Artificial intelligence (AI) is changing the way we live our lives; it is everywhere and here to stay. The concepts of Artificial intelligence started on the pages of science fiction, which introduced us to the notion of smart robots. With the invention of electronic digital computers in the early 1940s the pursuit of AI was made possible. The term itself was coined at a conference at Dartmouth in the summer of 1956, where scientists gathered to discuss ways to program computers to solve problems with the skills of a human. AI flourished for the next two decades and optimism was high that we would soon have machines with the general intelligence of an average human.

Just a few days ago, a team of Facebook engineers received the ACM SIGPLAN Most Influential POPL Paper Award which is one of most the covered awards in the machine learning research community. The award was based on the paper "Compositional Shape Analysis by Means of Bi-abduction", which describes some of the science behind one of my favorite machine learning applications of recent years: Project Infer. The goal of Project Infer seems extracted from an sci-fi movie: detecting bugs in mobile app code before it ships. Bugs in mobile apps are very costly. Discovering an error after a mobile app has been distributed to thousands of mobile devices is the nightmare facing any mobile developer.

Episode Summary: One of the promises of artificial intelligence is aiding humans in making smarter decisions. Whether it's in pharma, retail, or eCommerce companies, the idea of being able to pool together streams of data and coax out the insights that would help make the best call for the organization to reach its goals is the promise of artificial intelligence. As it turns out that same dynamic is sort of happening in the public sector where AI is now being used to inform policy. Previously, she was Program Director at the National Science Foundation. PhD in computer science and she runs the Data Science Initiatives at URI.

In his office in Paris's National Assembly, Cédric Villani opens a parcel: it contains a metallic spider. "Lovely," he says, putting it on a shelf, where a collection of spider-shaped objects sits next to his scientific decorations and a photo of him with Mark Zuckerberg. Villani is on a mission. Well, on several missions: the French mathematician, winner of the 2010 Fields Medal – often described as maths' Nobel Prize – sits as an MP for Emmanuel Macron's party La République en Marche, teaches at the University of Lyon, and is running for the Paris 2020 mayoralty. But the expert in mathematical analysis, famous for his academic achievements as well as for wearing spider-shaped pins on his three-piece suits, has a bigger goal: making France a leader in artificial intelligence. Appointed by the French president to set out a national AI strategy, in 2018 Villani published a report, "AI for Humanity", setting clear lines for the sector: "We must valorise our research, define our industrial priorities, work on the ethical and legal framework and on AI training," Villani says, sat among his spiders – one as big a pillow – in his office.

A survey of early work exploring how AI can be used in medicine, with somewhat more technical expositions than in the complementary volume Artificial Intelligence in Medicine."Each chapter is preceded by a brief introduction that outlines our view of its contribution to the field, the reason it was selected for inclusion in this volume, an overview of its content, and a discussion of how the work evolved after the article appeared and how it relates to other chapters in the book.

Artificial intelligence, or AI, is largely an experimental science—at least as much progress has been made by building and analyzing programs as by examining theoretical questions. MYCIN is one of several well-known programs that embody some intelligence and provide data on the extent to which intelligent behavior can be programmed. As with other AI programs, its development was slow and not always in a forward direction. But we feel we learned some useful lessons in the course of nearly a decade of work on MYCIN and related programs. In this book we share the results of many experiments performed in that time, and we try to paint a coherent picture of the work. The book is intended to be a critical analysis of several pieces of related research, performed by a large number of scientists. We believe that the whole field of AI will benefit from such attempts to take a detailed retrospective look at experiments, for in this way the scientific foundations of the field will gradually be defined. It is for all these reasons that we have prepared this analysis of the MYCIN experiments.

At HUAWEI CONNECT 2018, we unveiled our AI strategy and portfolio. At HUAWEI EC0-CONNECT EUROPE 2018, we explored how we can work with our partners and customers to create an open industry ecosystem and help build pervasive intelligence in Europe. We also spoke to Europe's industry leaders and prominent experts about artificial intelligence, including Marco Menichelli, CTO of XSENSE. The full interview transcript is below. Marco Menichelli: What I call Artificial Intuition.

In a venture capital firm, you want different talents that will enrich the investing team, such as a person from industry, say, mixed with people from the finance world, and perhaps people with a legal or public policy background. You may even want an automaton that crunches numbers. "Motherbrain" is the name that Henrik Landgren, operating partner, and his colleagues at venture capital firm EQT Ventures have given to the computer program that they increasingly turn to in order to get an early read on potential investments. Motherbrain uses convolutional neural networks, or CNNs, the most popular form of machine learning, to review time-series data about companies to help guide where the firm should invest. The technology has seriously improved EQT Ventures's ability to scope out deals early in the pipeline, Landgren said in an interview with ZDNet.

Sina Habibi, CEO of Cognetivity Neurosciences, spoke with INN about the company's partnership with DPUK and additional plans for 2019. At the recent Cantech Investment Conference, Sina Habibi, CEO of Cognetivity Neurosciences (CSE:CGN,OTCQB:CGNSF) spoke with the Investing News Network (INN) about the company's partnership with the Dementia Platform UK (DPUK) and additional plans for 2019. Habibi said the company will be putting more efforts into its artificial intelligence (AI) platform and collecting more data as it seeks to train its solutions to detect mental health disorders, like attention deficit hyperactivity disorder (ADHD). As it currently stands, Cognetivity is using AI and machine learning to aid in the early detection of dementia and Alzheimer's disease. On that note, in addition to the DPUK partnership, Habibi spoke to INN about a health application the company has that could be launched by the end of 2019.

To relieve the pain of manually selecting machine learning algorithms and tuning hyperparameters, automated machine learning (AutoML) methods have been developed to automatically search for good models. Due to the huge model search space, it is impossible to try all models. Users tend to distrust automatic results and increase the search budget as much as they can, thereby undermining the efficiency of AutoML. To address these issues, we design and implement ATMSeer, an interactive visualization tool that supports users in refining the search space of AutoML and analyzing the results. To guide the design of ATMSeer, we derive a workflow of using AutoML based on interviews with machine learning experts. A multi-granularity visualization is proposed to enable users to monitor the AutoML process, analyze the searched models, and refine the search space in real time. We demonstrate the utility and usability of ATMSeer through two case studies, expert interviews, and a user study with 13 end users.