Twitter has acquired Magic Pony, a London-based firm that has developed technology to improve video shot on smartphones and shared online. The company specializes in machine learning, the ability of computers to use built-in artificial intelligence to improve output. In the case of Magic Leap, that involves improved processing of video so that clips and live feeds look better shared on Twitter. Magic Pony had developed "novel machine learning techniques for visual processing," said Twitter CEO Jack Dorsey in a Twitter blog post announcing the acquisition Monday. "Machine learning is increasingly at the core of everything we build at Twitter. It's powering much of the work we're doing to make it easier to create, share, and discover the very best content so that every time you open Twitter you're immersed in the most relevant news, stories, and events for you."

TOKYO (Reuters) - Toyota Motor Corp is targeting developing in the next five years driver assistance systems that integrate artificial intelligence (AI) to improve vehicle safety, the head of its advanced research division said. Gill Pratt, CEO of recently set up Toyota Research Institute (TRI), the Japanese automaker's research and development company that focuses on AI, said it aims to improve car safety by enabling vehicles to anticipate and avoid potential accident situations. Toyota has said the institute will spend 1 billion over the next five years, as competition to develop self-driving cars intensifies. Earlier this month, home rival Honda Motor Co said it was setting up a new research body which would focus on artificial intelligence, joining other global automakers which are investing in robotics research, including Ford and Volkswagen AG VOWG_p.DE . "Some of the things that are in car safety, which is a near-term priority, I'm very confident that we will have some advances come out during the next five years," Pratt told reporters late last week in comments embargoed for Monday.

This week at the International Supercomputing Conference (ISC '16) we are expecting a wave of vendors and high performance computing pros to blur the borders between traditional supercomputing and what is around the corner on the application front--artificial intelligence and machine learning. For some, merging those two areas is a stretch, but for others, particularly GPU maker, Nvidia, which just extended its supercomputing/deep learning roadmap this morning, the story is far more direct since much of the recent deep learning work has hinged on GPUs for training of neural networks and machine learning algorithms. We have written extensively over the last year about how GPUs are being used in both deep learning and in HPC separately, but we might soon arrive at a fuller merger between the two areas, at least from a systems and hardware perspective. "Deep learning is not just an application segment, it's a whole new computing model," Ian Buck, VP of Accelerated Computing, tells The Next Platform. "If you had asked me at the launch of CUDA if GPUs would be in the largest supercomputers or revolutionizing artificial intelligence, I would have said that was a vision or even a pipe dream."

Facebook is well known for its early and increasing use of artificial intelligence. The social media site uses AI to pinpoint its billion-plus users' individual interests and tailor content accordingly by automatically scanning their newsfeeds, identifying people in photos and targeting them with precision ads. And now behind the scenes the social network's AI researchers are trying to take this technology to the next level--from pure data-crunching logic to a nuanced form of "common sense" rivaling that of humans. AI already lets machines do things like recognize faces and act as virtual assistants that can track down info on the Web for smartphone users. But to perform even these basic tasks the underlying learning algorithms rely on computer programs written by humans to feed them massive amounts of training data, a process known as machine learning.

The traditional definition of artificial intelligence is the ability of machines to execute tasks and solve problems in ways normally attributed to humans. Some tasks that we consider simple--recognizing an object in a photo, driving a car--are incredibly complex for AI. Machines can surpass us when it comes to things like playing chess, but those machines are limited by the manual nature of their programming; a 30 gadget can beat us at a board game, but it can't do--or learn to do--anything else. This is where machine learning comes in. Show millions of cat photos to a machine, and it will hone its algorithms to improve at recognizing pictures of cats.

Can Artificial Intelligence Help Save eBay? By The Street For eBay (EBAY), artificial intelligence could prove to be its salvation. The San Jose, Calif.-based e-commerce giant, which has been working to transform itself after spinning off PayPal Holdings (PYPL) last year, could reap some huge benefits if it can find out a way to effectively use AI, according to analysts. At the Code Conference earlier this month, eBay CEO Devin Wenig revealed that the company started getting very serious about using artificial intelligence last year, noting that e-commerce is essentially a data business and that AI may be more important for eBay than its peers due to the "breadth of its inventory." Wenig further explained that eBay plans to formulate a strategy focused on using data to predict what consumers might want and offering a wide selection of products.

Google is expanding its efforts in Artificial intelligence with a new project to teach machines what it terms "common sense". The technology giant has launched a new European research centre dedicated to advancing AI technology. Based in Zurich, the team will focus on three areas – machine learning, natural language understanding and computer perception. Emmanuel Mogenet, who will head the unit, said much of the research would be on teaching machines common sense. There was, he said, "no limit on how big I grow the team We are very ambitious in terms of growth. The only limiting factor will be talent," he told journalists gathered in Zurich to hear more about Google's AI plans.

Security and privacy expert Matthew Green reassures us, "Your iPhone is not going to kill you." But in his recent explanation of how Apple's differential privacy approach will send an obscured subset of our private activities to Apple, he explains that some studies demonstrate serious consequences to restricting privacy too much when collecting data related to medical research. Apple proposes initially to gather data in iOS 10 from typing to improve emoji substitution and predictive word suggestions for previously unrecognized words, and from deep links within apps (non-private internal destinations) to improve Spotlight search results. In macOS Sierra, it will use data to improve autocorrect. And in both, it will watch which Lookup Hints are selected in Notes to provide better help.

Facebook has developed an engine that will enable it to better understand the context of your posts. Called DeepText, it utilizes deep neural network architecture in order to understand the text being shared. The social networking company claims that DeepText is able to understand "with near-human accuracy" the content of several thousand posts per second across 20 languages. This technology was built on ideas developed around deep learning by Ronan Collobert and Yann LeCun from Facebook's AI Research Group. Although introduced today, DeepText is already being tested across some Facebook properties, such as Messenger.

Somewhere in the not so distance future, computers could help doctors diagnose diseases much more quickly than they can today. In fact, researchers from Beth Israel Deaconess Medical Center (BIDMC) and Harvard Medical School (HMS) have been working on a way to train artificial intelligence (AI) to read and interpret pathology images that doctors use to look for signs of cancer. Andrew Beck from BIDMC explains that the "method is based on deep learning." It is the method commonly used to train AI to recognize images, speech patterns, and objects. During a demonstration at the annual International Symposium of Biomedical Imaging, they were able to show how effective their training was.