It's a great documentary and really captures the history of AlphaGo very well - every time I watch it it takes me right back to the excitement of those months! If you are interested in AI, Go, or just like documentaries in general I really recommend you give it a try.
Usually in software, version numbers tend to go up, not down. With AlphaGo Zero, we did the opposite - by taking out handcrafted human knowledge, we ended up with both a simpler and more beautiful algorithm and a stronger Go program.
At the core is a self-improvement loop based on self-play and Monte Carlo Tree Search (MCTS): We start with a randomly initialized network, then use this network in the MCTS to play the first games. The network is …
You might have heard about our recent games with AlphaGocache in China, at the Future of Go summit. No only did we play the legendary Ke Jie, but there were also two new and exciting formats: Team Go and Pair Go.
This match was also very exciting on the technical side because we had improved AlphaGo to the point where we ran it on a single machine in the Google Cloudcache - that's one tenth of the computation powercache compared to the distributed version we used in the last match!
Personally, I also really enjoyed the Pair Go …
After 5 long and exciting games AlphaGo finally managed to win 4:1 against the legendary Lee Se-dol, the first time in history a computer program managed to defeat a 9 dan professional player in an even match. And not just any 9 dan player, probably the best player of the decade. It was even awarded an honorary rank of 9 dan professional itself!
Obviously we are all extremely proud of this achievement, you can find out more about the details in our Nature paper. Most importantly, we still used roughly the same amount of hardware! This was a true …
Today we published our paper on beating the human state of the art in Gocache, the only major board game where humans (or at least top professionals) could still beat computers. No more. Our program AlphaGo achieved a 99% winning rate against the strongest existing Go programs, and defeated the human European champion by 5 games to 0.
(That's me playing at 0:10)
The first major breakthrough in computer Go - after remaining at weak amateur level for decades - came with the advent of Monte Carlo Tree Searchcache (MCTS) around 2007, massively improving playing strength. Still, Go programs …
You may or may not have noticed that I've been working for DeepMind for a while, causing me to get exposed to lots and lots of cutting edge machine learning research. Most of that I can't share here, but there's plenty that's already public.
Firstly, yes, that's what all the fuzz is about. There's a great book by Sutton and Barto, Reinforcement Learning: An Introductioncache, with an in-progress version of the second edition available for free from their website!
The book is very good at introducing and explaining RL itself, but does not cover how to combine …
A great explanation of why Dropout is really good for training large neural networks, and why it's actually the same thing your brain is doing:
Recent advances in machine learning cast new light on two puzzling biological phenomena. Neurons can use the precise time of a spike to communicate a real value very accurately, but it appears that cortical neurons do not do this. Instead they send single, randomly timed spikes. This seems like a clumsy way to perform signal processing, but a recent advance in machine learning shows that sending stochastic spikes actually works better than sending precise real …
DeepMind just published a new paper Neural Turing Machinescache with some very interesting implications. For the ones pertaining to AI just read the paper; here I want to focus on what it means for the human brain.
The main achievement of the NTM paper is coupling a neural network to external traditional computer memory, implementing both content and address based access. This implies something similar could potential work with the human brain.
This is more or less just a random collection of links I've come across while researching Deep Learning, I hope they are as useful to you as to me.
Neural Networks, Manifolds, and Topologycache is a great post on how to visualize deep neural networks and get an intuition for them. Reading it was the first time I truly appreciated how the successive layers of NNs just transform the topology of the input data, until finally it becomes linearly separable.