At DeepMind, we conduct state-of-the-art research on a wide range of algorithms, from deep learning and reinforcement learning to systems neuroscience, towards the goal of building Artificial General Intelligence. A key factor in facilitating rapid progress is the software environment used for research. For nearly four years, the open source Torch7 machine learning library has served as our primary research platform, combining excellent flexibility with very fast runtime execution, enabling rapid prototyping. Our team has been proud to contribute to the open source project in capacities ranging from occasional bug fixes to being core maintainers of several crucial components. With Google's recent open source release of TensorFlow, we initiated a project to test its suitability for our research environment.

For a while now, Google's company DeepMind has been working on an artificial intelligence (AI) which plays Atari games better than you remember your older brother playing them in the 1980s. The AI is not only extremely proficient at playing these cult classics but has also learned to play 49 of them completely on its own. Despite this impressive feat, the DeepMind's creation isn't perfect and some games have simply proved to be too complicated for it to learn them on its own, Montezuma's Revenge being one of them. However, the Google-owned company has recently been hard at work correcting the flaws in its AI which has finally mastered the unforgiving 1984 platformer developed by the now-defunct Utopia Software. As its developers explain it, they had to make the AI "curious enough" for it to want to actually win the game.

Google wants to apply its machine learning technology to NHS patient data within the next five years, TechCrunch reports. The search giant's London-based artificial intelligence research lab, DeepMind, announced a partnership with the Royal Free NHS Trust in London in February but the full extent of the arrangement is only just becoming clear. A Memorandum of Understanding (MoU) between DeepMind and the Royal Free shows that the pair envisage a "broad ranging, mutually beneficial partnership, engaging in high levels of collaborative activity and maximizing the potential to work on genuinely innovative and transformational projects." The MoU -- obtained via a Freedom of Information (FoI) request from New Scientist -- states that DeepMind hopes to gain access to "data for machine learning research under appropriate regulatory and ethical approvals" within the next five years. Machine learning -- a subfield of computer science that gives computers the ability to learn without being explicitly programmed -- has the potential to speed up patient diagnosis and optimise their treatments.

If we want to create artificial intelligence that can teach itself how the world works, it needs to be curious. This has been a recurring theme in the world of AI in recent years, and newly published research from Google's DeepMind division shows exactly why this quintessentially human quality is important for making computers smart. Curiosity means rewarding the AI agent's exploration In the video above you can see DeepMind's AI agent tackling the infamously difficult Atari game Montezuma's Revenge. Unlike bots playing Unreal Tournament or StarCraft, the agent doesn't have access to all the information in the game, but is learning to play the same way humans do -- by looking at the screen, pushing buttons, and seeing what works. If this setup sounds familiar, it's because last February DeepMind unveiled an earlier iteration of the same agent, but when that bot tried to take on Montezuma's Revenge, it couldn't score a single point.

In the study they published, the team wrote that adopting a built-in rewards system "significantly improved exploration in a number of hard games, including the infamously difficult Montezuma's Revenge." Okay, you might not find it "infamously difficult," but it's tough for an AI to plan for the traps (and the platformer has plenty) that lie ahead. You can read the team's paper if you want to know more about the technique, but the video below can show you how the AI tackled the game. DeepMind, if you'll recall, is also behind AlphaGo, the program that bested Korean Go grandmaster Lee Sedol in four games out of five.

Musk was an early investor in AI firm DeepMind, which was later acquired by Google, and in March made an investment San Francisco-based Vicarious, another company working to improve machine intelligence. Speaking to US news channel CNBC, Musk explained that his investments were, "not from the standpoint of actually trying to make any investment return… I like to just keep an eye on what's going on with artificial intelligence. I think there is potentially a dangerous outcome there." "There have been movies about this, you know, like Terminator," Musk continued. "There are some scary outcomes. And we should try to make sure the outcomes are good, not bad."

Scientists from Google's artificial intelligence division, DeepMind, and Oxford University are developing a "kill switch" for AI. In an academic paper, they outlined how future intelligent machines could be coded to prevent them from learning to over-ride human input. It is something that has worried experts, with Tesla founder Elon Musk particularly vocal in his concerns. Increasingly, AI is being integrated into many aspects of daily life. Scientists Laurent Orseau, from Google DeepMind, and Stuart Armstrong, from the Future of Humanity Institute at the University of Oxford, set out a framework that would allow humans to always remain in charge.

Elon Musk is getting involved in AI, too, by supporting OpenAI, a non-profit research company focused on advancing digital intelligence for the common good. "Elon Musk has launched the OpenAI project with a star-studded list of backers – Palantir CEO Peter Thiel, LinkedIn founder Reid Hoffman and Y Combinator president Sam Altman," says an impressed Jones. OpenAI is headed up by machine learning expert Ilya Sutskever, ex-Google Brain Team member, and has just opened the OpenAI Gym in beta to help developers working with'reinforced learning', a type of machine learning that's central to AI. Essentially, it's about getting software to alter its behaviour in a dynamic environment in order to get a reward (you can't give Siri a biscuit every time she'found this on the web'). The arrival of AI means a changing of the guard in the tech industry, with disruption, innovation – and the complete domination of the cloud. Expertise, not investment, will become king.

As long as we've had idea of robots, we've had the idea of robot uprisings. Even the term'robot' originates with the Czech play R.U.R, which describes, you guessed it, a robot uprising. And now Google is taking the first definitive steps to make sure that never happens. DeepMind, AI research lab owned by Google, has released a paper exploring the best ways to prevent self-learning machines from turning off the "big red button" humans might use to shut them down. Of course, neither DeepMind or Google are referring to the paper, "Safely Interruptible Agents", as an anti-SkyNet.

Stuart Armstrong is a philosopher at the University of Oxford and one of the paper's authors. AI agents, as they're sometimes known, can already beat us at complex board games like Go and they're becoming more competent in a range of other areas. Now a London AI research lab owned by Google has carried out a study to make sure we can pull the plug on self-learning machines when we want to. DeepMind, acquired by Google for a reported 400 million in 2014, teamed up with scientists at the University of Oxford to find a way to make sure AI agents don't learn to prevent, or seek to prevent humans from taking control. The paper - titled "Safely Interruptible Agents [PDF]" and published on the website of the Machine Intelligence Research Institute (MIRI) - was written by Laurent Orseau, a research scientist at Google DeepMind, Stuart Armstrong at Oxford University's Future of Humanity Institute, and several others.