Predictive Memory and Particle Filters

I keep thinking about the Particle Filters AI method, so might as well right some of it down. Note that in AI Particle Filters has a particular meaning, different from the physics for particles in a real-world simulation or in a video game. In AI filtering is a method for localization of an object, which could be a large physical object like a robot. It involves probabilistic methods (Monte Carlo techniques) for predicting where the object might be, combined with sensory feedback. Typically it requires a pre-existing map of the territory (which for a robot is real, but which could be highly abstract in other cases) and a method for matching the feedback to the map. It has been most notably used as an essential software component of the Google self-driving car. A somewhat more extensive overview can be found at Particle Filters.

The reason I keep thinking about it is because I started noticing how I consciously (and probably subconsciously) localize myself in the physical world. Most notably, when I am navigating in the dark in my house. I do this to avoid backtracking: turning on a light, going back to the prior switch & turning it off, etc. In any case I know my house well, but in just a few feet in the dark it is easy to get a bit off course and bump into furniture or a wall where I expected a doorway. The stairs can be tricky too.

So I walk a few feet in what I guess is the right direction based on experience. Then I reach out to touch what I expect is a wall or piece of furniture. Or I note a change from rug to bare floor, or spot an LED. Usually I get the feedback I expect, but sometimes I find I am off course and need to make a correction.

In Particle Filter Localization terms, I project a probabilistic range of particles representing where I, the human robot, might be next. Then I check. Often I know exactly where I am after a check, but sometimes all I know is that I am not near a wall or furniture piece. Then I make another mental projection, take a step or two depending on my expectations and level of caution, and try again to touch something. I go through similar mental gymnastics when driving a car or taking a daylight walk, but the feedback is visual rather than touch.

I went through a similar analysis process after reading Jeff Hawkins theory of predictive memory in On Intelligence. The theory is that the brain largely operates by making constant predictions, based on memory of historical patterns, and checking them against sensory input. When input varies from predictions, neurons send special signals about detecting novelty to higher brain centers that deal with novelty. For instance, as you read this your brain knows a number of words might be included in this zebra. See, you expected "sentence" or maybe "essay" or the like, you were not expecting "zebra", and so the normal flow of processing was interrupted.

I have not seen a neural network model for particle filters. It could be that the resemblance between how human brains seem to work is only superficially like particle filters. More generally we are talking about feedback systems, which can take a number of forms. Even very primitive animals that lack differentiated neurons have feedback systems, so there could be many types of neural feedback schemes operating in the human brain.

I often notice my dog doing something that makes me think that most mammals, including pre-sapiens homo species, can do that. Yet when these processes, say chasing a rabbit, are abstracted, they can become quite complex. I can imagine chasing a rabbit. I can plan to chase a rabbit into a trap. I can pursue something more abstract, say a solution to a construction problem or even a math problem in a way that resembles chasing a rabbit. Let's see, the answer to the solution of this differential equation has disappeared, it is not obvious. I'll beat the bushes, using three techniques known to help solve this type of equation, most-likely technique first. Hopefully one technique will put the problem in a form that I recognize from the standard map for solving such equations.

How would particle filters be implemented with neural networks? Let me know your thoughts.

Stanford AI Class wrap up

I managed to muddle my way through the free Internet version of the Stanford Introduction to Artificial Intelligence (AI) course. "Congratulations! You have successfully completed the Advanced Track of Introduction to Artificial Intelligence ..." says my Statement of Accomplishment.

Before putting on my analyst mask, I would like to thank Stanford University and particularly the instructors, Sebastian Thrun and Peter Norvig, for conducting the course. In particular, for making it free of charge. I am hoping they will leave the instruction videos up for a while, there are some I would like to go over again.

I got a good review of Bayes Rule, basic probability, and some simple machine learning algorithms. I had not worked on Planning algorithms before, so that was of some interest. Markov Models had always been a bit vague to me, so that section helped me nail down the idea. Games and game theory seem to have made little progress since the 1940's, but I guess they have to be covered, and I did get clear on how MinMax works. Computer vision seemed kind of weak, but then you can't assume students know basic optics and at least we learned how to recognize simple features. Robotics was a prior interest for me, and I did not know about Thrun's obvious favorite, Particle Filters, which are a useful paradigm for spatial positioning (aka localization).

The last two units were on Natural Language Processing, and that is a good place to start a critique (keeping in mind that all this material was introductory). Apparently you can do a lot of tricks processing language, both in the form of sounds/speech and written text, without the algorithms understanding anything. They showed a way to do pretty decent inter-ethnic language translations, but the usefulness depends on humans being able to understand at least one language.

Plenty of humans do plenty of things, including paid work, without understanding what they are doing. I suppose that could be called a form of artificial intelligence. Pay them and feed them and they'll keep up those activities. But when people do things without understanding (I am pretty sure some of my math teachers fell into that category), danger lurks.

The Google Car that drives itself around San Francisco (just like Science Fiction!) just demonstrates that driving a Porsche proves little about your intelligence capabilities. Robot auto-driving was a difficult problem for human engineers to solve. They were able to solve it because they understood a whole lotta stuff. Particle Filters, which involve probability techniques combined with sensory feedback to map and navigate an environment, are a cool part of the solution. If I say "I understand now: I have been walking through a structure, and to get to the kitchen I just turn left at the Picasso reproduction," I may be using the word understand in a way that compares well with what we call the AI capabilities of the Google Car. Still, I don't think the Car meets my criteria for machine understanding. The car might even translate from French to English for its human cargo, but I still classify it as dumb as a brick.

Hurray! Despite my advancing age, lack of a PhD., less than brilliant business model, and tendency to be interested in too many different things to be successful in this age of specialization, no one seems to have gotten to the essence of how humans understand, and are aware of, the world and themselves.

If the human brain, or its neural network subcomponents, did Particle Filters, how would that work? I know from much practice that bumping around in the dark can lead to orientation or disorientation, depending on circumstances. On the other hand the random micro-fine movements of the eye might be a physical way of generating randomness to test micro-hypotheses that we are not normally consciously aware of.

We sometimes say (hear Wittgenstein in my voice) that someone has a shallow understanding of something. "Smart enough to add columns of numbers, not smart enough to do accounting," or "Good at game basics, but unable to make strategic decisions." Let me put it another way: in some ways the course itself was an intelligence test. I imagine it would be very rough for anyone without a background in algebra and basic probability theory. The students in the class already knew a lot, and had to learn difficult things.

I want to know how our bodies, our brains, learn difficult things. The only way I will be able to be sure that I understand how that is done is if I can build a machine that can do the same thing.

stuck

Maybe we need the equivalent of a quantum hypothesis for the brain, in the sense that it may not be possible to construct a corrrect model based on classic principles, no matter how complex.

Stanford AI Class thoughts, and a brief tour of AI history

"people who face a difficult question often answer an easier one instead, without realizing it"
— Daniel Kahneman,

Despite time management issues (which will only get worse this week) I managed to struggle through the first two weeks, or four units, of the online Stanford introduction to artificial intelligence course.

In the past I had already tried to struggle through Judea Pearl's Probabilistic Reasoning in Intelligent Systems. That was published well over 20 years ago, and yet this course uses many of the same examples. The course is much more about working out actual examples; it is practical, not so theoretical. We've covered Bayes networks and conditional probability, both concepts I had already learned because Numenta was using them. Pearl's book contains a lot of material about wrong directions to take; the Stanford course seems to be focussed on what actually works, at least for Google.

My impression is still that the Stanford AI paradigm, while very practical, is not going to provide the core methods for truly intelligent machines, which I characterize as machine understanding. I think this largely because I am ancient and have watched quite a few AI paradigms come and go over the decades.

When AI got started, let's say in the 1950's, there was an obsession with logic as the highest form of human intelligence (often equated with reasoning). That computers operated with logic circuits seemed a natural match. Math guys who were good at logic tended to deride other human activities as less difficult and requiring less intelligence. Certain problems, including games with limited event spaces (like checkers), could be solved more rapidly by computers (once a human had written an appropriate program) than by humans. By the sixties, at latest by the seventies, computers running AI programs would be smarter than humans. In retrospect, this was idiotic, but the brightest minds of those times believed it.

One paradigm that showed some utility was expert systems. To create one of these, experts (typical example: a doctor making a diagnosis) were consulted to find out how they made decisions. Then a flow chart was created to allow a computer program to make a decision in a similar manner. As a result a computer might appear intelligent, especially if provided by the then more difficult trick of an audio imitation voice output, but today no one would call such a system intelligent. That is no more intelligent than the early punch card sorters that once served as input and output for computers that ran on vacuum tubes.

In the 1980's there was a big push in artificial neural networks. This actually was a major step towards machines being able to imitate human brain functions. It is not a defunct field. Some practical devices today work with technologies developed in that era. But scaling, the problems grew faster than the solutions. No one could build an artificial human brain out of the old artificial neural networks. We know that if we can exactly model a human brain, down to the molecular (maybe even atomic) level, we should get true artificial intelligence. But simplistic systems of neurons and synapses are not easy to assemble into a funcioning human brain analog.

The Stanford model for AI has been widely applied to real world problems, with considerable success. This probabilistic model allows it to deal with more complex data than the old logic and expert system paradigms ever could. Machine systems really can learn new things about their environment and store and organize that information in a way that allows for practical decision making. Clearly that is one thing human brains can do, and it is a lot more difficult than playing in a set-piece world like tic-tac-toe or even chess.

Sad as the state of human reasoning can be at times, and as slow as we are to learn new lessons, and as proud as we are of our least bouts of creativity, (and as much as we may occasionally ignore the rule against run-on sentences), I think the Stanford model is not, by itself, going to lead to machine understanding. The human brain has a lot of very interesting structures at the gross level and at the synaptic level. Neurologists have not yet deciphered them. Their initial "programming," or hard-wiring is purely the result of human evolution.

When is imitated intelligence real intelligent? When does a machine (or a human, for that matter) understand something, as opposed to merely changing internal memory to reflect the external reality?

Then again, maybe a Stanford model computer/program/input/output system would have done better at the Stanford AI course than I have. I certainly have not been getting all the quizzes and homework problems right on the first try. On the other hand, I think it will be some good long time before a machine can read, say, a book on neurology and carry on an extended intelligent conversation about it.

Brain Modeling Computational Trajectory

SGI is a manufacturer of high-performance computers, what might be called small supercomputers. I was listening to their analyst day (I own stock in SGI) this morning and saw an interesting slide, which I reproduce here:


With regard to machine understanding, this is the direct assault method. At some point when the human brain is modeled in sufficient detail the construct should display human memory, intelligence, and even consciousness or self-awareness. It is conceivable that a detailed computer model might exhibit artificial intelligence or understanding but leave us still unable to comprehend the essence of what is happening. More likely we will be enlightened and therefore able to construct working machine entities that have true intelligence and understanding, but are not exactly modeled on the human nervous system.

There are different levels for modeling biological systems of neurons and brain matter. We might model on the atomic, molecular, sub-cellular, cellular, or neural-functional level. It is not clear what level of detail is assumed in the SGI projection. Best guess is that is has a model for individual neurons, but the complexity is added by adding additional neurons to the network. That would be the main difference between modeling a "cellular neocortical column," a mesocircuit, a rat brain, and a human brain.

SGI already makes computers for researchers in this field. Of course other vendors' computers can be used. The advantage SGI brings to the table today is the ability to build a large model in computer memory chips (the processors see a big, unified memory), as opposed to hard disks.

I would be very impressed if someone could correctly model a functioning rat brain by 2014. Keep in mind that just because the computer power is available to do it, does not mean that any given team's model is correct. I wonder what proof on concept would consist of? To test such a brain you would need a test environment. That might be a simulation, but it could also be robotic. Keep in mind that much of what a rat brain does relates not to what we think of as awareness of the environment, but to maintaining body functions.

Another issue is the initial state problem. Suppose that you "dissect" a rat so that you know the relative placement of every neuron in its central nervous system. Still, to make your model work, you would need a functional initial state for all the the cells. You need to know how synapses are weighted. Probably someone is working on the boot-up of mammal brains during embryonic development. Even just getting the genetically re-programmed neurotransmitter types for each synapse to each cell seems like a more difficult problem that making a generalized computer model based on a neural map plus a generalized neuron.

Apparently there is plenty of work on this project for everyone.

Stanford AI course

I signed up for the free Stanford course, An Introduction to Artificial Intelligence to be taught by Sebastian Thrun and Peter Norvig this fall. I figure it can't hurt, and free is a good price.

Naive Bayes Classifier in Python

I have been busy with many things: indexing a book on software management; trying to learn math things I would have learned when I was 18 or 19 if the Vietnam War had not made me decide to major in Political Science; advising investors about the value of Hansen Medical's robotics technology and Iteris's visual analysis software for cars and highway monitoring.

So even though I continue to think about MU, and to study, I have had nothing in particular worth reporting here. However, I came across a good introductory page on using Bayes probability with the Python programming language. If that is what you need, here it is:

Text Categorization and Classification in Python with Bayes' Theorem

And I would love to play with a Kinect, but where would the time come from?