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Sounds like active learning to me. It's a type of machine learning where a learner pro-actively ask for interesting data points to be labeled so that he can learn more about the system. :)
They saved "several percentage points" off of 4.4M MWh, so maybe 250M KWH, which might be $10-20M. At 30x earnings [1], they just made back most if not all of the purchase price of Deepmind [2] [1] https://ycharts.com/companies/GOOG/pe_ratio [2] https://techcrunch.com/2014/01/26/google-deepmind/
One might assume, though, that Google could've applied more homegrown ML to the same problem and achieved significant savings as well.
I've been involved with similar multi-million dollar projects that were reported to be bringing in XX million in revenue/savings over three years. What isn't reported is that the revenue was just being moved from another project that was canned, and a guy in an office for a week looking at a spreadsheet could have found similar savings without the expense/complexity. (Not that I'm saying that's what's happening in google s case, but the fluff peices are so universally fluffy, you aren't given enough information to know that it isn't)
They saved "several percentage points" off of 4.4M MWh, so maybe 250M KWH, which might be $10-20M. At 30x earnings [1], they just made back most if not all of the purchase price of Deepmind [2] [1] https://ycharts.com/companies/GOOG/pe_ratio [2] https://techcrunch.com/2014/01/26/google-deepmind/
That's a unique way of defining "made back"
Earlier quoted context omitted.
One might assume, though, that Google could've applied more homegrown ML to the same problem and achieved significant savings as well.
Indeed. One of the "elephant in the room" parts of all of these corporate/pr press releases dressed up as news/revenue reports is that they never report or investigate opportunity cost: say how much could google have saved with a simple algorithm, homegrown solution, or 4 heuristic if/then/else statements. I've been involved with similar multi-million dollar projects that were reported to be bringing in XX million in…
https://googleblog.blogspot.com/2014/05/better-data-centers-...
They need to find some medium sized city that will let them play with their traffic lights.
I've actually taken some sociology classes about this and the professor was a physicist who went into sociology and he used a lot of maths and modeling to tackle this problem in a local city. Unfortunately I do not have any data on this anymore but I remember it being surprisingly complex because human behaviour plays a big role, but there are massive improvements to be made with traffic lights, and so he did.
We're talking about simple physics. Heat transfer. Cooling systems. They should have been installed, operated and programmed correctly using very simple techniques.
It's an interesting application but I'm thinking this is a prima facie example of over-engineering.
OT: it's frustrating to pay Google Contributor to skip ads, and still get nagged by bloomberg.com for using an ad blocker (which I've never done). https://www.google.com/contributor/
Firefox + NoScript for the win.
The next step, of course, is to let the AI know which servers in the server-farm itself is running on so that it can optimize for self preservation.
... and then suddenly it magically develops the concept of self-preservation fully formed, and begins taking action based upon this.
Even if the AI were to have a concept of what it is, because the fundamental goal against which it measures success is power draw the AI would only select for "self-preservation" if that action had a beneficial effect on power draw.