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| Inceptionism Iteration |
Born from my dual interest in both building systems and
neural networks, this post is a bit off-track for Limbic Signal, but not really
– we’re looking here at neural networks, the ones mentioned in Hidden Scents. The neural
networks used to run Google’s Deep Mind are
similar to the workings of the olfactory bulb in the way they use layers of
feedback systems to recognize patterns.
Deep Mind is in the news because it cut the electricity
bills at one of Google’s buildings by a lot. The thing about these self-learning
algorithms, if you will, is that the way they work, or how they work, is really
unknown to us. They are using an optimization algorithm to generate their
results, which means making microtweaks on hundreds of variables and in
realtime. It’s the opposite of a silver bullet approach to energy efficiency (and it's also the way we learn to smell, and why smells mean different things to different people). I’ll
let the researchers themselves talk about it; this is from their blog:
20 JULY 2016, Rich
Evans, Research Engineer, DeepMind and Jim Gao, Data Centre Engineer, Google
“Each data centre has a unique architecture and
environment. A custom-tuned model for one system may not be applicable to
another. Therefore, a general intelligence framework is needed to understand
the data centre’s interactions.
...
“We accomplished this by taking the historical data that
had already been collected by thousands of sensors within the data centre --
data such as temperatures, power, pump speeds, setpoints, etc. -- and using it
to train an ensemble of deep neural networks. Since our objective was to
improve data centre energy efficiency, we trained the neural networks on the
average future PUE (Power Usage Effectiveness), which is defined as the ratio
of the total building energy usage to the IT energy usage. We then trained two
additional ensembles of deep neural networks to predict the future temperature
and pressure of the data centre over the next hour. The purpose of these
predictions is to simulate the recommended actions from the PUE model, to
ensure that we do not go beyond any operating constraints.
...
“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.
...
And furthermore, I’ve taken a piece from another one of
their posts:
17TH JUNE 2016, David
Silver, Google DeepMind
“However, deep Q-networks are only one way to solve the
deep RL problem. We recently introduced an even more practical and effective
method based on asynchronous RL. This approach exploits the multithreading
capabilities of standard CPUs. The idea is to execute many instances of our
agent in parallel, but using a shared model. This provides a viable alternative
to experience replay, since parallelisation also diversifies and decorrelates
the data. Our asynchronous actor-critic algorithm, A3C, combines a deep
Q-network with a deep policy network for selecting actions. It achieves
state-of-the-art results, using a fraction of the training time of DQN and a
fraction of the resource consumption of Gorila. By building novel approaches to
intrinsic motivation andtemporally abstract planning, we have also achieved
breakthrough results in the most notoriously challenging Atari games, such as
Montezuma’s Revenge.”
Post Script
I can’t talk about Deep Mind without mentioning Deep
Dream though: check out what it looks like for a computer to dream, it’s
basically a new artform called Inceptionism,
and it comes from these neural networks.
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