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Comment by tlarkworthy | Hacker News Reader

My PhD program was in neuroinformatics and it exactly was this intersection, it was not an accurate comment. Hinton knows neurophysiology, its clear in any of his talks he reads about the weird illusions that give us insight into the computational mechanisms going on in the inside. (e.g. how we recognize rotated objects but with symmetry unresolved)

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

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Computational mechanisms that Hinton uses in his ML models have very little to do with what is going on in brains. It's kind of like watching a bird fly and building a jet or a helicopter. Hawkins' comment was about building a machine that "flies like a bird" - actually looking at what's going on in real brains. Hinton seems to be inspired not by brain physiology or anatomy, but by psychology.

What about you? Have you implemented in software any of the ideas you learned from looking at the brain? I mean, the software designed to perform some intelligent task?

Using your analogy I would guess Geoff Hinton's goal is to understand aerodynamics and which parts of bird flight are actually necessary for flight and which are simply accidents of evolution.

Hinton does not really try to understand how brain does it. His latest ideas on capsules are a little bit closer to the brain anatomy, but still very far away from real brains. I think he admits that himself.

Jeff Hawkins is the guy who actually tries to understand "which parts of bird flight are actually necessary for flight".

Hinton explicitly states he was intreiged that we can recognise an upside down R, but not whether its reflected, and made a neural model that leveraged the same weakness That indicates he reads psychophysics papers and he uses the same loosening of problem constraints in his visual recognition models. That is drawing on cross discipline insights. I don't care he doesn't model ion channels or fine grained neuroanatomy, he is inspired by the coarse grained computational tricks employed in biology. For a primarily AI researcher thats an ideal level of abstraction.

He is looking at human behavior, sure. He is trying to copy some of the specifics of that behavior in his systems (like "pose" represented by columns of neurons. However, it still seems that he treats human brain as a black box, and he's not trying to understand the mechanisms of computation and information processing in the real brains - and I'm not talking about low level stuff like ion channels, I'm talking things like prediction, anomaly detection, or knowledge representation.

If you believe that the best way to build AI is to model human brain, then we need to look inside the brain, and Hinton does not do that.

If you are interested in the intersection of neuroscience and AI, Jeff Hawkins' HTM theory is the best we got so far. Unfortunately, most people talking about it can't be bothered to actually learn it. Just read HTM white paper [1], and decide for yourself.

[1] http://numenta.org/htm-white-paper.html

I've read it. Just not a good enough programmer to implement it. Maybe in time that will change. It's really exciting; I just wish people with the requisite skills would try to understand it more fully.

You don't need to implement it. Numenta implemented the theory as an open source project called Nupic. You can contribute by running experiments.

i've been reading about sparse distributed memory and numenta/nupid for the past few days. It sounds very interesting but I haven't actually seen any demos that show it solving things better than other avenues (like deep learning) in AI at the moment. Are you using it for anything?

Numenta's primary goal is not solving practical problems (such as those typical in ML field) - it's understanding how neocortex processes information. They believe that once they understand enough, they will be able to build a system that works like a brain (and then it will be able to solve any problems that a brain can solve). Obviously, this is work in progress. They might have a breakthrough in two years, or in two decades, but the point is that not many others are trying to do that at all (definitely no one in ML field).

What do you think about the hardware centric approaches like http://news.stanford.edu/news/2014/april/neurogrid-boahen-en...

Those are interesting projects, and sooner or later we will need specialized hardware to speed up AI algorithms. However, today AI development is not constrained by computing power, at least not directly. If you run IBM Watson algorithms 100 times faster, it won't be much smarter. In ML, we need better algorithms. In AI, we need better theories.

Brain simulations, such as Human Brain Project, is where we need more computing power. That is the field where 100 times faster can lead to major breakthroughs.

This also reminds me of the attempt by a researcher to develop an AI to solve bongard problems. He apparently gave up for supposed ethical reasons: http://www.foundalis.com/soc/why_no_more_Bongard.html . I suspect other reasons (i.e., couldn't actually create a program that would do so), but it isn't altogether important what they are. I'd like to see a real AI solve such problems - and it's going to take understanding HI to do that.

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