Google's artificial intelligence group, DeepMind, has unveiled the latest incarnation of its Go-playing program, AlphaGo – an AI so powerful that it derived thousands of years of human knowledge of the game before inventing better moves of its own, all in the space of three days. Named AlphaGo Zero, the AI program has been hailed as a major advance because it mastered the ancient Chinese board game from scratch, and with no human help beyond being told the rules. In games against the 2015 version, which famously beat Lee Sedol, the South Korean grandmaster, in the following year, AlphaGo Zero won 100 to 0. The feat marks a milestone on the road to general-purpose AIs that can do more than thrash humans at board games. Because AlphaGo Zero learns on its own from a blank slate, its talents can now be turned to a host of real-world problems. At DeepMind, which is based in London, AlphaGo Zero is working out how proteins fold, a massive scientific challenge that could give drug discovery a sorely needed shot in the arm.

Google's AlphaGo already beat us puny humans to become the best at the Chinese board game of Go. Now, it's done with humans altogether. DeepMind, the Alphabet subsidiary behind the artificial intelligence, just announced AlphaGo Zero. The latest iteration of the computer program is the most advanced yet, outperforming all previous versions. It's also different from its predecessors in one uniquely significant way: Whereas the older AlphaGos trained in Go from thousands of human amateur and professional games, Zero foregoes the need for human insight altogether. Like the unpopular kid in class, it will learn simply by playing alone, and against itself.

Artificial Intelligence (AI) presents a significant opportunity to solve problems previously either not easy to solve or worse, not possible to solve. The combination of AI along with today's Graphics Processing Unit (GPU) technology provides an added boost to those leveraging sophisticated algorithms in their deep learning solutions. These sophisticated systems are able to train deep learning models and ultimately lead to predictive insights. The objective is to move from reactive to proactive and finally to predictive insights. The breadth of opportunities that AI presents is wide, however, a significant opportunity is in the Cybersecurity space.

Google's AlphaGo already beat us puny humans to become the best at the Chinese board game of Go. Now, it's done with humans altogether. DeepMind, the Alphabet subsidiary behind the artificial intelligence, just announced AlphaGo Zero. The latest iteration of the computer program is the most advanced yet, outperforming all previous versions. It's also different from its predecessors in one uniquely significant way: Whereas the older AlphaGos trained in Go from thousands of human amateur and professional games, Zero foregoes the need for human insight altogether. Like the unpopular kid in class, it will learn simply by playing alone, and against itself.

We've written before about how Google is one of the most prominent tech companies leading the way when it comes to the development of artificial intelligence. As each month passes, its AI division, DeepMind, continues to reveal increasingly advanced AI capabilities, especially when it comes to AlphaGo. This particular AI is most well-known for mastering the ancient Chinese game of Go…and subsequently defeating 18-time world champion Lee Se-dol, which happened just last year. Since then, DeepMind has started adding imagination to its AI, and they also used gaming to teach the AI how to better manage tasks. AlphaGo even went on to defeat another top go player, Ke Jie, once again showing off its (potentially) unlimited potential to learn.

LONDON – DeepMind, the London-based artificial intelligence company owned by Alphabet Inc., is planning to let its software learn how to fold proteins, an important problem for drug discovery. The company is best known for AlphaGo, software that beat the world's top human players at the ancient strategy game go. But now it has created software based on a different design, called AlphaGo Zero, which can beat all previous versions of AlphaGo. Unlike earlier versions, AlphaGo Zero learned completely from scratch, with no knowledge of how humans play the game, DeepMind chief executive Demis Hassabis said at a news conference held ahead of the publication of the new research in the scientific journal Nature on Wednesday. DeepMind's latest project shows how its studies could be of increasing practical importance to its parent company.

A collaborative course incorporating labs in TensorFlow and peer brainstorming along with lectures.

Google's artificial intelligence group, DeepMind, has unveiled the latest incarnation of its Go-playing program, AlphaGo – an AI so powerful that it derived thousands of years of human knowledge of the game before inventing better moves of its own, all in the space of three days. Named AlphaGo Zero, the AI program has been hailed as a major advance because it mastered the ancient Chinese board game from scratch, and with no human help beyond being told the rules. In games against the 2015 version, which famously beat Lee Sedol, the South Korean grandmaster, AlphaGo Zero won 100 to 0. The feat marks a milestone on the road to general-purpose AIs that can do more than thrash humans at board games. Because AlphaGo Zero learns on its own from a blank slate, its talents can now be turned to a host of real-world problems. At DeepMind, which is based in London, AlphaGo Zero is working out how proteins fold, a massive scientific challenge that could give drug discovery a sorely needed shot in the arm.

As humans, we know that the picture in the header of this blog is that of a broken coffee cup. We've been conditioned, over time, to recognize the visual cues – the cup's edges, the handle, the color of the spilled liquid, and even the logo printed on the side. Our brains add it all up and within milliseconds come to the correct conclusion. But what if you've never seen a broken coffee cup before? The CloudSight.ai story starts with the problem statement: How do you go beyond traditional image recognition to deliver a deep learning-powered service that helps organizations tap new meaning from their data?

This post introduces eigenvectors and their relationship to matrices in plain language and without a great deal of math. It builds on those ideas to explain covariance, principal component analysis, and information entropy. The eigen in eigenvector comes from German, and it means something like "very own." For example, in German, "mein eigenes Auto" means "my very own car." This car, or this vector, is mine and not someone else's.