With the recent progress in artificial intelligence (AI) algorithms, dramatic increase in computational capacities, and availability of big data necessary for training deep neural networks, a lot of AI applications became available at the market and automation tendencies started to penetrate all spheres of human activities and all industries. While the topic of AI has been getting a lot of media coverage and public attention, profound research on its socio-economic and policy effects, especially with regard to entrepreneurship, has yet to be developed. Moreover, methodological papers in artificial intelligence field have been mainly published in very technical venues and it is difficult for a broader publics to grasp the most recent developments in this area. Therefore, the purpose of this special issue is to address these shortcomings. This special issue is the first initiative to interact the technical and methodological papers in AI with papers exploring socio-economic, entrepreneurship and policy effects of AI.

Note: This Editorial is appearing in Speaking of Medicine ahead of print. The final version will appear in PLOS Medicine at the end of December. PLOS Medicine Machine Learning Special Issue Guest Editors Suchi Saria, Atul Butte, and Aziz Sheikh cut through the hyperbole with an accessible and accurate portrayal of the forefront of machine learning in clinical translation. Artificial Intelligence (AI) as a field emerged in the 1960s when practitioners across the engineering and cognitive sciences began to study how to develop computational technologies that, like people, can perform tasks such as sensing, learning, reasoning, and taking action. Early AI systems relied heavily on expert-derived rules for replicating how people would approach these tasks.

This book aims to get you up to speed with what is, in my opinion, the most powerful data visualization stack going: Python and JavaScript. You'll learn enough about big libraries like Pandas and D3 to start crafting your own web data visualizations and refining your own toolchain. Expertise will come with practice, but this book presents a shallow learning curve to basic competence. If you're reading this, I'd love to hear any feedback you have. Please post it to pyjsdataviz@kyrandale.com. You'll also find a working copy of the Nobel visualization the book literally and figuratively builds toward at http://kyrandale.com/static/pyjsdataviz/index.html. The bulk of this book tells one of the innumerable tales of data visualization, one carefully selected to showcase some powerful Python and JavaScript libraries and tools which together form a toolchain. This toolchain gathers raw, unrefined data at its start and delivers a rich, engaging web visualization at its end. Like all tales of data visualization, it is a tale of transformation--in this case, transforming a basic Wikipedia list of Nobel Prize winners into an interactive visualization, bringing the data to life and making exploration of the prize's history easy and fun. A primary motivation for writing the book is the belief that, whatever data you have and whatever story you want to tell with it, the natural home for the visualizations you transform it into is the Web.

Research article: Soft ethics, the governance of the digital and the General Data Protection Regulation Luciano Floridi Research article: The fallacy of inscrutability Joshua A. Kroll Opinion piece: Constitutional democracy and technology in the age of artificial intelligence Paul Nemitz Research article: Artificial intelligence policy in India: a framework for engaging the limits of data-driven decision-making Vidushi Marda Research article: Algorithms that remember: model inversion attacks and data protection law Michael Veale, Reuben Binns, Lilian Edwards Research article: Ethical governance is essential to building trust in robotics and artificial intelligence systems Alan F. T. Winfield, Marina Jirotka Research article: Apples, oranges, robots: four misunderstandings in today's debate on the legal status of AI systems Ugo Pagallo Research article: Democratizing algorithmic news recommenders: how to materialize voice in a technologically saturated media ecosystem Jaron Harambam, Natali Helberger, Joris van Hoboken

This piece accompanies a dedicated series from Ben around intelligence, A.I, and data-driven design and development in retail – all of which you can find in our 7th Edition. Similarly, you will find references to other'features', which denote to the other editorial pieces in our 7th Edition Report.] Just as WhichPLM did for both of our previous special editorial examinations (covering 3D in 2015, and the Internet of Things in 2016) the last exclusive feature in our 7th Edition acts as the final piece of the puzzle, collecting guidance, food for thought, and practical recommendations for retailers and brands who may be looking to lay the long-term groundwork for their own A.I. initiatives, or to embark on a particular, more pressing project. The clearest question for prospective customers of A.I. solutions: are these viable products, with clear return on investment potential? Broadly speaking, the answer is yes. While general intelligence – a single machine to run everything, with mental capacities far in excess of our own, across essentially all of human endeavour – remains a pipe dream, more focused applications of narrow, specialised A.I. are limited only by customers' ability to find the right technology partner and to gain access to their own information and broader market data in sufficient volume to deliver results.

Lifelong Machine Learning, Second Edition is an introduction to an advanced machine learning paradigm that continuously learns by accumulating past knowledge that it then uses in future learning and problem solving. This book is thus suitable for students, researchers, and practitioners who are interested in machine learning, data mining, natural language processing, or pattern recognition. ISBN 9781681733029, 207 pages.

AI is shaping the world, who will be shaping AI? The future may be uncertain, but one certainty is that today's youth will be central to shaping it. Acorn Aspiration's mission is to empower and assist teens in learning and shaping the future of AI for positive contributions to the world. More than 1500 young people have participated in Acorn's Bootcamps/Hackathons, Accelerator programs, and conferences to date. Acorn's latest initiative, TeensInAI, was launched at the UN AI for Good Summit in May, and followed by a 5-day Bootcamp/ Hackathon.

The purpose of AI Magazine is to disseminate timely and informative articles that represent the current state of the art in AI and to keep its readers posted on AAAI-related matters. The articles are selected for appeal to readers engaged in research and

In the beginning of June, Neovision welcomed three new collaborators. With this additional potential, Neovision will continue to perform and match the challenges related to the deployment of Artificial Intelligence. A phenomenon that affects a growing number of companies, which are working on their digital transformation, as evidenced by the newly launched projects with EDF and Michelin! More than ever, Neovision remains attached to its values and makes AI accessible to everyone.

Latest Books on revolutionary changes in the field of artificial intelligence (AI) and data science, that will empower you by increasing your knowledge drastically. To ease your curiosity, and keep you up-to-date with the ideas, concepts and practicality of these subjects, I bring to you the 11 best technical reads in AI as well as data science that will help you stay ahead in these technology areas. These books are a work of non-fiction. The list is presented in no particular order. This is one of the most recent additions to the must read books in AI.