
Numenta releases brain-derived learning algorithm package NuPIC - gfodor
http://numenta.org/news/2013/06/03/introducing-nupic.html
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j4mie
Five years ago, I used the previous closed-source version of NuPIC for my MSc
thesis to build an evolutionary simulation of a predator/prey relationship to
evolve camouflage patterns. The NuPIC network was the "predator", trying to
categorise "prey" patterns against background images. I only scratched the
surface of what it could do, but it worked incredibly well.

It looks like this new version is a completely different implementation [1]
but, in my experience, the idea is sound and the approach is very promising.

I'd love to revisit the project at some point. The fact that this is now open
source makes that much more appealing.

[1]
[http://numenta.org/faq.html#whats_the_difference_between_thi...](http://numenta.org/faq.html#whats_the_difference_between_this_and_your_old_offering)

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brendano
Has Numenta done any of the empirical evaluations that are standard in the
mainstream applied machine learning communities? For example, if they're doing
image classification, one of the standard shared tasks from the field of
vision research. When you ask what research supports Numenta's approach, they
always point to the Hawkins book. It's a nice story, but there's quite a
history of nice stories in AI that haven't always been ready from prime time.

~~~
MrQuincle
Good point. There are so many benchmarks that can be performed. POMDP (Partial
Observable Markov Decision Processes) literature has a lot of benchmarks
(bandits, etc.). Reinforcement literature has many. There are many standard
problems in nonlinear control theory solved, not only the inverted pendulum.
It is common knowledge that an algorithm that performs well on task A, will be
outperformed on task B. What are the tasks B in Hawkins book? In what do these
specific type of recurrent networks excel, and in what do they sink as a
brick?

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seiji
They have some pretty fancy acronyms ("Cortical Learning Algorithm (CLA), but
also the Online Prediction Framework (OPF)"), but is this just an n-th
generation autoencoder? "Encoders turn raw values into sparse distributed
representations (SDRs)."

I feel like they're making up their own terminology in a bubble instead of
integrating with the rest of mainline research?

~~~
iandanforth
It's valid to say that CLA is somewhat separate from mainline research, and
some of the terms are a product of that. The specific ones you refer to arn't
misnomers however.

CLA - Closest 'mainline' term would be a recurrent neural network, but the
neurons and network organization are _very_ different.

OPF - This is more of a runtime environment or a set of tools to work with.

Encoders - These are actually a step below what you think of as an
autoencoder. They translate many data types into useable inputs for the
network. Then the spatial pooler takes over and tries to find efficient
representations.

While it's possible to describe Numenta's work with existing frameworks, and
unifying nomenclature is always useful, I hope you'll take the time to learn
the system so that we can apply the correct shared terminology!

Full Disclosure: I used to work for Numenta.

~~~
seiji
Are there any concise (non-book-length) tutorials or online courses about the
Hawkins Way?

~~~
ansible
The most recent HTM paper is here:

<https://www.groksolutions.com/technology.html#cla-whitepaper>

There were some earlier ones that used to be on the Numenta website (before
the name-change), but I don't see those posted now.

With the exception of being able to handle on-line learning, I have a slight
preference for their previous, more abstract approach. Their current approach
seems to model the details of the neural connections at a lower level than
what seems necessary to me. This is not, by the way, any kind of valid
scientific opinion, just the views of a widely-read amateur.

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modeless
I hope somebody makes an ImageNet classifier or does some other standard
machine learning benchmark so we can directly compare the performance of NuPIC
to the state-of-the-art in machine learning.

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kqr2
Quick link to Jeff Hawkins book _On Intelligence_ which describes the theory:

<http://www.amazon.com/On-Intelligence-ebook/dp/B003J4VE5Y/>

~~~
ra88it
I bought this as an audiobook on iTunes for a car trip or something, back in
~2006. It made a huge impact because its theories are so simple and easy to
understand that, if you are even a novice programmer, you can probably imagine
how you'd go about implementing such a brain. Especially if you find the
original white papers and read them too, as they practically include the
source code if I recall (I've got them printed and bound on a shelf
somewhere).

While I was in grad school, I spent several weekends tinkering with my own
implementation of a very simple HTM to process shapes in Go (the game). It
never did much in the way of playing Go, and I stopped messing with it after
something else more practical got in the way, but I'd love to dust off that
code and now seems like a great time to get back into it.

The narration on the audiobook is very high quality, and, for me at least, the
ideas were quite accessible through audio. I highly recommend it if you're the
type of person who might purchase such a book in paper and never get around to
reading it (but you have time to kill in the car).

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IAmAI343
I'm just going to throw this out there in the hope that someone enlightens me.

Ever since I first read the book "On Intelligence" I thought Jeff Hawkings was
on the right track with Strong AI. The insights I got from reading the book I
thought were invaluable. I don't know how close they are to Strong AI or if
they are closer then anybody else but I would expect that if they were making
any real head way into the field that Google would already have made an offer
to buy them.

At least that is what I would do if I wanted to be the first to control the
technology. The fact that nobody seems interested in acquiring them sends the
signal that they probably are no better than anybody else building AI tools.

~~~
Killah911
I've read "On Intelligence" too and I think Jeff Hawkings is onto something.
However your argument:

>>"The fact that nobody seems interested in acquiring them sends the signal
that they probably are no better than anybody else building AI tools"

Seriously? The bar to being successful is an acquisition offer from Google?
I'd understand if money was the driving factor. But Jeff Hawkings has already
made a bunch of that from his previous ventures and to me at least, it seems
that he's genuinely passionate about building something extraordinary. So, I'm
still rooting for Numeta.

In the SV bubble the measure of success may be getting "acquired", but I hope
Numeta is more about actually creating some groundbreaking advancements in AI
& not just getting acquired.

~~~
IAmAI343
>>Seriously? The bar to being successful is an acquisition offer from Google?
I'd understand if money was the driving factor. But Jeff Hawkings has already
made a bunch of that from his previous ventures and to me at least, it seems
that he's genuinely passionate about building something extraordinary. So, I'm
still rooting for Numeta.<<

The thing is, Since On Intelligence came out I haven't really seen any real
products from them. Sure, they created some tools that they expect other
developers to build projects on top of them but that is it. Honestly, with all
his talks claiming how his algorithms are much better than the current state
of the art I would expect really good image recognition, speech recognition or
something like IBM's Watson to come out of their labs. But everything I've
seen from them seems average. Not that much better than the state of the art.

If they had anything groundbreaking and I were google, I'd want to acquire
that technology to further the goal of Strong AI. Google is interested in
producing AI and will acquire anybody that can help it with that goal. Google
is not interested in Numenta's technology, and I'm sure they've checked their
technology, which makes me think that Numenta doesn't really have much to
offer.

Whether Jeff is interested in selling or not is irrelevant.

~~~
the_gigi
Google hired recently Ray Kurzweil as director of engineering. Ray's recent
book - "How to Create a Mind: The Secret of Human Thought Revealed" - is
influenced heavily by the ideas from the first generation of Numenta's
technology (via Dileep George). So, you could say that Google is following
Numenta to some degree. Jeff Hawkins is always careful about setting
expecetations and tries avoid as much hype as possible. The progress was
indeed slower than what some people imagined it to be, but it is a very hard
problem . Now, Numenta has Grok and its technology will be tested in the
field. It also released NuPIC as open source, which will let anyone interested
to dive in, run benchmarks, etc.

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Killah911
In all honestly though, the Google founders were Ray Kurzweil fans before he
wrote the "How to create a mind" (which is book that's a whole another
discussion altogether). I would be really surprised if there weren't already
some interest and some back and forth between Numeta and others in the AI
field. It's really about goals, and if goals align then "acquisitions" might
make sense. But it is far from a marker of success. There are plenty of acu-
hires to prove that.

~~~
Sven7
<http://www.youtube.com/watch?v=4y43qwS8fl4> \- Jeff Hawkins recent talk at
Google with Kurzweil in the audience asking questions.

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randallu
It'd be nice if they had some small useful examples.

Context: I'm not familiar with ML though I've been reading some papers
recently (I'd like to learn ML; I wanted to have some ML baddies in a game and
let them play each other, etc).

I imagine the ML component being a black box with inputs and outputs, but it's
probably more complicated than that. The FAQ says "The algorithm lends itself
well to high-speed temporal data" which makes me think of high frequency
trading or touch-screen input smoothing or something like that, but I don't
see examples for those kind of things in their github tree (but maybe I'm just
so unfamiliar with ML that I don't know the names of the concepts?).

~~~
joe_the_user
I think Dileep George's thesis offers some clues as to the relation between
Hawkin's Hierarchical-temporal ideas and other ML constructions:
[http://www.dileepgeorge.com/tiki/tiki-
download_file.php?file...](http://www.dileepgeorge.com/tiki/tiki-
download_file.php?fileId=28)

I recall George was the technical lead at Numenta and has since left.

Hawkins' model centers on having an overall model of the brain based on
prediction. I remember watching a Hawkins video where he makes the reasonable
point that neuroscience has failed to create overarching visions and it seems
plausible that such a vision (or several competing vision) could be useful.
But I also think that prediction in-and-of itself probably isn't sufficient.

FWIW, When I earlier scanned their website, Numenta was trying to sell its
software for big bucks and not giving any detailed documentation for their
algorithm. Now they seem to be giving everything away for free. That might be
an indication that this is the end of the road. But it's hard to know, of
course.

~~~
otoburb
>>I recall George was the technical lead at Numenta and has since left.

George Dileep left Numenta and co-founded Vicarious Systems[1].

[1] <http://vicarious.com/>

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KingMob
As a former cognitive neuroscientist, claiming that a region as large as the
neocortex has a single underlying principle that explains it all sets off my
oversimplification alarm bells...

I'm sure the code is great for machine learning purposes, but I doubt his
theory has much bearing on how brains work. The brain is one of the most
heterogenous and complex structures in the solar system.

~~~
joe_the_user
Care to go into greater detail?

The thing is, the hypothesis that there is a more-or-less basic neocortical
algorithm wouldn't be disproved by modest amounts of variation in the
neocortex.

And position that the neocortex is rather uniform is fairly standard
neurophysiology as far as I can tell.

[http://www.pnas.org/content/early/2013/01/02/1221398110.abst...](http://www.pnas.org/content/early/2013/01/02/1221398110.abstract)

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jgalt212
what is the difference between Numenta and Grok Solutions?

Per the wiki, the latter is the successor of former, but now it seems the may
have resurrected the old brand.

<https://en.wikipedia.org/wiki/Grok_(company)>

~~~
iandanforth
Correct. The Numenta name is now associated with the open source project and
NuPIC, whereas Grok is the commercial company.

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carlob
I couldn't find a paper describing the science behind this (did they publish
any?). Is there anything new here? Or is it just an iterative engineering
improvement on existing science?

~~~
xal
He wrote the book On Intelligence. Probably one of the most illuminating books
that exists.

~~~
seiji
Care to elaborate or point out some well written reviews?

~~~
smosher
I've read it and fwiw it contains some great information, but only at the
lowest levels of cognition. Its fascinating, but his conclusions are not
compelling because they're drawn from selective facts that have already been
tainted by opinions.

It reads like he's gone the long way around to learn the same things ML
already knows while resisting anything that ML doesn't already know.

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cdooh
I'm a third year computer science student from Maseno unversity, I'm really
interested in learning about AI and unfortunately we'll only be learning more
about machine learning and so on next academic year. We've done a basic unit
called Intelligent Systems. So my question is, what next from there? Can
someone please recommend what I should read to advance my rather basic
knowledge?

~~~
martingoodson
Pattern Recognition and Machine Learning by Christopher M. Bishop

Machine Learning: A Probabilistic Perspective (Adaptive Computation and
Machine Learning series) by Kevin Murphy

~~~
cdooh
Thanks!! I'll check then out. Do they start basic enough though?

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bjeanes
For anyone who is interested in this, Jeff Hawkins gave a fantastic key note
speech at last year's StrangeLoop conference entitled "Computing Like The
Brain" which goes into some detail about the theory behind this.

<http://www.infoq.com/presentations/Brain-Computing>

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agilord
In case you got interested in machine learning and neural networks, I've found
this course by Geoffrey Hinton refreshing and helpful:
<https://www.coursera.org/course/neuralnets>

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rdtsc
Looks interesting. Anyone have any idea how this research/product fits into
the larger landscape of ML theory?

They don't seem to reference much outside work as far as my cursory check
revealed.

~~~
mietek
According to the author's book, there was almost no preceding work in this
area; the one groundbreaking paper was largely ignored.

1\. "On Intelligence", Jeff Hawkins, 2004

2\. "An Organizing Principle for Cerebral Function", Vernon Mountcastle, 1978

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skilesare
Glad to see this bubbling to the top. I've been following its development for
almost a decade. It will be nice to finally have some code to reverse
engineer.

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dnautics
serious question - is there a reason why computer models of thought and 'weak
AI' focus on biologically-inspired models instead of generalizing to higher-
level cognitive phenomena? It seems like this is quite a bit like trying to
model the hydrodynamics of a river flowing by going down to the quantum
mechanical properties of water and dirt.

~~~
mietek
This has been tried without success. You may have heard about the AI winter,
or the failure of the symbolic approach, in the 1980s.

Check out "On Intelligence". It talks about their reasons for following
biological constraints, in opposition to your point of view, common among
computer scientists.

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sown
Has any of this been peer reviewed?

~~~
skilesare
The book has been out for almost a decade. I've read other published papers
over the last decade. They've shown remarkable result from what I've read. I'm
not a peer exactly but it seems that they are on to SOMETHING. That being
said, they've changed direction a bit since doing nupic. Grok takes a
different approach to solving the same problem(ie generalizing the structures
of the neocortex and using it to make predictions about incoming data).

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memming
only works with Python 2.6 and not 2.7??

~~~
danbmil99
Oh, then it can't be real AI.

