The Philosophy Of robot sophia artificial intelligence

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.
Artificial Intelligence robot sophia artificial intelligence

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.
Artificial Intelligence robot sophia artificial intelligence

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.
Artificial Intelligence robot sophia artificial intelligence

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.
Artificial Intelligence robot sophia artificial intelligence

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.

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