Talk:Hierarchical Temporal Memory
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[edit] Similarity with Bayesian Models
This article currently references HTMs as modeling different aspects of the neocortex as a bayesian model (17-10-2007). I think this is actually backwards based on what I've read of HTMs. The model is similar to a bayesian model, but differs in a number of important ways. Further, it's backwards because the model was built by starting with neuroscience and trying to fit a model to the data, rather than simply starting with a computer science model and describing how it is similar to the brain. (See Bionics for more information on starting with nature)
Does anyone have any further information on the overlap/differences with Bayesian networks? —Preceding unsigned comment added by Gokmop (talk • contribs) 17:58, 17 October 2007 (UTC)
[edit] Paste
This is obviously a paste of numenta's marketing materials. Furthermore it is more of an attempt to somehow legitimize the product, probably on behalf of Numenta, not on the behalf of education or reference.
- That is not true - the article is not an attempt to legitimize the product. Moreover, the machine learning community is smart enough to know that only prediction scores legitimize products, rather than worthless marketing materials. You are welcome to improve the article to enhance its NPOV. --Amit 21:40, 2 March 2007 (UTC)
[edit] Copyright
This article copies whole paragraphs out of Numenta's Copyright white paper and therefore needs major editing! quota 09:16, 24 July 2006 (UTC)
A request was made on July 25, 2006 to Phillip B. Shoemaker, Director, Developer Services, Numenta, to relicense the HTM Concepts paper under the GFDL. No response was received. --Amit 08:45, 7 August 2006 (UTC)
I will be editing the article further. --Amit 08:45, 7 August 2006 (UTC)
[edit] Possible POV statement
"may ultimately equal the importance of traditional programmable computers in terms of societal impact and financial opportunity." is a ridiculous claim for something that hasn't even been implemented yet!
- The statement has now been removed the article. --Amit 20:50, 20 September 2006 (UTC)
[edit] Out of date
The research release is out and the article at this time is seriously out of date. --Amit 04:16, 5 March 2007 (UTC)
[edit] Article
This article is completely POV. Hawkins is a rich guy, and no-one feels like telling him that his stuff is crap. He had a few smart people working for him at some point, but when they told him his ideas were half baked and not new, he just fired their asses.
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. At any rate, he seems to have donated money to fund a university research group in California. He probably can't advance science, but his money certainly can.
—Preceding unsigned comment added by 74.98.253.40 (talk • contribs)
- Doesn't matter for me. Even the Hierarchical Temporal Memory is a warming up of old ideas, its based on some very good ideas. Also its a existing and operational implementation which makes it very important in my eyes - and the Wired magzine would't have written a article about. You may be right if you think that the article is either marketing or copy&paste from the Numenta-Homepage, but that means that we just need to rewrite the article. Recognizing 8x8px images is very impressive, because its at the lowest layer while the whole picture is recognized at higher layers - this is a normal approach to do so. But what you should respect is that its working with moving patterns. I feel that very impressive, because it might get combined with other solutions like CBCL's (MIT) technique for doing the same with still images. Note that in the human brain there is a center in the cortex for still-images and one for moving images, so this two techniques might be a interesting combination. Also there is a project for improved optical sensors by the IOO of TU Berlin to mimicry the human eye, which might get used for this application field to. Combining such techniques can lead to very exiting applications, so I wouldn't corrode any of them.
- — MovGP0 00:37, 11 March 2007 (UTC)
[edit] IEEE Spectrum magazine article
There's an article on it:
http://spectrum.ieee.org/apr07/4982 41.241.197.221 19:01, 5 May 2007 (UTC)
[edit] Copyvio
Almost this entire article was a copyright violation from the texts concerning the subject. I have deleted the article and replaced it with a stub that contains all of the non copyvio text I could salvage. Please do not re-add any of the copyvio text; short excerpts from copyrighted works are acceptable for the purpose of critical commentary, but anything else will be removed on sight. Thanks --Spike Wilbury 15:05, 17 May 2007 (UTC)
[edit] htm vs neural networks
If anyone is familiar enough with this, could you throw in something on how HTM differs from traditional artificial neural networks? It sounds exactly the same, with the exception of the temporal aspect perhaps (which I'm sure has also been tacked onto existing nn applications), yet there is no mention of neural networks anywhere. —The preceding unsigned comment was added by 24.68.157.4 (talk) 00:55, August 22, 2007 (UTC)
[edit] Where's the beef?
Whatever the merits or otherwise of HTM, the article as it now stands contains almost no factual material. (I understand that it may have been removed due to copyright infringement, but paraphrasing is usually acceptable.) Without any substantive content, the article is unworthy of Wikipedia and as such should be withdrawn.
84.9.75.24 (talk) 21:14, 17 November 2007 (UTC)
