Toyota and Honda have unveiled separate initiatives for car connectivity and artificial intelligence, the latest moves by Japan's auto industry to counter emerging tech rivals in Silicon Valley. Toyota has formed a partnership to create a global communications platform that will support unified car connectivity worldwide, rather than relying on varying networks in different markets. Meanwhile, Honda will establish a Honda R&D Innovation Lab in Tokyo around September to work on "intelligent technologies" beyond mechanical engineering, such as vehicle connectivity, robotics, brain research and visual recognition. The projects, announced independently on Thursday, June 2, underscore a new urgency felt by Japanese automakers to invest more in next-generation technologies for autonomous driving and safety systems. They are stepping up as entrants from outside the industry, such as Google and Uber, take the lead in the fields.

Google's secretive AI division is working on a'big red button' that can halt its artificial intelligence software. Researchers have previously warned that AI could threaten humanity, with doomsday scenarios of AIs taking over, with one expert involved in the new paper admitting Google's historic win over Go world champion proves AI can be'unpredictable and immoral'. Now the DeepMind team say they have the answer - an off switch. Google's DeepMind team say AI agents are'unlikely to behave optimally all the time' and have called for'safe interruptibility' to be built into systems. Google has set up an ethics board to oversee its work in artificial intelligence.

Back in March, Microsoft had announced that Windows 10 has surpassed 300 million active devices since its launch in July 2015 and now the company claims it is the most successful operating system till date. Despite the initial success, Microsoft is moving away from excessive dependence on Windows to new growth areas like artificial intelligence and conversational bots. At the Build Tour in Pune on Friday, Microsoft announced how the company is realigning its strategies on three pillars – cognitive computing, Augmented Reality and conversational bots. Microsoft, which is essentially out of the mobile business, is now relying on platforms like cloud computing and Cortana intelligence suite for its next growth verticals. At the Build Tour, the consensus was strongly in favour of Windows 10.

One video, for me, changed everything. It's footage from the old Atari game Breakout, the one where you slide a paddle left and right along the bottom of the screen, trying to destroy bricks by bouncing a ball into them. You may have read about the player of the game: an algorithm developed by DeepMind, the British artificial intelligence company whose AlphaGo programme also beat one of the greatest ever Go players, Lee Sedol, earlier this year. Perhaps you expect a computer to be good at computer games? Once they know what to do, they certainly do it faster and more consistently than any human. DeepMind's Breakout player knew nothing, however. It was not programmed with instructions on how the game works; it wasn't even told how to use the controls. All it had was the image on the screen and the command to try to get as many points as possible. At first, the paddle lets the ball drop into oblivion, knowing no better. Eventually, just mucking about, it knocks the ball back, destroys a brick and gets a point, so it recognises this and does it more often.

Practicing the scientific approach to the data exploration one should know at what extent certain method can be applied. Neural Nets are futile for the stock market's predictions. Monte-Carlo algorithms couldn't offer much help either, and poorly implemented Random Forest algorithm can literally ruin your vacation in South-East Asia, especially if it was implemented by NSA. In this article we will briefly introduce machine learning methods classification and see how they are relevant to the different lines of business. From the cradle to the grave, we are making decisions – from our first decision to attract mother's attention to one of our last decisions when asking the doctor for pain treatment.

Dogo goes up stairs, rolls out in pairs, and fits on a squadmate's back. Dogo is a new remotely operated robot made by Israel's General Robotics, and inside its tiny frame it carries another, more familiar weapon. And then they reveal their deadly secret. Nestled sideways inside the Dogo is a 9mm Glock 26 compact pistol. It's like the kind of thing Nerf would make, only it's real and instead of foam darts this shoots actual bullets to kill actual people. Like the Nerf toy, Dogo is remotely controlled from a special handset, which displays live video and lets the human remotely fire the weapon.

Nearly 250 million video surveillance cameras have been installed throughout the world, and chances are you've been seen by several of them today. Most people barely notice their presence anymore -- on the streets, inside stores, and even within our homes. We accept the fact that we are constantly being recorded because we expect this to have virtually no impact on our lives. But this balance may soon be upended by advancements in facial recognition technology. Soon anybody with a high-resolution camera and the right software will be able to determine your identity.

Have you ever begun a Google search, only to click on the words the box lays before you? Tagged a friend's face when Facebook prompted it? Have you spoken to your iPhone? The artificial intelligence technology behind these tools is neither self-aware nor homicidal. But they are driven by a computational technique called machine learning, which is, at its simplest, a way to teach machines to teach themselves.

On The Tonight Show, in March 1978, the late astronomer Carl Sagan had lots to talk about. He had just published Dragons of Eden: Speculations on the Evolution of Human Intelligence--which would win the Pulitzer Prize--and Star Wars, released the year before, still captivated the public's imagination. When Johnny Carson, the show's then-host, asked Sagan to expand on some comments he'd made prior to the evening, about the film's indifference to scientific accuracy, Sagan said the "11-year-old in me loved" it, but it "could have made a better effort to do things right." His critique would resonate today: After making the biological point that the Star Wars scenario--humans evolving long ago, in a faraway galaxy--is vastly improbable, Sagan said there's another problem: "They're all white." Carson, pushing back a bit, said, "They did have a scene in Star Wars with a lot of strange characters."

Recent advances in neural variational inference have spawned a renaissance in deep latent variable models. In this paper we introduce a generic variational inference framework for generative and conditional models of text. While traditional variational methods derive an analytic approximation for the intractable distributions over latent variables, here we construct an inference network conditioned on the discrete text input to provide the variational distribution. We validate this framework on two very different text modelling applications, generative document modelling and supervised question answering. Our neural variational document model combines a continuous stochastic document representation with a bag-of-words generative model and achieves the lowest reported perplexities on two standard test corpora. The neural answer selection model employs a stochastic representation layer within an attention mechanism to extract the semantics between a question and answer pair. On two question answering benchmarks this model exceeds all previous published benchmarks.