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Comment by tormeh | Hacker News Reader

Neuroscience suggests that the brain has many fixed-purpose computational units, some neural nets and others not. While doing everything in a single network is indeed impressive, I am concerned about whether this is the most efficient approach.

EDIT: Downvoter, care to explain the reason for downvoting?

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This method actually does have fixed-purpose computational units if you will (that's why it's called guided policy search). During learning, it basically loops back and forth between a heuristic trajectory optimization (to keep a tight rein on the motor torques) and reinforcement learning with neural networks (to achieve high-level abstraction).

Why are you concerned? It's an approach. It seems to work pretty well. It's possible it could be improved, does that mean we should not display the results of this because it might not be the most efficient result?

I didn't downvote, but it's tiresome to hear "Yeah, that's all cool and everything, but I'm concerned that it might not be the absolute best." Nobody claimed it was the most efficient approach ever possible, only that it was cool, surprising, and reasonably ground breaking.

You can't really tell if it's the most efficient approach until you try it, and compare it with a model that you think might be a more efficient approach.

I said it was impressive and I meant it.

The neocortex is over half of the human brain by mass or volume, and it plausibly uses a small number of distinct algorithms (believed by some to correspond very loosely to those of artificial neural networks) to learn and remember a vast diversity of knowledge and skills.

>some neural nets and others not.

Source for the brain having computational units that aren't neural nets? I'd love to read more on this.

"In vertebrates, inter-aural time differences are known to be calculated in the superior olivary nucleus of the brainstem. According to Jeffress,[1] this calculation relies on delay lines: neurons in the superior olive which accept innervation from each ear with different connecting axon lengths."

http://en.wikipedia.org/wiki/Sound_localization

How is that not a neural network? This system is basically exploiting the property of action potential propagation down the axon to capture relative timing differences.

As an aside, there are a number of useful properties seen in biological neural networks that aren't yet incorporated into multi-layer perceptrons. E.g. short-term plasticity, axonal delays, spiking neurons, etc. I expect that some of these will find their way into the MLP formulation when we can figure out an effective mathematical way of doing so.

I mean that it's not a neural network in the computational sense. Sorry about the terminology issue.

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