
Jeff Hawkins: Brains, Data, and Machine Intelligence [video] - superfx
https://www.youtube.com/watch?v=cz-3WDdqbj0
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appreneur
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.

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java-man
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.

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kmike84
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/25lnbt/ama_yann_lecun/chigv2l)

* [http://www.reddit.com/r/MachineLearning/comments/2lmo0l/ama_...](http://www.reddit.com/r/MachineLearning/comments/2lmo0l/ama_geoffrey_hinton/clw89b4)

* [http://www.reddit.com/r/MachineLearning/comments/2iejpg/syst...](http://www.reddit.com/r/MachineLearning/comments/2iejpg/systematically_compared_with_other_pattern/)

~~~
java-man
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)?

~~~
kmike84
Sure they can, see e.g.
[http://nlp.stanford.edu/pubs/SocherChenManningNg_NIPS2013.pd...](http://nlp.stanford.edu/pubs/SocherChenManningNg_NIPS2013.pdf)
or
[http://arxiv.org/pdf/1301.3618v2.pdf](http://arxiv.org/pdf/1301.3618v2.pdf).

Check the first paper - when working on this problem researchers from Stanford
developed a way to measure the quality of an approach, evaluated the results
on 30k+ relation examples from 2 different datasets and compared their
algorithm with 4 other algorithms.

The problem with cortical.io or numenta (both commercial companies) is that
they don't compare their approaches with existing approaches and don't
evaluate them on public datasets. And when people do such comparison existing
approaches turn out to be better.

It is totally possible these algorithms are good and they provide something
that "traditional" methods don't provide. But this is yet to be shown; for
some reason authors decide not to "compete" on a same ground. Instead of
"promoting" their methods in scientific community via publications /
comparisions with existing approaches they seem to focus on people who have
little knowledge of modern machine learning. Also, they use their own
terminology and usually refer only to their own papers or to some obscure
papers from ten years ago, which doesn't help.

~~~
zo1
>" _But this is yet to be shown; for some reason authors decide not to
"compete" on a same ground. Instead of "promoting" their methods in scientific
community via publications / comparisions with existing approaches they seem
to focus on people who have little knowledge of modern machine learning._"

You'd have to provide them then with a decent argument about what
economic/competitive benefit they would get by what you suggest. You say
they're commercial companies, so then don't be surprised when their approach
is based on financial incentives. But trust me, they're probably _begging_ to
have someone show them a better alternative that will give them a competitive
edge.

So, either no one like you has given them that alternative/idea. Someone has
already, and they rejected the financial benefit. Or, finally, someone already
told them your idea but they discovered there was no financial benefit and the
only benefit was for the greater society.

~~~
kmike84
A cynical view on this could be the following: Numenta sells licenses,
cortical.io sells api requests, they benefit from more developers using their
tools. Commercial companies which fund deep learning research (like Microsoft
and Google ) develop their own products which are based on machine learning,
they benefit from advancing state of the arts, from better algorithms. So we
have quality publications and algorithms from Microsoft or Google and quality
marketing from Numenta or cortical.io.

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java-man
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
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.

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eli_gottlieb
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
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.

