The complexity of the problem faced by Numenta

I have been following Dileep George's new blog, and he made a couple of responses to posts by myself and Dave (perhaps the same Dave who occasionally posts here).

In my post, I asked Dileep how the traditional tree-shaped hierarchy can explain the vast number of qualities that can come into play when we recognise, for instance, a shoe. For example, when we see a shoe, we recognize that it is a certain color, a certain texture, has a certain design on it, and many other features. In other words, recognizing one object requires the brain to have connections to a number of other invariant representations of different types of objects and concepts. In my mind, I couldn't see how a simple tree structured hierarchy could represent this complexity, and Dileep confirmed that I was correct, stating that the brain likely has a number of different hierarchies that communicate with one another. Since that time, I will say that I think that I was mixing up how we recognize a particular instantiation of a shoe with how we recognize the invariant representation of "shoe" that is stored in the brain. The simple tree-shaped hierarchy might be sufficient to store the invariant "shoe" concept while not working to recognize a particular shoe.

Dave's question to Dileep focused on whether a single HTM network could recognize both an object (such as a shoe) and an action (like running or walking). Surprisingly to me, Dileep answered that you would need two separate HTM networks to handle two separate types of knowledge. My conclusion how is that the simple traditional tree shaped hierarchy is not sufficient even to represent all invariant concepts known by the brain, much less the particular instantiations of those representations that we learn (i.e. particular faces of persons as opposed to the general idea of "face").

This goes to show that even if Numenta's new algorithms have licked the problem of how the brain learns within a region and does inference, figuring out how the brain as a whole learns many types of objects and concepts, both invariantly and specifically, and how it ties all of this knowledge together in the amazing way that our brain works, is still something that we are only beginning to figure out.

Numenta's new website

Numenta redesigned its website. Here are a few nuggets from the new site:

1. Some new videos were added, including Hawkins' 2008 keynote from the HTM workshop and a speech by Subutai Ahmad from the 2009 workshop. Ahmad's talk was particularly interesting because he discussed a number of corporate partnerships and some early results from them. For instance, Numenta is/was working with a major automaker on the creation of a pedestrian detection system, where the car looks for pedestrians in front of the vehicle. The early testing resulted in 96-97% accuracy, or closer to 99% accuracy if one counts a false positive as a good result (situations where the system detects a pedestrian where there wasn't one). The talk also mentioned some interesting work that Numenta did with Tyzx, which provides computer vision systems. They used an HTM network to look for objects/persons in security camera footage. Subutai specifically mentioned robotics as a potential application. Interestingly, only three days ago Tyzx announced a deal with Irobot to provide vision systems for its military robots, including person detection capabilities. The press release did not mention whether HTM's are a part of that technology. It would be interesting to see what companies Numenta has been working within in the 14 months since Subutai's talk.

2. The website also contains a basic description of its new learning algorithms. It is difficult not to notice how huge of a leap forward that Numenta views these algorithms as. In one place, Numenta states that the new algorithms are a "radical" improvement. In another place, it states that the new learning algorithms are "far superior" to the old ones. One thing that I wish the website contained was some experimental results showing these huge improvements. One thing that I found confusing was its description of prediction in the new algorithms. It described prediction as something flowing up the hierarchy. That seems different from prediction as described in the original HTM theory, which envisioned incoming data flowing up the hierarchy and predictions flowing down the hierarchy. In any event, it was an interesting read.

Tidbits on Numenta

Wow has it been a slow summer for HTM news. I have never seen a period of time where Numenta's employees have made so few public appearances. Since Hawkins' talk in March, I haven't seen any mention of any Numenta speeches, interviews, or papers of any kind. A few things of note:

-In its June newsletter, Numenta mentioned that it decided not to attempt an HTM workshop this year, meaning that the next generation algorithms will not be out this year. In their words, they decided not to push for an "interim" release this year, but to delay the workshop and release to 2011. "Interim" was an interesting choice of words, suggesting that a more fully featured product will be the result when NUPIC 2.0 does come out.

-Dileep George has a new blog on his website. It is called Mind Matter, and is located at the following link.

-I saw an interesting blog post regarding a robot called Nao that can apparently show and understand emotion. The author claims that the creators of the new robot software were using HTM software in the robot. I have not been able to verify that claim. The only mention of HTM in the context of a Nao robot that I found was an article by Ben Goertzel in which he describes using an HTM for low level perception in a Nao robot. That article specifically states that he hasn't implemented the idea yet, however. Find the link here.

-Finally, Tomaso Poggio, a Professor in the Department of Brain and Cognitive Sciences at MIT, and one of the creators of a biology-based hierarchical learning model known at HMAX, has created a software model that uses GPUs to greatly accelerate software that is designed to emulate the cortex, such as HTM or HMAX. Poggio claims that the software accelerates these biology-based models by an amazing 80-100 times. Poggio is listed as a technical advisor on Numenta's website, so hopefully they are aware of this, especially given the increased computational demands of NUPIC 2.0.

Dileep George leaving Numenta

Big Numenta news today. Numenta's website indicates that Dileep George is on an extended "personal" leave of absence. He, along with Jeff Hawkins, co-founded Numenta back in 2005. It is hard to overstate his importance to the company over the years. He was the guy who read "On Intelligence" and figured out how to turn Hawkins' neuroscience theories into a mathematical expression that could be created in software. I went to George's website, and he says there that he left Numenta so that he could form a new company focused more on applications of the HTM technology.

I am not sure what to think about this. On the one hand, this could signal that George simply thinks that the technology is finally in a state that serious commercial applications can now be created with HTM. Numenta has always been more about the basics of the theory than applications, so it might be that Dileep just wants to hurry along the commercialization process. This could be a signal that the new algorithms really are going to be that big of a step forward for AI.

Hopefully this move doesn't mean some type of rift has opened between George and the company. Numenta could really use his talents down the road. Given George's stated reason for leaving, and given that it is called a "leave of absence" rather than outright resignation, I am inclined to go with the more optimistic interpretation.

May 2010 Numenta newsletter

In case you missed it, this week Numenta issued a newsletter with an update on the status of the new algorithms. Its an interesting read, with a write up by Jeff Hawkins himself. He talks about how last fall they decided to take a fresh look at their node learning algorithms, realizing that the current version had shortcomings that could not be overcome. They went back and looked at the brain for inspiration on how to improve the learning capabilities. Here are a few keys points I pulled out of the article:

1. The new algorithms can be learning and inferring at the same time. The old algorithms had a separate learning and inference stage, so this will be a significant improvement for many applications with real time data where the system needs to be able to learn, infer, and predict in real time (like a real brain).

2. The sparse distributed nature of the system makes it scale much better to large problems, and makes it very robust to noise. In other words, the system will work very well with messy, incomplete data.

3. Variable order sequence learning- A real brain can start listening to a song midway through and almost immediately identify the song. Likewise, we can predict the future based on learned sequences of various lengths that occurred a short time ago or years ago. The new software will be useful in doing these types of things.

4. Much more biologically realistic- This is the first version of the software that will basically be emulating the cortex at the level of neurons and synapses. Of course, the downside is the higher system requirements. Hawkins notes that Numenta is having to spent a great deal more time optimizing the software to be able to work on something that isn't a supercomputer.

As an aside, I am surprised at the level of skepticism of some of the mainstream AI people regarding Numenta. Ben Goertzel, for one, seems determined to believe that Numenta is on the wrong track. He went out of his way recently to claim that Itamar Arel's DESTIN system is a better hierarchical pattern recognition system. I have looked into DESTIN, and it actually seems very similar to Numenta's work. It learns temporal sequences of spatial patterns in a hierarchical nature, and performs bayesian inference. I have not been able to find any evidence showing that DESTIN has, so far, done more in the computer vision arena than HTM. If I am wrong, someone can correct me. For instance, in a December 2009 paper regarding DESTIN, Arel noted that they had conducted an experiment showing character recognition. It was recognition of letters in a binary (black or white) environment. Numenta was demonstrating that level of work at least three years ago. My sense is that DESTIN is on the right track, and perhaps Arel and Hawkins will collaborate at some point (maybe they already are) but I have no idea how Goertzel reaches his conclusion.

I was happy to see that Shane Legg (another AI critic of Numenta) seemed to change his mind about Numenta after seeing Hawkins' recent talk in March on the new algorithms. If Numenta can come through in a big way with its next software release, I think that there will be many more converts to the HTM theory of AI.

2nd public commercial HTM application

Looks like a press release was issued today by EDSA Power Analytics, noting that it is teaming up with Numenta to develop autonomous monitoring of electrical power systems. Apparently, for some applications, electrical power failures are hugely expensive. EDSA develops software to monitor power systems to attempt to prevent power failures. HTM can be used by EDSA to learn the difference between normal and non-normal electrical activity, such that the HTM becomes increasingly able to predict a future power failure.

http://www.businesswire.com/portal/site/home/permalink/?ndmViewId=news_view&newsId=20100420005529&newsLang=en

Hawkins explains next generation HTM algorithms

On March 18, Jeff Hawkins gave a talk to the computer science department at the University of British Columbia. In that talk, for the first time, he gave a detailed explanation regarding the upcoming HTM algorithms. To be honest, parts of it were difficult for a layperson (like me) to fully understand. It is a very interesting talk, though, and showed that Numenta is getting ever closer to a truly intelligent computer. This version of the algorithms appears to much more closely mimic the brain's method of learning than ever before. Here is a link to the speech:

http://www.youtube.com/watch?v=TDzr0_fbnVk