San Francisco-based startup Fove has developed eye-tracking for virtual reality -- that kernel of technology many feel is key for the illusion of becoming immersed in a setting. Or use a death stare to shoot down virtual spaceships. Watch a movie of a forest or a room and be able to look around wherever you want. "It allows you to go inside the world that's behind the display," said Yuka Kojima, Fove's co-founder and a rare female chief executive in male-dominated Japan Inc. Fove, which comes from "fovea," the part of the eye with the sharpest vision, from "field of view," and the word's similarity with "love," has devised a way to use tiny infrared sensors inside headset goggles to monitor the movements of a wearer's pupils. It's a small company, founded in 2014, with offices in Tokyo, San Francisco and Los Angeles, and employing just 17 people.

SAN FRANCISCO, March 21 (Reuters) - Andy Grove, the Silicon Valley elder statesman who made Intel into the world's top chipmaker and helped usher in the personal computer age, died on Tuesday at age 79, Intel said. The company did not describe the circumstances of his death but Grove, who endured the Nazi occupation of Hungary during World War Two, living under a fake name, and came to the United States to escape the chaos of Soviet rule, had suffered from Parkinson's. Grove was Intel's first hire after it was founded in 1968 and became the practical-minded member of a triumvirate that eventually led "Intel Inside" processors to be used in more than 80 percent of the world's personal computers. With his motto "only the paranoid survive," which became the title of his best-selling management book, Grove championed an innovative environment within Intel that became a blueprint for successful California startups. Grove, who was named man of the year by Time magazine in 1997, encouraged disagreement and insisted employees be vigilant of disruptions in industry and technology that could be major dangers - or opportunities - for Intel.

If 2015 was the year analytics tools became ubiquitous in enterprise software, 2016 is shaping up to do much the same for machine learning. Just last week artificial-intelligence startup Nervana launched an offering that promises "deep learning on demand," and on Thursday Hewlett Packard Enterprise released a product of its own for what it calls "machine learning as a service." Dubbed Haven OnDemand, the cloud platform offers machine-learning application programming interfaces (APIs) and services designed to enable developers and businesses to build data-rich mobile and enterprise applications. Face-detection capabilities are included in Haven OnDemand. Haven OnDemand entered beta back in 2014, and at the time it had just a few APIs, said Fernando Lucini, HPE's CTO for big data.

With the lopsided 4-1 rout by Google's AlphaGo over Go grandmaster Lee Sedol, the easy takeaway is that artificial intelligence (AI) has achieved another milestone against humans, raising the specter that machines may eventually replace people, even managers. But by winning even in such convincing fashion, AlphaGo has revealed that AI still has a number of shortcomings, particularly when it comes to machine-made intuition. Google acquired DeepMind, the developer of AlphaGo, in 2014, in a 500 million bid to expand its burgeoning AI portfolio. AlphaGo's deep-learning algorithm allows both a "policy network" and a "value network" to store not only millions of past games played by the masters but also those played against tweaked versions of itself. The naming of the two networks is managerial-sounding and is aimed at promoting efficiency, not just raw computing power.

It was nearly 30 years ago that I first got infatuated with Artificial Intelligence (AI) and I ended up focusing both my undergraduate and graduate engineering research on applications of Artificial Neural Networks (ANNs). My first two jobs after graduate school stayed in the same groove; over 6 years I developed AI and machine learning techniques to address real world problems that ranged from recognizing human speech and natural language, to converting handwriting to searchable digitized text, and to streamlining maintenance procedures in nuclear reactor cores. So it is with a mix of amazement and amusement that I am soaking up the resurgence of AI and machine learning as the buzzword-du-jour: "Deep Learning". Deep Learning is very visible in the high hopes we hold for driverless cars and in the triumph of machines over chess champions. It is less conspicuously and more frequently used in the form of Apple's Siri, Amazon's Echo, playlists generated on Spotify, that auto-tag feature on Facebook Photos, the voice assistant that answers the phone when you call your bank, or when your fingerprint is recognized by a machine.

Image recognition is a hot area of research using artificial intelligence, and now IBM offers an online demo to let anyone test out the capabilities offered by its Watson cognitive computing system. Six sample photos are provided for illustration, or you can upload your own and ask Watson to analyze them.

It may not look like one of Isaac Asimov's robots or sound like HAL from "2001: A Space Odyssey," but artificial intelligence is here, and it is already having a huge impact on how the world works. From the way you shop for a pair of shoes online to how fast a Formula 1 team can push its car's engine, AI is helping businesses across the globe save millions by improving performance and efficiency. Still, problems like trust and security, not to mention fears of the so-called singularity, when artificial intelligence would overtake human thinking, remain hurdles that the technology must overcome before it goes mainstream. AI hit the news this week after a program called AlphaGo, developed by engineers at DeepMind, the AI startup acquired by Google in 2014 for 580 million, defeated the world's No. 1 Go player Lee Sedol. AlphaGo beat Sedol 4 games to 1, claiming a 1 million prize.

The field of intellectual property is rapidly evolving, both with respect to the law and the technologies being considered for protection. This session provides a primer about what a patent is, current best practices for protecting speech technologies and defending against assertion, and the recent evolution of intellectual property law in the United States, with emphasis on speech, software user interfaces, and mobile technologies. Fraudsters are using robodialing and ANI spoofing to wreak havoc on call centers. From the illegal practice of toll-free traffic pumping and international revenue-sharing fraud, to the more villainous acts of financial account fraud, identity theft, and drug trafficking, this seminar explores the unusual ways criminals are hacking our businesses. We also examine simple and cost-effective practices to protect our businesses, and our customers.

Network data appear in a number of applications, such as online social networks and biological networks, and there is growing interest in both developing models for networks as well as studying the properties of such data. Since individual network datasets continue to grow in size, it is necessary to develop models that accurately represent the real-life scaling properties of networks. One behavior of interest is having a power law in the degree distribution. However, other types of power laws that have been observed empirically and considered for applications such as clustering and feature allocation models have not been studied as frequently in models for graph data. In this paper, we enumerate desirable asymptotic behavior that may be of interest for modeling graph data, including sparsity and several types of power laws. We outline a general framework for graph generative models using completely random measures; by contrast to the pioneering work of Caron and Fox (2015), we consider instantiating more of the existing atoms of the random measure as the dataset size increases rather than adding new atoms to the measure. We see that these two models can be complementary; they respectively yield interpretations as (1) time passing among existing members of a network and (2) new individuals joining a network. We detail a particular instance of this framework and show simulated results that suggest this model exhibits some desirable asymptotic power-law behavior.

We all know that robots are already here, changing the way industries across the world work. The robotics industry is evolving at a fast pace. In addition to the more traditional industrial robots, there is an emerging demand for modern cobots which are intended to physically interact with humans in a shared workspace. One example of these modern cobots is ABB's YuMi, which was officially introduced to the marketplace at the end of 2015. YuMi is a "robotic co-worker" that will, according to the company, change the way we think about assembly automation.