The research is representative of a new generation of computer science that is exploiting the falling cost of computing and the availability of huge clusters of computers in giant data centers. It is leading to significant advances in areas as diverse as machine vision and perception, speech recognition and language translation. Although some of the computer science ideas that the researchers are using are not new, the sheer scale of the software simulations is leading to learning systems that were not previously possible. And Google researchers are not alone in exploiting the techniques, which are referred to as "deep learning" models. Last year Microsoft scientists presented research showing that the techniques could be applied equally well to build computer systems to understand human speech.

Three years ago, researchers at the secretive Google X lab in Mountain View, California, extracted some 10 million still images from YouTube videos and fed them into Google Brain -- a network of 1,000 computers programmed to soak up the world much as a human toddler does. After three days looking for recurring patterns, Google Brain decided, all on its own, that there were certain repeating categories it could identify: human faces, human bodies and … cats1. Google Brain's discovery that the Internet is full of cat videos provoked a flurry of jokes from journalists. But it was also a landmark in the resurgence of deep learning: a three-decade-old technique in which massive amounts of data and processing power help computers to crack messy problems that humans solve almost intuitively, from recognizing faces to understanding language. Deep learning itself is a revival of an even older idea for computing: neural networks.

Particle collisions at the Large Hadron Collider produce huge amounts of data, which algorithms are well placed to process. The next generation of particle-collider experiments will feature some of the world's most advanced thinking machines, if links now being forged between particle physicists and artificial intelligence (AI) researchers take off. Such machines could make discoveries with little human input -- a prospect that makes some physicists queasy. Driven by an eagerness to make discoveries and the knowledge that they will be hit with unmanageable volumes of data in ten years' time, physicists who work on the Large Hadron Collider (LHC), near Geneva, Switzerland, are enlisting the help of AI experts. On 9–13 November, leading lights from both communities attended a workshop -- the first of its kind -- at which they discussed how advanced AI techniques could speed discoveries at the LHC. Particle physicists have "realized that they cannot do it alone", says Cécile Germain, a computer scientist at the University of Paris South in Orsay, who spoke at the workshop at CERN, the particle-physics lab that hosts the LHC.

Robin Li, head of China's web giant Baidu, unveils the firm's intelligent digital assistant, Duer. When Andrew Ng joined Google from Stanford University in 2011, he was among a trickle of artificial-intelligence (AI) experts in academia taking up roles in industry. Five years later, demand for expertise in AI is booming -- and a torrent of researchers is following Ng's lead. The laboratories of tech titans Google, Microsoft, Facebook, IBM and Baidu (China's web-services giant) are stuffed with ex-university scientists, drawn to private firms' superior computing resources and salaries. "Some people in academia blame me for starting part of this," says Ng, who in 2014 moved again to become chief scientist at Baidu, working at the company's research lab in California's Silicon Valley.

Google's Go-playing program AlphaGo will take on world champion Lee Sedol in a series of games in Korea next month with a $1 million prize for the winner. Go is a 2,500-year-old game that was mentioned by Confucius. Players take turns placing black or white stones on a grid, capturing opponents' stones or surrounding spaces to take territory. AlphaGo has already bested a ranking Go master in Europe. AlphaGo, developed by Google's London-based artificial intelligence team DeepMind, trounced the best Go-playing AI programs in a series of matches, as reported by Nature last month.

Google has purchased DeepMind, a British artificial intelligence company whose goal reportedly is to make computers think like humans. The Mountain View, Calif., tech giant confirmed the deal with technology blog Recode, formerly known as AllThingsD, which first reported on the deal. The British Independent said Google is paying more than $500 million. The Independent also said Google beat out Facebook for the acquisition of the London startup. With the deal, Google appears more interested in talent than anything else.

South Korean Go player Lee Sedol reviews the match after finishing the Google DeepMind Challenge Match against Google's artificial intelligence program, AlphaGo, in Seoul, South Korea, on March 15, 2016. Google's Go-playing computer program defeated its human opponent in a 4:1 victory. South Korean Go player Lee Sedol reviews the match after finishing the Google DeepMind Challenge Match against Google's artificial intelligence program, AlphaGo, in Seoul, South Korea, on March 15, 2016. Google's Go-playing computer program defeated its human opponent in a 4:1 victory. South Korean Go player Lee Sedol reviews the match after finishing the Google DeepMind Challenge Match against Google's artificial intelligence program, AlphaGo, in Seoul, South Korea, on March 15, 2016.

The development of artificial intelligence (AI) is rapidly advancing thanks to a machine learning process called "deep learning," raising expectations of radical changes and greater convenience in people's lives. The AI boom began early in the 2010s when major information technology companies such as U.S. companies Google Inc. and Facebook Inc. established research institutes, and the development of deep learning has added fuel to the fire. Deep learning is a branch of machine learning in which a computer system mimics human neural circuits and processes information in multiple processing layers. It is a "revolution" in the foundation of AI, said Yutaka Matsuo, an associate professor at the University of Tokyo and expert on information technology and AI. The AI in a computer system teaches itself by processing huge amounts of data. When, for example, AI processes input data and outputs them, it repeatedly learns the features of the data so as to bring out the same data as those entered.

SAN FRANCISCO – Tesla Motors Inc. Chief Executive Officer Elon Musk and other prominent tech executives are pouring $1 billion into a nonprofit aimed at creating artificial intelligence that augments humans' capabilities, rather than making them obsolete. The effort announced Friday, called OpenAI, joins significant investments from companies such as Alphabet Inc.'s Google, Facebook Inc. and Amazon.com Inc., which have used artificial intelligence to sharpen their businesses with services such as facial recognition or language processing. But the OpenAI founders suggested they have set their sights on bigger problems. "Our goal is to advance digital intelligence in the way that is most likely to benefit humanity as a whole, unconstrained by a need to generate financial return," a blog post on Open-AI's website (bit.ly/1lBMdz9) said.

Canadian financial services firm Manulife Financial Corp. is banking on artificial intelligence being a significant asset for its next project. The company's Toronto-based Lab of Forward Thinking (LOFT) is collaborating with Silicon Valley-based Nervana Systems on a new AI-based application that could help portfolio managers analyze the high volume of online information, financial news, emails, and documents they receive when researching investments. "If you think about all of the things that a portfolio manager or researcher might look at when making decisions… a lot of it is social data – emails, industry reports, stock and economic data," Ace Moghimi, Manulife's Boston-based head of innovation, North America, told ITBusiness.ca. "The system we're putting in place essentially uses natural language processing, supported by an underlying deep learning neural network, to go through vast stores of unstructured data, allowing researchers to analyze the information much faster than they would be able to on their own," he said. The project's genesis lies within the LOFT division itself, which researches ways that Manulife can incorporate emerging technology and unconventional business processes into its products and services – and its Toronto-based data scientists were particularly taken with AI and deep learning, Moghimi said.