Artificial intelligence* is developing much faster than we thought. Just last month, Google's DeepMind AI beat Lee Sedol, a legendary Go player, at his own game in a defining moment for the industry. What enabled this win is a relatively new AI technique called deep learning, which is transforming AI. Until deep learning was introduced, even the best AI systems were always highly tuned for specific problems and required many rules to operate successfully. But deep learning has changed that, causing many researchers to abandon classical AI approaches.

A conference dedicated to a versatile computer chip is expected to draw thousands of researchers and hundreds of tech companies to San Jose next week for a look at advances in some of Silicon Valley's hottest technologies. Now in its seventh year, the Nvidia GPU Technology Conference opening Tuesday at the San Jose Convention Center celebrates the graphics processing unit, or GPU, a chip that has become the Swiss Army knife of computing. Some industry observers credit the annual conference for helping spark the research that has led to recent leaps forward in artificial intelligence. Patrick Moorhead, a semiconductor analyst with Moor Insight and Solutions, said that the San Jose Convention Center conference -- now in its seventh year -- became a meeting ground for scientists, academics and developers. "What happened is that once you bring these researchers together in one place and get them focused on this whole notion of using graphics to do a compute engine, they find these new ways to use it. That's exactly what happened," Moorhead said.

In the very same week that Artificial Intelligence lost one of its greatest pioneers, Marvin Minsky, it saw major progress on a decades-old challenge of playing human-level Go. There is much to shout about, but also a lot of hype and confusion about what we just saw. With so much at stake as people try to handicap the future of AI, and what it means for the future of employment and possibly even the human race, it's important to understand what was and was not yet accomplished. Fact: The paper published yesterday in Nature by DeepMind represents major progress in getting AI to play Go, a game that has been notoriously difficult for machines. Confusion: The European champion of Go is not the world champion, or even close.

Stephen Hawking, Bill Gates, and Elon Musk have something in common, and it's not wealth or intelligence. They' re all terrified of the AI takeover. Also called the AI apocalypse, the AI takeover is a hypothetical scenario where artificially intelligent machines become the dominant life-form on Earth. It could be that robots rise and become our overlords, or worse, they exterminate mankind and claim Earth as their own. But can the AI Apocalypse really happen?

April 02--A conference dedicated to a versatile computer chip is expected to draw thousands of researchers and hundreds of tech companies to San Jose next week for a look at advances in some of Silicon Valley's hottest technologies. Now in its seventh year, the Nvidia GPU Technology Conference opening Tuesday at the San Jose Convention Center celebrates the graphics processing unit, or GPU, a chip that has become the Swiss Army knife of computing. Some industry observers credit the annual conference for helping spark the research that has led to recent leaps forward in artificial intelligence. Patrick Moorhead, a semiconductor analyst with Moor Insight and Solutions, said that the San Jose Convention Center conference -- now in its seventh year -- became a meeting ground for scientists, academics and developers. "What happened is that once you bring these researchers together in one place and get them focused on this whole notion of using graphics to do a compute engine, they find these new ways to use it. That's exactly what happened," Moorhead said.

I went to school in Cambridge University in England, then went to Stanford to do my master's around 2000. I met up with Sergey Brin around then when Google was a tiny company and he invited me to join Google. So I started working part-time for Google. It was a fantastic time at the company--200 people, one building, and bright, idealistic, change-the-world kind of people. Naturally, when I finished my master's I joined full-time.

The Second National Data Science Bowl, a data science competition where the goal was to automatically determine cardiac volumes from MRI scans, has just ended. We participated with a team of 4 members from the Data Science lab at Ghent University in Belgium and finished 2nd! The team kunsthart (artificial heart in English) consisted of Ira Korshunova, Jeroen Burms, Jonas Degrave (@317070), 3 PhD students, and professor Joni Dambre. It's also a follow-up of last year's team Deep Sea, which finished in first place for the First National Data Science Bowl. This blog post is going to be long, here is a clickable overview of different sections. The goal of this year's Data Science Bowl was to estimate minimum (end-systolic) and maximum (end-diastolic) volumes of the left ventricle from a set of MRI-images taken over one heartbeat. These volumes are used by practitioners to compute an ejection fraction: fraction of outbound blood pumped from the heart with each heartbeat.

It's no longer the preserve of artificial-intelligence researchers and born-digital companies like Amazon, Google, and Netflix. Machine learning is based on algorithms that can learn from data without relying on rules-based programming. It came into its own as a scientific discipline in the late 1990s as steady advances in digitization and cheap computing power enabled data scientists to stop building finished models and instead train computers to do so. The unmanageable volume and complexity of the big data that the world is now swimming in have increased the potential of machine learning--and the need for it. In 2007 Fei-Fei Li, the head of Stanford's Artificial Intelligence Lab, gave up trying to program computers to recognize objects and began labeling the millions of raw images that a child might encounter by age three and feeding them to computers. By being shown thousands and thousands of labeled data sets with instances of, say, a cat, the machine could shape its own rules for deciding whether a particular set of digital pixels was, in fact, a cat.1 1.Fei-Fei Li, "How we're teaching computers to understand pictures," TED, March 2015, ted.com.

In The Social Express, a cast of animated characters help kids with autism learn helpful social skills. Both Katie and her teacher look like they'd be right at home in a Pixar film, and at first their conversation seems like it would fit in one too. The ponytailed and pink-clad Katie really wants to sharpen her pencil, but her teacher won't let her until the other kids in the class finish taking a test. Katie asks again, but the teacher offers the same, frustrating answer. "Katie seems upset that her teacher said'no.'

Britain Prime Minister David Cameron warned Western leaders Friday the Islamic State plans to use drones to spray nuclear material over Western cities. The UK Daily Telegraph reported that there is growing concerns among world leaders that extremists are looking to buy commercial drones to launch a dirty bomb attack over major metropolitan cities, which could kill thousands. Cameron warned the dangers of ISIS getting hold of nuclear material were "only too real." He met with leaders from the U.S., France and China to plan out a reaction response to such an attack, the newspaper reported. US officials reportedly fear that extremists could steal radioactive material from a medical facility and sold through the "dark web." Cameron said he would deploy counterterrorism police and the UK Border Force while British leaders hold a Cobra meeting.