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.

A man has been able to move all four of his paralysed limbs using a groundbreaking mind-controlled exoskeleton, scientists have said. The tetraplegic 30-year-old, known only as Thibault, said his first steps in the robotic suit felt like being "the first man on the Moon". The system, which works by recording and decoding brain signals, was trialled in a two-year study by French researchers at biomedical research centre Clinatec and the University of Grenoble. Scientists conceded the suit was an experimental treatment far from clinical application but said it had the potential to improve patients' quality of life and autonomy. Wearing the robotic limbs, Thibault was able to walk and move his arms using a ceiling-mounted harness for balance.

Minecraft's open-ended play environment could be ideal for AI research, some researchers say.Credit: Microsoft To see the divide between the best artificial intelligence and the mental capabilities of a seven-year-old child, look no further than the popular video game Minecraft. A young human can learn how to find a rare diamond in the game after watching a 10-minute demonstration on YouTube. Artificial intelligence (AI) is nowhere close. But in a unique computing competition ending this month, researchers hope to shrink the gap between machine and child -- and in doing so, help to reduce the computing power needed to train AIs. Competitors may take up to four days and use no more than eight million steps to train their AIs to find a diamond.

Today, Amazon announced a new approach that it says will put machine learning technology in reach of more developers and line of business users. Amazon has been making a flurry of announcements ahead of its re:Invent customer conference next week in Las Vegas. While the company offers plenty of tools for data scientists to build machine learning models and to process, store and visualize data, it wants to put that capability directly in the hands of developers with the help of the popular database query language, SQL. By taking advantage of tools like Amazon QuickSight, Aurora and Athena in combination with SQL queries, developers can have much more direct access to machine learning models and underlying data without any additional coding, says VP of artificial intelligence at AWS, Matt Wood. "This announcement is all about making it easier for developers to add machine learning predictions to their products and their processes by integrating those predictions directly with their databases," Wood told TechCrunch.