"“By not using human data — by not using human expertise in any fashion – we’ve actually removed the constraints of human knowledge,” said AlphaGo Zero’s lead programmer, David Silver, reports to the Verge.
“It’s therefore able to create knowledge itself from first principles; from a blank slate.”"
"I hope these kind of algorithms will be routinely working with us as scientific experts medical experts on advancing the frontiers of science and medicine – that’s what I hope.”
“GANs (Generative Adversarial Networks) could even deliver unsupervised learning, something that doesn’t really exist today. Currently, neural network can learn to recognize cats by analyzing several million cat photos, but humans must carefully identify the images and label them as cat photos. People are still very much in the mix, and that’s often a problem, whether the issue is bias or the sheer scale of human labor needed to train an AI. Researchers like LeCun are pushing toward systems that can learn without such heavy human involvement, something that could accelerate the evolution of AI.”
"Everything the stick figure is doing in this video is self-taught. The jumping, the limboing, the leaping — all of these are behaviors that the computer has devised itself as the best way of getting from A to B. All DeepMind’s programmers have done is give the agent a set of virtual sensors (so it can tell whether it’s upright or not, for example) and then incentivize to move forward. The computer works the rest out for itself, using trial and error to come up with different ways of moving.
The novelty here is that the researchers are exploring how difficult environments can teach an agent complex and robust movements (i.e., using its knee to get purchase on top of a high wall). Usually, reinforcement learning produces behavior that is fragile, and that breaks down in unfamiliar circumstances, like a baby who knows how to tackle the stairs at home, but who can’t understand an escalator. This research shows that isn’t always the case, and that RL can be used to teach complex movements."
"“[AlphaGo Zero’s] games against AlphaGo Master will surely contain gems, especially because its victories seem effortless,” wrote Andy Okun and Andrew Jackson, members of the American Go Association, in a Nature News and Views article. “At each stage of the game, it seems to gain a bit here and lose a bit there, but somehow it ends up slightly ahead, as if by magic… The time when humans can have a meaningful conversation with an AI has always seemed far off and the stuff of science fiction. But for Go players, that day is here.”"
“Earlier this year the AlphaGo artificial intelligence program ended humanity’s 2,500 years of supremacy at the board game go. Not content with its 3–0 victory over the world’s top player, AlphaGo creator DeepMind Technologies on Wednesday unveiled an enhanced version—AlphaGo Zero—which the company says soundly thumped its predecessor program in an AI face-off, winning all 100 games played. But perhaps even more significant than these victories is how AlphaGo Zero became so dominant. Unlike the original AlphaGo, which DeepMind trained over time using large quantities of human knowledge and supervision, the new system’s algorithm taught itself to master the game.”
"Just when I think it’s left me
it returns, revived; renewal:
That sole feeling of existence,
When surprise is like a miracle.
Keep it continual
in this continuum of days,
Kick back and listen to the waves…"