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If you look more closely at the details here (beyond just deepminds blog post) you'll see that 1. It has not yet been deployed to all data centers. They just t

If you look more closely at the details here (beyond just deepminds blog post) you'll see that 1. It has not yet been deployed to all data centers. They just turned the system off for a period of time and looked at how much extra money it cost to run the data center like that, then extrapolated. 2. There was already a Google engineer back in 2012 who applied neural networks to this problem and saw huge gains (There is a blog post about this somewhere). In the deepmind blog post, they don't compare to this system, do for all we know it could just be a small refinement of that system. It is actually not clear whether deep RL was actually used or not from the deepminds blog post: it is only kind of implied.

gwern 9y ago
> If you look more closely at the details here (beyond just deepminds blog post) Where would that be? As far as I know, the DM blog post and Hassabis's occasional discussions are the most detailed public information available. And they don't mention that it's just a brief demo. > 2. There was already a Google engineer back in 2012 who applied neural networks to this problem and saw huge gains (There is a blog post about this somewhere) I don't remember this.
- dontreact 9y ago
  https://googleblog.blogspot.com/2014/05/better-data-centers-through-machine.html?m=1 https://googleblog.blogspot.com/2014/05/better-data-centers-...
  - dontreact 9y ago
    Misremembered the year. Actually 2014
  - gwern 9y ago
    So, that was 2 years deeper into the deep learning revolution, post-DeepMind acquisition, doesn't actually say it was a NN (the diagram could be literally any ML model from linear model to random forest), doesn't say they reduced costs by anything approaching 40%, or even are using it in production at all aside from the one instance they patched around some downtime.
    - FreakLegion 9y ago
      "Today we’re releasing a white paper (PDF) on how we’re using neural networks to optimize data center operations and drive our energy use to new lows."
      - gwern 9y ago
        Ah, missed that. In any case, the paper confirms what I said: they haven't used it in practice, and the only time they have was the brief one mentioned in the post where it resulted in a small PUE saving (it quotes 0.02, off an unspecified reduced load but note for comparison the average PUE of ~1.12, so saving anything remotely like 40% is unlikely).
        dontreact 9y ago
        Here is a followup from the same lead author of the paper referred to in that first blog post (Jim Gao) who apparently was involved in Deepmind's project. Note the conspicuous lack of any sort of reference to deep reinforcement learning https://blog.google/topics/environment/deepmind-ai-reduces-energy-used-for/ https://blog.google/topics/environment/deepmind-ai-reduces-e...
        gwern 9y ago
        Using forecasting for 'control' doesn't make too much sense (why the need to train a second ensemble to prevent overshoot if it's just supervised learning?), and the first author on that post, is not Gao but Richard Evans who is a DeepMind deep RL researcher (most recent publications: "Deep Reinforcement Learning in Large Discrete Action Spaces", "Deep Reinforcement Learning with Attention for Slate Markov Decision Processes with High-Dimensional States and Actions", "Reinforcement Learning in a Neurally Controlled Robot Using Dopamine Modulated STDP").
gwern 9y ago
https://www.theverge.com/2017/5/30/15712300/alphago-ai-humanity-google-artificial-intelligence-ke-jie https://www.theverge.com/2017/5/30/15712300/alphago-ai-human... "Say you’re a data center architect working at Google. It’s your job to make sure everything runs efficiently and coolly. To date, you’ve achieved that by designing the system so that you’re running as few pieces of cooling equipment at once as possible — you turn on the second piece only after the first is maxed out, and so on. This makes sense, right? Well, a variant of AlphaGo named Dr. Data disagreed. “What Dr. Data decided to do was actually turn on as many units as possible and run them at a very low level,” Hassabis says. “Because of the switching and the pumps and the other things, that turned out to be better — and I think they’re now taking that into new data center designs, potentially. They’re taking some of those ideas and reincorporating them into the new designs, which obviously the AI system can’t do. So the human designers are looking at what the AlphaGo variant was doing, and then that’s informing their next decisions.” Dr. Data is at work right now in Google’s data centers, saving the company 40 percent in electricity required for cooling and resulting in 15 percent overall less energy usage."
- dontreact 9y ago
  At the very least I think this is deceptive because the source (If you keep clicking on links) for the 40 percent savings is the original blog post, and there's no other information saying things have been rolled out fully, yet the Verge is seems to imply that here. I am somewhat convinced that something resembling RL was used based on your argument in the other thread, but I think even you would agree that calling it "a variant of alphago" is a pretty big stretch.

gwern 9y ago

> If you look more closely at the details here (beyond just deepminds blog post) Where would that be? As far as I know, the DM blog post and Hassabis's occasional discussions are the most detailed public information available. And they don't mention that it's just a brief demo. > 2. There was already a Google engineer back in 2012 who applied neural networks to this problem and saw huge gains (There is a blog post about this somewhere) I don't remember this.

dontreact 9y ago
https://googleblog.blogspot.com/2014/05/better-data-centers-through-machine.html?m=1 https://googleblog.blogspot.com/2014/05/better-data-centers-...
- dontreact 9y ago
  Misremembered the year. Actually 2014
- gwern 9y ago
  So, that was 2 years deeper into the deep learning revolution, post-DeepMind acquisition, doesn't actually say it was a NN (the diagram could be literally any ML model from linear model to random forest), doesn't say they reduced costs by anything approaching 40%, or even are using it in production at all aside from the one instance they patched around some downtime.
  - FreakLegion 9y ago
    "Today we’re releasing a white paper (PDF) on how we’re using neural networks to optimize data center operations and drive our energy use to new lows."
    - gwern 9y ago
      Ah, missed that. In any case, the paper confirms what I said: they haven't used it in practice, and the only time they have was the brief one mentioned in the post where it resulted in a small PUE saving (it quotes 0.02, off an unspecified reduced load but note for comparison the average PUE of ~1.12, so saving anything remotely like 40% is unlikely).
      - dontreact 9y ago
        Here is a followup from the same lead author of the paper referred to in that first blog post (Jim Gao) who apparently was involved in Deepmind's project. Note the conspicuous lack of any sort of reference to deep reinforcement learning https://blog.google/topics/environment/deepmind-ai-reduces-energy-used-for/ https://blog.google/topics/environment/deepmind-ai-reduces-e...
        gwern 9y ago
        Using forecasting for 'control' doesn't make too much sense (why the need to train a second ensemble to prevent overshoot if it's just supervised learning?), and the first author on that post, is not Gao but Richard Evans who is a DeepMind deep RL researcher (most recent publications: "Deep Reinforcement Learning in Large Discrete Action Spaces", "Deep Reinforcement Learning with Attention for Slate Markov Decision Processes with High-Dimensional States and Actions", "Reinforcement Learning in a Neurally Controlled Robot Using Dopamine Modulated STDP").

gwern 9y ago

https://www.theverge.com/2017/5/30/15712300/alphago-ai-humanity-google-artificial-intelligence-ke-jie https://www.theverge.com/2017/5/30/15712300/alphago-ai-human... "Say you’re a data center architect working at Google. It’s your job to make sure everything runs efficiently and coolly. To date, you’ve achieved that by designing the system so that you’re running as few pieces of cooling equipment at once as possible — you turn on the second piece only after the first is maxed out, and so on. This makes sense, right? Well, a variant of AlphaGo named Dr. Data disagreed. “What Dr. Data decided to do was actually turn on as many units as possible and run them at a very low level,” Hassabis says. “Because of the switching and the pumps and the other things, that turned out to be better — and I think they’re now taking that into new data center designs, potentially. They’re taking some of those ideas and reincorporating them into the new designs, which obviously the AI system can’t do. So the human designers are looking at what the AlphaGo variant was doing, and then that’s informing their next decisions.” Dr. Data is at work right now in Google’s data centers, saving the company 40 percent in electricity required for cooling and resulting in 15 percent overall less energy usage."

dontreact 9y ago
At the very least I think this is deceptive because the source (If you keep clicking on links) for the 40 percent savings is the original blog post, and there's no other information saying things have been rolled out fully, yet the Verge is seems to imply that here. I am somewhat convinced that something resembling RL was used based on your argument in the other thread, but I think even you would agree that calling it "a variant of alphago" is a pretty big stretch.