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Google Testing AI System To Cool Data Center Energy Bills
An anonymous reader writes: Google is looking at artificial intelligence technology to help it identify opportunities for data center energy savings. The company is approaching the end of an initial 2-year trial of the machine learning tool, and hopes to see it applied across the entire data center portfolio by the end of 2016. The new AI software, which is being developed at Google's DeepMind, has already helped to cut energy use for cooling by 40%, and to improve overall data center efficiency by 15%. DeepMind said that the program has been an enormous help in analyzing data center efficiency, from looking at energy used for cooling and air temperature to pressure and humidity. The team now hopes to expand the system to understand other infrastructure challenges, in the data center and beyond, including improving power plant conversion, reducing semiconductor manufacturing energy, water usage, and helping manufacturers increase throughput.
So by weak AI we've had AI since the first programmable device?
So by weak AI we've had AI since the first programmable device?
No. There's a very large difference between machine learning-based systems and human-programmed systems. One very obvious one is that human-programmed systems can be explained by pointing out the specific rules that are being applied, in what order and for what reasons. With machine learning, especially the deep neural networks that are currently proving so effective, we can explain the structure of the system and the mechanisms used to "train" it, and how inputs flow through it to produce outputs, but we can't explain what logical "rules" are being applied, or when or why.
In theory, it should be possible to derive the rules by examining the weights in the neural network. In theory, it should be possible to implement those rules with traditional, human-defined logical rules. In practice, the details of how the trained system works are usually beyond us, and typically better than we can do with explicit logic.
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LOL. There is no difference. A machine learning system is still programmed the same. It is running algorithms and rules just like any other program. People stick "neural net" in there and think that it works like neurons. Here is a hint: a neural net isn't anything like a brain. And now they just add "Deep" to the front of "neural net" or "learning" and think that is must be, like, really good now, because it is "deep" learning. We aren't any closer to AI than we were 40 years ago.
Here is a hint: a neural net isn't anything like a brain.
There are some vague structural similarities between a neural net and the basic structure of the brain, but they're certainly very different, yes.
And now they just add "Deep" to the front of "neural net" or "learning" and think that is must be, like, really good now, because it is "deep" learning.
You don't have any idea what the "deep" means there, do you? It's not a content-free buzzword, it has a very specific meaning... and you might consider what the fact that you don't know what it is says about your knowledge in this space.
We aren't any closer to AI than we were 40 years ago.
Right. We've made, for example, no progress at all on voice recognition.
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