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.

Developing robust quantitative trading strategies is an intensive, rigorous, time-consuming process with no guarantee for success. In this webinar, you will learn how to apply techniques from the Artificial Intelligence and machine learning fields to improve the quantitative strategy development process and maximize your chances of success with every strategy. Attendees will learn practical applications that they can apply to their own trading and will come away with a strategy they can actually trade live. Attendees should have a basic understanding of quantitative and algorithmic trading. No programming experience is required.

While many tech moguls dream of changing the way we live with new smart devices or social media apps, one Russian internet millionaire is trying to change nothing less than our destiny, by making it possible to upload a human brain to a computer, reports Tristan Quinn. "Within the next 30 years," promises Dmitry Itskov, "I am going to make sure that we can all live forever." It sounds preposterous, but there is no doubting the seriousness of this softly spoken 35-year-old, who says he left the business world to devote himself to something more useful to humanity. "I'm 100% confident it will happen. Otherwise I wouldn't have started it," he says. It is a breathtaking ambition, but could it actually be done?

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Within the rail industry, anything which helps keep trains moving, avoiding operational delays and improves customer experience, is worth pursuing. Many OEMs are now investing significant resources into one of the most valuable and potentially rewarding currencies in business: Big Data. In rail, and specifically when it comes to rolling stock maintenance, big data is synonymous with Condition Based Maintenance (CBM) and Predictive Maintenance (PM). Thanks to the rapidly expanding scale of manufacturing and asset maintenance industries, they are now adapting to the wider applications of advanced algorithms on consumer generated big data. Though CBM and PM are commonly adopted practices in rail industry, the scope of CBM is far wider than that of PM.

This helps to validate DeepMind's machine learning techniques and the neural network construction behind it. Having proven their mettle in Go, the DeepMind team could now have the confidence (and funding) to tackle more complex AI challenges. ARTIFICIAL INTELLIGENCE (AI) just overcame a new hurdle: learning to play Go, a game considered thousands of times more complex than chess--well enough to beat the greatest human player at his own game. South Korean national Lee Se-dol, one of the world's top Go players, won only one of the five matches against Google's AlphaGo, missing out on the 1-million prize up for grabs in a recent'challenge' held in Seoul. AlphaGo, an AI system developed by Google DeepMind, just bested the best Go-playing human currently alive. This was not supposed to happen.

The World Science Festival Brisbane's'Shared Space Bots' performance demonstrations at Queensland University of Technology used pint-sized, futuristic floor robots to reveal research into the technologies that will allow humans to communicate with driverless cars, and allowed audience members a chance to'test drive' the systems safely. Shared Space Bots were demonstrated by internationally acclaimed technologist Christopher Lindinger from Austrian R&D company Ars Electronica Futurelab. They have been developed as part of the ongoing research cooperation between Mercedes-Benz and Ars Electronica Futurelab on the topic of future mobility.

Artificial intelligence is breathing down our necks: Software built by Google startled the field last week by easily defeating the world's best player of the Asian board game Go in a five-game match. Go resembles chess in the deep, complex problems it poses but is even harder to play and has resisted AI researchers longer. It requires mastery of strategy and tactics while you conceal your own plans and try to read your opponent's. Mastering Go fits well into the ambitious goals of AI research. It shows us how much has been accomplished and forces us to confront, as never before, AI's future plans.

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.