AlphaGo Zero: Discovering new knowledge
Key Takeaways
The video discusses AlphaGo Zero, a computer program that learned to play the game of Go from scratch without human data, and its ability to discover new knowledge and achieve a high level of performance. The program uses tabula rasa learning, starting from a blank slate and figuring out how to play the game through self-play.
Full Transcript
[Music] alphago has been through many generations now the first generation of alphago which we published in our original nature paper was able to beat a professional player for the first time now we have the final version of alphago alphago zero which has learned completely from scratch from first principles without using any human data and has achieved the highest level of performance overall the most important idea in alphago zero is that it learns completely tabula rasa that means it starts completely from a blank slate and figures out for itself only from self play and without any human knowledge without any human data without any human examples or features or intervention from humans it discovers how to play the game of Go completely from first principles so tabula rasa learning is extremely important to our goals and ambitions that deep mind and the reason is that if you can achieve tabula rasa learning you really have an agent that can be transplanted from the game of go to any other domain you untie yourself from the specifics of the domain you're in and you come up with an algorithm which is so general that it can be applied anywhere for us the idea of alphago is not to go out and defeat humans but actually to discover what it means to to do science and for a program to be able to learn for itself what knowledge is so what we starts to see was that alphago zero not only rediscovered the common patterns and openings that humans tend to play these joseki patterns that humans play in the corners it also learned them discovered them and ultimately discarded them in preference for its own variants which humans don't even know about or play at the moment and so we can say that really what's happened is that in a short space of time alphago zero has understood all of the go knowledge that has been accumulated by humans over thousands of years of playing and it's analyzed it and it started to look at it and discover much of this knowledge for itself and sometimes it's chosen to actually go beyond that and come up with something which the humans hadn't even discovered in this time period and developed new pieces of knowledge which were creative and and novel in many ways we're all really excited by how far alpha go zero has cotton but I think what we're most excited about is how far it can go in the real world that the fact that we've seen a program can achieve a very high level of performance in domain as complicated and challenging as go should mean that now we can start to tackle some of the most challenging and impactful problems for Humanity
Original Description
DeepMind's Professor David Silver describes AlphaGo Zero, the latest evolution of AlphaGo, the first computer program to defeat a world champion at the ancient Chinese game of Go. Zero is even more powerful and is arguably the strongest Go player in history.
Previous versions of AlphaGo initially trained on thousands of human amateur and professional games to learn how to play Go. AlphaGo Zero skips this step and learns to play simply by playing games against itself, starting from completely random play. In doing so, it quickly surpassed human level of play and defeated the previously published champion-defeating version of AlphaGo by 100 games to 0.
If similar techniques can be applied to other structured problems, such as protein folding, reducing energy consumption or searching for revolutionary new materials, the resulting breakthroughs have the potential to positively impact society.
Find out more here: https://deepmind.com/blog/alphago-zero-learning-scratch
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AlphaGo Zero: Discovering new knowledge
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