Jeff Hawkins' Cortex Sim Platform Available 126
UnreasonableMan writes "Jeff Hawkins is best known for founding Palm Computing and Handspring, but for the last eighteen months he's been working on his third company, Numenta. In his 2005 book, On Intelligence, Hawkins laid out a theoretical framework describing how the neocortex processes sensory inputs and provides outputs back to the body. Numenta's goal is to build a software model of the human brain capable of face recognition, object identification, driving, and other tasks currently best undertaken by humans. For an overview see Hawkins' 2005 presentation at UC Berkeley. It includes a demonstration of an early version of the software that can recognize handwritten letters and distinguish between stick figure dogs and cats. White papers are available at Numenta's website. Numenta wisely decided to build a community of developers rather than trying to make everything proprietary. Yesterday they released the first version of their free development platform and the source code for their algorithms to anyone who wants to download it."
Future Plans (Score:2, Funny)
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Jeff Hawkins's advisor (Score:1)
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Right... (Score:5, Insightful)
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Plus, he's sure because he's proposing a solution to the 'unsolved problem.'
Re: Not one year, seven or eight years (Score:4, Informative)
Actually, its content was produced seven or eight years ago.
Its publishing date was "December 2005". But publishers will lie about the publication date of a book if it allows them to sell more books. And in this case, I wouldn't be surprised if the book came out hot off the presses in December 2004 with a postdate of "December 2005"
Furthermore, this book was based on the scientific proceedings of a conference which occurred six years before the book was finally edited (or finally published). I'm actually not sure of the year of the scientific conference itself, because the information supplied to sell the book doesn't give the actual year.
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Re:Right... (Score:4, Insightful)
That is a wonderful thing, though. First of all, claims can be tested. They'll either live up to the description, or they won't. If the don't, another path not to go down in a particular manner has been identified, and that is useful. OTOH, if they are verified, then we may have a key to a form of cognition. Whether it is our kind or not is really not as important as just the fact that it is some kind.
Aside from that, I found some very interesting things in his descriptions of the HTM. For instance, I found the following precise description of enabling religious behavior: First, he describes how HTMs handle specific, non-overlapping domains (and of course this doesn't mean that another HTM can't relate those to each other.) One might handle financial markets, another speech, another cars. Then he says "After initial training, an HTM can continue to learn or not" Emphasis mine. So you can set up an HTM in a learning situation where you limit the input to descriptions consisting of sensory data of any arbitrarily limited set of patterns you like, get it to see the world represented by those patterns as you wish, and then disable learning for that particular HTM. Other HTMs can continue to learn, but that one is "frozen." Sounds like the perfect recipe for a priest or supplicant to me. Does that not sound like the very core definition of "unshakable faith"?
For all the doubt being thrown this fellow's way, you know, eventually someone will come up with something like this and it will be a working model of such a system. It's a tough problem, very abstract and requiring a lot of insight, but as with all problems discovered to date where we can actually get our hands on the system under study, there is no indication that any part of it exists in any way outside the sphere of nature and the natural rules we already know - and we know a lot of basic rules.
Kudos to him for sinking his teeth into the problem, and for coming up with results that can be tested, and for letting them loose into the word for such testing. If he's wrong, he's helping. If he's right - he's going to be mentioned in the same breath with a lot of very important people for a very, very long time to come.
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No, it sounds more like you should share whatever it is you've been smoking...
What you've described applies equally well to, say,
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Oh, I do have strange "beliefs", if you'd measure them, as most would, by comparing them to the majority outlook. In fact, I try not to have any at all, preferring a confidence-based outlook derived from consensual evidence. So my beliefs... yes, strange or non-existent. You're certainly spot-on about that. :-) The rest, not so much. But you are certainly welcome to your opinion; there's no rule that I know of that says you have to be correct in order to speak out.
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Most "grand-scale theories of brain operation", in fact, fail to make claims that can be tested, at least not in the foreseeable future. They predict the large-scale algorithms by which the brain operates. They do not make any claims as to the behavior of any individual neurons, and this is the dat
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I'm an experimental neuroscientist, and I think that's a little harsh. I think people who work at the level that Hawkins does (not necessarily him in particular) can provide things that are useful
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There is an even simpler truth that underlies all this; you and I, using nothing but our brains and what we've learned, can multiply 7 times 9 in our heads and get the right answer. Computer methods can do that, and far faster and more reliably than we can - though they certainly don't do it the same way. At least most of us don't convert to binary and use ALUs. :-)
The task, however, is useful because it is well accomplished. Nothing in particular that is very exciting is presented to neuroscience, but
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To Hawkins' credit, near the end of the book he explicitly lists several claims his theory makes, and also suggests studies which would validate or invalidate his claims.
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Sounds like the perfect recipe for a priest or supplicant to me. Does that not sound like the very core definition of "unshakable faith"?
If by "unshakable faith" you mean the ability to do the right thing 100% of the time without ever thinking to do the wrong thing, then I believe you're correct...
However, if by that term you mean the ability to choose, despite strong opposition from the surrounding forces of culture and pier pressure as well against the very nature that corrupts the spirit we have, to
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No, I was thinking more along the lines of after the Turin shroud was dated to a much more recent period than the time when Christ's death is written to have occurred, a good sized chunk of the Christian community went right on believing it was contemporary with that time. I was thinking about children being trained in religion, and being unable to escape those myths as adults
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The same principal that prevents him from converting to another religion (faith) is the same idea that prevents you from suddenly believing that the sun revolves around the earth.
Given that you cannot at any given moment detect the true center of the solar system, you realize you are and always have been taking it as an article of faith that wha
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That's not a problem; that's a fact.
Science is a method that produces information and models that can be repeatedly tested in the natural world, not only by the originator of the idea, but by anyone to whom the idea is transferred. Likewise, should a new test be created that causes the model to fail or the data to become a poor or imposs
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I know how science works. The topic is AI, and your brain does not work the way science does. In fact I would wager that the reason AI has not progressed very far is due to the fact that smart people like to think their brains are basing conclusions on fact. So they try to make machines that make decisions based on provable facts and find that they can't do it.
A brain can't know anything more than it can sense or infer from those senses and past knowledge. So the vast majority
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I know what you're trying to get across; but your base presumption, that the filter of the scientific method is of equal value as the filter of "gee, that's a nice story" in getting things into your brain and pulling them out again or using them to proceed forward inductively, is insufficient to the task of holding up the rest of your argument. The data - human technological progress - show
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I think maybe my use of the worth faith is the problem here. I should have used something else like maybe confidence. You have confidence that scientific principal works based on observation and pa
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No - really, it doesn't. All it requires is that I have a reasonable amount of confidence in the process. The reason why this is so is because I can, if I so choose, verify the results myself, or arrange to have them verified. This is because science deals with the natural world and all science involves consensual experience. This applies directly to your example, and yes, it
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Surely we have plenty of humans available to do tasks 'curently best undertaken by humans' :)
Seriously though... while it might be useful to develop AI systems in this area as timesaving devices, the examples given above aren't really in that category - IMO AI research could be better applied to tasks humans can't achieve so eas
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Yes; his reasoning is laid out in the beginning of this document. [numenta.com] The thinking seems quite reasonable to me, as far as it goes. AI is my area of research.
Re:Right... (Score:5, Interesting)
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Life's work (Score:3, Funny)
Some people spend their entire adult lives trying to overcome alcohol addiction, or trying not to beat their spouse. To others, it comes naturally.
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Google "Ornithopter
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If we wanted an artifact that can do what birds can (e.g. land on a tree branch) we probably would start by building a mechanical bird. It's quite hard, and I doubt we can do that today. We can't build a mechanical brain (i.e. an artifact that can do what a brain does), either.
Why (Score:2)
Its been my experience that the most brilliant people have a fiduciary target at some point, and its oft quoted here that the best are those whole love it and do it for the pleasure, rewards aside. Recent studies re funding of the kernel would bear out my point. Personally i feel a core of dedicated staff with external input will yield the best results (ala firefox) but this is not open per se.
This will cause problems (Score:2, Insightful)
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So the outsourced captcha-breakers really are being replaced by a very small shell script?
--Rob
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it has to be said (Score:2, Redundant)
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Books (Score:2)
I hang out, via e-mail, with people involved in the Loebner Prize Contest, so I
High-Quality Video Link (Score:5, Informative)
Enter the Matrix (Score:3, Funny)
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drawing recognition (Score:2)
Yeah, but can it distinguish the invention of PalmOS Graffiti from the invention of PARC Unistroke? That would have been handy...
Software you can really get into... (Score:2)
Then, of course, there's always the dream of eventually being able to really 'get into the code' and debug it from the inside, leading to the soviet joke where "the code debugs you."
Ryan Fenton
Barrier to entry (Score:2)
Re:Barrier to entry (Score:5, Insightful)
Not so much dare to be stupid, but rather the Socratic, don't be afraid of exposing your own ignorance - don't lose your opportunity to learn by merely being embarrassed of people thinking you dumb while you take your first few steps in a new landscape.
But do take notes and research the small topics you are uncertain of after your first adventure into to the topic. Perhaps you'll need to learn a bit about XML/XSL, perhaps you'll need to find out the anatomy of a nerve cell to understand some explanations. If nothing else though - get into it because it is a fun adventure and a lot of cool stuff to learn.
Ryan Fenton
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I agree completely.
The same goes for those of us who may hold some kind of expertise in one area already. Every time we explore a new area, we must allow ourselves to start from scratch over and over again. In this thought, I'm often reminded by what
Re:Barrier to entry (Score:4, Informative)
He's also done some lectures available on Google Video [google.com].
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If you are interested in the field of AI with neural like computing, your best bet is to learn a huge amount of math. Really you can't understand anything without knowing at least 2nd year linear algebra. That's if you just want to basically understand what's going on. If
Multivariate linear algebra: try chemometrics (Score:1)
I say "dumbed-down" in the nicest possible sense, in that they focus on solving practical problems in industry and laboratory, as opposed to rigorous statistical proofs as to why these algorithms work.
Well,
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As an amateur programmer who's dabbled with AI and game design, some things I found helpful and interesting were:
-Stephen Pinker's How the Mind Works and The Language Instinct (readable and entertaining)
-Douglas Hofstadter's Godel, Escher, Bach (brilliant but baffling, touching on a lot of topics, some of which are worth skipping over; not the best book to pick up lightly)
-Hofstadter's Fluid C
System Requirements (Score:3, Insightful)
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Confidentiality agreement a killer (Score:5, Informative)
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No. That line refers to anything you get from the company. Note that it doesn't say "and" in front of "anything you obtain..." -- it's referring to the same "HTM Algorithms, HTM Algorithms Source Code, etc." described before. It's definitely not referring to anything you learn by using it.
It's pretty easy to misread, I admit.
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I respectfully disagree:
The agreement starts like this:
I can't read that any other way than agreeing that anything you learn from them is their confidential property (regardless of whether the information is in the public domain, is patented/copyrighted
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They aren't providing either of these under that Agreement.
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that you obtain or learn from Numenta
is their property, where Numenta is the corporation not the technology. I would seem to not cover derived discoveries: ie I assume that if you discover the secret to the universe from a physics HTM you would still own it, but algorithm's and the ideas in their whitepapers are theirs.
This still, like all such contracts, does contaminate you with their IP: restricting what you can later work on, even risking independent work suddenly becoming their property. If you work in even a sli
Starting companies to be heard? (Score:2, Interesting)
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"
The purpose of this document is to compare HTMs with several existing technologies for modeling
data. HTMs use a unique combination of the following ideas:
* A hierarchy in space and time to share and transfer learning
* Slowness of time, which, combined with the hierarchy, enables efficient learning of intermediate levels of the hierarchy
* Learning of causes by using time continuity and actions
* Models of attention and specific memories
* A p
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Bring on the face recognition that isn't fooled by dark sunglasses and a false mustache!
Yeah, especially since human brains often can't even do that.
Before anyone else says it (Score:2, Insightful)
Hmm.... (Score:2, Interesting)
1) All the research into cortical circuitry is done in non-humans. There are definite similarities between our cortex and that of a rat, but there are also drastic differences, if there weren't then rats would be able to talk, think, and reason like we do. (Yes lots of research is being done in non-human primates, but this work is EXTREMELY expensive and even non-human primates have different cortical
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(Not only are the cortices of different species drastically different, scientists often chose regions of cortex that have no correlation in humans. Many neuroscientists are studying the Barrel Cortex. It is a region of cortex that is specifically designed to integrate the signals from the whiskers of a Rodent. Humans don't have whiskers and we also don't have Barrel Cortex. Anything learned about the circuitry of the Barrel Cortex will not necessarily correlate to human cortex.
Correct me if I'm wrong, but isn't one of the main points of Hawkins' theory that all parts of the neocortex perform the same algorithm, no matter what the input, be it eyes, ears, or even whiskers? I'm not a neuroscientist and I'm definitely no expert on this, but I did read the book and I seem to remember that being a prominent point. Also, I was under the impression (from the book) that the only thing making animals less intelligent than humans was the size of the cortex. Again, please correct my mis
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I think that's a very good, and very accurate summary. And I am an expert, or at least as much so as anyone in the field is, these days. :)
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That's an entirely invalid simplification. There are large variations on structure, on sensory input, etc between species. Any one of which could set back - or set sideways, more interestingly - performance. For instance, bats process sounds into direction one heck of a lot better than we do. Cats and raptors, to name but two, process balance and visual information into far more athletic capability than we do.
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Hmm hmm (Score:2)
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Well that depends on how you define neocortex some scientists call all cortex neocortex while others only refer to the frontal lobe in humans as neocortex. If you look at the circuitry of all the cortical areas then no they don't use the same algorithm and if Hawkins claims this then he is wrong. I haven't studied the frontal lobe that much so I can't say much about the circuitry there but I would be extremely surprised to see if the
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As a current student in neuroscience, you should know better than to make such a sweeping and inaccurate presumption. There are many paths to working models and working theories, and very few of them include "integrating all levels of research" or anything remotely similar. It is entirely possible to code up (for example) a brand new, highly functional sorting method without either knowin
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"Barrel cortex" is a descrip
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First, the different cognitive abilities of rats could be due either to smaller size or different connectivity of cortical modules that were absolutely identical. I'm not saying either of these is the case, but your argument isn't exactly solid. Second,
almost... (Score:3, Interesting)
http://en.wikipedia.org/wiki/Baum-Welch_algorithm [wikipedia.org] http://en.wikipedia.org/wiki/Viterbi_algorithm [wikipedia.org]
The first is an alogorithm which utilizes forward and back-tracking "to find the unknown parameters of a hidden Markov model." The second is a similar algorithm used for learning 'known' causes (for reference).
I work in computational linguistics and the time an algorithm takes to run and the amount of memory it requires are serious limitations. That's why ad-hoc systems are so common.
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So what?
Infringes on Thaler's Neural Network (NN)Patents? (Score:1)
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And yet again, we see the potential of the patent system to retard progress instead of stimulate it; to favor cashing in over invention; to stifle, crush and force back progress, however isolated from the original inventor such progress may have originated. The PTO is a hive of scum and villainy.
Abolish it. It is out of hand.
If you think that's impressive, just wait. (Score:1)
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Old Code (Score:2)
I played around with some of his publicly available code a few months ago. It was pretty impressive on a toy problem (recognizing a small set of characters) but was very, very slow at training (on the order of hours or days to learn the simple problem).
But on the other hand, I can't think of any sort of technology that could do better than it (I am into machine learning and AI.) Also, it is not a big
Cortex Sim == Bullsh*t (Score:5, Interesting)
Here is what many people in machine learning and computer vision think about Hawkins stuff:
- it's way, way behind what other people in vision and machine learning are doing. Several teams have biologically-inspired vision systems that can ACTUALLY LEARN TO RECOGNIZE 3D OBJECTS. Hawkins merely has a small hack that can recognize stick figures on 8x8 pixel binary images. Neural net people were doing much more impressive stuff 15 years ago.
- Hawkins's ideas on how the brain learns are not new at all. Many scientists in machine learning, computer vision, and computational neuroscience have had general ideas similar to the ones described in Hawkins's book for a very long time. But scientists never talk about philosophical ideas without actual scientific evidence to support them. So instead of writing popular book with half-baked conceptual ideas, they actually build theories and algorithms, they build models, and they apply them to real data to see how they work. Then they write a scientific paper about the results, but they rarely talk about the philosophy behind the results.
It's not unusual for someone to come up with an idea they think is brand new and will revolutionize the world. Then they try to turn those conceptual ideas into real science and practical technologies, and quickly realize that it's very hard (the things they thought of as mere details often turn out to be huge conceptual obstacles). Then, they realize that many people had the same ideas before, but encountered the same problems when trying to reduce them to practice (which is why you didn't hear about their/your ideas before). These people eventually scaled back their ambitions and started working on ideas that were considerably less revolutionary, but considerably more likely to result in research grants, scientific publications, VC funding, or revenues.
Most people go through that "naive" phase (thinking they will revolutionize science) while they are grad students. A few of them become successful scientists. A tiny number of them actually manage to revolutionize science or create new trends. Hawkins quit grad school and never had a chance to go through that phase. Now that he is rich and famous, the only way he will understand the limits of his idea is by wasting lots of money (since he obviously doesn't care about such things as "peer review"). In fact, many reputable AI scientists have made wild claims about the future success of their latest new idea (Newell/Simon with the "general theorem prover", Rosenblatt with the "Perceptron", Papert who thought in the 50's that vision would be solved over the summer, Minsky with is "Society of Minds", etc......).
No scientist will tell Hawkins all this, because it would serve no purpose (other than pissing him off). And there is a tiny (but non-zero) probability that his stuff will actually advance the field.
- Anonymous Scientist
I read the book and tried the software (Score:2)
His work might have been inspired by Kohonen's classic Springer-Verlag book "Self-Organization and Associative Memory".
I downloaded their software last night but have had little time doing anything but building and running two examples. When I get 20 hour
Unfinished business (Score:1)
Old stuff? (Score:1)
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Well, when I read this:
Numenta's goal is to build a software model of the human brain capable of face recognition, object identification, driving, and other tasks currently best undertaken by humans.
I thought: Hey! That's been my goal my whole career. Nobody pays me for tha
Of 2 Minds on Computerized Minds (Score:1)
Not too long ago two of IT's top original thinkers and innovators, Jeff Hawkins and Ray Kurzweil, appeared at an MIT emerging tech conference to discuss artificial intelligence. Both see computing mirroring the functions of the human brain. But they disagree on how fast scientists and engineers will develop technologies that exhibit the most complex cerebral traits of humans: self-awareness, emotion, and even a sense of one's own mortality.
Because of technology's exponential growth, Kurzweil sees emotion-
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That's not a "debunking", that's a closed-minded opinion-fest. Reminds me of Papert's and Minsky's huge rants on how neural nets couldn't do this and that, exemplified by the (incorrect) claim they couldn't even be made to do an XOR. They published, just ran off at the mouth like college kids with their first exposure to ideas orthogonal to their thinking, then were proved soundly wrong by the facts.
Some advice for the closed minded: Judge this fellows work by his actual results; not what other people t
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"Perceptrons" by Minsky and Papert was correct regarding perceptrons' limited computational expressiveness. Following that they incorrectly conjectured that their negative result would hold for 3 or more perceptron layers, which later turned out not to be true. So cut them some slack, they gave the research community significant useful results, and probably acted with best judgment.
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I really don't think they deserve any slack, particularly Minsky. There were huge personal issues, they published an outright attack based on Minsky's emotional bias [wikipedia.org] (it certainly wasn't based on the facts, because they weren't in possession of them), they didn't do any testing of the claims they made, and they were wrong. Their results affected research for years in the wrong direction. It was a bone-headed stunt with nothing about it that redeems it. I watched it happen; it seemed vindictive and puerile
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