaces the function of human arm, moving and moving in space. Generally, the structure is composed of mechanical body, controller, servo mechanism, and sensor, and some specified actions are set to complete according to the actual production requirements. The manipulator has flexible operation, good stability, and high safety, so it is widely used in industrial automation production line. With the development of science and technology, many practical production requirements for the function of the manipulator are more and more refined, especially in the high-end research field. For example, medical devices, automobile manufacturing, deep-sea submarines, and space station maintenance put forward higher requirements for it. In terms of miniaturization and precision, it can meet the needs of scientific research and actual production. This paper mainly introduces the research of manipulator control system based on AI wearable acceleration sensor, aiming to provide some ideas and directions for the research of wearable manipulator. It is used for the research and experiment of manipulator control system based on AI wearable acceleration sensor. The experimental results show that the average matching rate of the manipulator control system based on AI wearable acceleration sensor is as high as 88.

With the upgrading of modern sensors and microprocessors, robotics has developed rapidly. How to make the control method of the robotic arm more effective and convenient is currently the research hotspot. At present, most manipulators are roughly divided into two types according to the control method. The first is a dedicated manipulator that can complete preprogrammed processing actions by itself. Most of these control methods are industrial manipulators, which are often used in assembly lines in factories.

The robotic arm is one of the most widely used automation devices in the field of robotics science and technology. At present, the traditional manipulator control methods are mostly completed by preprogramming processing or command input from external devices. Such control methods are usually complicated and cumbersome and require operators to familiarize themselves with specific programming methods or according to different types of manipulators control instruction. With the advent of accelerometers, a brand-new noncontact somatosensory technology has been rapidly developed, showing a broad application prospect in the field of intelligent robots. In order to realize a more convenient and flexible manipulator control method, this paper designs a manipulator control system based on AI wearable acceleration sensor.

Then, use 3D CAD design software to model and design VRNC-210 indexing table, expansion disc, and L-shaped sensor fixture. Firstly, the model of VRNC-210 is established, and the model effect is that 32 L-shaped clamps are fixed on the expansion disk through 4 M2.5 screws, and the sensors are connected through 3 positioning holes at different positions on the L-shaped clamp, with a design diameter of 283 mm.

The high- and low-temperature box and the servo drive are respectively provided with RS-485 interface and RS-422 interface. First, the PCI slot of the industrial control computer can be installed with a model interface converter to expand a RS-485 and RS-422 hardware interface, respectively.

In different scenes, facing different objects, the robotic arm can take different grasping actions to reduce the load of its steering gear and make the grasping objects more stable.

Calibration requires two steps to complete: first, open the control glove upper computer software, which can realize data reception and data transmission. When the control glove is calibrated, its data receiving function is mainly used; then, wear the control glove and turn on the data glove ON button. If the indicator light is on, it is the working state. After pressing the calibration button on the circuit board, hold the five fingers and spread the five fingers in the opposite direction. Repeat this action several times to maximize the grip and stretch range of the ADXL345 tilt sensor.

Sometimes, the temperature determination of the object to be grasped is also very important. Whether the temperature of the object is suitable for grasping is the problem to be solved. Under certain conditions, the temperature of the object to be grasped is too high or too low to damage the end jaws of the robotic arm. Therefore, judging the temperature of the object to be grasped is also a task to protect the wearable robotic arm system.

DS18B20 is a temperature sensor with a single bus interface. Its temperature measurement range is −55° to +125°, and the accuracy is very high in the range of −10° to +85°.

Stick the DS18B20 probe on the sensitive area of the gripper end of the robotic arm to feel the temperature of the object being grasped. When the gripper at the end of the robotic arm grips the object, the patch temperature sensor DS18B20 will touch the temperature measurement object. If there is a high temperature pop-up prompt, the operator should consider whether to continue grasping.

This research uses RFP602 chip piezoresistive pressure sensor and conversion module to solve this problem. Put the mechanical arm patch type piezoresistive pressure sensor on the jaw end of the actuator to solve the problem of measuring the pressure required to grasp different objects. When the external force acts on the sensing point, its resistance will become regular with the external force. When there is no pressure, the resistance value of the resistance is large and the maximum value. As the pressure value continues to increase, its resistance is continuously decreasing, which is inversely proportional to the pressure value. Connect the sensor to the voltage conversion module and connect it to the STM32 microcontroller to obtain the collected voltage. The pressure information is obtained through the resistance-pressure relationship conversion formula.

After using FPGA acceleration, the time for SIFT feature extraction is about 0. Therefore, after using FPGA acceleration, the target recognition time is reduced to 2.

With the development of computer technology, robotics technology has also matured. It combines technologies from computers, electronics, machinery, sensors, and control and is widely used in military, industry, agriculture, medicine, education, scientific research, and other fields.

In this paper, the robot arm control system of the AI wearable acceleration sensor is designed to realize the remote grasping operation, which requires the whole body movement. The structure of the system is designed according to its functional requirements, and the main hardware is designed at the same time. In the process of wireless data transmission, data security and anti-interference ability are not considered.

Primer on artificial intelligence and robotics

This article provides an introduction to artificial intelligence, robotics, and research streams that examine the economic and organizational consequences of these and related technologies. We describe the nascent research on artificial intelligence and robotics in the economics and management literature and summarize the dominant approaches taken by scholars in this area.

Scholars have been increasingly interested in the economic, social, and distributive implications of artificial intelligence, robotics, and other types of automation.

This article is a primer on artificial intelligence, robotics, and automation. To begin, we provide definitions of the constructs and describe the key questions that have been addressed so far. We also describe ways in which organizational scholars have been using artificial intelligence tools as part of their research methodology.

Studies of artificial intelligence and robotics base their theory and analysis on constructs of automation, robotics, artificial intelligence and machine learning, and automation. It is important that organizational scholars carefully define any such constructs in their studies and to avoid confusing these related but distinct constructs.

Automation is not a new concept, as innovations such as the steam engine or the cotton gin can be viewed as automating previously manual tasks.

While artificial intelligence, robotics, and automation are all related concepts, it is important to be aware of the distinctions between each of these constructs. Robotics is largely focused on technologies that could be classified as “manipulators” as per the IFR definition, and accordingly, more directly relates to the automation of physical tasks. On the other hand, artificial intelligence does not require physical manipulation, but rather computer-based learning. The distinction between the two technologies can become fuzzier as applications of artificial intelligence may involve robotics or vice versa.

In many cases, a computer or robot may be able to complete relatively low-value tasks, freeing up the human to focus efforts instead on high-value tasks.

Similarly, artificial intelligence and robotics technology have the capacity to reshape firms and change the structure of organizations dramatically. As discussed above, the adoption of artificial intelligence and robotics technologies will likely alter the bundle of skills and tasks that many occupations are comprised of. By that aspect alone, these technologies will reshape organizations and force firms to restructure themselves to account for these changes. In addition, the composition of the labor force may change to adopt to the new set of skills that are most valued.

There are a variety of other questions surrounding artificial intelligence and robotics that we encourage organizational scholars to turn to. One topic that has yet to be explored in much detail surrounds the establishment and firm-level consequences for adoption of artificial intelligence and robotics technology. Research could examine performance consequences as well as outcomes related to firm organization and strategy. Scholars can study in what circumstances and in what kinds of firms such adoption has the greatest impact. The adoption of the technology itself can be viewed as an outcome, and scholars can examine what circumstances and factors encourage or discourage the use of these technologies. Certain industries, management styles, or organizational forms may be particularly quick to adopt, and market level forces may also impact the adoption decision. Industry and organizational factors may play a role as well as the backgrounds of individuals and managers within organizations.

There will be a need to evaluate what skills and tasks are still valuable in the labor market compared to skills and tasks that can now be fully automated.

Sex and gender differences and biases in artificial intelligence for biomedicine and healthcare

We address their existing and potential biases and their contribution to create personalized therapeutic interventions. We examine the sex and gender issues involved with the generation and collection of experimental, clinical and digital data. Furthermore, we review a number of technologies to analyze and deploy this data, namely Big Data Analytics, Natural Language Processing and Robotics. Those technologies are becoming increasingly relevant for Precision Medicine while being exposed to potential sex and gender biases.

Top 6 Lessons About artificial intelligence female robot 2021

Fair data generation and explainable algorithms are fundamental requirements for the design and application of artificial intelligence to optimize for health and wellbeing across the sex and gender spectrum.

A desirable bias implies taking into account sex and gender differences to make a precise diagnosis and recommend a tailored and more effective treatment for each individual. This represents a much more accurate approach than collapsing all sex and gender categories into a single one, such as data generated from mixed sex or gender cohorts16.

Another source of undesirable bias is the misrepresentation of the target population, leaving minorities out.

Even nowadays, male mouse models are overall more represented than female models in basic, preclinical, and surgical biomedical research25. A recent analysis of data on 234 phenotypic traits from almost 55,000 mice showed that existing findings were influenced by sex26.

Differences in the physiology of sexes33 might translate into clinically relevant differences in pharmacokinetics and pharmacodynamics of drugs. These differences, taken together with the underrepresentation of women in clinical trials, can explain why women typically report more adverse event reactions compared with men34.

Accounting for sex and gender differences leads to a better understanding of the pharmacodynamic and pharmacokinetic action of a drug.

For instance, data from GWAS targeting smoking behaviour have shown sex-associated genetic differences that influence smoking initiation and maintenance55. Interestingly, these differences complement the differential effectiveness of tobacco control initiatives based on the sex of the individuals that receive the preventative messages56.

Awareness of sex and gender differences through biomedical Big Data could lead to a better risk stratification.

A case of undesirable biases in NLP is the use of text corpora containing imprints of documented human stereotypes that can propagate into AI systems85.

A flourishing area of NLP is that of medical chatbots, aiming to improve users’ wellbeing through real-time symptom assessment and recommendation interfaces. A dialogue of a chatbot can be modelled with available metadata to adjust to features of the replier in terms of gender, age, and mood90. Although both proved to be effective in clinical trials, the lack of data on their long-term effects is raising certain concerns.

Robots can serve a diverse range of roles in improving a human’s tasks, health and quality of life.

Despite the progress of AI models in recent years, the complexity of their internal structures has led to a major technological issue termed the ‘Black box’ problem.

On one hand, an explanation of the decisional process would enable to find potential mistaken conclusions derived by training an algorithm with misrepresented data. This will facilitate the identification of undesirable biases generally found in clinical data with unbalanced sex and gender representation.

For instance, a widely used approach to ensure fairness in data processing is to remove some sensitive information, such as sex or gender, and all other possible correlated features112.

Although affirmative action represents a remedy for unfair algorithmic discrimination, ensuring the quality of the data used for algorithm training is also crucial. For instance, a study found that only 17% of cardiologists correctly identified women as having greater risk for heart disease than men114.

Fairness is highly context-specific and requires an understanding of the classification task and possible minorities.

The development and application of fair approaches will be critical for the implementation of unbiased and interpretable models for Precision Medicine106,116.

Technological advances in machine learning and AI are transforming our health systems, societies, and daily lives120.

The ambitious goals set by Precision Medicine will be achieved using the latest advances in AI to properly identify the role of inter-individual differences. The proper use of innovative technologies will pave the way towards tailored and personalised disease prevention and treatment, accounting for sex and gender differences and extending towards generalized wellbeing.

The 2014 Survey: Impacts of AI and robotics by 2025

Among the key themes emerging from 1,896 respondents’ answers were: – Advances in technology may displace certain types of work, but historically they have been a net creator of jobs. – We will adapt to these changes by inventing entirely new types of work, and by taking advantage of uniquely human capabilities. – Technology will free us from day-to-day drudgery, and allow us to define our relationship with “work” in a more positive and socially beneficial way. – Ultimately, we as a society control our own destiny through the choices we make. – Automation has thus far impacted mostly blue-collar employment; the coming wave of innovation threatens to upend white-collar work as well.

These two groups also share certain hopes and concerns about the impact of technology on employment.

The robots are here: To hire us

But even before applicants encounter this system, Peluso suggested, the job description they run into may be automatically generated. AI can analyze the needs of a particular job, match them to the job description, and determine whether the description is appropriate.

In fact, some professionals believe that the hiring process may eventually become entirely automated.

Already, many companies have introduced AI into the onboarding and training process, often through gamified software.

On top of this, AI today can act as a coach.

Rise Of The Machines: Experts Look At AI, Robotics And The Law

NEW YORK — Artificial intelligence, robots, and the law, are all changing a rapid pace.

And with facial recognition, he said, research found packages for gender recognition did much less well for men than women, but do notably bad for dark-skinned women. He said beyond just getting things wrong, sometimes there is a dehumanizing quality of an interaction with a technology that fails to recognise you as a person at all.

He also talked about mistakes made by Google searches based on errors of input.

Crootof talked about autonomous weapons systems, which are capable of independently selecting and engaging a target based on pre-programmed constraints, and are being used in the field.

Like any new technology, it raises a number of concerns, such as morality, strategic, security, and legal issues. AWS create a break in the chain between the human element in force and when that force is exercised, she said, which raises a question of responsibility. “Who or what should be responsible when there’s a malfunction?” she asked.

She noted that criminal law assigns guilt for blameworthy acts, while tort law assigns liability for accidents.

“We are not going to ban autonomous weapons systems to protect robots,” she noted.

We should not look at who do we hold morally responsible or accountable. Instead, we ask what is the appropriate liability to minimize the possibility that these acts are going to occur in the first place. So that is like tort law, not criminal law, leading Crootof to argue for using a “tort law lens” to look at the problem. There could be overlap, but while criminal law finds blame for the acts, tort law is often assigned for accidents.

Felten said there is a lot of discourse about how to provide transparency, accountability and governance in AI systems.

Artificial intelligence does perhaps raise the stakes on this challenge, he said, but it does not change the underlying problem. For computer scientists there is not a single method or approach.

This can be used to determine that some bad outcome can never happen or will never happen. But it does not tell you everything you might want to know about what the system will actually do when you turn it on.

Second is to provide hands-on access to the system, Felten said. For example, auditors might be allowed to interact with the system.

Testing is good for telling if something can happen, he said, but it cannot tell you what would happen in a case you didn’t test. And for any interesting system, the number of possible scenarios would be vastly astronomically larger than you can test for. So testing is a way to look at a tiny fraction to see if a situation might come up. So testing can tell you if something can happen, but it cannot tell you if a bad outcome cannot happen.

Third is doing due diligence on the engineering process, according to Felten. Just like in other studies you want to ensure got a good result, looking at the process is important. Computer science and software are still so immature it is hard to know what process you could follow to be sure you get to good results, he said.

The good news is there are methods out there to help to control its behaviour even if you don’t fully understand it. The concept is to take a complex, unruly system and put it inside of some kind of wrapper that will govern its behaviour.

This should be thought about in the design rather than trying to “bolt it on” after the fact.

Finally, he compared thinking about an AI making decisions to a bureaucracy full of people making decisions. “We don’t just let the people in that building do what they want to do,” he said.

“It can be expensive to be poor,” she said, noting that access to credit is critical.

He asked participants to think about the algorithms behind financial tech systems.

Pasquale described a situation in which a person cannot access credit because of a “thin credit file. The person takes the loan and makes the repayments on time. But she does not know what is done with her data and cannot find out due to trade secrecy.

Humanoid Robot: ASIMO

Abstract:Today we find most robots working for people in industries, factories, warehouses, and laboratories. For instance, it boosts economy because businesses need to be efficient to keep up with the industry competition. Therefore, having robots helps business owners to be competitive, because robots can do jobs better and faster than humans can, e. Yet robots cannot perform every job; today robots roles include assisting research and industry.

A HUMANOID ROBOT is a robot with its body shape built to resemble of the human body. Some humanoid robots may also have heads designed to replicate human facial features such as eyes and mouths.

Although the initial aim of humanoid research was to build better orthotics and prosthesis for human beings, knowledge has been transferred between both disciplines.

Regular jobs like being a receptionist or a worker of an automotive manufacturing line are also suitable for humanoids.

Knowledge of and Attitudes on Artificial Intelligence in Healthcare: A Provincial Survey Study of Medical Students

At one medical school, the survey was sent out in a newsletter to the MD student body. At all schools, the survey was open for four weeks, with a reminder email sent two weeks after the first email. Participation was voluntary and was not related to the students’ ongoing curricular activities. Students were offered entry into a gift card raffle for completing the survey. Consent for study participation was obtained through the first page of the survey, and respondent anonymity was guaranteed by design.

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Students’ perceptions of AI’s potential capability in the domains of individual health, health systems, and population health are described in Supplementary Table 1. Perceptions regarding the timeline in which these capabilities will be achieved are described in Supplementary Table 2.

Students were also concerned about how AI will affect the medical job market. They believe AI will raise ethical and social implications yet are unconvinced that our health system is equipped to deal with these novel challenges. Overall, students agree that medical education must do more to prepare students for the impact of AI in medicine.

AIBx, artificial intelligence model to risk stratify thyroid nodules

Nodules were excluded if there was no definitive diagnosis of benignity or malignancy. 482 nodules met the inclusion criteria and all available images from these nodules were used to create the AI models.

By using image similarity AI models, we can eliminate subjectivity and decrease the number of unnecessary biopsies.

The History of Artificial Intelligence

Breaching the initial fog of AI revealed a mountain of obstacles. The biggest was the lack of computational power to do anything substantial: computers simply couldn’t store enough information or process it fast enough. In order to communicate, for example, one needs to know the meanings of many words and understand them in many combinations. Hans Moravec, a doctoral student of McCarthy at the time, stated that “computers were still millions of times too weak to exhibit intelligence.

Ironically, in the absence of government funding and public hype, AI thrived. During the 1990s and 2000s, many of the landmark goals of artificial intelligence had been achieved. In 1997, reigning world chess champion and grand master Gary Kasparov was defeated by IBM’s Deep Blue, a chess playing computer program. In the same year, speech recognition software, developed by Dragon Systems, was implemented on Windows. This was another great step forward but in the direction of the spoken language interpretation endeavor. It seemed that there wasn’t a problem machines couldn’t handle.

The application of artificial intelligence in this regard has already been quite fruitful in several industries such as technology, banking, marketing, and entertainment. We’ve seen that even if algorithms don’t improve much, big data and massive computing simply allow artificial intelligence to learn through brute force. There may be evidence that Moore’s law is slowing down a tad, but the increase in data certainly hasn’t lost any momentum.

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12 Example Artificial Intelligence Humanoid 2021

Artificial intelligence Humanoid robots knocking at the door

Traditional entertainment industry wants to develop in the direction of intelligence. The business process is divided into entertainment, service, delivery, payment, service feedback and resources. The traditional hospitality industry needs to consider which businesses to add intelligent robots to. For example, the traditional hospitality industry mentioned in the case faces challenges in hosting foreign guests, so multilingual Artificial Intelligence humanoid 2021 robots should be applied in hospitality.

Meet Sophia, World’s First AI Humanoid Robot

In addition, the use of the delivery robot needs to be determined in conjunction with the actual situation of the merchant. The results of the interviews and case studies show that the robot often needs to consider whether the item is suitable for robotic transmission when receiving and delivering items. Cases and interviews clearly indicate that the items being transferred need to be classified in detail.

The first step was to set up an international research network so neuroscientists, computer scientists and other specialists from different disciplines could exchange information and ideas. Philosophers are on board as well, as this research touches on ethical issues. Cooperation on this ambitious scale can only run smoothly if exchange inside the scientific community improves. HBP hopes to change this, and will allow researchers from all over the world to access the project’s findings for use in their own projects.

While HBP scientists in Geneva investigate the structure of the brain, researchers in Munich are working on a robot that will imitate the full sophistication of the human body. Dubbed Roboy, this robot is not just the face of the HBP. It is also the arms and legs, ankles, the swinging hips and blinking eyes of the project, so to speak. In return, the Roboy team contributed high-quality hardware to their projects. The Roboy robot can ride a bicycle and operate a turntable. By 2020 its makers say it will be able to diagnose medical conditions.org The robot has since moved to the Technical University of Munich, where every semester a new team of students from various disciplines works to teach Roboy something new. Roboy’s skill set now includes things like the ability to talk two people at once or play the xylophone. All their findings are freely available on a robotics platform on the Internet. And the little robot is a fast learner: Roboy can ride a bike and operate a record player.

By 2030, the HBP hopes to have reached its goal of designing a computer that can keep pace with the human brain. There are still a few hurdles to clear along the way though. It will take quite a few more technical steps to get to one exaFLOPS of computing power, for example. This raises obvious and fundamental ethical questions, which is why an ethics committee has always been an integral part of the project.


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For example, the observer must at least be able to understand symbolic questions, make decisions, and formulate responses. The ability to follow instructions, in itself, involves a fairly advanced degree of mental reasoning.

Meltzoff7 noted this challenge and proposed the need for non-verbal tests to assess theory of mind. For example, one non-verbal assessment of ToM involves testing whether a child will mobilize to assist another person that appears to be struggling to open a door.
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The Design Model for Robotic Waitress

With the rapid development of traditional industries, intelligent robots have been widely used in the hospitality industry. It not only affects the interaction experience between users and robots, but also prevents companies from getting valuable feedback in a timely manner. In order for intelligent robots to effectively utilize interactive information, the user experience of robot entertainment is improved.

This paper proposes and establishes a basic technical model called iRCXM. Combining the iRCXM model with a decision tree classification algorithm is excepted effectively improve the interaction experience between humans and robots in hospitality. This paper designs a model of intelligent robot based on decision tree algorithm. The model divides the user into three sections, each corresponding to a different standard function.

Using a decision tree classification algorithm model is excepted effectively judge users’ current stage and whether they can move to the next stage. When the user reaches the final stage, it proves that the user has obtained a good interactive experience. In addition, the research developed a robot user interaction system based on the existing technology. The developed samples were tested in a real environment and feedback from customer experience was collected through semi-structured interviews.

Artificial Intelligence artificial intelligence humanoid 2021

Radiate: When the customer meets the conditions of the first phase, they can convert to that stage. At this point, the merchant will provide customers with more mobile social media channels in addition to email. The information and content of these channels can often be collected and analyzed. Customers can evaluate the products and services posted by merchants at these channels.

Align: When the user satisfies the characteristics of the above two stages, the quality of the customer is evaluated at this stage. The main evaluation criteria for customer quality is the customer’s access value.

At this stage, merchants should be memberized for key customers with high quality. And build a community of members to share information between members. The preferential policies offered by members should use at least A/B.

Nurture: This phase enhances the customer experience by linking more channels and increasing overall conversion rates. The main implementation of customer-centric trigger-based dialogue, collecting explicit and implicit customer behavior, automated processes. The main criterion for this stage is whether the customer has accepted the entry of the face recognition function provided by the intelligent robot.

Engage: Connect different online and offline customer databases to support a single view of the customer. It is popular to say that this stage will provide customers with a recommendation system based on machine learning. Old users can be recommended for new products or activities based on their preferences. The criterion for this stage is whether the customer has a positive experience evaluation of the recommendation system. Negative evaluations of customers will be promptly feedback to the merchants and adjusted.

Lifetime customers: When users meet the criteria for all of the above stages, they can be considered as lifetime customers. The KPI at this stage is the retention rate of the customer’s lifetime value. The merchant will predict the behavior of the customer based on the data collected at all stages above.

The decision conditions at different stages in the framework can be considered features. And ask the merchant to collect and build a user behavior database. The above framework clarifies that the ultimate goal of the user is to become the life customers of the merchant. Merchants want to be able to predict whether different customers can become Lifetime customers and choose how to improve the user experience based on predictions. This type of problem is more like a decision tree classification problem in machine learning. In addition to the face recognition and similarity-based recommendation systems mentioned in the framework.
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This process corresponds to the division of the feature space and also corresponds to the construction of the decision tree. The initial idea of the study was to calculate the entropy values of the different features.

Customers can command robots to raise or lower their arms, introduce themselves, turn on the lights and change the color of LED lights in the welcome interface. In addition, customers can enter the face recognition system through this interface. Customers take pictures of their own facial information by calling the camera of the robot. The captured images need to be added to the face database for recognition. In the process of adding to the face database, customers can set their own identity information such as name. Next time customers do not need to add faces repeatedly, they just need to take pictures and start recognizing. When the recognition is successful, the robot will say your name and welcome you.
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After dividing the traditional business process, the assigned business should be assigned responsibility. The violation of the law by the robot shall be the responsibility of its responsible person. The robot after the task division should be assigned the corresponding manager. The way managers and robots can be distributed can be in the form of small groups, for example, three delivery robots are assigned a person in charge.

Businesses should consider data protection measures when collecting data with service intelligence and robots. Most cases show that consumers don’t want their data to be leaked. In addition, network information security and data protection laws and related policies need to be considered in the process of collecting data using robots.

Use the HRI interactive mode specified by iRCXM to select the appropriate service intelligence technology.

In the face recognition interface, new customers can choose to add faces to the face database and enter names.

The sensor on the Sanbot robot can feel the touch of the customer.

This experiment was designed in accordance with framework for intelligent robotics for Hospitality. The robot has an interface that interacts with the user, and the robot can collect user information and data. Use the Http request sent to the cloud server to upload and identify the customer’s face information. The face database encrypts the access rights by using an encryption algorithm to ensure the security of the user data.

During the test, the researchers found that the robot’s sensor could not sense the baby’s touch screen. The researchers believe that the baby is at risk of interacting with the robot, so a guardian is required to monitor and avoid accidents when interacting. In addition, some older people think that the robot is not high enough. This makes it very inconvenient for older people to be interactive. The robot’s screen can’t sense the touch of the nail, and some women may not be able to easily master the interaction skills at the beginning.

It is often unclear what the robot can do when interacting with the robot, which causes the customer to be hindered while interactive. Researchers believe that adding operational hints to the robot interface is more conducive to user interaction with the robot. In addition, 8 of the 12 customers who experienced the experience proposed whether or not the ordering operation can be performed on the robot. This proves that the robot needs to have the corresponding functions in the current environment in different hospitality environments.

This study demonstrates the feasibility of the framework for intelligent robotics for hospitality described in this study by using a large number of case studies and experiments. This study summarizes the current problems of intelligent robots in the hospitality industry mainly in terms of HRI, user experience, data confidentiality and effective service intelligence. The feasibility and development potential of the model combined with decision tree algorithm are proved by interviewing customers. In addition, the study also found that robots that are too similar to humans can cause panic among customers. Experiments show that robots with service intelligence have positive development potential in the hospitality industry.

Predicted Influences of Artificial Intelligence on Nursing Education: Scoping Review

Methods: This scoping review followed a previously published protocol from April 2020. In addition to the use of these electronic databases, a targeted website search was performed to access relevant grey literature. Abstracts and full-text studies were independently screened by two reviewers using prespecified inclusion and exclusion criteria. Included literature focused on nursing education and digital health technologies that incorporate AI.

Additionally, nurse educators need to adopt new and evolving pedagogies that incorporate AI to better support students at all levels of education.

Additionally, as the majority of papers included in this review were expository papers and white papers, there is a need for more research in this context. Further research is needed to continue identifying the educational requirements and core competencies necessary for specifically integrating AIHTs into nursing practice.

Nurse educators in clinical practice and academic institutions around the world have an essential leadership role in preparing nurses and nursing students for the future state of AIHTs.

To our knowledge, this is the first scoping review to examine AIHTs and their influence on nursing education. While there has been research conducted on AIHTs and on nursing education as separate research topics, now is the time to realize the critical relationship between these two entities. AIHTs cannot be implemented in an effective manner without the solid foundation of nursing education, in both academic and clinical practice settings.

The History of Artificial Intelligence

Breaching the initial fog of AI revealed a mountain of obstacles. The biggest was the lack of computational power to do anything substantial: computers simply couldn’t store enough information or process it fast enough. In order to communicate, for example, one needs to know the meanings of many words and understand them in many combinations. Hans Moravec, a doctoral student of McCarthy at the time, stated that “computers were still millions of times too weak to exhibit intelligence.

Ironically, in the absence of government funding and public hype, AI thrived. During the 1990s and 2000s, many of the landmark goals of artificial intelligence had been achieved. In 1997, reigning world chess champion and grand master Gary Kasparov was defeated by IBM’s Deep Blue, a chess playing computer program. In the same year, speech recognition software, developed by Dragon Systems, was implemented on Windows. This was another great step forward but in the direction of the spoken language interpretation endeavor. It seemed that there wasn’t a problem machines couldn’t handle.

The application of artificial intelligence in this regard has already been quite fruitful in several industries such as technology, banking, marketing, and entertainment. We’ve seen that even if algorithms don’t improve much, big data and massive computing simply allow artificial intelligence to learn through brute force. There may be evidence that Moore’s law is slowing down a tad, but the increase in data certainly hasn’t lost any momentum.

AI, a new artist for the 21st century

This is the case of the humanoid robot, Ai-Da, developed by the University of Oxford, and protagonist of one of this year’s news. On June the 12th it was announced that her works of art were going to be exhibited, including paintings and sculptures, in a gallery of London.

Both examples of 2019 creative AI follow other artistic milestones achieved by AI previously.

And for the worst, there should always be someone responsible behind the machines, that is, in case a machine violates the law.

Opinion: Robots and AI could soon have feelings, hopes and rights … we must prepare for the reckoning

For some, the question of whether or not the human race will live to see a 22nd century turns upon this latter consideration. While forecasting the imminence of an AI-centric future remains a matter of intense debate, we will need to come to terms with it.

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It is clear, however, that the European Parliament is making inroads towards taking an AI-centric future seriously. Included in this draft proposal is preliminary guidance on what it calls “electronic personhood” that would ensure corresponding rights and obligations for the most sophisticated AI.

Yes, driverless cars are problematic, but only in a world where traditional cars exist.

It goes without saying that the very notion of making separate, transferable, editable copies of human beings embodied in robotic form poses both conceptual and practical legal challenges.

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K2 leverages state of the art Artificial Intelligence technology and initiates conversation without any need for wake-up commands. Keeping in mind the needs of the specially abled,K2 can respond to queries with text display along with Speech. K2 can address general and specific HR-related employee queries as well as handle personal requests for actions like providing payslip, tax forms etc.

Ubiquitious Artificial Intelligence

The best way to develop a truly intelligent system is to use the known properties of the only intelligent system that we know: humans. Intelligent techniques are playing an increasingly important role in engineering and science having evolved from a specialized research subject to mainstream applied research and commercial products. Manufacturing systems in industries has dramatically changed as a result of advanced manufacturing technologies employed in today’s factory. Factories are now trying to attend and maintain a world-class status through automation that is possible by sophisticated computer programs. The development of CAD/CAM system is evolving towards the phase of intelligent manufacturing system.

A tremendous amount of manufacturing knowledge is needed in an intelligent manufacturing system. Artificial intelligence techniques are designed for capturing, representing, organizing, and utilizing knowledge by computers, and hence play an important role in intelligent manufacturing. Artificial intelligence has provided several techniques with applications in manufacturing like; expert systems, artificial neural networks, genetic algorithms and fuzzy logic.

A “knowledge engineer” interviews experts in a certain domain and tries to embody their knowledge in a computer program for carrying out some task. How well this works depends on whether the intellectual mechanisms required for the task are within the present state of AI. When this turned out not to be so, there were many disappointing results. In the present state of AI, this has to be true.

Banks use artificial intelligence systems to organize operations, invest in stocks, and manage properties. In August 2001, robots beat humans in a simulated financial trading competition. Financial institutions have long used artificial neural network systems to detect charges or claims outside of the norm, flagging these for human investigation. AI is more S/W related so the game can be easier or harder.Banks use intelligent software applications to screen and analyze financial data.

How AI will give IT staffing a major boost

It surely is a no-brainer that AI will be revolutionizing the current landscape of employment and IT industry. With technology becoming powerful and accessible around the globe, from mundane to crucial tasks can be automated which humans take long and tedious hours to complete. Self-service machines, driverless cars, and smart homes have already marked the beginning of the rise of machines. The intelligent machines are quicker and accurate in arduous data computation and organizing hundreds of spreadsheets in the fraction of seconds.

Facebook CEO Mark Zuckerberg is quite optimistic with Artificial Intelligence believing the human flaws can be mended with the intelligence of robots.

The robots and machines call for human intervention and data in a timely manner to function effectively and efficiently.

The Accenture PLC’s global study, over a thousand big companies, have already started using or testing AI & machine-learning systems. The study also revealed the emergence of various new and unique human jobs amidst the AI landscape and these are the jobs that need high-tech skills and training.

The IT professionals would need to design and develop the algorithms which help the devices think like a human. For example, Yahoo engineers recently developed a sarcasm detector in order to make the language processing system learn what people literally mean on social media and discussion forums.

Until now, we have witnessed major AI breakthroughs with the development of driverless cars, social humanoid robot Sophia, Amazon’s Alexa, the E-Patrol Robot Sheriff, etc. But, it is expected during the next decade that every small or big task of our job and personal life will be automated. This will include AI translators which guarantee 100% accuracy in language translation and the brilliant driverless vehicles prompt in taking a wise decision when dealing with rare real-world situations.

With the introduction of AI, the industries worldwide are running out of skilled, competitive, and qualified talent. Providing them with the right talent is the prime choice for IT staffing companies.

The 2014 Survey: Impacts of AI and robotics by 2025

Among the key themes emerging from 1,896 respondents’ answers were: – Advances in technology may displace certain types of work, but historically they have been a net creator of jobs. – We will adapt to these changes by inventing entirely new types of work, and by taking advantage of uniquely human capabilities. – Technology will free us from day-to-day drudgery, and allow us to define our relationship with “work” in a more positive and socially beneficial way. – Ultimately, we as a society control our own destiny through the choices we make. – Automation has thus far impacted mostly blue-collar employment; the coming wave of innovation threatens to upend white-collar work as well.

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These two groups also share certain hopes and concerns about the impact of technology on employment.

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What first artificial intelligence robot 2021

The History of Artificial Intelligence

The application of artificial intelligence in this regard has already been quite fruitful in several industries such as technology, banking, marketing, and entertainment. We’ve seen that even if algorithms don’t improve much, big data and massive computing simply allow artificial intelligence to learn through first artificial intelligence robot 2021. There may be evidence that Moore’s law is slowing down a tad, but the increase in data certainly hasn’t lost any momentum.

Top 10 Most Advanced Humanoid Robot

Where is the human got to go? Artificial intelligence, machine learning, big data, digitalisation, and human–robot interaction in Industry 4.0 and 5.0

General Electrics in Hawthorne, USA, noticed ergonomic problems with production lines and piece-rate work. The Hawthorne Studies were launched with the goal to increase productivity by ergonomic improvements, for example, work place illumination. The most important result was, however, the discovery of the Hawthorne-Effect: No matter, whether light was optimised or worsened, employees continuously produced more relays and other electrical components. The reason was the continuous attention and appreciation of the experimenters., employee-oriented management and open communication is also true today, a century later and in Industry 4.

Artificial Intelligence first artificial intelligence robot 2021

Humans are smart—irrespective of the doubt’s engineers in general and computer scientists in particular have facing the attitude–behaviour discrepancies. People realise dead ends of their organisation and cleverly find work-arounds. Only exceptionally, like in BAM, the organisation’s leaders are long-sighted internal stakeholders. Very often, shareholders or change managers from outside decide and frequently offend staff sensibilities. At this stage, the workforce has suffered a long time from the lack of leadership and strategy. Many employees developed interventive, preventive, and innovative ideas to change for the better, yet they weren’t heard. Change managers are mostly educated and trained to firstly reduce fix costs rapidly and massively, which often means firing people.

Accordingly, in this paper, we argue for an improved assessment of the perceived threats of AI and propose a survey scale to measure these threat perceptions. First, a broadly usable measurement would need to address perceived threats of AI as a precondition to any actual fear experienced. This conceptual difference is subsequently based on the literature on fear and fear appeals. Second, the perceived threat of AI would need to take into account the context-dependency of respective fears as most real-world applications of AI are highly domain-specific. AI that assists in the medical treatment of a person’s disease might be perceived vastly different from an AI that takes over their job.

The collected data supports the factorial structure of the proposed TAI scale. Still, such distinct perceptions do also differ between the domains tested. Contrarily, autonomous decision-making in which an AI unilaterally decides on the proscribed treatment was met with relatively bigger apprehension.
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While item 3 broadly queries the fear of AI in general, item 2 specifically inquiries about its specific impacts on the economic sector. Items 1 and 4 query a specific functionality of AI, with item 4 focusing on the human-machine connection. Thus, the items are mixed in their expressiveness and aim at different aspects of AI’s impact.

As a consequence, we decided to focus our measurement solely on AI as it depicts the core issue of the nascent technology, i.

Visual behavior modelling for robotic theory of mind

In children, the capacity for ToM can lead to playful activities such as “hide and seek”, as well as more sophisticated manipulations such as lying3.

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Researchers typically refer to the two agents engaged in Behavior Modeling or ToM as “actor” and “observer.” The actor behaves in some way based on its own perception of the world.

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In the simplest case, the actor behaves deterministically, and the observer has full knowledge of the world external to the actor.

For example, the observer must at least be able to understand symbolic questions, make decisions, and formulate responses. The ability to follow instructions, in itself, involves a fairly advanced degree of mental reasoning.

Meltzoff7 noted this challenge and proposed the need for non-verbal tests to assess theory of mind. For example, one non-verbal assessment of ToM involves testing whether a child will mobilize to assist another person that appears to be struggling to open a door.
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Exploring the impact of Artificial Intelligence and robots on higher education through literature-based design fictions

Maybe it’s a bit weird to say, but it’s about developing mutual understanding and… respect. Like the bots can sense your feelings too and chip in with a word just to pick you up if you make a mistake. And you have to develop an awareness of their needs too. Know when is the right time to say something to them to influence them in the right direction. When you watch the best teams they are always like talking to each other.

Sitting on the bus I look at the plan for the day suggested in the University app. A couple of timetabled classes; a group work meeting; and there is a reminder about that R205 essay I have been putting off.

Primer on artificial intelligence and robotics

This article provides an introduction to artificial intelligence, robotics, and research streams that examine the economic and organizational consequences of these and related technologies. We describe the nascent research on artificial intelligence and robotics in the economics and management literature and summarize the dominant approaches taken by scholars in this area.

Scholars have been increasingly interested in the economic, social, and distributive implications of artificial intelligence, robotics, and other types of automation.
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This article is a primer on artificial intelligence, robotics, and automation. To begin, we provide definitions of the constructs and describe the key questions that have been addressed so far. We also describe ways in which organizational scholars have been using artificial intelligence tools as part of their research methodology.

Studies of artificial intelligence and robotics base their theory and analysis on constructs of automation, robotics, artificial intelligence and machine learning, and automation. It is important that organizational scholars carefully define any such constructs in their studies and to avoid confusing these related but distinct constructs.

Automation is not a new concept, as innovations such as the steam engine or the cotton gin can be viewed as automating previously manual tasks.

While artificial intelligence, robotics, and automation are all related concepts, it is important to be aware of the distinctions between each of these constructs. Robotics is largely focused on technologies that could be classified as “manipulators” as per the IFR definition, and accordingly, more directly relates to the automation of physical tasks. On the other hand, artificial intelligence does not require physical manipulation, but rather computer-based learning. The distinction between the two technologies can become fuzzier as applications of artificial intelligence may involve robotics or vice versa.

In many cases, a computer or robot may be able to complete relatively low-value tasks, freeing up the human to focus efforts instead on high-value tasks.

Similarly, artificial intelligence and robotics technology have the capacity to reshape firms and change the structure of organizations dramatically. As discussed above, the adoption of artificial intelligence and robotics technologies will likely alter the bundle of skills and tasks that many occupations are comprised of. By that aspect alone, these technologies will reshape organizations and force firms to restructure themselves to account for these changes. In addition, the composition of the labor force may change to adopt to the new set of skills that are most valued.

There are a variety of other questions surrounding artificial intelligence and robotics that we encourage organizational scholars to turn to. One topic that has yet to be explored in much detail surrounds the establishment and firm-level consequences for adoption of artificial intelligence and robotics technology. Research could examine performance consequences as well as outcomes related to firm organization and strategy. Scholars can study in what circumstances and in what kinds of firms such adoption has the greatest impact. The adoption of the technology itself can be viewed as an outcome, and scholars can examine what circumstances and factors encourage or discourage the use of these technologies. Certain industries, management styles, or organizational forms may be particularly quick to adopt, and market level forces may also impact the adoption decision. Industry and organizational factors may play a role as well as the backgrounds of individuals and managers within organizations.

There will be a need to evaluate what skills and tasks are still valuable in the labor market compared to skills and tasks that can now be fully automated.

Predicted Influences of Artificial Intelligence on Nursing Education: Scoping Review

Methods: This scoping review followed a previously published protocol from April 2020. In addition to the use of these electronic databases, a targeted website search was performed to access relevant grey literature. Abstracts and full-text studies were independently screened by two reviewers using prespecified inclusion and exclusion criteria. Included literature focused on nursing education and digital health technologies that incorporate AI.

Additionally, nurse educators need to adopt new and evolving pedagogies that incorporate AI to better support students at all levels of education.

Additionally, as the majority of papers included in this review were expository papers and white papers, there is a need for more research in this context. Further research is needed to continue identifying the educational requirements and core competencies necessary for specifically integrating AIHTs into nursing practice.

Nurse educators in clinical practice and academic institutions around the world have an essential leadership role in preparing nurses and nursing students for the future state of AIHTs.

To our knowledge, this is the first scoping review to examine AIHTs and their influence on nursing education. While there has been research conducted on AIHTs and on nursing education as separate research topics, now is the time to realize the critical relationship between these two entities. AIHTs cannot be implemented in an effective manner without the solid foundation of nursing education, in both academic and clinical practice settings.

Artificial intelligence, robotics and eye surgery: are we overfitted?

Historically, the first in-human–robot-assisted retinal surgery occurred nearly 30 years after the first experimental papers on the subject. Similarly, artificial intelligence emerged decades ago and it is only now being more fully realized in ophthalmology. The delay between conception and application has in part been due to the necessary technological advances required to implement new processing strategies.

Chief among these has been the better matched processing power of specialty graphics processing units for machine learning. Transcending the classic concept of robots performing repetitive tasks, artificial intelligence and machine learning are related concepts that has proven their abilities to design concepts and solve problems.

The implication of such abilities being that future machines may further intrude on the domain of heretofore “human-reserved” tasks. Although the potential of artificial intelligence/machine learning is profound, present marketing promises and hype exceeds its stage of development, analogous to the seventieth century mathematical “boom” with algebra. Nevertheless robotic systems augmented by machine learning may eventually improve robot-assisted retinal surgery and could potentially transform the discipline.

In conclusion, neither artificial intelligence nor robotics is a novel concept, until artificial intelligence is strategically incorporated into robotic systems. Many obstacles exist to human end user adoption of robotics including but not limited to cost, size, functional limits, accuracy, human acceptance and importantly, clearly superior outcomes and safety. In retinal procedures, robotic platforms show a promising role and first human studies are encouraging. That artificial intelligence might enhance these systems is logical, the form that such augmentation takes is only now emerging. What the ultimate form will be is anyone’s guess, as is the eventual role of humans in microsurgery.

Accelerated AI development for autonomous materials synthesis in flow†‡

All experiments presented in this work cover material exploration from a starting position of no prior knowledge.

Such estimates, uncertainties, and covariances are then used in subsequent decision-making policies to calculate expected rewards/regret for running a particular experiment.

Developing Self-Awareness in Robots via Inner Speech

Such a dialogue accompanies the introspection of mental life and fulfills essential roles in human behavior, such as self-restructuring, self-regulation, and re-focusing on attentional resources. Although the underpinning of inner speech is mostly investigated in psychological and philosophical fields, the research in robotics generally does not address such a form of self-aware behavior. Existing models of inner speech inspire computational tools to provide a robot with this form of self-awareness.

The information flow from the working memory to the perception module provides the ground for the generation of expectations on possible hypotheses. The flow from the phonological store to the proprioception module enables the self-focus modality, i.

The cognitive cycle of the architecture starts with the perception module that converts external signals in linguistic data and holds them into the phonological store. Thus, the symbolic form of the perceived object is produced by the covert articulator module of the robot. The cycle continues with the generation of new emerging symbolic forms from long-term and short-term memories. The sequence ends with the rehearsing of these new symbolic forms, which are further perceived by the robot.

On the one side, expectations are related to the structural information stored in the symbolic knowledge base, as in the previous example of the action of grasping.

On the other side, expectations are also related to purely associative mechanisms between situations. Suppose that the system learned that when there is a grasp action, then the action is typically followed by a move action.

The focus of research is investigating the role of inner and private speech in the robot task of the exploration of a scene. To the knowledge of the authors, no other robot system employed inner or private speech, as described in the previous sections.

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One effort will be to test the establishment of self-awareness in AI agents empirically. Our approach offers the advantage that robots’ inner speech will be audible to an external observer, making it possible to detect introspective and self-regulatory utterances. Measures and assessment of the level of trust in human-robot interaction involving vs.

The Effects of Physically Embodied Multiple Conversation Robots on the Elderly

When one robot in the Question state queried a person, the other robot showed a backchannel in the Backchannel state. Subsequently, the robot that asked in the Question state produced a comment in the Comment state. In the next Question state, the two robots alternated roles with each other.

In this study, we carried out an experiment comparing two conditions: physical and virtual. In the physical condition, an elderly participant talked with a conversation system that operated two physical robots, namely CommUs. In the virtual condition, an elderly participant interacted with the system that operated two virtual 3D characters that resembled CommUs, namely virtual CommUs. The participants were asked to answer the questionnaire after talking with either pair of robots.

The first set, consisting of five questions, was employed to get the user accustomed to the conversation with physical or virtual robots. At first, the robots introduced themselves and requested the participant to answer questions. Then, they asked questions based on the proposed model described in section Related Works. After completing five questions, they said that the training session was over and asked him/ her to wait for a while until the next conversation would start.

The second set was used as the experimental stimuli and consisted of 20 questions, each of which belonged to either type of topic: relatively light and serious. The former consisted of 14 questions about childhood memory as well as experience and preference for travel. The latter consisted of six questions about health condition, feelings in daily lives, and expectations or anxiety for the future. Table 1 shows the questions and the order in which they were presented. The robots first asked the participant to answer questions as in the training session and then started asking questions. As with the first scenario, the system was allowed to activate the listening function for only half of the questions on light topics, namely seven out of the 14 questions.

The questions marked with one or more asterisks in Table 1 correspond to the listening function. After finishing them, they thanked the participants for answering their questions. Note that they terminated the conversation after 15 min even if they did not finish asking all questions.

For each question in both scenarios, some expected user replies were listed. In addition, a backchannel and comment utterances were prepared for each expected word, which were produced when the system detected the user utterance containing it. Meanwhile, another ambiguous comment was prepared for each question, which was used when it did not detect any expected words.

First, the participant received an explanation about the procedure of the experiment from an experimenter in a waiting room. The participant then moved to the experimental room and sat down in front of the robots. After the experimenter confirmed it through the camera installed in the room, the participant made the system start the first conversation for practice. Then, the system terminated the conversation either when 5 min had passed or all five questions were asked. The experimenter then made the system start the next conversation, which was the experimental stimulus. The system lasted the conversation until either when 15 min had passed or when all 20 questions were asked.

The virtual robot is limited in its movement and facial expression. Regardless that, one of the advantages of a virtual robot is the capability of arbitrary non-verbal expression which is difficult for a physical robot. However, the most effective expression in the conversation for the virtual robot is not apparent. As the first step, therefore, we compared the virtual robot with the physical robot under the same conditions. It is noteworthy that the current result did not suggest that the advantages of having a physical body are always shown under any conditions.

Accordingly, to prevent potential variance in the data, the order and the topics in the experiment were limited to be fixed for all participants.

In this study, aiming to develop a robot as a conversation partner for the elderly, we investigated whether the robot should have a physical body or a virtual body. We implemented conversation systems in which two physical or virtual robots interacted with an elderly person. We conducted an experiment with 40 participants to confirm which type of robot they would interact with more and feel closer to. The results of the experiment indicated that the elderly, who is successfully responded to by robots, engaged more in the conversation with the physical robots than the virtual robots.

The 2014 Survey: Impacts of AI and robotics by 2025

Among the key themes emerging from 1,896 respondents’ answers were: – Advances in technology may displace certain types of work, but historically they have been a net creator of jobs. – We will adapt to these changes by inventing entirely new types of work, and by taking advantage of uniquely human capabilities. – Technology will free us from day-to-day drudgery, and allow us to define our relationship with “work” in a more positive and socially beneficial way. – Ultimately, we as a society control our own destiny through the choices we make. – Automation has thus far impacted mostly blue-collar employment; the coming wave of innovation threatens to upend white-collar work as well.

These two groups also share certain hopes and concerns about the impact of technology on employment.

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Development and Application of Artificial Intelligence in Auxiliary TCM Diagnosis

As the first of the four TCM examinations, inspection has the characteristics of intuitiveness and simplicity and plays an important role in the TCM diagnosis. Through inspection, the physician observes the patient’s general or local appearance and morphology, thus achieving the goal of determining the patient’s disease state.

The tongue and internal viscera and bowels are connected by meridians. Exuberance and debilitation of the healthy qi or pathogenic qi and the changes of qi, blood, fluid, and humor can be obtained by observing the tongue manifestation. Tongue diagnosis mainly includes looking at the tongue body and tongue fur. The tongue body mainly reflects the patient’s exuberance and debilitation of qi and blood and strength and weakness of the viscera and bowels. The location and nature of the disease can all be reflected by tongue fur.

AI has great potential in the development of healthcare and presents an opportunity to modernize the development of TCM diagnostics. Over the past decades, many scientists and medical scientists have contributed to the combination of them. Combining AI with TCM diagnosis cleverly avoids the malpractice of uncertainty in doctors’ subjective judgment, makes the diagnosis information more real, and improves the accuracy of clinical diagnosis.

Although AI has made some achievements in the application of TCM diagnosis, there is still a lot of room for development. For AI diagnostic accuracy in auxiliary TCM diagnosis, there seems to be a lack of relevant reports. For inspection, the application of AI is limited only to facial and tongue diagnosis, and inspection has not been given to other parts of human body. The tongue color is easily influenced by food and medication, and it is worthwhile to explore how to intelligently identify whether it is influenced by such factors. In the facial diagnosis, it is mainly limited to the analysis of facial color. Although there are studies on the analysis of facial expressions in some countries, it is incapable of establishing a connection between facial changes and TCM symptoms. However, there are still problems such as the language of inquiry is not standardized and the inquiry of complex diseases is not yet realized.

The intelligence of TCM diagnosis will be the path to the early modernization of TCM. Secondly, the state needs to improve relevant policies and regulations to protect patients’ personal data and disease-related information from being leaked and applied for other purposes. In addition, a reasonable cost standard for the use of smart diagnostic devices should be set.

AI and Law What should a robot be allowed to do?

On the other hand, there is the question of who should benefit when AI produces intellectual property. The works they created, however, were mostly based on random algorithms that cannot be compared in any way with human intelligence. In the past ten years, however, AI seems to have “reached a new level of development”, as the BMWi acknowledged in its paper. Today robots write entire film scripts and compose pieces of music. It can hardly be compared with the randomised doodles from back then. So can a robot become a creator – an originator? Lawyers like to refer to a precedent from the animal world. Slater gave his camera to a macaque called Naruto, who snapped a “monkey selfie” that went viral three years later and spread around the world. The animal rights organisation, Peta, tried to sue, on behalf of Naruto, for the proceeds from the photo. This was followed by a lawsuit lasting several years, which was fought in the United States. In 2017, Slater agreed to an out-of-court settlement and pledged to donate a quarter of the future proceeds from the Naturo selfie to Peta. The San Francisco Court of Appeal, however, did not accept the settlement. The lawsuit was dismissed on the grounds that Naturo itself had no say in the settlement and the aim all along had been to set a precedent. In addition, Peta had to pay the photographer’s legal fees. He later sued the German punk band, Terrorgruppe, for using the photo on a record cover without his authorisation. The US Copyright Office stated that copyrights can only be granted to humans and therefore not to animals – or robots. Currently, courts and governments do not absolve people of their responsibility for the AI they have developed, even if their inventions become inventors themselves. The rights and obligations remain with the users of the AI or with those who operate it. The British Copyright Designs and Patent Act came to this decision back in 1988 when the first home computers raised questions similar to those posed by the “learning robot” today. The EU Commission also seems to be sympathetic to this idea.

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A Literature Survey on Artificial Intelligence

Much of the research covered in this review could be applicable to developing strong AI. Creating a machine capable of understanding the concepts behind the words is important because it allows for more humanlike conversations as well as improved translation. There is also fascinating research into detecting human emotions through audio and video cues. In particular, this paper provides a full review of recent developments within the field of artificial intelligence and its applications. The work is targeted at new aspirants to the artificial intelligence field.

In the last few years, there has been an arrival of large amount of software that utilizes elements of artificial intelligence. Subfields of AI such as Machine Learning, Natural Language processing, Image Processing and Data mining have become an important topic for todays tech giants. Machine Learning is actively being used in Googles predictive search bar, in the Gmail spam filer, in Netflixs show suggestions. Image Processing is necessary for facebooks facial recognition tagging software and in Googles self driving cars. Data Mining has become a slang for software industry due to the mass amounts of data being collected every day.

There are abundant complications when trying to create an intelligent system. Much of the old or simple AI is a list of conditions for what reaction to have based on expected stimuli.

Many complications involve Human Machine interaction because of the complexity of human interaction. A lot of the communication that happens that happens between humans cannot be coded facts a machine could simply recite. There are hundreds of subtle ways that humans interact with each other that affect communication. Innovation in voices, body language, and response to various stimuli, emotions, popular culture facts, and slang all affect how two people might communicate.

Handling large amount of inconsistent data is another complication, because inconsistent data is inevitable but difficult to process.

The second type of case happens whenever we fail to fully align the AIs goal with ours, which is strikingly difficult.

Lets just assume that your system gets hacked or crashed down then it will be quite a problem.

By designing radical latest technologies, and thus produced super intelligent system might be able to help us wipe out poverty, disease or may be even war.

Webinar 30 March: Trust and Transparency in Artificial Intelligence

Perspective for Future Medicine: Multidisciplinary Computational Anatomy-Based Medicine with Artificial Intelligence

The MCA-based medicine might be one of the best solutions to overcome the difficulties in the current medicine.

Designing and Applying a Moral Turing Test

This study attempts to develop theoretical criteria for verifying the morality of the actions of artificial intelligent agents, using the Turing test as an archetype and inspiration. This study develops ethical criteria established based on Kohlberg’s moral development theory that might help determine the types of moral acts committed by artificial intelligent agents. Subsequently, it leverages these criteria in a test experiment with Korean children aged around ten years. The study concludes that the 10-year-old test participants’ stage of moral development falls between the first and second types of moral acts in moral Turing tests. We evaluate the moral behavior type experiment by applying it to Korean elementary school students aged about ten years old.

Artificial intelligence has proven its effectiveness through many applications in society: medical diagnostics, e-commerce, robot control and remote sensing. It has been able to advance many fields and industries including finance, education, transportation and others.

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The political choreography of the Sophia robot: beyond robot rights and citizenship to political performances for the social robotics market

This playful dialogue took place between David Hanson and his designed robot at a robotics trade show in Austin, Texas in March 2016. David Hanson, founder of Hanson Robotics, launched the robot sophia artificial intelligence by ‘chatting’ with it. A video released by CNBC about Sophia quickly garnered millions of views. The world’s leading newspapers including The New York Times, The Guardian, The China Daily, The Times of India and The Sydney Morning Herald published stories about Sophia.

According to Goertzel, the worldwide media attention the Sophia robot garnered starting in 2017 was not a planned publicity stunt by the company.
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The traditional media have played a pivotal role in giving publicity to Hanson Robotics to advance its technological utopia about the future of humanoid robots. The robot evolved into an iconic figure in a fairly short time promoting the idea of the robot as an almost living being. Hanson Robotics has closely monitored Sophia’s public image by preventing the media and journalists from asking Sophia questions that are too difficult or politically sensitive.

While we should recognize the joint human/nonhuman agency in these performances, we should also ask who choreographs us.

It looks like that the embodiment as the special ability offered by social robots is also a stumbling block for designers and social robot business. Desktop assistants, such as Amazon Echo and Google Home, have provided many features at a much lower cost than social robots. Due to the high price, most social robots are mainly marketed for use by companies and public organizations under the headings of care robots or educational robotics. However, it is highly questionable how beneficial the technologies have actually been in these contexts.
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The FII investment forum in Riyadh, Saudi Arabia in 2017 was preceded by the media spectacle in which the Sophia robot was granted Saudi citizenship. What that citizenship meant in practice was not specified in detail by the Saudi authorities or Hanson Robotics. However, the granting of citizenship can be seen as a kind of the culmination point in the political choreography of the Sophia robot.

It should be noted that the FII event was launched and hosted by the Crown Prince of Saudi Arabia, Mohammad bin Salman. His policy was widely condemned in the West after the assassination of journalist Jamal Khashogg by a 15-member squad of Saudi assassins. The extensive arrangements for the 2018 FII Economic Forum were largely cancelled when many invited speakers, companies and media houses refused bin Salman’s invitation to come to Riyadh. Although the FII 2018 Economic Forum eventually failed for Saudi Arabia, the 2017 Forum appears to be a successful media performance from the perspective of both Hanson Robotics and bin Salman.

Does it exist a human-like artificial intelligence?

I would say that we’re not even close to a “real” human-like AI. For all the wonderful things that applications like Siri, Cortana and the like can do, they’re actually really dumb compared to even a child. Of course part of that, IMO, is that most AI applications are not embodied and don’t experience the world the way humans do.

‘Every Great Science Discovery, Invention, Is The Stuff Of Dreams, Not The Stuff Of Reason’: Interview With David Hanson Of Hanson Robotics

Artificial Intelligence robot sophia artificial intelligence

It was not until computer animated characters became profitable and popular that there was a kind of a gold rush for computer animation.

However there has not been a Toy Story moment for character robots. Pixar got into that dominant industry position because they had just enough moral, production and financial backing, but they were considered to be a big risk. Disney did not know whether they were going to succeed or not.

Sure there is formal IP protection to be had, and also discoveries that can go into the public domain. My general principle is 70 percent open and public domain and 30 percent proprietary. For code or designs release it could be 90 percent open but it is the 10 remaining percent that have high value.

Sophia is a good example of what many call the “uncanny valley.” Generally, the more robots resemble us, the more comfortable we feel around them—until they reach a breaking point.
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Artificial Intelligence: Does Consciousness Matter?

Artificial Intelligence Master or Minion?

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As futurists believe, there would more integration of robots in our daily lives, possibly having robot physician, robot care giver, robot driver and even robots as partners.

We need to talk about AI

Kai is Professor of Information Technology and Organisation in the Discipline of Business Information Systems at The University of Sydney Business School.

They are coming for our jobs, and worse, they’re coming to get us. We are besieged by ever more dire warnings about artificial intelligence from increasingly loud voices.
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One frequently cited analysis by Oxford economists suggests that 47 per cent of workers in the US have jobs at high risk of automation over the next two decades. Even though such studies have been heavily critiqued, they feature prominently in the media, albeit these days alongside more nuanced takes on the effects of automation and AI.

At the University of Sydney Business School we have looked at all the news stories from the past year to understand the public conversation around artificial intelligence.

The seductive hyperbole obscures the mounting complexity that is embedded in algorithms which are based on deep neural networks. For example, given the black box nature of how these networks learn, even experts have a lot of difficulty understanding how such systems are reaching the decisions they do.

Companies are grappling with removing such biases from systems without affecting their usefulness but they are often not explicitly revealed and so are difficult to correct.

They are even used to judge English fluency for those seeking Australian skilled migrant visas.

These rules emphasise first and foremost that we cannot and must not attribute agency to an AI. A bank, for instance, is fully accountable for the algorithms it uses.

The second rule ensures AI cannot be used to deceive people into believing they are dealing with another human being. Recently, researchers from the University of Chicago demonstrated that machine learning algorithms can write totally believable fake product reviews.

Formulating a set of ethical rules to guide how we deploy this technology in society is a good first step.

There have of course been calls not to regulate AI at all. This would ensure private entities, corporates and governments would be able to experiment and develop the technology unencumbered. However, issues of responsibility, explainability and inability to fully audit AI make this technology unpredictable.

This content has been produced by Sydney Business Insights, an initiative of the University of Sydney Business School, in commercial partnership with AFR BOSS magazine.

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Social Robots – a New Perspective in Healthcare

Social robots can interact with humans in both collaborative settings, such as shopping malls, and personal settings, performing tasks within domestic services and healthcare.

Unmanned Aircraft Systems in the Cyber Domain

Autonomous systems currently exist in seven different areas: air, ground, sea, underwater, humanoids, cyber, and exoskeletons.

Technology flourishes as free markets expand, and free-roaming robotics will be the key to market expansion for the robotics revolution. Advanced artificial intelligence is the key enabler to expand market breadth and depth. Policy and ethical issues come as the autonomous revolution continues accelerating productivity without the underlying stability of a constant linear growth rate.

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Or the fact, that Saudi women and their children are denied citizenship if they marry foreign men.

Marketing Artificial Intelligence: Creating the AI Archetype for Evoking the Personality Trust

There has also been a study done into creating machines that may exhibit emotions. We are still a considerable way off from seeing a system that may seem to be alive.

It is against this backdrop that this paper seeks to understand how the developments in AI are imposing specific requirements on the process of its marketing. In the next sections, we will examine how AI can be marketed using the framework of the traditional marketing mix.

Deep learning-based question answering system for intelligent humanoid robot

The development of Intelligent Humanoid Robot focuses on question answering systems that can interact with people is very limited. This kind of robot can be used widely in hotels, universities, and public services. The Humanoid Robot should consider the style of questions and conclude the answer through conversation between robot and user.

Our research goal is to make a question answering system using deep learning with self-learning capability in the Humanoid Robot. Previously we have tried to find a question answering system journal for Humanoid Robot, but we have not found another journal that discusses it properly.

Deep learning is a specific subset of Machine Learning, which is a specific subset of Artificial Intelligence. Computer vision and Natural Language Processing are examples of a task that Deep Learning has transformed into something realistic for robot applications. Using Deep Learning to classify and label images and text will be better than actual humans.

For the knowledge base, our Humanoid Robot can get the knowledge by 3 methods. The robot will listen and save knowledge to the knowledge base.

For models using RNN based Encoder, we get the optimum model at 93.000th iteration, while the model using the CNN based Encoder, we get the optimal result at 43. From two different approaches, RNN based Encoder gives better EM and F1 score results. The EM and F1 scores between dev and test have much better results, because we use 10% of training data, for testing data. The proposed model successfully makes our Intelligent Humanoid Robot to accept questions and respond to the user with appropriate answers.

Our model is successfully obtained knowledge using big data technology and answer the questions from the user using deep learning.

What topic do you want to hear about?: A bilingual talking robot using English and Japanese Wikipedias. InThe 26th International Conference on Computational Linguistics, Proceedings of COLING 2016 System Demonstrations 2016 Dec 11.

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I was born in the EU, therefore I am a citizen of the EU and, at the same time, one of its member states.

Created and activated in 2015 in Austin, Texas, US, Sophia is a robot modeled after actress Audrey Hepburn. Sophia uses artificial intelligence, visual data processing and facial recognition to interact with the environment and has made many impressive appearances at various events.

In October 2017, Sophia was awarded an honorary citizenship by Saudi Arabia.

On the one hand, we can’t help but contribute to the development of AI, watch in wonder when its applications spring around us and enjoy its perks. Self-driving cars are here: California already approved the testing of driverless cars on roads without human safety drivers. The US Congress is debating how the government and law enforcement can use AI to provide better services to the population.

A common theme in pop culture is that robots will realize that we are weak and that they can dominate us.

A 2015 McKinsey study showed that “45% of the activities individuals are paid to perform can be automated by adapting currently demonstrated technologies”. Financial managers, physicians, and senior executives will also be forced to redefine what they do. In 2016, the same analyst firm analyzed which industries are more likely to be affected by automation. The study found manufacturing, food service, accommodations and retailing to be among the most automatable activities. Managing and developing people, decision making, planning, or creative work will be more difficult to automate.

Sex and gender differences and biases in artificial intelligence for biomedicine and healthcare

We address their existing and potential biases and their contribution to create personalized therapeutic interventions. We examine the sex and gender issues involved with the generation and collection of experimental, clinical and digital data. Furthermore, we review a number of technologies to analyze and deploy this data, namely Big Data Analytics, Natural Language Processing and Robotics. Those technologies are becoming increasingly relevant for Precision Medicine while being exposed to potential sex and gender biases.

Fair data generation and explainable algorithms are fundamental requirements for the design and application of artificial intelligence to optimize for health and wellbeing across the sex and gender spectrum.

A desirable bias implies taking into account sex and gender differences to make a precise diagnosis and recommend a tailored and more effective treatment for each individual. This represents a much more accurate approach than collapsing all sex and gender categories into a single one, such as data generated from mixed sex or gender cohorts16.

Another source of undesirable bias is the misrepresentation of the target population, leaving minorities out.

Even nowadays, male mouse models are overall more represented than female models in basic, preclinical, and surgical biomedical research25. A recent analysis of data on 234 phenotypic traits from almost 55,000 mice showed that existing findings were influenced by sex26.

Differences in the physiology of sexes33 might translate into clinically relevant differences in pharmacokinetics and pharmacodynamics of drugs. These differences, taken together with the underrepresentation of women in clinical trials, can explain why women typically report more adverse event reactions compared with men34.

Accounting for sex and gender differences leads to a better understanding of the pharmacodynamic and pharmacokinetic action of a drug.

For instance, data from GWAS targeting smoking behaviour have shown sex-associated genetic differences that influence smoking initiation and maintenance55. Interestingly, these differences complement the differential effectiveness of tobacco control initiatives based on the sex of the individuals that receive the preventative messages56.

Awareness of sex and gender differences through biomedical Big Data could lead to a better risk stratification.

A case of undesirable biases in NLP is the use of text corpora containing imprints of documented human stereotypes that can propagate into AI systems85.

A flourishing area of NLP is that of medical chatbots, aiming to improve users’ wellbeing through real-time symptom assessment and recommendation interfaces. A dialogue of a chatbot can be modelled with available metadata to adjust to features of the replier in terms of gender, age, and mood90. Although both proved to be effective in clinical trials, the lack of data on their long-term effects is raising certain concerns.

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Robots can serve a diverse range of roles in improving a human’s tasks, health and quality of life.

Despite the progress of AI models in recent years, the complexity of their internal structures has led to a major technological issue termed the ‘Black box’ problem.

On one hand, an explanation of the decisional process would enable to find potential mistaken conclusions derived by training an algorithm with misrepresented data. This will facilitate the identification of undesirable biases generally found in clinical data with unbalanced sex and gender representation.

For instance, a widely used approach to ensure fairness in data processing is to remove some sensitive information, such as sex or gender, and all other possible correlated features112.

Although affirmative action represents a remedy for unfair algorithmic discrimination, ensuring the quality of the data used for algorithm training is also crucial. For instance, a study found that only 17% of cardiologists correctly identified women as having greater risk for heart disease than men114.

5 Things To Demystify robot sophia artificial intelligence

Fairness is highly context-specific and requires an understanding of the classification task and possible minorities.

The development and application of fair approaches will be critical for the implementation of unbiased and interpretable models for Precision Medicine106,116.

Technological advances in machine learning and AI are transforming our health systems, societies, and daily lives120.

The ambitious goals set by Precision Medicine will be achieved using the latest advances in AI to properly identify the role of inter-individual differences. The proper use of innovative technologies will pave the way towards tailored and personalised disease prevention and treatment, accounting for sex and gender differences and extending towards generalized wellbeing.

The Future of Customer Experience in the Information Age of Artificial Intelligence – Get Ready for Change

Enterprises are constantly seeking to accomplish the mission of enriching customers through outstanding service by supplanting legacy networking systems with today’s most advanced technology solutions.

Our Privacy Policy

We will not sell, distribute or lease your personal information to third parties unless we have your permission or are required by law to do so. 

If you believe that any information we are holding on you is incorrect or incomplete, please write to or email us as soon as possible, at the above address. We will promptly correct any information found to be incorrect.

  1. Max Infosys may change this policy from time to time by updating this page. You should check this page from time to time to ensure that you are happy with any changes.
  2. While we use encryption to protect sensitive information transmitted online, we also protect your information offline.
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We have implemented the following:

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Artificial Intelligence
Why the business of artificial intelligence Is The Only Skill You Really Need Primer on artificial intelligence and robotics This
Artificial Intelligence
What Is robotics and artificial intelligence? In times of medical crisis, robotics and artificial intelligence helps humans manage emergencies and
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What is Artificial Intelligence Examples What is Artificial Intelligence Examples - A computational agent is an agent whose decisions about
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What first artificial intelligence robot 2021 The History of Artificial Intelligence The application of artificial intelligence in this regard has
Artificial Intelligence
The Ultimate Guide To use of artificial intelligence and robotics in healthcare industry The use of artificial intelligence and robotics
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6 Super Useful Tips To Improve the development of artificial intelligence Artificial Intelligence in Civil Engineering Development of Artificial intelligence
Artificial Intelligence
Artificial Intelligence for Every Individual? It’s Easy if You Do it Smart ARTIFICIAL NARROW INTELLIGENCE Commonly called Weak AI, ANI

13 Easy Ways To ai business school microsoft

However, according to recent developments , Ai business school microsoft AI editors are already showing signs of inaccuracy. The AI reportedly used a photo of Leigh-Anne Pinnock on a story about her fellow bandmate Jade Thirlwall’s experiences with racism.


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A third, particularly significant application of AI is in combating the massive global problem of climate change. Sometimes, a breakthrough may take as long as a decade, but machine learning can accelerate it in much the same way as drug development.

How Cortana help and enhance Business Intelligence?

How China’s AI experts can beat Google and Microsoft by 2030

Over 40 per cent of the top AI-related academic papers published worldwide in 2015 had at least one or more Chinese researchers. Chinese AI-based patent applications grew 186 per cent between 2010 and 2014, a huge increase from the previous five-year period.

These favourable policies have inspired innovations from both smaller firms and internet giants in China.

Baidu, for example, has developed a cutting-edge neural-network-based machine translation system that has achieved a speech recognition accuracy higher than that of humans. It has also launched an open-source platform for autonomous driving solutions, namely Project Apollo, to speed up the -development of self-driving vehicles.

AI research in academia has spread from being a focus at a few elite universities to those across China. Chinese academics have built a robust research community, which allows them to tap AI resources in both Chinese and English. Large numbers of Chinese science and engineering graduates are now flocking to the industry.
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China is more than capable of becoming a leader in AI.

As of now, groundbreaking research is still mostly being done in the West, where the focus is on the science and infrastructure behind AI technology. Chinese academics, on the other hand, tend to research new applications of pre-existing technology.

Chinese companies are very good at launching new products and features quickly to the market, as they are well-versed in tapping newly identified opportunities. In the same vein as academia, Chinese companies primarily rely on new applications of pre-existing technologies rather than creating new ones.

China needs a fundamental change to truly become a leader in AI. There needs to be a greater emphasis on developing the science behind the technology rather than emphasising new applications.

To achieve the goal of becoming a global AI leader by 2030, China will need to take at least two essential steps. First, it should redraft its incentives policies to motivate local companies and academics to conduct research on new AI technologies.
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Andrew Ng, a leading American AI researcher, once said that AI would become the “new electricity” – transforming not just one industry, but all of them. AI can be and, in fact, is already being utilised across different sectors, creating unparalleled opportunities to “activate new businesses”.

Another failed attempt of AI replacing humans: Microsoft AI Editor already shows signs of inaccuracies

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Ready or Not, AI Comes— An Interview Study of Organizational AI Readiness Factors

In sum, our paper is a first step toward comprehensively conceptualizing and operationalizing organizational AI readiness. As such, we provide additional empirical groundwork for theorizing on technology adoption and readiness in general. Further, our AI readiness factors serve as the necessary foundation for purposeful decisions in the entire AI readiness and adoption process. Hence, we extend the body of descriptive knowledge on AI readiness and provide a first building block for prescriptive knowledge to guide organizations toward successful AI adoption.

This paper’s theoretical foundation is twofold: First, with AI being a technological innovation, the literature on innovation adoption provides the scaffold of our research. Second, research on organizational readiness for change emphasizes readiness as a necessary precursor for organizational change, such as AI adoption.

In sum, research so far provides fruitful theoretical groundwork but cannot provide relevant organizational AI readiness factors. Drawing on this previous work, we seek to provide a sound set of organizational AI readiness factors and corresponding indicators for AI readiness assessments.

The AI readiness categories and factors describe the organizational chassis for developing AI readiness. Besides, our explorative interviews provide insights that help to understand the hurdles for successful AI adoption. Consequently, our findings are a necessary precursor to indicate how organizations can explore AI’s potentials. However, owing to AI’s characteristics as GPT, AI adoption differentiates from previously discussed technology adoption. Organizations define and pursue individual AI adoption purposes that describe how they seek to accrue value from the wide range of AI’s potential application scenarios. Thus, AI adoption can have different facets depending on the distinct adoption purpose. The AI adoption purpose can span from single use-cases to self-contained AI-driven business models.

Building on prototypes, experiments, and preceding projects as steps of AI adoption, companies may then push their AI adoption purpose to shift over time. For example, they may opt to start with internal applications before involving the customer interface.

In the following, we will position the results of our exploratory interview study within the existing adoption and readiness literature. Thereby, we conceptualize AI readiness as a valuable addition to the scholarly knowledge base and a necessary foundation for successful AI adoption. Further, we discuss the interdependencies between AI readiness and AI adoption as intertwined concepts.
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First, AI readiness comprises 18 readiness factors along five categories that provide the organizational chassis for developing AI readiness. Second, beyond the specific factors, AI readiness entails the understanding of purposeful AI adoption.

Regarding practical implications, our paper grants insights into opportunities and challenges for AI adoption. The AI readiness factors provide comprehensive guidance to decision-makers on relevant managerial action fields. Based on an AI readiness assessment, decision-makers may reflect and adapt the factors to specific organizational needs. Setting and developing adequate AI readiness target levels is compulsory in order to derive actionable measures for successful AI adoption.

We combined insights from interviews with 25 AI experts with findings from scientific and practitioner literature to compile 18 AI readiness factors and 58 illustrative indicators in five categories. Further, we discussed that organizations must continuously assess and develop their AI readiness in the AI adoption process and described relevant aspects to consider. This includes AI’s nature as a GPT, the context- and purpose-specifics, and the mutually reinforcing interplay of AI readiness and AI adoption.

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6 Resources To Help Improve Your Data Science Skills

Jobs in the field of data science are becoming increasingly popular. However, they require you to have a particular skillset that might not be built into the curriculum of your graduate program.

Many of the platforms below have implemented “digital badging” for their courses. Digital badges are certifications that you have completed a course or training module. LinkedIn and Twitter allow you to embed these badges into your profile, and you can also include them on your CV. Host platforms may maintain directories of all users who have been awarded badges so that companies can confirm your credentials.

Coursera offers many courses and certificates in data science including deep learning, data visualization, biostatistics in public health, and more. While courses are hosted by Coursera, they are developed by universities and companies such as IBM, Google Cloud Platform, and Johns Hopkins.

DataCamp offers courses and “skill tracks” designed to help sharpen your data science skills in R, Python, SQL, git, shell, spreadsheets, theory, Scala, and Tableau. The free subscription to DataCamp includes the first chapters in all courses, 100+ coding challenges and 7 projects.

The Data Incubator Data Science Fellowship Program is an 8-week data science bootcamp specifically for PhD and Master’s students and is free for admitted fellows. There are weekly mini projects as well as a capstone project in which you build a web application.

Insight’s program is a 7-week post-doctoral bootcamp that is tuition-free and provides need-based scholarships to help cover living expenses. During the bootcamp, you will undergo self-directed, project-based learning under the guidance of top industry data scientists. The program culminates in the completion of a capstone project that you present during job interviews with mentor companies.

AI Transparency & Explainability.

Pamela developed a proprietary Credit Derivative trading system for Deutsche Bank and a quantitative market risk VaR system for Nomura. Pamela is the CEO of Jasper Consulting Inc, a consulting firm through which she provides advisory and audit services for AI Ethics governance.

Renée Cummings is an AI ethicist, data activist, criminologist, criminal psychologist and therapeutic jurisprudence specialist. She’s also the historic first Data Activist in Residence, at the University of Virginia’s School of Data Science and a community scholar at Columbia University.

Renée also specializes in AI for social good, justice-oriented AI design, social justice in AI policy and governance, and using AI to save lives.

Nikita Lukianets, a Founder of the Open Ethics initiative that fosters the inclusive dialogue between experts and citizens to design systems where humans and AI successfully work together.

Nikita Lukianets has more than 10 years of experience in Human-Computer Interaction and has partnered with multiple organizations to help them build human-centered interfaces.

While human-level artificial intelligence has not been achieved yet, the implications that arise from the integration of AI into human societies are visible in the narrow fields already.

Open Ethics is about making clear how autonomous technologies make their decisions.

AI for social good: unlocking the opportunity for positive impact

Results from several recent studies hint at the potential benefits of using AI for social good. Amnesty International and ElementAI demonstrated how AI can be used to help trained human moderators with identifying and quantifying online abuse against women on Twitter19.

This wealth of projects, sometimes isolated, has led to several meta-initiatives. For example, the Oxford Initiative on AIxSDGs26, launched in September 2019, is a curated database of AI projects addressing SDGs that indexes close to 100 projects. Once publicly accessible, it should support a formal study of such projects’ characteristics, success factors, geographical repartition, gaps, and collaborations.

Related principles are encoded in the Montreal Declaration for Responsible AI35 and the Toronto Declaration36. The European Commission states that AI needs to be lawful, ethical and robust, to avoid causing unintended harm.

A recent UN report40 details how over 30 of its agencies and bodies are working towards integrating AI within their initiatives. According to the report, AI4SG projects need to be approached as a collaborative effort, bringing communities together to carefully assess the complexities of designing AI systems for SDGs. These initiatives should aim to involve NGOs, local authorities, businesses, the academic community, as well as the communities which these efforts support.

This process involved setting up focused working groups around key topics and repeatedly coming together to disseminate the results, obtain feedback and discuss within the wider group.

The results of the study have revealed worrying patterns of online abuse, estimating 1.1 million toxic tweets being sent to women in the study across the year, black women being 84% more likely than white women to experience abuse on the platform. The core of the analysis was based on using machine learning approaches to pre-filter the data, followed by applying computational statistics methods. The team has additionally evaluated the feasibility of using a fine-tuned deep learning model for automatic detection of abusive tweets61.

We encourage AI experts to actively seek out opportunities for delivering positive social impact. Ethics and inclusivity should be central to AI systems and application-domain experts should inform their design.

Top 8 open source AI technologies in machine learning

com aspires to publish all content under a Creative Commons license but may not be able to do so in all cases. You are responsible for ensuring that you have the necessary permission to reuse any work on this site. Red Hat and the Red Hat logo are trademarks of Red Hat, Inc.

Artificial Intelligence Holds Enticing Promise, Needs Framework, Say OECD, Microsoft, IEEE

As artificial intelligence technology spreads its wings, governance issues are emerging, as are international discussions, including a range of activities planned for 2018.

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Masahiko Tominaga, vice-minister for policy coordination at the MIC, presented artificial intelligence with the potential for solving various problems and with “enormous benefits” to be expected.

Under the leadership of Japan, the OECD started an international, multi-stakeholder dialogue on AI, she explained, taking stock of who is doing what and what differences are emerging.

According to an OECD spokesperson, in 2018, the OECD is planning to produce and analytical/policy report on AI building on the OECD conference held last October.

Guidelines for clinical trial protocols for interventions involving artificial intelligence: the SPIRIT-AI Extension

The SPIRIT-AI extension includes 15 new items, which were considered sufficiently important for clinical trial protocols of AI interventions. These new items should be routinely reported in addition to the core SPIRIT 2013 items.

The SPIRIT-AI and CONSORT-AI extensions were simultaneously developed for clinical trial protocols and trial reports. Both guidelines were developed in accordance with the EQUATOR Network’s methodological framework.29 The SPIRIT-AI and CONSORT-AI steering group, consisting of 15 international experts, was formed to oversee the conduct and methodology of the study.

Class activation map—Class activation maps are particularly relevant to image classification AI interventions.

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The title should be understandable by a wide audience; therefore, a broader umbrella term such as “artificial intelligence” or “machine learning” is encouraged. More precise terms should be used in the abstract, rather than the title, unless broadly recognised as being a form of artificial intelligence/machine learning.

Explanation: The intended use of the AI intervention should be made clear in the protocol’s title and/or abstract. This should describe the purpose of the AI intervention and the disease context.1936 Some AI interventions may have multiple intended uses, or the intended use may evolve over time.

Explanation: The measured performance of any AI system may be critically dependent on the nature and quality of the input data.40 The procedure for how input data will be handled—including data acquisition, selection, and pre-processing before analysis by the AI system—should be provided. Completeness and transparency of this process is integral to feasibility assessment and to future replication of the intervention beyond the clinical trial.

Poor quality or unavailable data can also affect non-AI interventions. For example, suboptimal quality of a scan could impact a radiologist’s ability to interpret it and make a diagnosis. It is therefore important that this information is reported equally for the control intervention, where relevant.

Explanation: A description of the human-AI interface and the requirements for successful interaction when handling input data should be described.21 A description of any planned user training and instructions for how users will handle the input data provides transparency and replicability of trial procedures. Poor clarity on the human-AI interface may lead to a lack of a standard approach and carry ethical implications, particularly in the event of harm.

Explanation: Reporting performance errors and failure case analysis is especially important for AI interventions. AI systems can make errors which may be hard to foresee but which, if allowed to be deployed at scale, could have catastrophic consequences.45 Therefore, identifying cases of error and defining risk mitigation strategies are important for informing when the intervention can be safely implemented and for which populations. The protocol should specify whether there are any plans to analyse performance errors.

The SPIRIT-AI extension provides international consensus-based guidance on AI-specific information that should be reported in clinical trial protocols alongside SPIRIT 2013 and other relevant SPIRIT extensions.446 It comprises 15 items: three elaborations to the existing SPIRIT 2013 guidance in the context of AI trials and 12 new extensions.

This study is set in the current context of AI in health; therefore, several limitations should be noted. First, at the time of SPIRIT-AI development there were only seven published trials and no published trial protocols in the field of AI for healthcare. Thus, the discussion and decisions made during the development of SPIRIT-AI are not always supported by existing real-world examples. As the science and study of AI evolves, we welcome collaboration with investigators to co-evolve these reporting standards to ensure their continued relevance. Third, the initial candidate items list was generated by a relatively small group of experts consisting of steering group members and additional international experts.

Currently, most applications of AI/ML involve disease detection, diagnosis, and triage, and this is likely to have influenced the nature and prioritisation of items within SPIRIT-AI. As wider applications that utilise “AI as therapy” emerge, it will be important to re-evaluate SPIRIT-AI in the light of such studies. Additionally, advances in computational techniques and the ability to integrate them into clinical workflows will bring new opportunities for innovation that benefits patients.

Not all data is created equal: the promise and peril of algorithms for inclusion at work

In 2016, Microsoft unveiled its first AI chatbot, Tay, developed to interact and converse with users in real-time on Twitter and engage Millennials.

On March 23, Tay took its first steps on Twitter, posting mostly innocuous messages and jokes, like “humans are super cool”.

First, machine learning algorithms are driven by the data they are fed. Consequently, their outcomes are only as unbiased as the data they are based on. Second, AI and machine learning models can learn and adapt over time as new data is incorporated.

The story of Tay also shows these algorithms can learn and adapt based on the data they are presented with. By implementing improved processes now, firms can reduce bias in datasets and set AI on a positive path of supporting inclusion, rather than perpetuating existing discrimination.

Teresa Almeida – LSE The Inclusion Initiative Teresa Almeida is a research officer at LSE’s The Inclusion Initiative. She has run B2B campaigns across some of the largest enterprise businesses in the area of information and communication technology. Teresa is fascinated with the world of behavioural science and decision-making, with an emphasis on applying insight to deliver tangible results.

Indeed, a plethora of sophisticated and successful tools support companies on this path. This roadmap is the synthesis of the multiple research on the subject and tries to draw the best from the practices of innovative companies. First, Unification of data sources, then Process optimisation and finally Metamorphosis. Unification – Optimisation – Metamorphosis The roadmap shows how learning algorithms should be oriented to transform the business in small incremental steps that together produce powerful change.

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10 Myths About examples of artificial intelligence in education

Exploring the impact of artificial intelligence on teaching and learning in higher education

This paper explores the phenomena of the emergence of the use of artificial intelligence in teaching and learning in examples of artificial intelligence in education. It investigates educational implications of emerging technologies on the way students learn and how institutions teach and evolve.

Adding Artificial Intelligence (AI) to your Learning System with Open eLMS

From AI to Straight A’s: Artificial Intelligence Within Education

I believe that the system could be bolstered by recent advancements in artificial intelligence technology, such as automation and adaptive learning, including gamification and knowledge monitoring.

Artificial Intelligence examples of artificial intelligence in education

When I think back on my own educational experience, I can distinctly remember the impact of technology as the years passed.

By 5th grade, we had all survived Y2K, and the digital revolution had officially begun. Our class had a small set of Alphasmart 2000 keyboard devices so we could begin learning how to type. In 6th grade, our classroom was the site of the school’s first SMART Board, and there was a bulky desktop computer for every 2-3 students.

, but eventually class notes, presentations, modules, and assignments moved online as well.

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Machine learning for human learners: opportunities, issues, tensions and threats

Harm caused by algorithmic activity can be hard to detect and find its cause. Creators of machine learning systems/models should be held accountable for any issues of bias and transparency.

Artificial Intelligence examples of artificial intelligence in education

As we discuss later machine learning is used for a range of different types of human learning situations.

Users may accept its value based on either their own experience of using the system or on studies that have compared its use with other learning approaches.

Principally these relate to the composition of the group and its time-bounded activities. Although the group comprised experts from Europe, Oceania and North America, the voices of Asia and other lands were missing. Also, machine learning is making huge strides as new applications emerge almost daily. There are both parallels and major differences between human deep learning and deep machine learning. As we educate our students about machine learning, they can be encouraged to find out more about their own mental processes. An increased coverage of basic elements of neuroscience starting in primary schools could support students’ developing understanding of both human learning and machine learning.
Artificial Intelligence examples of artificial intelligence in education

Recent research, discussed in this paper, suggests that children aged 11 upwards can undertake such activities but developing associated basic literacies including algorithmic literacy can start much earlier. The specific content and sequencing of such curricula are topics for future research and development as discussed later in this paper.

Artificial Intelligence

For example, in a tutoring system to teach elementary physics, such as mechanics, the system may present the theory and worked-out examples. This should then affect what is presented and what other questions are asked of the student.

Ubiquitious Artificial Intelligence

The best way to develop a truly intelligent system is to use the known properties of the only intelligent system that we know: humans. Intelligent techniques are playing an increasingly important role in engineering and science having evolved from a specialized research subject to mainstream applied research and commercial products.

Manufacturing systems in industries has dramatically changed as a result of advanced manufacturing technologies employed in today’s factory. Factories are now trying to attend and maintain a world-class status through automation that is possible by sophisticated computer programs. The development of CAD/CAM system is evolving towards the phase of intelligent manufacturing system.

A tremendous amount of manufacturing knowledge is needed in an intelligent manufacturing system. Artificial intelligence techniques are designed for capturing, representing, organizing, and utilizing knowledge by computers, and hence play an important role in intelligent manufacturing. Artificial intelligence has provided several techniques with applications in manufacturing like; expert systems, artificial neural networks, genetic algorithms and fuzzy logic.

A “knowledge engineer” interviews experts in a certain domain and tries to embody their knowledge in a computer program for carrying out some task. How well this works depends on whether the intellectual mechanisms required for the task are within the present state of AI. When this turned out not to be so, there were many disappointing results. In the present state of AI, this has to be true.

Banks use artificial intelligence systems to organize operations, invest in stocks, and manage properties. In August 2001, robots beat humans in a simulated financial trading competition. Financial institutions have long used artificial neural network systems to detect charges or claims outside of the norm, flagging these for human investigation. AI is more S/W related so the game can be easier or harder.Banks use intelligent software applications to screen and analyze financial data.

Research Methods for Education With Technology: Four Concerns, Examples, and Recommendations

The success of education with technology research is in part because the field draws upon theories and methods from multiple disciplines. However, drawing upon multiple disciplines has drawbacks because sometimes the methodological expertise of each discipline is not applied when researchers conduct studies outside of their research training.

The focus here is on research using methods drawn largely from psychology, for example, evaluating the impact of different systems on how students perform. The methodological concerns discussed are: low power; not using multilevel modeling; dichotomization; and inaccurate reporting of the numeric statistics.

The methods—both the design of the study and the statistical procedures—were examined for concerns that a reviewer might raise. These were chosen both by how much they may affect the conclusions and how easily they can be addressed. While these comments are critical, the purpose of the paper is to be constructive for the field. These were picked because of how well they illustrate the concern. Before doing this, some background on hypothesis testing is worth providing. Some statistical knowledge about this procedure is assumed in this discussion.
Artificial Intelligence examples of artificial intelligence in education

The choice of MED is sometimes influenced by the observed effects from similar studies. However, if you are confident that your true effect size is X, then there is no reason for the study.

The first was chosen because it uses a common, but much criticized, procedure called a median split. The second example involves the authors using a complex method to dichotomize the data.

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The easiest of these to address is errors in the numbers reported in tables and statistical reports. These types of errors will always be part of any literature, but it is important to lessen their likelihoods.

There is no reason to think that this was a deliberate fabrication.

About Education Works

The author starts by mentioning a few examples of how AI is/ may be used outside of education – both risky and useful.

Artificial Intelligence examples of artificial intelligence in education

It’s not hard to see how this could apply in educational contexts. There are probably ways to prevent this, but awareness of potential bias is needed for this to be done. All this considered, educators seeing AI as a tool that is free of bias would be rather worrying.

For this meeting, I read ‘How Artificial Intelligence Can Change Higher Education,’ a profile of Sebastian Thrun. The article detailed Thrun’s involvement with the popularization of massive open online courses and the founding of his company, Udacity.

At the heart of this question is the role that autonomous systems might have in helping to manage this kind of large scale educational system.

Like in the hype surrounding self-driving cars, the promises for a new educational paradigm that were put forward in this 2012 article still seem far off.

This short piece talked about how Robots are now being used for educational purposes and which ones are being used. The article talked a lot about how Robots can be used in a way that enhances learning by learning things themselves.

There were some aspects of the article that did make some sense on how Robots could aid learning, but these ideas didn’t go into much depth. It was discussed how Robots could talk in several languages so could be able to converse comfortably with a wider range of students. It also talked about how Robots could act as mediators to students, being able to check in, or provide advice at any time of the day.

As mentioned in the article ‘many people have an inherent distrust of advancing technologies.’ There are several questions to ask on how much a Robot is integrated into a learning environment, and when does it become too much.

They created a robot to ‘reduce loneliness and social isolation through warm technology’. AVI was a Robot created for children who are too ill to go to school. The robot sits in the class and the child at home can connect through it. Using an app, the children can take part in the classroom. They can raise their hand to answer questions, talk to nearby students, ask questions, and just listen if they want to.

Virtual studios are also described as “hubs”, an idea I would have liked to explore further. I wanted to know how a hub is different from a community.

First, it focused on place based learning as not being solely the province of lessons conducted in the field, away from the classroom.

As students explore their local communities, they can both explore critical issues facing the community and build on their own experiences in order to support their learning.

An important development in place based learning has been the rise in the ubiquity of smartphones and other location-aware devices. By tapping into GPS and other forms of location networks, it becomes possible to develop applications that allow learners to dynamically access information about their surroundings.

In this programme, children between 5-13 years old get visits in their school class every 3 weeks from a local baby, their parent and a Roots of Empathy instructor. The children observe how the baby and its feelings develop and its interactions with the parent. The curriculum is broken down into themes, which are then broken down further into age ranges. While the activities focus on feelings, some use knowledge and skills from school subjects, e.

Michael read ‘Using learning analytics to scale the provision of personalized feedback,’ a paper by Abelardo Pardo, Jelena Jovanovic, Shane Dawson, Dragan Gasevic and Negin Mirriahi. As it was designed, the system allowed instructors to create small, one or two sentence pieces of feedback for each activity within a course. Based on these, each week students would be able to receive a set of ‘personalized’ feedback that responded to their level of participation.

In the study, the authors found an improvement in student satisfaction with the feedback they received, but only a marginal improvement in performance, as compared to previous years. First, it was admirable in the way that it sought to use learning analytics techniques to improve feedback in large courses.

For world 1 , human led and closed, I was concerned that lots was only available to “higher paying students” and there was no mention at all of collaborative learning.

I liked it because it gave both pros and cons in a concise way.

8.7:c

Emerging technologies: artificial intelligence

Thus all the general affordances of computing in education set out in Section 5 of this chapter will apply to AI. This section aims to tease out the extra potential that AI can offer in teaching and learning.

every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it.

Indeed, so-called intelligent tutoring systems, automated multiple-choice test marking, and automated feedback on such tests have been around since the early 1980s. The closest to modern AI applications appear to be automated essay grading of standardised tests administered across an entire education system.

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It could be argued that all AI does is to encapsulate the existing biases in the system. The problem though is that this bias is often hard to detect in any specific algorithm, and that AI tends to scale up or magnify such biases.

In terms of what AI is actually doing now for teaching and learning, the dream is way beyond the reality. What works well in finance or marketing or astronomy does not necessarily translate to teaching and learning contexts. In doing the research for this section, it proved very difficult to find any compelling examples of AI for teaching and learning, compared with serious games or virtual reality.

This is mainly due to the difficulty of applying ‘modern’ AI at scale in a very fragmented system that relies heavily on relatively small class sizes, programs, and institutions. Probably for modern AI to ‘work’, a totally different organizational structure for teaching and learning would be needed.

Students often learn better when they feel that the instructor or teacher cares. In particular, students want to be treated as individuals, with their own interests, ways of learning, and some sense of control over their learning. Because of these emotional and personal aspects of learning, students need to relate in some way to their teacher or instructor. Learning is a complex activity where only a relatively minor part of the process can be effectively automated. Learning is an intensely human activity, that benefits enormously from personal relationships and social interaction.

However, to develop the skills and knowledge needed in a digital age, a more constructivist approach to learning is needed.

AI advocates often argue that they are not trying to replace teachers but to make their life easier or more efficient. The key driver of AI applications is cost-reduction, which means reducing the number of teachers, as this is the main cost in education.

Another problem with artificial intelligence is that the same old hype keeps going round and round. The same arguments for using artificial intelligence in education go back to the 1980s.

The trick though is to recognise exactly what kind of applications these new AI developments are good for, and what they cannot do well. In other words, the context in which AI is used matters, and needs to be taken account of.

Knowledge of and Attitudes on Artificial Intelligence in Healthcare: A Provincial Survey Study of Medical Students

At one medical school, the survey was sent out in a newsletter to the MD student body. At all schools, the survey was open for four weeks, with a reminder email sent two weeks after the first email. Participation was voluntary and was not related to the students’ ongoing curricular activities. Students were offered entry into a gift card raffle for completing the survey. Consent for study participation was obtained through the first page of the survey, and respondent anonymity was guaranteed by design.

Students’ perceptions of AI’s potential capability in the domains of individual health, health systems, and population health are described in Supplementary Table 1. Perceptions regarding the timeline in which these capabilities will be achieved are described in Supplementary Table 2.

Students were also concerned about how AI will affect the medical job market. They believe AI will raise ethical and social implications yet are unconvinced that our health system is equipped to deal with these novel challenges. Overall, students agree that medical education must do more to prepare students for the impact of AI in medicine.

Quo Vadis, Artificial Intelligence?

Rapid advances in research and technology in various fields have created environments into which artificial intelligence could embed itself naturally and comfortably. The scope for artificial intelligence in neuroscience and systems biology is extremely wide.

The forthcoming sections investigate how AI is situated in this extended environment. Initially, Section 2 takes a closer look at the interplay between AI, neuroscience, synthetic biology, and systems biology.

A feature that unites systems biology and synthetic biology is the tremendous complexity that is inherent in both fields.

Predicted Influences of Artificial Intelligence on Nursing Education: Scoping Review

Methods: This scoping review followed a previously published protocol from April 2020. In addition to the use of these electronic databases, a targeted website search was performed to access relevant grey literature. Abstracts and full-text studies were independently screened by two reviewers using prespecified inclusion and exclusion criteria. Included literature focused on nursing education and digital health technologies that incorporate AI.

Additionally, nurse educators need to adopt new and evolving pedagogies that incorporate AI to better support students at all levels of education.

Additionally, as the majority of papers included in this review were expository papers and white papers, there is a need for more research in this context. Further research is needed to continue identifying the educational requirements and core competencies necessary for specifically integrating AIHTs into nursing practice.

Nurse educators in clinical practice and academic institutions around the world have an essential leadership role in preparing nurses and nursing students for the future state of AIHTs.

To our knowledge, this is the first scoping review to examine AIHTs and their influence on nursing education. While there has been research conducted on AIHTs and on nursing education as separate research topics, now is the time to realize the critical relationship between these two entities. AIHTs cannot be implemented in an effective manner without the solid foundation of nursing education, in both academic and clinical practice settings.

Ethical principles in machine learning and artificial intelligence: cases from the field and possible ways forward

In this contribution, we have examined the ethical dimensions affected by the application of algorithm-driven decision-making.

As noted by a perceptive reviewer, ML systems that keep learning are dangerous and hard to understand because they can quickly change. Thus, could a ML system with real world consequences be “locked down” to increase transparency? If yes, the algorithm could become defective. If not, transparency today may not be helpful in understanding what the system does tomorrow. This issue could be tackled by hard-coding the set of rules on the behaviour of the algorithm, once these are agreed upon among the involved stakeholders. This would prevent the algorithm-learning process from conflicting with the standards agreed.

Thomas Hodgson, Jill Walter Rettberg, Elizabeth Chatterjee, Ragnar Fjelland and Marta Kuc-Czarnecka for their useful comments in this venue.

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Another application of AI was significantly noted in diagnostic imaging departments. Offering remote clinics with restricted resources access to tools for reading imaging needed for active clinical interventions. Feeding into these AI systems is a wealth of comparative studies to predict and describe abnormal studies, and enhance its predictions.

Additionally, AI has been used in monitoring patient’s vitals, and predicting deteriorating clinical course, requiring early resource utilization and critical decision making in a timely manner.

I personally have always had a utopian vision of how far health informatics can take our clinical practice, specifically EM.

What is Learning Analytics?

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Although machine learning is a field within computer science, it differs from traditional computational approaches. In traditional computing, algorithms are sets of explicitly programmed instructions used by computers to calculate or problem solve. Machine learning algorithms instead allow for computers to train on data inputs and use statistical analysis in order to output values that fall within a specific range.

We’ll explore which programming languages are most used in machine learning, providing you with some of the positive and negative attributes of each.

In supervised learning, the computer is provided with example inputs that are labeled with their desired outputs.

For example, with supervised learning, an algorithm may be fed data with images of sharks labeled as fish and images of oceans labeled as water.

A common use case of supervised learning is to use historical data to predict statistically likely future events. It may use historical stock market information to anticipate upcoming fluctuations, or be employed to filter out spam emails.

Unsupervised learning is often used for anomaly detection including for fraudulent credit card purchases, and recommender systems that recommend what products to buy next.

Correlation is a measure of association between two variables that are not designated as either dependent or independent. Regression at a basic level is used to examine the relationship between one dependent and one independent variable.

The k-nearest neighbor algorithm is a pattern recognition model that can be used for classification as well as regression. Often abbreviated as k-NN, the k in k-nearest neighbor is a positive integer, which is typically small.

In the simplified decision tree above, an example is classified by sorting it through the tree to the appropriate leaf node. This then returns the classification associated with the particular leaf, which in this case is either a Yes or a No.

A true classification tree data set would have a lot more features than what is outlined above, but relationships should be straightforward to determine.

Questions for Future Research

This access often allows certain apps, webpages, or extensions to be blocked to protect student information, which helps minimize the risk of data and/or security breaches.

Assistive technologies that use a form of AI may increase student engagement more than assistive technologies that do not include an AI component.

Teachers can help students protect their personal data by ensuring that personal profiles — to which educational technology companies have access — contain as little identifiable information as possible. Parental support for the use of assistive technologies could also be obtained, and school divisions could generate student log-in information that does not expose students’ identities. Students using personal devices should take additional measures to ensure that their privacy and security is maintained.

Allowing students to choose the assistive technology tools that could help them achieve their educational goals can promote greater independence and autonomy.

Our Privacy Policy

We will not sell, distribute or lease your personal information to third parties unless we have your permission or are required by law to do so. 

If you believe that any information we are holding on you is incorrect or incomplete, please write to or email us as soon as possible, at the above address. We will promptly correct any information found to be incorrect.

  1. Max Infosys may change this policy from time to time by updating this page. You should check this page from time to time to ensure that you are happy with any changes.
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7 Unforgivable Sins Of microsoft artificial intelligence business school

Microsoft Artificial intelligence business school is often considered the “next frontier” of technological advancement, and sought after for the improvements that it can make to business performance. But its use does not stop at the business and economic case.

Another area where AI can make a major difference is in education.
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A third, particularly significant application of AI is in combating the massive global problem of climate change. Sometimes, a breakthrough may take as long as a decade, but machine learning can accelerate it in much the same way as drug development.

How China’s AI experts can beat Google and Microsoft by 2030

Over 40 per cent of the top AI-related academic papers published worldwide in 2015 had at least one or more Chinese researchers. Chinese AI-based patent applications grew 186 per cent between 2010 and 2014, a huge increase from the previous five-year period.

These favourable policies have inspired innovations from both smaller firms and internet giants in China.
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Baidu, for example, has developed a cutting-edge neural-network-based machine translation system that has achieved a speech recognition accuracy higher than that of humans. It has also launched an open-source platform for autonomous driving solutions, namely Project Apollo, to speed up the -development of self-driving vehicles.

AI research in academia has spread from being a focus at a few elite universities to those across China. Chinese academics have built a robust research community, which allows them to tap AI resources in both Chinese and English. Large numbers of Chinese science and engineering graduates are now flocking to the industry.

China is more than capable of becoming a leader in AI.

As of now, groundbreaking research is still mostly being done in the West, where the focus is on the science and infrastructure behind AI technology. Chinese academics, on the other hand, tend to research new applications of pre-existing technology.

Chinese companies are very good at launching new products and features quickly to the market, as they are well-versed in tapping newly identified opportunities. In the same vein as academia, Chinese companies primarily rely on new applications of pre-existing technologies rather than creating new ones.
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China needs a fundamental change to truly become a leader in AI. There needs to be a greater emphasis on developing the science behind the technology rather than emphasising new applications.

To achieve the goal of becoming a global AI leader by 2030, China will need to take at least two essential steps. First, it should redraft its incentives policies to motivate local companies and academics to conduct research on new AI technologies.

Andrew Ng, a leading American AI researcher, once said that AI would become the “new electricity” – transforming not just one industry, but all of them. AI can be and, in fact, is already being utilised across different sectors, creating unparalleled opportunities to “activate new businesses”.

Another failed attempt of AI replacing humans: Microsoft AI Editor already shows signs of inaccuracies

However, according to recent developments, Microsoft’s AI editors are already showing signs of inaccuracy. The AI reportedly used a photo of Leigh-Anne Pinnock on a story about her fellow bandmate Jade Thirlwall’s experiences with racism.

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AI for social good: unlocking the opportunity for positive impact

Results from several recent studies hint at the potential benefits of using AI for social good. Amnesty International and ElementAI demonstrated how AI can be used to help trained human moderators with identifying and quantifying online abuse against women on Twitter19.

This wealth of projects, sometimes isolated, has led to several meta-initiatives. For example, the Oxford Initiative on AIxSDGs26, launched in September 2019, is a curated database of AI projects addressing SDGs that indexes close to 100 projects. Once publicly accessible, it should support a formal study of such projects’ characteristics, success factors, geographical repartition, gaps, and collaborations.

Related principles are encoded in the Montreal Declaration for Responsible AI35 and the Toronto Declaration36. The European Commission states that AI needs to be lawful, ethical and robust, to avoid causing unintended harm.

A recent UN report40 details how over 30 of its agencies and bodies are working towards integrating AI within their initiatives. According to the report, AI4SG projects need to be approached as a collaborative effort, bringing communities together to carefully assess the complexities of designing AI systems for SDGs. These initiatives should aim to involve NGOs, local authorities, businesses, the academic community, as well as the communities which these efforts support.

This process involved setting up focused working groups around key topics and repeatedly coming together to disseminate the results, obtain feedback and discuss within the wider group.

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The results of the study have revealed worrying patterns of online abuse, estimating 1.1 million toxic tweets being sent to women in the study across the year, black women being 84% more likely than white women to experience abuse on the platform. The core of the analysis was based on using machine learning approaches to pre-filter the data, followed by applying computational statistics methods. The team has additionally evaluated the feasibility of using a fine-tuned deep learning model for automatic detection of abusive tweets61.

We encourage AI experts to actively seek out opportunities for delivering positive social impact. Ethics and inclusivity should be central to AI systems and application-domain experts should inform their design.

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Ready or Not, AI Comes— An Interview Study of Organizational AI Readiness Factors

In sum, our paper is a first step toward comprehensively conceptualizing and operationalizing organizational AI readiness. As such, we provide additional empirical groundwork for theorizing on technology adoption and readiness in general. Further, our AI readiness factors serve as the necessary foundation for purposeful decisions in the entire AI readiness and adoption process. Hence, we extend the body of descriptive knowledge on AI readiness and provide a first building block for prescriptive knowledge to guide organizations toward successful AI adoption.

This paper’s theoretical foundation is twofold: First, with AI being a technological innovation, the literature on innovation adoption provides the scaffold of our research. Second, research on organizational readiness for change emphasizes readiness as a necessary precursor for organizational change, such as AI adoption.

In sum, research so far provides fruitful theoretical groundwork but cannot provide relevant organizational AI readiness factors. Drawing on this previous work, we seek to provide a sound set of organizational AI readiness factors and corresponding indicators for AI readiness assessments.

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The AI readiness categories and factors describe the organizational chassis for developing AI readiness. Besides, our explorative interviews provide insights that help to understand the hurdles for successful AI adoption. Consequently, our findings are a necessary precursor to indicate how organizations can explore AI’s potentials. However, owing to AI’s characteristics as GPT, AI adoption differentiates from previously discussed technology adoption. Organizations define and pursue individual AI adoption purposes that describe how they seek to accrue value from the wide range of AI’s potential application scenarios. Thus, AI adoption can have different facets depending on the distinct adoption purpose. The AI adoption purpose can span from single use-cases to self-contained AI-driven business models.

Building on prototypes, experiments, and preceding projects as steps of AI adoption, companies may then push their AI adoption purpose to shift over time. For example, they may opt to start with internal applications before involving the customer interface.

In the following, we will position the results of our exploratory interview study within the existing adoption and readiness literature. Thereby, we conceptualize AI readiness as a valuable addition to the scholarly knowledge base and a necessary foundation for successful AI adoption. Further, we discuss the interdependencies between AI readiness and AI adoption as intertwined concepts.

First, AI readiness comprises 18 readiness factors along five categories that provide the organizational chassis for developing AI readiness. Second, beyond the specific factors, AI readiness entails the understanding of purposeful AI adoption.

Regarding practical implications, our paper grants insights into opportunities and challenges for AI adoption. The AI readiness factors provide comprehensive guidance to decision-makers on relevant managerial action fields. Based on an AI readiness assessment, decision-makers may reflect and adapt the factors to specific organizational needs. Setting and developing adequate AI readiness target levels is compulsory in order to derive actionable measures for successful AI adoption.

We combined insights from interviews with 25 AI experts with findings from scientific and practitioner literature to compile 18 AI readiness factors and 58 illustrative indicators in five categories. Further, we discussed that organizations must continuously assess and develop their AI readiness in the AI adoption process and described relevant aspects to consider. This includes AI’s nature as a GPT, the context- and purpose-specifics, and the mutually reinforcing interplay of AI readiness and AI adoption.

The Ethics of AI Ethics: An Evaluation of Guidelines

The current AI boom is accompanied by constant calls for applied ethics, which are meant to harness the “disruptive” potentials of new AI technologies. As a result, a whole body of ethical guidelines has been developed in recent years collecting principles, which technology developers should adhere to as far as possible.

The selection and compilation of 22 major ethical guidelines were based on a literature analysis. During the analysis of the search results, I also sifted through the references in order to manually find further relevant guidelines. Furthermore, I used Algorithm Watch’s AI Ethics Guidelines Global Inventory, a crowdsourced, comprehensive list of ethics guidelines, to check whether I missed relevant guidelines. Via the list, I found three further guidelines that meet the criteria for the selection.

In Table 1, I only inserted green markers if the corresponding issues were explicitly discussed in one or more paragraphs.

At first glance, the most obvious potential for improvement of the guidelines is probably to supplement them with more detailed technical explanations—if such explanations can be found. Ultimately, it is a major problem to deduce concrete technological implementations from the very abstract ethical values and principles.

Ethics thus operates at a maximum distance from the practices it actually seeks to govern.

Nevertheless, in several areas ethically motivated efforts are undertaken to improve AI systems. This is particularly the case in fields where technical “fixes” can be found for specific problems, such as accountability, privacy protection, anti-discrimination, safety, or explainability. Again, as mentioned earlier, the list of omissions is not exhaustive and not all omissions can be justified equally.

Checkbox guidelines must not be the only “instruments” of AI ethics.

On the one hand, a stronger focus on technological details of the various methods and technologies in the field of AI and machine learning is required. This should ultimately serve to close the gap between ethics and technical discourses. It is necessary to build tangible bridges between abstract values and technical implementations, as long as these bridges can be reasonably constructed.

Exploring the impact of artificial intelligence on teaching and learning in higher education

This paper explores the phenomena of the emergence of the use of artificial intelligence in teaching and learning in higher education. It investigates educational implications of emerging technologies on the way students learn and how institutions teach and evolve.

The future of higher education is intrinsically linked with developments on new technologies and computing capacities of the new intelligent machines.

AI Transparency & Explainability.

Pamela developed a proprietary Credit Derivative trading system for Deutsche Bank and a quantitative market risk VaR system for Nomura. Pamela is the CEO of Jasper Consulting Inc, a consulting firm through which she provides advisory and audit services for AI Ethics governance.

Renée Cummings is an AI ethicist, data activist, criminologist, criminal psychologist and therapeutic jurisprudence specialist. She’s also the historic first Data Activist in Residence, at the University of Virginia’s School of Data Science and a community scholar at Columbia University.

Renée also specializes in AI for social good, justice-oriented AI design, social justice in AI policy and governance, and using AI to save lives.

Nikita Lukianets, a Founder of the Open Ethics initiative that fosters the inclusive dialogue between experts and citizens to design systems where humans and AI successfully work together.

Nikita Lukianets has more than 10 years of experience in Human-Computer Interaction and has partnered with multiple organizations to help them build human-centered interfaces.

While human-level artificial intelligence has not been achieved yet, the implications that arise from the integration of AI into human societies are visible in the narrow fields already.

Open Ethics is about making clear how autonomous technologies make their decisions.

Indeed, a plethora of sophisticated and successful tools support companies on this path. This roadmap is the synthesis of the multiple research on the subject and tries to draw the best from the practices of innovative companies. First, Unification of data sources, then Process optimisation and finally Metamorphosis. Unification – Optimisation – Metamorphosis The roadmap shows how learning algorithms should be oriented to transform the business in small incremental steps that together produce powerful change.

Artificial Intelligence Holds Enticing Promise, Needs Framework, Say OECD, Microsoft, IEEE

As artificial intelligence technology spreads its wings, governance issues are emerging, as are international discussions, including a range of activities planned for 2018.

Masahiko Tominaga, vice-minister for policy coordination at the MIC, presented artificial intelligence with the potential for solving various problems and with “enormous benefits” to be expected.

Under the leadership of Japan, the OECD started an international, multi-stakeholder dialogue on AI, she explained, taking stock of who is doing what and what differences are emerging.

According to an OECD spokesperson, in 2018, the OECD is planning to produce and analytical/policy report on AI building on the OECD conference held last October.

How Artificial Intelligence Will Revolutionize the Energy Industry

Although AI is in its early stages of implementation, it is poised to revolutionize the way we produce, transmit, and consume energy.

High costs for infrastructure and distribution lines, as well as stringent governmental regulations, naturally create opportunities for monopolies to develop in the market.

, the average age of power plants is over 30 years and of power transformers is over 40 years. This deteriorating transmission system led to the 2003 Northeast blackout, the largest failure in U. history according to the federal task force charged with its investigation. It left 50 million people without power for several days when an overloaded transmission line sagged and struck a tree.

An additional challenge is the rise of distributed generation, where private users generate and use their own electricity from renewable sources, such as wind and solar.

The current system was not built to accommodate this diversification in energy sources, especially not the rise in renewable resources. Rather, when demand outpaces supply, utilities turn on backup fossil fuel-powered plants, known as ‘peaker plants’, at a minute’s notice to avoid a cascading catastrophe.

The technology will continuously collect and synthesize overwhelming amounts of data from millions of smart sensors nationwide to make timely decisions on how to best allocate energy resources.

As a result, large regional grids will be replaced by specialized microgrids that manage local energy needs with finer resolution.

On the demand side, smart meters for consumers, including homes and businesses, and sensors along transmission lines will be able to constantly monitor demand and supply. Further, briefcase-sized devices known as ‘synchrophasers’ would measure the flow of electricity through the grid in real time, allowing operators to actively manage and avoid disruptions. These sensors would communicate with the grid and modify electricity use during off-peak times, thereby relaxing the workload of the grid and lowering prices for consumers.

Fortunately, industry leaders are aware of this challenge and are already taking steps to in the right direction. The three leading greenhouse gas emmitters in this industry, computer makers, data centers, and telecoms are looking to reduce emmissions in many ways.

For those looking to make a difference in shaping the future of society, the interface between AI and energy is a great place to start. Technological innovation is drastically changing the way we think about these two industries and their integration is in its early stages.

6 Resources To Help Improve Your Data Science Skills

Jobs in the field of data science are becoming increasingly popular. However, they require you to have a particular skillset that might not be built into the curriculum of your graduate program.

Many of the platforms below have implemented “digital badging” for their courses. Digital badges are certifications that you have completed a course or training module. LinkedIn and Twitter allow you to embed these badges into your profile, and you can also include them on your CV. Host platforms may maintain directories of all users who have been awarded badges so that companies can confirm your credentials.

Coursera offers many courses and certificates in data science including deep learning, data visualization, biostatistics in public health, and more. While courses are hosted by Coursera, they are developed by universities and companies such as IBM, Google Cloud Platform, and Johns Hopkins.

DataCamp offers courses and “skill tracks” designed to help sharpen your data science skills in R, Python, SQL, git, shell, spreadsheets, theory, Scala, and Tableau. The free subscription to DataCamp includes the first chapters in all courses, 100+ coding challenges and 7 projects.

The Data Incubator Data Science Fellowship Program is an 8-week data science bootcamp specifically for PhD and Master’s students and is free for admitted fellows. There are weekly mini projects as well as a capstone project in which you build a web application.

Insight’s program is a 7-week post-doctoral bootcamp that is tuition-free and provides need-based scholarships to help cover living expenses. During the bootcamp, you will undergo self-directed, project-based learning under the guidance of top industry data scientists. The program culminates in the completion of a capstone project that you present during job interviews with mentor companies.

Bringing Big Data to Bear in Environmental Public Health: Challenges and Recommendations

Current public health and EHS students are interested in these issues, which means that public health schools need to integrate computer and data science into their core curriculums. There are a handful of existing programs that currently provide the skill set needed to apply data science to public health research. Within the status quo, students are presented with the option of either an MPH or an MS in Data Science, with little crossover between the two.

SC, AA, and MV formulated the research question/premise, conducted research and literature reviews, and wrote the first draft of the manuscript. NK and RH contributed writing and research to sections of the manuscript. VV, NK, LJ, and RH assisted with conceptualizing the research question and contributed to manuscript editing.

Automation and early-stage artificial intelligence systems are already changing the nature of employment and working conditions in multiple sectors.

Data reflects the social, historical and political conditions in which it was created. Artificial intelligence systems ‘learn’ based on the data they are given. This, along with many other factors, can lead to biased, inaccurate, and unfair outcomes.

As artificial intelligence systems are introduced into our core infrastructures, from hospitals to the power grid, the risks posed by errors and blind spots increase.

Automation and early-stage artificial intelligence systems are already changing the nature of employment and working conditions in multiple sectors.

Data reflects the social, historical and political conditions in which it was created. Artificial intelligence systems ‘learn’ based on the data they are given. This, along with many other factors, can lead to biased, inaccurate, and unfair outcomes.

As artificial intelligence systems are introduced into our core infrastructures, from hospitals to the power grid, the risks posed by errors and blind spots increase.

Is Data Science a Discipline?

To drive progress in the field of data science, we propose 10 challenge areas for the research community to pursue. We preface our enumeration with meta-questions about whether data science is a discipline.

Data science as a field of study is still too new to have definitive answers to all these meta-questions. Their answers will likely evolve over time, as the field matures and as members of the contributing established disciplines share scholarship and perspectives from their respective disciplines.

CardioSmart365: Artificial Intelligence in the Service of Cardiologic Patients

The rest of the paper is organized as follows: related work is presented in Section 2 followed by motivation in Section 3. Section 4 presents literature information about the different types of health records, the existing health record providers, and their evaluation. Section 5 presents a thorough description of the implemented system, including its architecture, functionality, components, and software framework.

The direct implication of humans, in particular patients, presupposes that the new technologies incorporated have to be safe, reliable and to offer proven solutions.

First, patients entering data into their health records can elect to submit the data into their clinicians’ EHRs. The PHR may also become a conduit for improved sharing of medical records.

All these capabilities render HealthVault to be a valuable tool of import and management of health-related information. A major limitation should be stressed; using HealthVault is available only to residents of the United States of America, due to legal obstacles.

Authorised end users have access to the integrated system through client applications, a web application, and a native mobile application for smartphones, with friendly- and easy-to-use interfaces. Great emphasis has been given in the design of user friendly and functional interfaces for both physicians and patients. In particular, the interface of mobile devices is designed in such a way to require the minimum volume of typing data. In order to achieve platform independence, the client applications communicate and exchange data with the database through web services, which allow data interchange through heterogeneous systems. The web services provide functionality with which specific information can be accessed by client applications after authenticated access. CardioSmart365 utilizes the Microsoft HealthVault platform as a backend platform, to store and manage important information of patients’ EMRs and measurements, into a uniform format.

An extra control module based on fuzzy sets is developed to check out-of-range measurement values and alert the attending MD.

Information about a new cardiovascular examination is stored by the cardiologist or general doctor who performs it. Periodical measurements of vital signs are performed at home, are imported by patients, and are available to cardiologists for a more comprehensive patient monitoring and decision making.

Most of the information described above is also stored in Microsoft HealthVault, including medication, laboratory examinations, periodical measurements, and demographics. This way, the information will be available to third parties after patient’s approval.

Table 1 presents the basic criteria used in the decision mechanism for each one of the five CPMs. Some of the criteria are fuzzified into fuzzy variables, while others are better exploited as crisp variables.

Every day clinical practice concerns MDs, nurses, hospital staff, outhospital healthcare organisations, and patients. In this way, better collaboration is established between all the involved working teams inside and outside the hospital.

After all, patients suffering from cardiologic diseases, most of the time in reflect chronic pathologies that require medication and followup for the rest of their life.

Research and Science strongly benefited towards better monitoring and understanding of cardiologic diseases. CardioSmart365 is a tool for recording and studying scientifically validated data elements of cardiologic diseases.

CardioSmart365 can be used for a reliable estimation of the economical cost that a patient encumbered a healthcare system. CardioSmart365 stores data concerning the examinations of a patient that their cost is usually the higher cost that burdens a healthcare system.

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CardioSmart365 is designed to offer an easy way for the incorporation of economic costs customized to different healthcare systems. Moreover, tools for estimation and various indices used for better monitoring and prediction of health involved costs will be established.

Using the web applications and services of CardioSmart365, feedback from institutional centers specialized on cardiologic diseases will be collected and incorporated to future versions of CardioSmart365. The cardiologic patient modules will be continuously updated in an automated way through the tools that will be developed. Knowledge from experts will be further continuously incorporated to the DSS of CardioSmart365, optimizing their support to MDs, leading towards personalized patient profiles and personalized medicine.

CardioSmart365 will further adopt clinical data and data involved in healthcare in a greater detail.

Our Privacy Policy

We will not sell, distribute or lease your personal information to third parties unless we have your permission or are required by law to do so. 

If you believe that any information we are holding on you is incorrect or incomplete, please write to or email us as soon as possible, at the above address. We will promptly correct any information found to be incorrect.

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The Increasing Role of Artificial Intelligence in Health Care: Will Robots Replace Doctors in the Future?

The use of AI in health care is poised for many-fold growth.4 Current applications of ai include disease diagnostics, drug development, the personalization of treatment, supportive health services, and gene editing. The use of AI in medicine can be classified into visual and physical domains.

Driving an analogy from biological neural networks, ANN is a computer program that simulates human thinking, such as with learning and retrieving data from previous experiences. An ANN observes examples of solutions to previous problems, collects information from those examples, processes this information through learning algorithms, and develops a response. Unlike a pre-program, an ANN learns, reasons, and responds as humans do. The quality of results depends on the amount of data it processes.

Future of Artificial intelligence

FES is a rule-based AI that is programmed with expert knowledge in a specific field and that mimics the response of an expert. The advantage of an FES system is that the knowledge of experts will be perpetually more accessible. EC is a computer program that is inspired by biological evaluation to provide an optimal or near-optimal solution. Essentially, EC processes data and information, suggests suitable solutions, and evolves with bigger data while eliminating unsuitable solutions.

AI is finding applications in more and more clinical areas and there has been a significant amount of research on developing new ones. The authors of a recent review synthesized three previously reported systematic reviews on the use of AI to reduce lower back pain.

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Applications and Challenges of Implementing Artificial Intelligence in Medical Education: Integrative Review

Conclusions: The primary use of AI in medical education was for learning support mainly due to its ability to provide individualized feedback. Little emphasis was placed on curriculum review and assessment of students’ learning due to the lack of digitalization and sensitive nature of examinations, respectively. Big data manipulation also warrants the need to ensure data integrity. Methodological improvements are required to increase AI adoption by addressing the technical difficulties of creating an AI application and using novel methods to assess the effectiveness of AI.

The titles and abstracts of identified articles were screened for the previously identified search criteria, and exclusion criteria were applied. All articles screened to be relevant or inconclusive were assessed in full text.

Based on the findings from our review, we propose that future research should focus on assessing the effectiveness of AI in medical education. Only one study that reviewed expert-led training in rheumatology has thus far shown the benefit of the use of an AI-driven system as compared to traditional methods.

As technology continues to advance, the potential uses of AI will continue to increase in medical education. One such development will be the use of AI, combined with immersive technologies such as virtual reality and augmented reality. As presented in our results, such studies have already been reported.

The scope of this review covers a broad spectrum of the current applications of AI in medical education. In the field of medicine, where the practices of each subspecialty vary tremendously, the use of AI in education may also vary. It may therefore be too early to make an overarching statement about the benefits of AI in medical education.

This review identified the current uses of AI in medical education, which include curriculum assessment and improvement of students’ learning, with research mainly existing on the latter.

Machine intelligence today: applications, methodology, and technology

This article is structured as follows: In the next section we present selected AI applications in various domains, namely culture, education, and industrial manufacturing. The following section focuses on AI methodology, namely aspects of machine learning and knowledge representation.

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What are the AI technologies currently used to fight the coronavirus pandemic?

Access to health care and resources at a moment’s notice is vital for battling the spread of the coronavirus.

AI in Cardiac Imaging: A UK-Based Perspective on Addressing the Ethical, Social, and Political Challenges

Imaging and cardiology are the healthcare domains which have seen the greatest number of FDA approvals for novel data-driven technologies, such as artificial intelligence, in recent years. The increasing use of such data-driven technologies in healthcare is presenting a series of important challenges to healthcare practitioners, policymakers, and patients. In this paper, we review ten ethical, social, and political challenges raised by these technologies. We also consider the approaches being taken by healthcare organizations and regulators in the European Union, the United States, and other countries.

The question also remains as to whether the use of AI will create new health inequalities.

There is still some way to go in addressing these questions. Furthermore, given the complexity of these technologies, a truly multidisciplinary approach is required.

We are extremely grateful to our colleague Nika Strukelj for her contribution to the original research cited in this paper.

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How to cite this report

Along with the insurers and AI a third factor in the diffusion of AI in the insurance sector is the consumer. Purchasing insurance and making claims are part of a relationship between the consumer and the insurer. Most current applications of AI are used to strengthen the capabilities and knowledge of the insurer and not the consumer. As the information asymmetry increases, the insurer can provide lower quality services at lower prices. The higher quality more expensive services may stop being offered as they can no longer compete on price.

This raises the question how the user will react to their weaker position and how they should be ‘compensated’. Other approaches are to offer lower prices or a better service.

The development and use of ML and AI technologies have quickly become topics of national interest and debate. Since 2015, the federal government alone has increased funding for unclassified AI research and development by more than 40%.

Also at this summit, a new Select Committee on Artificial Intelligence comprised of the government’s most senior AI experts was announced.

There is currently no universally agreed-upon definition of artificial intelligence. The term ”intelligence” is understood as a measure of a machine’s ability to successfully achieve an intended goal. Like humans, machines exhibit varying levels of intelligence subject to the machine’s design and training data.

Such autonomous agents could open new ethical and legal complications that will need to be adequately assessed and planned for. For instance, autonomous agents or programs may, as a product of their autonomy, operate outside the expectations of their creators. In the event that the agent or program’s creators have not implemented comprehensive stopgaps, the agent or program may inadvertently cause unintended harm to allies or adversaries.

This is most certainly the general public’s understanding of AI.

Another scientific controversy surrounding machine learning and AI is the extent to which these technologies may mimic, or even exacerbate, human bias in judgment and decision making. One of the main sources of this bias is when developers use insufficiently diverse or representative datasets to initially train the AI. Media Lab showed that the accuracy of popular facial recognition programs varied by the gender and race of the individual in the photo.

In particular, the programs more accurately identified the gender and race of white men than black women. And ProPublica reported that a computer program used to rate the likelihood that a criminal defendant will commit a future crime was biased against black defendants.

Topics: artificial intelligence

In the public sector, AI-enabled governance may afford new efficiencies that have the potential to transform a wide array of public service tasks.

Barriers and Facilitators to Genetic Service Delivery Models: Scoping Review

Background: Advances in diagnostics testing and treatment of genetic conditions have led to increased demand for genetic services in the United States. At the same time, there is a shortage of genetic services professionals.

Results: There were a number of challenges identified, including the limited number of genetics specialists, wait time for appointments, delivery of services by nongenetics providers, reimbursement, and licensure.

Advances in diagnostic testing and treatment options for genetic conditions have led to increased demand for genetic services in the United States. For example, newborn screening programs test all infants shortly after birth for a variety of genetic conditions. Other avenues include clinical diagnosis from a broad array of specialists, such as neurologists, oncologists, and geneticists. We sought to understand how genetic services are provided and identify the most cost-effective methods of meeting growing needs for services.

Researchers independently reviewed each abstract and made notations related to reported challenges and potential solutions regarding the delivery of genetic services. Researchers reviewed each other’s notations, discussed any areas of disagreement, and ultimately came to a consensus on whether the article should be obtained and included in the review.

After an initial review, 93 articles related to genetic service models from across the 3 searches were selected for a full-text review. Three researchers categorized the articles by theme together and carried out full-text reviews.

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The literature review pointed out several challenges that are currently facing the field of genetics.

The use of telehealth is one of the main ways to increase access to genetic services. On the basis of the literature review, there is ample evidence to support the use of telehealth. First, because of the vastness of the United States and the dearth of genetic service providers, each of the genetic services centers serves a large catchment area. In addition, telehealth can facilitate the use of remote translation services. Finally, there is a nationwide shortage of genetic service providers across the continuum. Telehealth expansion can help with workforce issues by obviating the need for staff to travel to multiple locations.

In addition to telehealth, there are a few possible solutions related to clinical workflow that may improve access to genetic services. The use of GCAs has been rated favorably in many of the studies included in the literature review.

Another possible solution to improve the knowledge and expertise of nongenetics professionals is to embed genetics providers within primary care settings. Typically, genetic counselors are hired to be part of primary care settings to assist with referrals and provide long-term support to patients. This approach, though, may be more challenging to implement, given there is still an insufficient number of genetic counselors, although the field is growing.

The literature review illuminated the challenges and identified possible solutions that could be implemented to improve the delivery of genetic services. Options that include telehealth applications may be the most straightforward and immediate option for genetic centers to pilot. However, a more long-term investment will be to complement telehealth models with the education of nongenetics professionals.

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Applications of Artificial Intelligence and Big Data Analytics in m-Health: A Healthcare System Perspective

Section 2 shows the motivation and scope of this work along with the systematic reviews and meta-analysis process. Section 3 depicts the definition of m-health and its schematic representation along with the mobile sensors and their applications in m-health. Section 4 explains a detailed review about the applications of AI in m-health along with the performance measurement indicators used to examine the quality of m-healthcare. Section 5 presents the applications of big data analytics in m-health followed by the additional summary of its applications in the healthcare sector. Section 6 presents the proposed model based on the AI and big data analytics for m-health.

After completing the process of searching the article, the authors concealed the titles and abstracts of the retrieved articles using an inclusion and exclusion criteria. At last, 106 articles were obtained and kept for the review process.

Various concepts of analytics such as data mining and AI can be used to analyze the obtained data. These analytical approaches in big data can be used to identify the anomalies by analyzing a huge amount of data from various datasets and their sources. Figure 4 shows an example of the smartphone-based m-health model with the combination of AI and big data analytics.

The authors proposed a framework for the organizations in healthcare in providing intelligence-based smart services. Their detailed research depicts a novel framework for the smart healthcare system based on big data and also makes the research directions interdisciplinary. In fact, the proposed framework is the combination of three technical streams such as the AI, agent-based systems, and data mining along with the smart health.

These are combined to convey the perception of enabling a decision-making process in real time. Various concepts of analytics such as data mining and AI are used to analyze the obtained data from a patient. The AI-based engine comprises two modules such as the stream analysis module and the AI-based report management tool.

It is also used as a platform for the disease control, treatment, and diagnosis tool. It also detects the irregular records which are present in the EHR.

The big data analysis engine consists of two modules such as storage for big data and a statistical data analysis tool. The statistical data analysis tool retrieves the input data, processes it into queries, and then sends it to the AI-based engine.

The system can never be too accurate to replace the humans and their predictions. These systems have been made to ease out the health structure but they cannot be a substitute to human.

These m-health systems also make a user/patient to be dependent completely on them. If the user loses his or her mobile phone and user id/password, there is a possibility for all the information to be lost temporarily or even permanently. There might be a chance for various issues in the privacy and security of the health data present in it.

m-Health is a technique which uses mobile devices and technology for health interventions and is the biggest technological advancement of recent research. Similarly, the application of AI and the analytics of big data in healthcare are considered as one of the important achievements for the intelligent healthcare system. In this paper, a detailed review of the m-healthcare system is proposed based on the application of AI and big data analytics. Various advantages from this combination for the m-health perspective are presented. Particularly, all applications of relevant technological areas and the building blocks such as communications, sensors, and computing which are associated with mobile health are explained in detail. The role of various tools of machine learning within the current m-health model is also illustrated.

Artificial Intelligence in Medicine: Applications, implications, and limitations

However, while some algorithms can compete with and sometimes outperform clinicians in a variety of tasks, they have yet to be fully integrated into day-to-day medical practice.

Generally, the jobs AI algorithms can do are tasks that require human intelligence to complete, such as pattern and speech recognition, image analysis, and decision making. However, humans need to explicitly tell the computer exactly what they would look for in the image they give to an algorithm, for example.

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Advances in computational power paired with massive amounts of data generated in healthcare systems make many clinical problems ripe for AI applications.

Applications of AI in classical software engineering

The analysis results that major achievements and future potentials of AI are a) the automation of lengthy routine jobs in software development and testing using algorithms, e. AI thus contributes to speed up development processes, realize development cost reductions and efficiency gains.

The systematic review of prior empirical studies explicitly refers to experiences with AI application at the respective stages of the development life cycle. The review includes more than 60 publications in peer-reviewed journals and conference papers published between 2010 and 2020, to ensure topicality and academic quality of the results.

AI comprises several novel technologies and their development lines are still open.

As of today, AI indirectly enhances project planning mechanisms, according to participant 2. The analysis of data pools of earlier projects provides realistic estimates of failure quotas and iteration routines in earlier projects and locates potential areas of difficulties.

Participant 3 esteems predictive analysis equipment which as of today accesses large online data pools to predict trends and outcomes of new applications. Predictive analyses enable software designers to plan their products more proactively and adjust to new technological trends in their emergence.

AI, according to participant 1, provides structured access to immense amounts of data which are retrieved from earlier similar projects, for instance. The number of expected bugs and their location is reliably predicted on that basis and error avoidance routines are established more effectively. AI has sped up the design speed of software projects, according to participant 2, by enabling programs to execute routine tasks, which previously had to be done by human developers.

The tool has reduced software development times and improves output quality.

Participant 3 is experienced with automated code compliers, which support the transformation of high-level programming language codes in machine-executable instructions.

Participant 5 agrees that developers can foresee and advocate for change, while AI routines can only apply and process existing knowledge.

Table 2 summarizes technologies, achievements, limitations and future development potentials of AI for the six stages of the software engineering life cycle as available from previous studies and the interviews.

The review has shown that the basic principles and technologies underlying AI supported software engineering are similar across the life cycle stages. However, AI comes to its limits when novel insights are sought and new problem sets are meant to be discovered and, innovative routines have to be developed. These fundamental activities so far remain at the hands of human designers and developers. Future AI routines could become more self-reliant if they could compose new tasks and solutions without human support.

Artificial Intelligence and Its Applications

In the paper entitled “A wavelet-based robust relevance vector machine based on sensor data scheduling control for modeling mine gas gushing forecasting on virtual environment,” W. present a wavelet-based robust relevance vector machine based on sensor data scheduling control for modeling mine gas gushing forecasting. Morlet wavelet function can be used as the kernel function of robust relevance vector machine. Mean percentage error has been used to measure the performance of the proposed method in this study. As the mean prediction error of mine gas gushing of the WRRVM model is less than 1.5% and the mean prediction error of mine gas gushing of the RVM model is more than 2.

Virtually in CFSO3, just the initial values of positions and velocities of the swarm members have to be randomly assigned.

In the paper entitled “Research on the production scheduling optimization for virtual enterprises,” M. An improved genetic algorithm is proposed in the model to solve the time complexity of virtual enterprise production scheduling.

In the paper entitled “Interesting activities discovery for moving objects based on collaborative filtering,” G. propose a method of interesting activities discovery based on collaborative filtering. First, the interesting degree of the objects’ activities is calculated comprehensively. Then, combined with the newly proposed hybrid collaborative filtering, similar objects can be computed and all kinds of interesting activities can be discovered.

The presented method predicts general context based on probability theory through a novel graphical data structure, which is a kind of weighted directed multigraphs. They also consider the periodic property of context data and devise a good solution to context data with such property.

In the paper entitled “Study on semi-parametric statistical model of safety monitoring of cracks in concrete dams,” C. consider that cracks are one of the hidden dangers in concrete dams. The study on safety monitoring models of concrete dam cracks has always been difficult. Previous projects show that the semiparametric statistical model has a stronger fitting effect and has a better explanation for cracks in concrete dams than the parametric statistical model. However, when used for forecast, the forecast capability of the semiparametric statistical model is equivalent to that of the parametric statistical model.

In the paper entitled “Efficient secure multiparty computation protocol for sequencing problem over insecure channel,” Y. believe that secure multiparty computation is more and more popular in electronic bidding, anonymous voting, and online auction, as a powerful tool in solving privacy preserving cooperative problems. Privacy preserving sequencing problem that is an essential link is regarded as the core issue in these applications. However, due to the difficulties of solving multiparty privacy preserving sequencing problem, related secure protocol is extremely rare. In order to break this deadlock, their paper presents an efficient secure multiparty computation protocol for the general privacy-preserving sequencing problem based on symmetric homomorphic encryption.

In the paper entitled “Nighttime fire/smoke detection system based on a support vector machine,” C. If smoke appears within the monitoring zone created from the diffusion or scattering of light in the projected path, the camera sensor receives a corresponding signal. Characterization of smoke is carried out by a nonlinear classification method using a support vector machine, and this is applied to identify the potential fire/smoke location.

9 Mesmerizing Examples Of artificial intelligence examples applications

present “Robust quadratic regression and its application to energy-growth consumption problem.” The paper proposes a robust quadratic regression model to handle the statistics inaccuracy. First, they give a solvable equivalent semidefinite programming for the robust least square model with ball uncertainty set. Then the result is generalized to robust models under – and -norm criteria with general ellipsoid uncertainty sets. In addition, they establish a robust regression model for per capita GDP and energy consumption in the energy-growth problem under the conservation hypothesis.

In the paper “Identification of code-switched sentences and words using language modeling approaches,” L. A code-switched sentence is detected on the basis of whether it contains words or phrases from another language. Once the code-switched sentences are identified, the positions of the code-switched words in the sentences are then identified. Experimental results show that the language modeling approach achieved an F-measure of 80. For the identification of code-switched words, the word-based and POS-based models achieved F-measures of 41.

In the paper entitled “Matching cost filtering for dense stereo correspondence,” Y. propose a new cost-aggregation module to compute the matching responses for all the image pixels at a set of sampling points generated by a hierarchical clustering algorithm. The complexity of this implementation is linear both in the number of image pixels and in the number of clusters. Experimental results demonstrate that the proposed algorithm outperforms state-of-the-art local methods in terms of both accuracy and speed.

3% of image SIFT keypoints, uSee exceeded prior literature results by achieving an accuracy of 99.

In the paper entitled “Solving the balanced academic curriculum problem using the ACO metaheuristic,” J. consider that the balanced academic curriculum problem consists in the assignation of courses to academic periods satisfying all the load limits and prerequisite constraints. They present the design of a solution to the balanced academic curriculum problem based on the ACO metaheuristic, in particular via the best-worst ant system.

In the paper entitled “Hybrid functional-neural approach for surface reconstruction,” A. Gálvez introduce a new hybrid functional-neural approach for surface reconstruction. The approach is based on the combination of two powerful artificial intelligence paradigms: on one hand, they apply the popular Kohonen neural network to address the data parameterization problem. On the other hand, they introduce a new functional network, called NURBS functional network, whose topology is aimed at reproducing faithfully the functional structure of the NURBS surfaces. These neural and functional networks are applied in an iterative fashion for further surface refinement.

In the paper “Optimum performance-based seismic design using a hybrid optimization algorithm,” S. present a hybrid optimization method to optimum seismic design of steel frames considering four performance levels. These performance levels are considered to determine the optimum design of structures to reduce the structural cost. A pushover analysis of steel building frameworks subject to equivalent-static earthquake loading is utilized.

We would like to express our gratitude to all of the authors for their contributions and the reviewers for their effort providing constructive comments and feedback.

Applications of AI in classical software engineering

The analysis results that major achievements and future potentials of AI are a) the automation of lengthy routine jobs in software development and testing using algorithms, e. AI thus contributes to speed up development processes, realize development cost reductions and efficiency gains.

The systematic review of prior empirical studies explicitly refers to experiences with AI application at the respective stages of the development life cycle. The review includes more than 60 publications in peer-reviewed journals and conference papers published between 2010 and 2020, to ensure topicality and academic quality of the results.

AI comprises several novel technologies and their development lines are still open.

As of today, AI indirectly enhances project planning mechanisms, according to participant 2. The analysis of data pools of earlier projects provides realistic estimates of failure quotas and iteration routines in earlier projects and locates potential areas of difficulties.

Participant 3 esteems predictive analysis equipment which as of today accesses large online data pools to predict trends and outcomes of new applications. Predictive analyses enable software designers to plan their products more proactively and adjust to new technological trends in their emergence.

AI, according to participant 1, provides structured access to immense amounts of data which are retrieved from earlier similar projects, for instance. The number of expected bugs and their location is reliably predicted on that basis and error avoidance routines are established more effectively. AI has sped up the design speed of software projects, according to participant 2, by enabling programs to execute routine tasks, which previously had to be done by human developers.

The tool has reduced software development times and improves output quality.

Participant 3 is experienced with automated code compliers, which support the transformation of high-level programming language codes in machine-executable instructions.

Participant 5 agrees that developers can foresee and advocate for change, while AI routines can only apply and process existing knowledge.

Table 2 summarizes technologies, achievements, limitations and future development potentials of AI for the six stages of the software engineering life cycle as available from previous studies and the interviews.

The review has shown that the basic principles and technologies underlying AI supported software engineering are similar across the life cycle stages. However, AI comes to its limits when novel insights are sought and new problem sets are meant to be discovered and, innovative routines have to be developed. These fundamental activities so far remain at the hands of human designers and developers. Future AI routines could become more self-reliant if they could compose new tasks and solutions without human support.

Artificial intelligence for structural glass engineering applications — overview, case studies and future potentials

6 are discussed at this point, as the conduction of every step is essential for building a sensible AI/ML application. This step may take minutes to months in dependence of the problem and the data structure of the respective environment. It is advisable to consider standardization protocols for this step in order to guarantee data consistency within a company.

As shown in Fig 16, the trained U-Net is well suited to create a mask image from the original image without the need for further human interaction. A slight improvement of the mask images created by AI could be achieved by the cut-off condition or binary prediction. The presented NN for predicting the cut glass edge is therefore very accurate and saves a significant amount of time in the prediction and production of mask images. In addition, the mask images can be further processed, for example to make statistical analyses of the break structure of the cut glass edge.

Providing this AI-based method delivers remarkable economic and ecological advantages.

The careful study of the organization learned by each model revealed the existence of a deeper bias, or architectural style.

To summarize this section, AI has the potential to accelerate design and structural verification processes to a great demand while customization wishes may enter more naturally and affordably. The authors are currently at a stage, where first knowledge and experiences are gathered with these ideas.

Artificial Intelligence Applications in Dermatology: Where Do We Stand?

Although most applications involve analyzing and classifying images, there are other tools such as risk assessment calculators. The most progress thus far has taken place in the field of melanoma diagnosis, followed by ulcer and psoriasis assessment tools, then followed by numerous less frequently studied applications. However, critical barriers and literature gaps exist that significantly limit AI’s applicability to clinical practice at this time. For the less common applications, technological papers and commentaries are needed to improve capabilities and provoke interest. For the more saturated topics, there is a larger need for clinical trials providing evidence of clinical efficacy, while successfully overcoming the identified barriers.

Artificial Intelligence

This section outlines four application domains that will be developed in examples throughout the book.

Opportunities and Challenges for Artificial Intelligence Applications in Infrastructure Management During the Anthropocene

National Science and Technology Council says in its 2016 report, “There is no single definition of AI that is universally accepted by practitioners. Some define AI loosely as a computerized system that exhibits behavior that is commonly thought of as requiring intelligence. Others define AI as a system capable of rationally solving complex problems or taking appropriate actions to achieve its goals in whatever real world circumstances it encounters.” Herein, we use “AI” to include big data and analytics dimensions, but ultimately describe the leadership and intelligence capabilities that are needed to replace or augment people.

How is Bayes’ Theorem used in artificial intelligence and machine learning?

Bayesian network formalism was invented to allow efficient representation of, and rigorous reasoning with, uncertain knowledge. This approach largely overcomes many problems of the probabilistic reasoning systems to the 1960s and 70s; it now dominates AI research on uncertain reasoning and expert systems.

Like predicting a particular disease based on the symptoms and physical condition of the patient. There are many algorithms currently in use that are based on this theorem, like binary and multi-class classifier, for example, email spam filters.

Since you are a highschool student I will try to express it easier. It is a problem for a machine to make a decision if you haven’t given that information to it before. But sometimes there can be so many cases, here Data Mining, Neural Networks, Fuzzy Logic etc are used withing AI.

Advanced Applications of Neural Networks and Artificial Intelligence: A Review

Neural Networks can result in such devices which are able to detect faults in those areas where fault detection is difficult for human beings. Eg: fault detection in tracks of Railways, Metros and Roller coasters.Neural networks can detect patterns so it can detect faults when the railway track donot resemble its original shape i. having distorted shapes or having gaps, cracks or bents in it. These neural can be combined with a gps to locate their position as well as the position of crack. Also Thehealth of tracks can be checked by measuring their width, inclination and condition of screws. This in turn can be of great use to avoid accidents.

The ability of recognising the patterns can be further extended to recognise 3D objects. Recognition of 3D objects can help us in finding the objects. We can implement this technique in robotics so that the robots could identify objects. It will be very helpful in industries where robots can work as assistants for humans. Using this technique we will be able to search useful objects from trashes. The same can also be used to flter the non recyclable waste from recyclable waste in garbage treatment plant. Robots which are able to search 3D objects can also search humans during a rescue mission.
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Artificial Intelligence with MIT App Inventor

MIT is building tools into App Inventor that will enable even beginning students to create original AI applications that would have been advanced research a decade ago.

26 Artificial Intelligence and Machine Learning

Classification algorithms are used when the outputs are restricted to a limited set of values. For an algorithm that identifies spam emails, the output would be the prediction of either “spam” or “not spam”, represented by the Boolean values true and false. Regression algorithms are named for their continuous outputs, meaning they may have any value within a range.

In unsupervised learning, the algorithm builds a mathematical model from a set of data which contains only inputs and no desired output labels. Unsupervised learning algorithms are used to find structure in the data, like grouping or clustering of data points. Unsupervised learning can discover patterns in the data, and can group the inputs into categories, as in feature learning.

INSPIRE standards as a framework for artificial intelligence applications: a landslide example

The similarity score between a given model and instance is used as a proxy of landslide susceptibility. A high similarity score between an instance and a landslide susceptibility model signals a high susceptibility to that type of landslide.

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From a data structure perspective, INSPIRE code lists cannot currently host multi-hierarchical taxonomies. This limits the nature of reasoning that can be brought to bear on them.

The extensibility of INSPIRE allows for domain-specific applications, like the approach presented in this paper, to fit within the INSPIRE framework. However, problems may also arise from the fact that INSPIRE is extensible. Extensibility allows greater precision in terminology and schema for a specific application, but this allows different public and private institutions to implement separate and eventually incompatible extensions. Much work remains at the level of thematic clusters to implement as many standardized vocabularies and schemas as possible.

Ontologies provide the formal structure for INSPIRE code lists to run algorithms similar to that applied here.

This study also illustrates that, in their current state of development, the INSPIRE standards are not sufficiently expressive to support complex landslide susceptibility mapping. We provided an example of how INSPIRE’s extension capabilities may be implemented to add the required expressivity. Through its Re3gistry register, this extension framework ensures that the expressivity extensions are documented and available to all interested parties for reuse.

This project was first presented at the Helsinki 2019 INSPIRE data challenge and won the first prize. The authors would also like to acknowledge Massimiliano Alvioli et al.avaflow code, and WeTransform GmbH for the Hale Connect and Hale Studio software licences. We would also like to thank the reviewers Ivan Marchesini and Omar F.

Oftentimes a business or organization may wish to do the same task over and over again, and there is a lot of data at its disposal. The lecture discusses differences between prediction systems and recommendation tasks, supplemented by examples from industry that include e-commerce applications, language modeling, and image analysis.

Artificial Intelligence in Clinical Health Care Applications: Viewpoint

They may also serve to facilitate communication between scientists involved in AI and medical doctors.

Chapter 13. AI, Visitor Experience, and Museum Operations: A Closer Look at the Possible

1 This concept has shifted over the last few decades and there have been fluctuations in the application of AI technologies.

There are multiple classifications of AI technologies2 and among the most common methods we find are computer vision, machine learning, robotics, and natural language processing. All of these methods offer a way to speed processes which would otherwise involve human labor and costs, such as language translation or image identification. Although we may be in another hype phase of the term “AI,” popularity of the tools are normalizing its usage in research and practice.

Big Data in Machine Learning

Many, large companies from the control and automation segment are already infected with the “ML virus”. But according to industry experts, the use of machine learning in industrial applications is still in its infancy.

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