Above the Trend Line: machine learning industry rumor central, is a recurring feature of insideBIGDATA. In this column, we present a variety of short time-critical news items such as people movements, funding news, financial results, industry alignments, rumors and general scuttlebutt floating around the big data, data science and machine learning industries including behind-the-scenes anecdotes and curious buzz. Our intent is to provide our readers a one-stop source of late-breaking news to help keep you abreast of this fast-paced ecosystem. We're working hard on your behalf with our extensive vendor network to give you all the latest happenings. Be sure to Tweet Above the Trend Line articles using the hashtag: #abovethetrendline.

Google has released technical paper on AI safety produced in collaboration with researchers from Stanford University, University of California, Berkeley and OpenAI.

When we see two people meet, we can often predict what happens next: a handshake, a hug, or maybe even a kiss. Our ability to anticipate actions is thanks to intuitions born out of a lifetime of experiences. Machines, on the other hand, have trouble making use of complex knowledge like that. Computer systems that predict actions would open up new possibilities ranging from robots that can better navigate human environments, to emergency response systems that predict falls, to Google Glass-style headsets that feed you suggestions for what to do in different situations. This week researchers from MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) have made an important new breakthrough in predictive vision, developing an algorithm that can anticipate interactions more accurately than ever before.

Investors backed more AI companies in the first quarter of 2016 (Q1'16) than in any other quarter, according to research from venture capital analysis firm CB Insights, which supports the idea that AI is the next major revolution in computing. In Q1'16, there were over 140 deals to startups focused on AI, CB Insights data wrote on its blog on Tuesday. Data startup Trifacta, DNA testing startup Pathway Genomics, and cognitive computing business Digital Reasoning Systems were among the AI-powered companies that raised equity funding rounds in Q1 from investors including Goldman Sachs, Accel Partners, Greylock Partners, and the IBM Watson Group. So far in 2016, more than 200 AI-focused companies have collectively raised nearly 1.5 billion ( 1 billion). The pick up in AI funding activity comes as businesses look to make their platforms and systems more human-like.

Twitter Inc. agreed to acquire a London-based artificial intelligence startup to make tweeted live videos look more professional. In a blog post Monday, Twitter Chief Executive Officer Jack Dorsey said he was buying Magic Pony Technology "so Twitter can continue to be the best place to see what's happening and why it matters, first." Seeking to shore up slowing growth, the social media company has in recent months begun emphasizing video on its site. Magic Pony uses machine learning, a way of teaching software to perform tasks without explicit programming instructions based on pattern recognition, a technology that's "increasingly at the core of everything we build at Twitter," Dorsey said. Twitter paid about 150 million for Magic Pony, according to a person familiar with the matter.

Google is diving deeper into artificial intelligence, with the company opening a dedicated machine learning research center in its Zurich office, the search company announced on Thursday, June 16. The Google Research Europe center will focus on three areas: Machine intelligence, natural language processing and understanding, and machine perception. The research center aims to deliver machine learning that can be put into practical use, to improve the machine learning infrastructure, and to assist the research community overall. "Google's ongoing research in machine intelligence is what powers many of the products being used by hundreds of millions of people a day -- from Translate to Photo Search to Smart Reply for Inbox," Emmanuel Mogenet, head of Google Research Europe, wrote in the blog post announcing the center. Mogenet noted machine learning software engineers and researchers will be able to develop products and conduct research at the Zurich center, which also holds the largest Google engineering office outside of the US.

Intel's chip arsenal appears to have some glaring weaknesses. One of them is the lack of a high-end graphics processor, which is important for gaming, virtual reality and machine learning. However, the company does have powerful alternatives: two monster chips that will be ammunition to take on GPUs and rival chips in the areas of machine learning and supercomputing, which are important to the company. In 2018, Intel will likely release a faster and more power-efficient Xeon Phi, a supercomputing chip that is already used in some of the world's fastest computers. Intel is also looking beyond CPUs to FPGAs (field programmable gate arrays), which can be faster at key tasks.

In a Monday blog post, the leadership of artificial intelligence (AI) research company OpenAI said that the group wants to modify'off-the-shelf' robots so they can perform common household tasks. "We're working to enable a physical robot (off-the-shelf; not manufactured by OpenAI) to perform basic housework," the group said in a blog post authored by Research Director Ilya Sutskever, Chief Technology Officer Greg Brockman, Sam Altman and Elon Musk. This futuristic target is second only to the primary goal laid out in the organization's blog post, which is to develop AI that could learn to improve its ability over time. Meeting such a goal would provide an underpinning for the perhaps more glamorous concept of robots that can clean your home, but the post goes onto say that domestic robots themselves would provide a solid foundation for approaching other problems in AI. "There are existing techniques for specific tasks, but we believe that learning algorithms can eventually be made reliable enough to create a general-purpose robot. More generally, robotics is a good testbed for many challenges in AI," the blog post reads.

Riddance to bad rubbish: Islamic State is still in Fallujah. Britain's EU referendum: Hoping that demography is not destiny Flying high: A new crop of hands-on universities is transforming how... Visit The Economist e-store and you'll find a range of carefully selected products for business and pleasure, Economist books and diaries, and much more

SYDNEY: Marketers are looking toward artificial intelligence (AI) to boost capability in measurement and targeting, according to an expert in the field. Karen Nelson-Field, Associate Professor at the University of South Australia and the author of Viral Marketing: The Science of Sharing, addressed this topic at the AdNews Media Summit in Sydney. And she outlined potentially significant opportunities for advertisers in the areas of viewability, ad avoidance, audience measurement and contextual programmatic targeting in real-time. While biometrics and similar technology have been used before to track people's responses to ads in a laboratory setting, Nelson-Field argued this is too removed from how people interact with advertising in real life. She suggested that the next step for marketers is in biometrics with vision AI behind it, a phase that will harness subconscious recollection and provide a more accurate picture of how consumers interact with advertising in real life.