The Turing test detects if a machine can truly think like a human. If you were to mash the two together to create a particularly messy Venn diagram, the overlap shall henceforth be known as the Ex Machina Zone. In writer/director Alex Garland's thought-provoking new film--out Friday--we meet Ava (Alicia Vikander), an artificially-intelligent robot. Ava's creator, genius tech billionaire Nathan (Oscar Isaac), has asked his employee Caleb (Domhnall Gleeson) to determine whether Ava's thinking is indistinguishable from a human's. Until she meets Caleb, Ava has only ever met her maker and one other woman. Her existence, and her ability to learn how to interact, is a fascinating study of what makes us human.

After months of delays and much anticipation, the Federal Aviation Administration finally released its final rule on small drones. The rule represents an important, long-delayed step toward establishing comprehensive regulations for flying small unmanned aerial systems and integrating them into the national airspace. Industry experts have argued for years that the United States risked falling behind in the burgeoning drone market and missing out on countless opportunities for growth. The new rule should expand the growth of the US commercial drone industry, catalyze American innovation in the global marketplace, and maintain the technological superiority of US military drones. But there's work left to do.

Cortana, Microsoft's answer to Apple's Siri, is already become more intelligent every day, Nadella said, echoing his recent comments in Slate, where he argued that humans and machines will work together, not against each other. "With all the abundance we have of computers and computing, what is scarce is human attention and time," Nadella said. "So having the personal digital assistant really helps me regain my time -- empowers me to get more out of every moment of my life." Nadella spoke alongside Walter Isaacson, CEO of the Aspen Institute and author of "Steve Jobs," at the Aspen Ideas Festival in Colorado, where speakers covered broad topics like the role of machines in policy and the future of Mars. The interview coincided with the announcement of Nadella's new book, "Hit Refresh," that will focus on the future of intelligent machines.

Britain's fintech, artificial intelligence, and genetics startups have been going great guns recently, but Brexit has killed their momentum, according to a London startup CEO who is now considering whether he should open up additional offices outside the UK. Damian Kimmelman, CEO and founder of business information platform DueDil, told Business Insider: "I think the UK has three massive centres of excellence. "Those aren't going to go away very quickly but the wind has been knocked out of their sails." Companies like TransferWise, DeepMind, and Sophia Genetics have helped the UK to build a reputation as one of Europe's leading technology hubs. Kimmelman added: "In order to do something that really stimulates the tech economy, we have to look at this [Brexit] as an opportunity." The entrepreneur believes the UK should "be bold" and rip out the old financial services infrastructure before introducing a new one. "Use technologies such as blockchain and make that more pervasive," he said. "Other countries won't have the goal to take such audacious measures.

KisanHub, a Cambridge, UK-based agtech startup, raised over 1m in funding. The round was led by Notion Capital with participation from NIAB, an international center for plant research, crop evaluation and agronomy. The company intends to use the funds for R&D, sales and customer delivery. Led by Dr Sachin Shende, CEO, Giles Barker, COO, KisanHub provides a SaaS based farming enterprise software platform which leverages precision agriculture, big data, cloud computing, machine learning and mobile to deliver decision points to over one thousand farmers and multiple large enterprises. The company also has operations in Pune.

The Turing test, the quintessential evaluation designed to determine if something is a computer or a human, may have a fatal flaw, new research suggests. The test currently can't determine if a person is talking to another human being or a robot if the person being interrogated simply chooses to stay silent, new research shows. While it's not news that the Turing test has flaws, the new study highlights just how limited the test is for answering deeper questions about artificial intelligence, said study co-author Kevin Warwick, a computer scientist at Coventry University in England. "As machines are getting more and more intelligent, whether they're actually thinking and whether we need to give them responsibilities are starting to become very serious questions," Warwick told Live Science. "Obviously, the Turing test is not the one which can tease them out."

"Make an appointment for 4pm today with Gary," I say to my assistant as I hang up from a promising phone call with a potential client. There was a time when you had to be high up in an organisation to have an assistant. My assistant is an artificially intelligent piece of software that lives in my smart phone. It makes me wonder if technologies like these could help a business grow. A report called The Future of Employment: How susceptible are jobs to automation was published in 2013 by researchers from Oxford University.

We propose a nonparametric procedure to achieve fast inference in generative graphical models when the number of latent states is very large. The approach is based on iterative latent variable preselection, where we alternate between learning a 'selection function' to reveal the relevant latent variables, and use this to obtain a compact approximation of the posterior distribution for EM; this can make inference possible where the number of possible latent states is e.g. exponential in the number of latent variables, whereas an exact approach would be computationally unfeasible. We learn the selection function entirely from the observed data and current EM state via Gaussian process regression. This is by contrast with earlier approaches, where selection functions were manually-designed for each problem setting. We show that our approach performs as well as these bespoke selection functions on a wide variety of inference problems: in particular, for the challenging case of a hierarchical model for object localization with occlusion, we achieve results that match a customized state-of-the-art selection method, at a far lower computational cost.

This is the second part of'A Brief History of Game AI Up to AlphaGo'. Part 1 is here and part 3 is here. In this part, we shall cover just about four decades of progress, from the first victories of computers against people at Checkers and Chess all the way up to DeepBlue's victory against humanity's then-best living Chess player. By the late 1950s, the industrious engineers at IBM were far from the only ones working on AI -- excitement for the new field filled research groups in universities from the US to the Soviet Union. One such group was made up of Allen Newell and Herbert Simon (both attendants of the Dartmouth Conference) from Carnegie Mellon University, and Cliff Shaw from RAND Corporation. They collaborated on Chess AI from 1955 to 1958, culminating in "Chess Playing Programs and the Problem of Complexity"1 which both summarized existing Chess AI research and contributed new ideas that they tested with the NSS (Newell, Shaw, and Simon) Chess program. Just as Shannon noted that master players use intuition to think selectively about moves, Newell, Shaw and Simon considered heuristics to be an important aspect of human Chess-playing.

I visited my local health-insurance office a few months ago. After entering the building, I was welcomed into a long and dark corridor, full of nervous people carrying bloated folders. The atmosphere was gloomy, and it was obvious that no one wanted to be there. After about 30 minutes I realized why: During that time, the line had barely moved, and it took me the better part of the day to reach a clerk. As a result, I was late for two other errands I had planned.