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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.

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