After helping to launch the Partnership on AI with Google, Facebook and others; and doubling down on AI research, today Microsoft unveiled a new initiative that points to how it plans to target specific verticals in what can potentially be a very nebulous field -- while also raising the public image of AI as some grow concerned about the implications of its encroaching influence. The event was led by Harry Shum, Microsoft's EVP of its AI and Research Group, along with Emma Williams, GM of Bing Studio; Chris Bishop, Distinguished Scientist and Laboratory Director at Microsoft Research Cambridge; and Eric Horvitz, technical fellow and director of Microsoft Research Labs. Microsoft's R&D labs in Cambridge, UK (the first Microsoft set up outside of the US) are 20 years old this year, and Microsoft is unveiling some other new programs in the area -- including a new Microsoft Research AI group; a new "Aether Advisory Panel" (an acronym for "AI and Ethics in Engineering and Research") that will report directly to senior management; a new partnership with the Amsterdam Machine Learning Lab; and a few new experimental products that are using AI, such as a new PowerPoint Presentation Translator. This is an interesting and important twist on the AI challenge: many worry about how AI will replace humans, and/or will quietly help evade ethical and privacy oversights -- "societal angst" as Microsoft's Emma Williams, the GM of Bing Studio and its "EQ Expert", put it (EQ: emotional quotient).

As an artificial intelligence researcher, I often come across the idea that many people are afraid of what AI might bring. It's perhaps unsurprising, given both history and the entertainment industry, that we might be afraid of a cybernetic takeover that forces us to live locked away, "Matrix"-like, as some sort of human battery. And yet it is hard for me to look up from the evolutionary computer models I use to develop AI, to think about how the innocent virtual creatures on my screen might become the monsters of the future. Might I become "the destroyer of worlds," as Oppenheimer lamented after spearheading the construction of the first nuclear bomb? I would take the fame, I suppose, but perhaps the critics are right. Maybe I shouldn't avoid asking: As an AI expert, what do I fear about artificial intelligence?

It's perhaps unsurprising, given both history and the entertainment industry, that we might be afraid of a cybernetic takeover that forces us to live locked away, "Matrix"-like, as some sort of human battery. And yet it is hard for me to look up from the evolutionary computer models I use to develop AI, to think about how the innocent virtual creatures on my screen might become the monsters of the future. Might I become "the destroyer of worlds," as Oppenheimer lamented after spearheading the construction of the first nuclear bomb? I would take the fame, I suppose, but perhaps the critics are right. Maybe I shouldn't avoid asking: As an AI expert, what do I fear about artificial intelligence?

LONDON (Reuters) - The world's leading drug companies are turning to artificial intelligence to improve the hit-and-miss business of finding new medicines, with GlaxoSmithKline unveiling a new $43 million deal in the field on Sunday. Other pharmaceutical giants including Merck & Co, Johnson & Johnson and Sanofi are also exploring the potential of artificial intelligence (AI) to help streamline the drug discovery process. The aim is to harness modern supercomputers and machine learning systems to predict how molecules will behave and how likely they are to make a useful drug, thereby saving time and money on unnecessary tests. AI systems already play a central role in other high-tech areas such as the development of driverless cars and facial recognition software. "Many large pharma companies are starting to realize the potential of this approach and how it can help improve efficiencies," said Andrew Hopkins, chief executive of privately owned Exscientia, which announced the new tie-up with GSK. Hopkins, who used to work at Pfizer, said Exscientia's AI system could deliver drug candidates in roughly one-quarter of the time and at one-quarter of the cost of traditional approaches.

LAS VEGAS – "Mark Minevich didn't mince words in his Rise of the Autonomous Enterprise presentation here at Interop last week: "Whoever doesn't get involved in AI will die and get phased out." Minevich, a senior advisor to the government's Council of Competitiveness in Washington D.C., ticked off several examples of companies that have transformed industries by leveraging AI technologies, including Amazon with its personalized recommendations and Tesla. "Tesla has accumulated over 750 million miles of data to make cars smarter," said Minevich. The good news is that the U.S. is widely viewed as a leader in AI. Minevich said there's more AI investment in Silicon Valley, for example, than anywhere else in the world and it's growing. Last year estimates put AI investments at $2.5 billion, a figure that's forecast to double to $5 billion 2017. Pointing to forecasts by consulting giants Accenture and McKinsey, Minevich said "AI will deliver a massive trillion-dollar economy and the U.S. ...

Here is a fragment of my presentation for the text "Artificial Intelligence in China in its direct sources. Volume I. S...",, which I just published on the Amazon / Kindle platform. "...In recent years, several countries have announced and initiated strategic programs to support the AI, so far in 2017, particularly Canada and China. In both cases, AI is seen as a tool for development... The present work continues the methodology already used in the case of the Pan-Canadian Artificial Intelligence Strategy, consisting of gathering, providing thematic cohesion and continuity in the edition, recapitulating the essential sources of the subject in question. Under this format, the use for researchers, specialists, and public policy makers is greatly expanded, since not only the information but also the original source is available if the transcribed material (APA methodology) is required to be collated.

Freeway merging in congested traffic is a significant challenge toward fully automated driving. Merging vehicles need to decide not only how to merge into a spot, but also where to merge. We present a method for the freeway merging based on multi-policy decision making with a reinforcement learning method called {\em passive actor-critic} (pAC), which learns with less knowledge of the system and without active exploration. The method selects a merging spot candidate by using the state value learned with pAC. We evaluate our method using real traffic data. Our experiments show that pAC achieves 92\% success rate to merge into a freeway, which is comparable to human decision making.

Researchers have tapped into artificial intelligence to help overcome some of fusion energy's greatest challenges. By feeding a machine-learning program data from past experiments, it can reveal links between processes that cause complications in the plasma's behaviour This could help to avoid such disruptions, which lead to rapid loss of stored thermal and magnetic energy, and can even threaten the machine itself. According to the researchers, this approach could be used to analyze the behaviour of plasma inside a tokamak. Fusion involves placing hydrogen atoms under high heat and pressure until they fuse into helium atoms. When deuterium and tritium nuclei - which can be found in hydrogen - fuse, they form a helium nucleus, a neutron and a lot of energy.

After twice being denied visas, a team of Afghan girls has been permitted to travel to the U.S. for a robotics competition. The team takes a photo at Herat International Airport on Thursday. After twice being denied visas, a team of Afghan girls has been permitted to travel to the U.S. for a robotics competition. The team takes a photo at Herat International Airport on Thursday. It has been an odyssey, but finally, a team of six Afghan girls will be able to travel to the United States to compete in a robotics tournament.

To build Penny, the researchers used household income data from the U.S. Census Bureau, mapped it, and overlaid it with satellite imagery. They color-coded areas to represent different income brackets. In the picture below, green represents areas with the highest quartile of annual income (averaging $71,876 and above), red represents areas with the lowest annual income (averaging $34,176 and below) and orange and yellow represent the middle levels of income (averaging between $34,176 and $49,904, and $49,904 and $71,876, respectively).