Watch DeepMind's program AlphaGo take on the legendary Lee Sedol (9-dan pro), the top Go player of the past decade, in a 1M 5-game challenge match in Seoul. This is the livestream for Match 3 to be played on: 12th March 13:00 KST (local), 04:00 GMT; note for US viewers this is the day before on: 11th March 20:00 PT, 23:00 ET. In October 2015, AlphaGo became the first computer program ever to beat a professional Go player by winning 5-0 against the reigning 3-times European Champion Fan Hui (2-dan pro). That work was featured in a front cover article in the science journal Nature in January 2016.

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.

The topic of Machine Learning has increasingly gained popularity over the last few years. Although it's not a new science, it continues to gain momentum. Allowing computers to find hidden insights that can guide better and faster decisions in real time without the use of human intervention allows this science to continue to grow. However, what does the future hold for Machine Learning? Let's assume analytical solutions can be built by studying past data models; where does that kind of data technology play its cards in the future?

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.

XGBoost is a library that is designed for boosted (tree) algorithms. It has become a popular machine learning framework among data science practitioners, especially on Kaggle, which is a platform for data prediction competitions where researchers post their data and statisticians and data miners compete to produce the best models. For structured learning problems on Kaggle, it can be difficult to get into the top 10 without including XGBoost. Typically, data scientists use multi-thread single machines to train XGBoost models. Very few people have deployed XGBoost on a distributed environment and achieved good performance.

Joining the conversation today is Jakob Foerster; an artificial intelligence expert who recently helped develop a machine that solved the notoriously difficult '100 hat riddle' used by Google and Goldman Sachs to weed out the highest calibre candidates during interviews . The neural network had to first figure out a way of communicating with other AIs before going on to solve the problem –Jakob explains that this is'basically a first step toward having machines that can communicate and collaborate'. Despite these achievements Jakob maintains a level head about the near future believing humans will continue to rule the machines for a while yet. This is a refreshing position given the recent warning issued by Stephen Hawking and Elon Musk that we may be near to creating something so powerful it cannot be controlled. Regardless of which future awaits us Jakob provides a fascinating insight into how artificial intelligence is already here and controlling everything from vehicles to the stock market and may very soon be creating music more emotional and beautiful than anything we can conceive of today.

As an example of this theory applied, Littman outlines a startup project for robots to help recovering alcoholics refrain from another drink. The robot would essentially perform the role of a counselor. Though inherently less dynamic than a professional human counselor, the robot could engage the subject in similar ways, like by asking poignant questions such as "How long have you been sober for?" The hope is that these questions would grant the subject another perspective and encourage him to reflect on his actions. Littman says, "By having the robot play the role of counselor or therapist, we will to make the insights and capabilities of highly-trained counselors more available to a wider set of people."

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.

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Next time you ask Google for directions or run an image search, thank Le Viet Quoc. The 34-year-old Vietnamese engineer is part of the team behind Google Brain, an artificial intelligence (AI) research project whose technology is responsible for such features, reports VnExpress. Part of Google's not-so-secret research outfit X, which pioneers cutting-edge technology like self-driving cars and delivery drones, Quoc works in a field known as "deep learning" which uses the human brain as a model to create "neural networks" for computers. Though deep learning's development has been slow, engineers like Quoc are making progress: in 2012, Google Brain made headlines when its network of 16,000 computer processors successfully learned how to search for cat videos on YouTube, despite being given no information prior to the test on how to identify such animals. The Stanford grad, who holds a doctorate in computer science and was named one of MIT's Innovators Under 35, is still working toward the creation of better, more intelligent machines.