> Our partnership with Google’s data centre team used AlphaGo-like techniques to discover creative new methods of managing cooling, leading to a remarkable 15% improvement in the buildings’ energy efficiency.
Which itself is an understatement of the achievement:
> Our machine learning system was able to consistently achieve a 40 percent reduction in the amount of energy used for cooling, which equates to a 15 percent reduction in overall PUE overhead after accounting for electrical losses and other non-cooling inefficiencies. It also produced the lowest PUE the site had ever seen.
Basically, they built an AI that was able to tune the "large industrial equipment such as pumps, chillers and cooling towers" to react to the dynamic, nonlinear interactions that vary within and between datacenters (weather, utilization, etc).
They also describe this AI as "General":
> Because the algorithm is a general-purpose framework to understand complex dynamics, we plan to apply this to other challenges in the data centre environment and beyond in the coming months.
They seem to imply that this technique could make almost any industrial process more efficient with minimal oversight/training/customization.
Nonlinear control with neural networks has existed for a long time, though, it's not exactly new. Is there reason to think they did something profoundly new in that specific instance?
Yes. When this came first up, someone on /r/machinelearning scoffed that using NNs to control fans/coolers/power systems to optimize was trivial and obvious and had been done for decades. When I challenged him for a ref, he produced a PDF which listed dozens of industrial applications for various nonlinear NN algorithms... none of which were actually autonomously controlling anything and most were predictive.
It's not secret sauce, the algos are well known, read carefully, the guy who proposed it was an ML outsider. And besides, they stated they plan to release it.
Lol, no. It's just the scale of the use that makes it valuable. Google datacenter cooling problems only exist inside of Google data centers, and the control knobs therein are 1. measurable AND measured 2. deterministic 3. simple.
They're innovating in a problem space that only exists at Google, Microsoft, and Amazon.
> In its ability to identify and share new insights about one of the most contemplated games of all time, AlphaGo offers a promising sign of the value AI may one day provide, and we're looking forward to playing more games in 2017.
What I find fascinating is how different AlphaGo's impact was from the impact of early chess engines. Once chess engines became decent (not even good—before even Deep Blue), they identified tons of inaccuracies in published chess literature. These were missed tactics, hard-to-see moves, requiring only relatively shallow calculation but a computer's precision. These inaccuracies were found even in classical annotations of well-known chess games, as well as standard books about openings. I believe John Nunn was known for this kind of work.
AlphaGo hasn't achieved nearly the same impact. Have they even tried to identify the same types of inaccuracies in classical Go books? Can you imagine how absolutely cool it would be for a go engine to find errors in Invincible? Maybe they tried, but didn't find any inaccuracies, so now this negative result is sitting in one of their file drawers? I really wish they were more active with this sort of thing.
Well, AlphaGo beat Lee Sedol, so I think it wouldn't be fair to compare timelines so directly. With chess engines, the compute power just wasn't there in the beginning, but it's all already there for go. Also, speaking as an optimist, yeah! why not? To be honest, my suspicion is that they aren't trying.
I suspect this is a visibility problem. Go is a more popular game in the East I bet there are a few grad student level problems doing just what you described at Tokyo U and other prestigious Schools far from the West.
Good point, I forgot about DeepZenGo. But it's very low-hanging fruit, though, which is the main reason why I'm surprised they haven't already done this.
Are there any open source or widely available Go engines that are even remotely as strong as AlphaGo (which I think we can safely assume plays beyond the 9p level, i.e., at superhuman strength)? If the only seriously strong engine is completely tied down behind the closed doors of a company that has largely already moved on to other challenges, I think that's the issue right there -- or at least a large part of it.
I think the strongest commercial engines are Zen and CrazyStone. Zen has certainly become pro-strength on fairly modest hardware (it had an exciting BO3 against a Japanese 9-dan, which it lost 1-2), but the strong version isn't available to the public yet, I think.
Interestingly, for the past 3 or 4 days there's been a mystery Go Bot playing on 2 servers calling itself "Master" which is currently about 50-0 (!) against professional opponents, many at the very highest level. Nobody knows its identity yet. It's stronger than the commercial engines, and its presence must be some sort of stunt or test by someone.
Google translate helps with the comments but they don't add much to the diagrams.
They are playing fast games with 3 time periods of 30 seconds per move. It means that one can exceed the 30 seconds limit and move on to the next period. When the limit is exceeded in the last period the game is over.
Fast games usually increase the chances of the stronger player if the difference is large.
Go is different than chess. There is no huge blunders to be found in well-known games. What Alphago can find are creative moves that happen to be very slightly better than "normal" ones.
Since a week, I hear news about a bot named "Master" that may be an evolution (or a concurrent clone) of Alphago.
Deepmind is more focused on general AI (AGI) than the game of Go. The AGI community has always considered beating a Go champion to be a significant milestone towards AGI, and it was expected to be years in the future. Now that Deepmind won that battle, there's little PR benefit - or benefit to AGI progress - to focusing on go.
I don't mean to slight the game of Go, but it is a perfect information game which could make it non-central to AGI research.
I'm excited for WaveNet to get faster/more accessible. I'm not very technically inclined when it comes to downloading things of github and hacking them together.
My goal is audio books - I'd love to hear them read by my favorite movie characters.
I assume Google wants to have them doing their own thing instead of just borg-similating it. Also they have Google brain, which is doing similar things
AI tends to perform poorly for many real-time games because they constitute many, many more "moves" than a game like backgammon. A backgammon game will typically take < 100 moves. For a game like Ms. Pac Man the AI will have to make that many decisions in a timespan of about four seconds. When you train a reinforcement learning algorithm, the AI has to update the parameters that were used to make several thousand decisions, and the only information it has to go on is its score at the end of the game. (This is known as the credit assignment problem --- of all the decisions you made in the game, only a few probably had a substantial impact on the outcome. How do you decide which ones were the important ones?)
A game like go is hard because there are many possible moves every turn. But the games themselves are not very many turns, so it is easier to find the important moves, because in some sense all the moves were more important. A game like Ms. Pac Man is hard because, while you only have 5 moves each turn, the games themselves are many, many turns, and it is very difficult for the NNs to learn these long-term dependencies.
It's true that someone could pretty easily hand-craft an AI that plays Ms. Pac-Man pretty well. But that's not really the point. Because after all that work, you still only end up with a computer program that can play Ms. Pac-Man. It won't play Pong, it won't play Doom, it won't even be able to play regular Pac-Man! The goal is to build a general system that can learn how to play specific games on its own. So, there is potentially high value to developing a Ms. Pac-Man AI, but only if it's not really a Ms. Pac-Man AI, but actually a more general AI, for which playing Ms. Pac-Man is a specific instance.
I agree with the parent comment that some sort of new approach is probably needed. The main issue with reinforcement learning is just that the algorithm gets too little feedback. This ends up discarding an enormous amount of data on each run (or, more accurately, learning from that data extremely inefficiently). There's a huge amount of data present, but the learning from that data has to be unsupervised. A child can do it easily --- they only have to play Ms. Pac-Man once or twice to get the hang of it. Computers currently cannot. Getting them to do that will definitely require something different, but no one is quite sure what.
How much more complex is Ms. Pac-Man when compared to Atari 2600 games? Deep Minds DQN did master (as in better then any human can play them) a whole bunch (49) of 2600 games a while back
It's a boring tautology. Arcade games require "clever" (read: not generalizable) solutions to optimize for speed in decision making.
"Learning" AI requires the broadest feasible search space for any solution and successful designs require clusters of machines. Clusters of machines have network latency and coordination overhead.
This is the same latency vs throughput design decision that every system must make, and its not impressive when a throughout oriented system struggles with being latency sensitive.
Note that most of recent work in reinforcement learning focus on creating a model which works on all or most of Atari games without using any game-specific knowledge. If researchers were to focus on only Ms. Pac Man, and ignore the "no game-specific tuning" rule, then Ms. Pac Man, and most Atari games would be solved to near perfection. The challenge that the Atari suite provides is not their individual difficulty, but the difficulty imposed by their diversity, i.e. finding a general algorithm which works well on all Atari games is hard.
It's also inferring the game state from looking at the screen rather than being spoonfed the data.
One could conceivably perform a depth-limited search on the actual game state if it were available and then use an AlphaGo-like DNN to predict what a deeper search would find, no?
Dunno really. A human would watch the ghosts behaviour and guess their likely future behaviours based on that. I'm not sure if the software gets that, as it were, or how you'd tweak it to do so.
OpenAI (https://openai.com/). OpenAI's research vision is at a similarly ambitious scale as DeepMind. They're also relatively more open in their research, and known to release papers, code, and models quickly. Currently, they are not as big though, more like how DeepMind was immediately after publishing their first Atari work in 2013. Give them time, and I expect them to become a very formidable opponent to DeepMind.
As a bonus, OpenAI is a nonprofit that always allows publishing. They also have some of the top names in DL: Ian Goodfellow, Ilya Sutskever and Zaremba. In DRL (deepmind's arena), they have Abbeel and Schulman, both absolute houses.
https://deepmind.com/blog/deepmind-ai-reduces-google-data-ce...
> Our partnership with Google’s data centre team used AlphaGo-like techniques to discover creative new methods of managing cooling, leading to a remarkable 15% improvement in the buildings’ energy efficiency.
Which itself is an understatement of the achievement:
> Our machine learning system was able to consistently achieve a 40 percent reduction in the amount of energy used for cooling, which equates to a 15 percent reduction in overall PUE overhead after accounting for electrical losses and other non-cooling inefficiencies. It also produced the lowest PUE the site had ever seen.
Basically, they built an AI that was able to tune the "large industrial equipment such as pumps, chillers and cooling towers" to react to the dynamic, nonlinear interactions that vary within and between datacenters (weather, utilization, etc).
They also describe this AI as "General":
> Because the algorithm is a general-purpose framework to understand complex dynamics, we plan to apply this to other challenges in the data centre environment and beyond in the coming months.
They seem to imply that this technique could make almost any industrial process more efficient with minimal oversight/training/customization.