Elon Musk is a well-known harbinger of the potential for ill held by artificial intelligence. The Tesla and SpaceX CEO also helped start OpenAI, a group with a broad mandate that focuses on developing AI out (as the name implies) in the open, rather than behind closed doors as the exclusive province of high-powered governments and secretive private contractors. Musk, it turns out, was in on the AI train early with an investment in DeepMind, which was later acquired by Google. Musk wasn't in DeepMind for a return, as is the case with most investments; he wanted access to greater insight regarding DeepMind's progress, and the progress of AI in general, according to a new feature in Vanity Fair. The enterprising CEO wanted to be able to see how fast AI was improving, and what he found was a rate of gains that he hadn't expected, and that he thought most people would not possibly expect. This was the insight that Musk needed to begin a campaign warning against the potential dangers of AI, and to develop his own efforts to responsibility develop the tech via OpenAI.

Despite his own role in the advancement of artificial intelligence, Elon Musk has long warned that the technology built by humans could one day lead to our destruction. And, the tech giant has now revealed he's kept a'wary eye' on the growth of AI for years as an investor in DeepMind, which was acquired by Google in 2014. While humans may be able to stop a runaway algorithm, there would be'no stopping' a large, centralized AI that calls the shots, Musk argues in a recent interview with Vanity Fair. Despite his own role in the advancement of artificial intelligence, Elon Musk has long warned that the technology built by humans could one day lead to our destruction. And, the tech giant has revealed he's kept a'wary eye' on the growth of AI for years as an investor in DeepMind Last summer, when asked at the Code Conference in southern California if the answer to the question of whether we are in a simulated computer game was'yes', Elon Musk said the answer is'probably'.

THE world reeled when Lee Sedol – one of the great modern players of the ancient board game Go – was beaten by Google's DeepMind artificial intelligence (AI) program, AlphaGo. The AI managed to outmaneuver Lee at his own game, one which rewards players' strategic judgment and creative analyses. To achieve this, DeepMind provided AlphaGo with the basic framework of the game, recordings of previous games and made it play itself continuously. The software mimics the processes of human learning – and as it went along, AlphaGo learned to be a better player over time. The day of the face-off, AlphaGo beat Lee four games to one and was awarded the highest Go game-master ranking.

The deal between Google and the U.K. National Health Service was profiled by Digital Journal last November. The agreement centered on plans to develop a platform capable of sharing patient data with the aim of improving patient outcomes. This was by providing information about medical conditions with the aid of artificial intelligence. A secondary aim was to reduce the amount of paperwork by digitizing patient records. One aspect of the project involve sharing some million patient records, provided by London's Royal Free Hospital, with DeepMind.

'Some of AlphaGo's moves puzzled observers, because they did not fit usual human patterns of play. DeepMind CEO Demis Hassabis explained the reason for this difference as follows: "unlike humans, the AlphaGo program aims to maximize the probability of winning rather than optimising margins". If this binary logic – in which the only thing that matters is winning while the margin of victory is irrelevant – were built into an autonomous weapons system, it would lead to the violation of the principle of proportionality, because the algorithm would see no difference between victories that required it to kill one adversary or 1,000.'

OpenAI is a non-profit AI research company, discovering and enacting the path to safe artificial general intelligence.

It is now table stakes for artificial intelligence algorithms to "learn" about the world around them. The next level: For AI bots to learn how to talk to each other -- and develop their own shared language. New research released last week by OpenAI, the artificial intelligence nonprofit lab founded by Elon Musk and Y Combinator president Sam Altman, details how they're training AI bots to create their own language, based on trial and error, as the bots move around a set environment. This is different from how artificial intelligence algorithms typically learn -- using large sets of data, like to recognize a dog by taking in thousands of pictures of dogs. The world the researchers created for the AI bots to learn in is a computer simulation of a simple, two-dimensional white square.

On Monday, Google announced plans to launch a new peer review journal and "ecosystem" for machine learning. Scientists need to develop notations, analogies, visualizations, and explanations of ideas. It's deeply tied to the heart of science. "That's why, in collaboration with OpenAI, DeepMind, YC Research, and others, we're excited to announce the launch of Distill, a new open science journal and ecosystem supporting human understanding of machine learning. Distill is an independent organization, dedicated to fostering a new segment of the research community. "Modern web technology gives us powerful new tools for expressing this human dimension of science.

Many problems in image processing and computer vision (e.g. colorization, style transfer) can be posed as 'manipulating' an input image into a corresponding output image given a user-specified guiding signal. A holy-grail solution towards generic image manipulation should be able to efficiently alter an input image with any personalized signals (even signals unseen during training), such as diverse paintings and arbitrary descriptive attributes. However, existing methods are either inefficient to simultaneously process multiple signals (let alone generalize to unseen signals), or unable to handle signals from other modalities. In this paper, we make the first attempt to address the zero-shot image manipulation task. We cast this problem as manipulating an input image according to a parametric model whose key parameters can be conditionally generated from any guiding signal (even unseen ones). To this end, we propose the Zero-shot Manipulation Net (ZM-Net), a fully-differentiable architecture that jointly optimizes an image-transformation network (TNet) and a parameter network (PNet). The PNet learns to generate key transformation parameters for the TNet given any guiding signal while the TNet performs fast zero-shot image manipulation according to both signal-dependent parameters from the PNet and signal-invariant parameters from the TNet itself. Extensive experiments show that our ZM-Net can perform high-quality image manipulation conditioned on different forms of guiding signals (e.g. style images and attributes) in real-time (tens of milliseconds per image) even for unseen signals. Moreover, a large-scale style dataset with over 20,000 style images is also constructed to promote further research.

The company, which is on a mission to "solve intelligence," has hired some of the brightest minds in the world, including academics from Oxbridge and research scientists from firms like Facebook and Microsoft. Exactly how DeepMind's researchers work together has been something of a mystery but the FT story sheds new light on the matter. Researchers at DeepMind are divided into four main groups, including a "neuroscience" group and a "frontiers" group, according to the report. The frontiers group is said to be full of physicists and mathematicians who are tasked with testing some of the most futuristic AI theories. "We've hired 250 of the world's best scientists, so obviously they're here to let their creativity run riot, and we try and create an environment that's perfect for that," DeepMind CEO Demis Hassabis told the FT.