Jeff Hawkins: Brains, Data, and Machine Intelligence [video]

https://www.youtube.com/watch?v=cz-3WDdqbj0

by superfx • 12 years ago

61 15 12 years ago

java-man4• 12 years ago

I think this is the most interesting direction of research in CS one can get involved with today. In 10-20 years, half of CS graduate will be working in computational biology and the other half developing SDR-based machine intelligence.

kmike843• 12 years ago

It is not clear that these methods work better than "traditional" deep learning methods; in fact, they haven't produced any good results yet, and most experts think the hype around CLA is not because of its technical properties.

See e.g.

* http://www.reddit.com/r/MachineLearning/comments/25lnbt/ama_...

* http://www.reddit.com/r/MachineLearning/comments/2lmo0l/ama_...

* http://www.reddit.com/r/MachineLearning/comments/2iejpg/syst...

java-man4• 12 years ago

Can "traditional deep learning methods" replicate the kind of unsupervised learning demonstrated by cortical.io's semantic retina (fox eats rodent example in the video)?

arketyp• 12 years ago

As far as this technology will deliver I belive it will become a engineering or scientific branch in its own right. I suspect it will be a bit like economics, with lots of clever people deriving sophisticated models but expert knowledge is all about heuristics (at least this is my layman view of economics): anyone can understand the basic principles but one would be hard pushed to claim there is anyone who really grasps the thing in entirety.

java-man4• 12 years ago

... except those who control it. :-)

java-man4• 12 years ago

What might be interesting is to stream stock market data (trades as well as company's press releases) into the Grok algorithm. People who will manage this by the next market crash will be very rich indeed.

malux85• 12 years ago

Im working on this now, but not using grok, using NuPIC core directly. Its been fun maintaining my own private fork of the repo while I scale the algorithms to run on > 1 machine i.e. be able to build a model of any topology where the regions aren't necessarily on the same machine, but still must be fast.

appreneur• 12 years ago

I was fortunate to discover Jeff Hawkins in the early 2007 , when he was exploring brain science .. .I went through his lectures for entrepreneurs at ecorner.stanford.edu , being indian ,I had to literally slow down the video to understand his speech and he is like Bill gross, very very fast in articulating and highly energetic in their talk. You can feel their passion in their work.

Incidently Jeff Hawkins was the first to work on Palm software.....you can say early versions of today's smartphones.

Coming back to brain science , I somehow feel it's more to do with complexity (Santafe.edu).

I am waiting for jeff Hawkins to launch a university like santafe.edu ( complexity) which works exclusively on brain science. Perhaps that would increase application of brain science.

eli_gottlieb• 12 years ago

Please stop talking about machine intelligence without reference to normatively correct principles of quantitative reasoning. "I tried it and it scored well in validation" just shows that you've managed to apply some kind of statistical principle, not that you've found a way to build complete machine intellect.

niklasni1• 12 years ago

Does Hawkins do that? I've not watched this one yet, but his talks are always much more high-level than that I think, and while his work is under the general umbrella of artificial intelligence, what he is actually trying to do is to emulate the human neo-cortex in gritty detail.

Jeff Hawkins: Brains, Data, and Machine Intelligence [video]

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