Nils J. Nilsson, a computer scientist who helped develop the first general-purpose robot and was a co-inventor of algorithms that made it possible for the machine to move about efficiently and perform simple tasks, died on Sunday at his home in Medford, Ore. His death was confirmed by his wife, Grace Abbott. Dr. Nilsson was a member of a small group of computer scientists and electrical engineers at the Stanford Research Institute (now known as SRI International) who pioneered technologies that have proliferated in modern life, whether in navigation software used in more than a billion smartphones or in such speech-control systems as Siri. The researchers had been recruited by Charles Rosen, a physicist at the institute, who had raised Pentagon funding in 1966 to design a robot that would be used as a platform for doing research in artificial intelligence. Although the project was intended to create a general-purpose mobile "automaton" and be a test bed for A.I. programs, Mr. Rosen had secured the funding by selling the idea to the Pentagon that the machine would be a mobile sentry for a military base.

As a teenager in Maryland in the 1950s, Mary Allen Wilkes had no plans to become a software pioneer -- she dreamed of being a litigator. One day in junior high in 1950, though, her geography teacher surprised her with a comment: "Mary Allen, when you grow up, you should be a computer programmer!" Wilkes had no idea what a programmer was; she wasn't even sure what a computer was. The first digital computers had been built barely a decade earlier at universities and in government labs. By the time she was graduating from Wellesley College in 1959, she knew her legal ambitions were out of reach. Her mentors all told her the same thing: Don't even bother applying to law school.

Drawing on the records of nearly 600,000 Chinese patients who had visited a pediatric hospital over an 18-month period, the vast collection of data used to train this new system highlights an advantage for China in the worldwide race toward artificial intelligence. Because its population is so large -- and because its privacy norms put fewer restrictions on the sharing of digital data -- it may be easier for Chinese companies and researchers to build and train the "deep learning" systems that are rapidly changing the trajectory of health care. On Monday, President Trump signed an executive order meant to spur the development of A.I. across government, academia and industry in the United States. As part of this "American A.I. Initiative," the administration will encourage federal agencies and universities to share data that can drive the development of automated systems. Pooling health care data is a particularly difficult endeavor.

An expert on artificial intelligence has called for all algorithms that make life-changing decisions – in areas from job applications to immigration into the UK – to be halted immediately. Prof Noel Sharkey, who is also a leading figure in a global campaign against "killer robots", said algorithms were so "infected with biases" that their decision-making processes could not be fair or trusted. A moratorium must be imposed on all "life-changing decision-making algorithms" in Britain, he said. Sharkey has suggested testing AI decision-making machines in the same way as new pharmaceutical drugs are vigorously checked before they are allowed on to the market. In an interview with the Guardian, the Sheffield University robotics/AI pioneer said he was deeply concerned over a series of examples of machine-learning systems being loaded with bias.

Neural Information Processing Systems (NeurIPS) is a multi-track machine learning and computational neuroscience conference that includes invited talks, demonstrations, symposia and oral and poster presentations of refereed papers. Following the conference, there are workshops which provide a less formal setting.

Organisations are becoming so overwhelmed with data relating to cybersecurity that they are having to turn to artificial intelligence (AI) in order to keep abreast of it all. More than half of them reported that they were using or looking to use AI because their organisations had too much data to deal with. The machine-learning systems can help by processing huge volumes of data in a way that would be impossible for human analysts. Some cyber-attacks can be identified and blocked automatically. The AI can also alert human analysts to areas of data that they should be paying particular attention to, allowing them to respond to threats more effectively.

Software development history, typically represented as a Version Control System log, is a rich source of insights into how the project evolved as well as how its developers work. What's probably more important is events from the past can predict the future. Vadim Markovtsev is a Google Developer Expert in Machine Learning and a Lead Machine Learning Engineer at source {d} (sourced.tech) His academic background is compiler technologies and system programming. Vadim is also author of several published papers about Machine Learning on Source Code.

Rev started its own competitor in this realm earlier this year. In Friday's Q. and A., contractors asked if they were being kept around just to train the company's artificial intelligence -- something Mr. Chicola vehemently denied. So far at least, the machine-powered alternatives do not appear to be eating into the work available for skilled transcribers. Paula Kamen, who runs Transcription Professionals from her home near Chicago, said that when she began her company in 1995, she was convinced that Dragon -- the buzzy speech recognition software of that time -- would soon make her business obsolete. But she said she has continued to grow at a steady rate because the advances in speech recognition technology have come alongside the proliferation in recording devices and people wanting to see their words turned into text.

In recent days, Microsoft's improvements to Power BI include the release of the October update for On-premises data gateway, the introduction of new contact lists for reports and dashboards, and plenty more. Earlier this year, the Redmond firm revealed the public preview of Automated Machine Learning (AutoML) for Dataflows in Power BI. Today, AutoML has reached general availability in all public cloud regions that offer Power BI Premium and Embedded services. A bunch of new capabilities have been added to the service ever since its preview version became available in April. For those unaware, AutoML allows business analysts to easily develop machine learning (ML) models.

Graphical causal inference as pioneered by Judea Pearl arose from research on artificial intelligence (AI), and for a long time had little connection to the field of machine learning. This article discusses where links have been and should be established, introducing key concepts along the way. It argues that the hard open problems of machine learning and AI are intrinsically related to causality, and explains how the field is beginning to understand them.