A British agritech start-up has won a prestigious Horizontal Innovation Award from the IET and the High Value Manufacturing Catapult (HVMC) to help develop'Harry', the company's drilling and planting robot. Small Robot Company, based in Shropshire, harnesses the power and precision of robots and Artificial Intelligence (AI) to improve the way that food is produced. The £50,000 funded research award will look to develop'Harry' from concept through to in-field prototype. Addressing key challenges around the use of robotics in agriculture, the development of'Harry's' punch planting mechanism will be supported by the Manufacturing Technology Centre, one of seven centres of excellence which make up the High Value Manufacturing Catapult (HVM Catapult), which is sponsored by Innovate UK. The technology is built on 15 years of robotics research by Professor Simon Blackmore, the world's leading expert on precision farming at Harper Adams University.

PLOS Medicine, PLOS Computational Biology and PLOS ONE announce a cross-journal Call for Papers for high-quality research that applies or develops machine learning methods for improvement of human health. The team of Guest Editors for this Collection seeks research with direct clinical and health policy implications, studies that elucidate biological processes underlying health and disease, innovations in machine learning methodology and data provision, and other advances in the field. Research accepted for publication in PLOS Medicine will appear in a Special Issue to be published in late Fall 2018, along with commentary from leading experts in the field. The broader Collection, comprising all articles published in PLOS Computational Biology, PLOS ONE and PLOS Medicine, will launch in late Fall and continue into 2019. Articles must be submitted by May 25, 2018.

PLOS Medicine, PLOS Computational Biology and PLOS ONE are excited to announce a cross-journal Call for Papers for high-quality research that applies or develops machine learning methods for improvement of human health. The team of Guest Editors for this Collection seeks research with direct clinical and health policy implications, studies that elucidate biological processes underlying health and disease, innovations in machine learning methodology and data provision, and other advances in the field. Research accepted for publication in PLOS Medicine will appear in a Special Issue to be published in late Fall 2018, along with commentary from leading experts in the field. The broader Collection, comprising all articles published in PLOS Computational Biology, PLOS ONE and PLOS Medicine, will launch in late Fall and continue into 2019. Articles must be submitted by May 25, 2018.

The 11th edition of Capgemini's flagship publication, the Digital Transformation Review, focuses on Artificial Intelligence (AI). This edition – Digital Transformation Review: Artificial Intelligence Decoded – presents a nuanced perspective on AI to help cut through the hype and fog. We spoke to leaders and experts from a broad spectrum: large corporates, tech startups, academics, venture capitalists, and technology vendors. Often an entry point for organizations looking to explore the benefits of artificial intelligence, we also cover our research on voice assistants. "AI will be the most debated, invested in, and disruptive business technology trend over the coming years. Organizations must cut through the fog and understand how to apply this innovation and become a truly intelligent enterprise."

Neural networks (NNs) and deep learning (DL) currently provide the best solutions to many problems in image recognition, speech recognition, natural language processing, control and precision health. NN and DL make the artificial intelligence (AI) much closer to human thinking modes. However, there are many open problems related to DL in NN, e.g.: convergence, learning efficiency, optimality, multi-dimensional learning, on-line adaptation. This requires to create new algorithms and analysis methods. Practical applications both require and stimulate this development.

The 11th edition of the Digital Transformation Review will be launched soon and it focuses on Artificial Intelligence (AI). AI has received significant bad press so why should we pay attention?.

There's no question artificial intelligence and machine learning technologies are enabling important discoveries in healthcare, but there can be a bit of a disconnect among the various stakeholders using them. A panel discussion at the upcoming CNS Summit in Boca Raton, Fla. presents a rare opportunity to bring the parties together and foster collaboration. The healthcare ecosystem includes academic medical centers, biopharmaceutical companies, solution providers, disease foundations and more. Each has its own interests and reasons for using AI and machine learning technology and important experiences to share about hits, misses and lessons learned. With that in mind, GNS Healthcare proposed bringing these groups together and get them talking about their experiences at the 2017 Collaborating for Novel Solutions Summit, which is focused on diseases that afflict the central nervous system.

In this book, we explore the task of automatically predicting human decision-making and its use in designing intelligent human-aware automated computer systems of varying natures - from purely conflicting interaction settings (e.g., security and games) to fully cooperative interaction settings (e.g., autonomous driving and personal robotic assistants). We explore the techniques, algorithms, and empirical methodologies for meeting the challenges that arise from the above tasks and illustrate major benefits from the use of these computational solutions in real-world application domains such as security, negotiations, argumentative interactions, voting systems, autonomous driving, and games. The book presents both the traditional and classical methods as well as the most recent and cutting edge advances, providing the reader with a panorama of the challenges and solutions in predicting human decision-making. Top Description Table of Contents Author Information Table of Contents Preface Acknowledgments Introduction Utility Maximization Paradigm Predicting Human Decision-Making From Human Prediction to Intelligent Agents Which Model Should I Use? Concluding Remarks Bibliography Authors' Biographies Index Top Description Table of Contents Author Information About the Author(s)Ariel Rosenfeld, Weizmann Institute of Science Ariel Rosenfeld is a Koshland Postdoctoral Fellow at Weizmann Institute of Science, Israel. He obtained a B.Sc. in Computer Science and Economics, graduating magna cum laude from Tel Aviv University, and a Ph.D. in Computer Science from Bar-Ilan University.

It is online only book developed by Chester Ismay and Albert Y. Kim and covers the basics of using R to analyze data and create data stories using R and statistics. Check the samples of "data stories" from one of the classes here from one of the authors of the book.

For the Perfect Strangedroids discussion panel on Wednesday, Engadget hosted a trio of robotics experts.. Sabri Sansoy, CEO and Chief Roboticist of Orchanic; Nader Hamda, Founder and CEO of Ozobot; and Stu Lipoff, IEEE Life Fellow and President of IP Action Partners all took the Engadget stage at CES 2018 with senior editor Andrew Tarantola moderating. Click here to catch up on the latest news from CES 2018. Click here to catch up on the latest news from CES 2018. Andrew has lived in San Francisco since 1982 and has been writing clever things about technology since 2011. When not arguing the finer points of portable vaporizers and military defense systems with strangers on the internet, he enjoys tooling around his garden, knitting and binge watching anime.