Until recently, [Europe's] contribution to the innovation and commercialisation of machine intelligence technologies has been under-appreciated. We now see growing self-confidence borne of the success, and continued presence, of local acquired startups like VocalIQ, Swiftkey, Deepmind, Magic Pony Technology, and PredictionIO. London is Europe's startup centre, mixing capital, proximity to markets, and world-class research hubs.

Right on cue, this past week heralded in an announcement of OpenAI, a new non-profit started by a number of tech luminaries to spearhead AI research that is publicly accessible. The motivation is that apparently these scions of capitalism lose faith in Adam Smith's invisible hand when it comes to AI R&D. Musk continues to promote the idea that AI will be humanity's largest existential threat. Challenging this view, the HBR asks if "OpenAI [is] Solving the Wrong Problem", pointing to the implied lack of trust in capitalism. This is similar to my own parry: that the biggest existential threat to humanity is humanity.

OpenAI Gym is a open-source Python toolkit for developing and comparing reinforcement learning algorithms. This R package is a wrapper for the OpenAI Gym API, and enables access to an ever-growing variety of environments. If you encounter a clear bug, please file a minimal reproducible example on github.

The evolution of the AI programs is reaching many fields: for example, to allow the AI to communicate in an ever more human way, Google's DeepMind developers have started to make it read hundreds of romance novels to help it improve its dialectical skills and develop a minimum of personality. The choice fell on the romantic novels because they have very linear plots and simple narrative schemes but also they are very similar to each other, an element that AI can learn to manage and rework to interact with a human being. The next step is to draft long and elaborate sentences, or even writing entire novels. Not surprisingly, a recent book written by a computer has passed a literary prize screening. The Japanese literary prize Hoshi Shinichi is also open to works produced by artificial intelligences and the jury – without knowing its origin – admitted the book "The day a computer writes a novel", written by the program of a professor of the Hakodate Future University.

Given the tensions surrounding AI at the moment, it's not surprising DeepMind is couching this breakthrough in the most mundane terms. The proffered example given in their Nature paper was DNC's ability to successfully navigate a London subway map from previous experience, finding the shortest path between specified points and inferring the missing links in randomly generated graphs. Finding an optimal route between locations is something we are already familiar with computers doing, so it's calculated to underwhelm.

In recent months, security researchers have shown that machine learning algorithms can be reverse-engineered and made to expose user data, like personal photos or health data. So how can we protect that information? New research from OpenAI and Google shows a way to build AI that never sees personal data, but is able to function as if it had. Ian Goodfellow, a researcher at OpenAI, compares the system to medical school. "The doctors who teach in medical school have learned everything they know from decades of experience working with specific individual people, and as a side effect they know a lot of private medical histories," Goodfellow says.

In his best-selling 2011 book Thinking, Fast and Slow, Nobel Prize-winning economist Daniel Kahneman hypothesized that thinking could be broken down into two distinct processes -- aptly named fast and slow thought. The former is all about your gut, the initial automatic responses you have to things, while the later is calculated, reflective and time-consuming. A new algorithm from DeepMind is beginning to show us that so-called "slow" thinking may soon be within the reach of machine learning. In a new paper published in Nature, the Google subsidiary DeepMind explained a new approach to machine learning that uses something called a differentiable neural computer. Neural networks operate using what essentially amounts to a very sophisticated trial and error process, eventually arriving at an answer.

The Billionaire, who also runs a not-for-profit artificial intelligence research company, warned that the futuristic technology could be deadly if it falls into the wrong hands. He has previously said that the research company, OpenAI, wants to "contract large corporations who may gain too much power by owning super-intelligence systems devoted to profits, as well as governments which may use AI to gain power and even oppress their citizens" but has extended that warning further. Speaking to Sam Altman, co-chairman of OpenAI, he claimed that countries would attempt to steal control away from its owner. However, Mr Musk reassured the public that AI technology would not develop a mind of its own and attack like scenes fictionalised in science fiction hit Terminator.

LONDON Jaguar Land Rover, Ford and Tata Motors are testing connected cars which can communicate with each other using technology designed to speed up journeys and cut accidents, the first such trials in Britain.

We are indeed living in interesting times, where we celebrate human-built machines defeating the best human minds at variety of activities. IBM Deep Blue's win against Chess champion Gary kasparov in 1997, IBM watson acing Jeopardy in 2011 and now Google DeepMind reportedly wining'Go' with high precision, being cited as a major breakthrough in AI, which even Facebook claims their team came close to acing the game as well. DeepMind goes against the'Go' champion, to be streamed live for the world to witness. While these feats are undoubtedly remarkable, and as understandable its creating quite a buzz in the AI community; as it provides the glimpse to the future seen only in sci-fi. As exciting as it may sound, it leaves a few questions before us.