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One in a series of posts explaining the theories underpinning our research. Bugs and software have gone hand in hand since the beginning of computer programming. Over time, software developers have established a set of best practices for testing and debugging before deployment, but these practices are not suited for modern deep learning systems. Today, the prevailing practice in machine learning i
Just over a year ago we presented WaveNet, a new deep neural network for generating raw audio waveforms that is capable of producing better and more realistic-sounding speech than existing techniques. At that time, the model was a research prototype and was too computationally intensive to work in consumer products. But over the last 12 months we have worked hard to significantly improve both the
Just over a year ago, we saw a major milestone in the field of artificial intelligence when AlphaGo took on and defeated one of the world’s top Go players, the legendary Lee Sedol, in a feat that experts agreed was a decade ahead of its time. While a lot of the immediate focus was on the achievement of beating a top professional player, if you looked beneath the surface something quite magical was
Human Level Control Through Deep Reinforcement LearningAbstract The theory of reinforcement learning provides a normative account deeply rooted in psychological and neuroscientific perspectives on animal behaviour, of how agents may optimize their control of an environment. To use reinforcement learning successfully in situations approaching real-world complexity, however, agents are confronted wi
The story of AlphaGo so farAlphaGo is the first computer program to defeat a professional human Go player, the first program to defeat a Go world champion, and arguably the strongest Go player in history. AlphaGo’s first formal match was against the reigning 3-times European Champion, Mr Fan Hui, in October 2015. Its 5-0 win was the first ever against a Go professional, and the results were publis
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