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.
Showing posts with label Jeff Hawkins. Show all posts
Showing posts with label Jeff Hawkins. Show all posts
Sunday, January 22, 2012
Monday, May 10, 2010
New Algorithms from Numenta
My study of Machine Understanding was on pause for a couple of weeks while I compiled an index for a 802.11n networking book. On May 5 I received a Numenta Newsletter, the key point of which is that Jeff Hawkins and crew have been working on a better algorithm for their HTM systems. Sadly, I still have not gone into the details of the old algorithm!
I'll quote the key passage from Jeff:
"Last fall I took a fresh look at the problems we faced. I started by
returning to biology and asking what the anatomy of the neocortex
suggests about how the brain solves these problems. Over the course
of three weeks we sketched out a new set of node learning algorithms
that are much more biologically grounded than our previous algorithms
and have the promise of dramatically improving the robustness and
performance of our HTM networks. We have been implementing these new
algorithms for the past six months and they continue to look good."
Sure. Even my own limited reading so mostly-outdated neurology texts seemed to indicate that the early versions of HTM are simplistic (compared to systems of human brain neurons). The new version, styled FDR (Fixed-sparsity Distributed Representation), are somewhat more complicated, but Jeff believes they are more capable. In particular, they deal better with noise and variable-length sequences.
On the other hand, we are certainly hoping to get machines to actually understand the world without having to duplicate (in software) a human brain molecule by molecule.
Jeff gave a lecture on the new algorithm at the University of British Columbia, which will have to do for the rest of us until details are posted at the Numenta web site:
http://www.youtube.com/watch?v=TDzr0_fbnVk
See also my Machine Understanding main web page.
In the meantime I intend, in addition to doing my own thinking & tinkering, to resume my program of going through the already-posted, earlier version examples of HTM.
I'll quote the key passage from Jeff:
"Last fall I took a fresh look at the problems we faced. I started by
returning to biology and asking what the anatomy of the neocortex
suggests about how the brain solves these problems. Over the course
of three weeks we sketched out a new set of node learning algorithms
that are much more biologically grounded than our previous algorithms
and have the promise of dramatically improving the robustness and
performance of our HTM networks. We have been implementing these new
algorithms for the past six months and they continue to look good."
Sure. Even my own limited reading so mostly-outdated neurology texts seemed to indicate that the early versions of HTM are simplistic (compared to systems of human brain neurons). The new version, styled FDR (Fixed-sparsity Distributed Representation), are somewhat more complicated, but Jeff believes they are more capable. In particular, they deal better with noise and variable-length sequences.
On the other hand, we are certainly hoping to get machines to actually understand the world without having to duplicate (in software) a human brain molecule by molecule.
Jeff gave a lecture on the new algorithm at the University of British Columbia, which will have to do for the rest of us until details are posted at the Numenta web site:
http://www.youtube.com/watch?v=TDzr0_fbnVk
See also my Machine Understanding main web page.
In the meantime I intend, in addition to doing my own thinking & tinkering, to resume my program of going through the already-posted, earlier version examples of HTM.
Tuesday, January 12, 2010
Bayesian Wasteland?
Despite being overloaded with other concerns, I continue to slog through two books that may (or may not) be helpful with the Machine Understanding project. On the information technology front we have Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference by Judea Pearl. I'm at page 116 as of last night. On the biological front I continue From Neuron to Brain by Stephen W. Kuffler; I'm on page 386.
Regarding Probabilistic Reasoning, so far I have seen a lot of interesting work on the problem of combining probability calculations with logic. I just finished the section on Markov Networks and am about to read up on Bayesian Networks. My problem so far is that I don't see any advantages to marrying anything I've seen of probabilistic reasoning to Jeff Hawkin's theory of predictive memory. Despite having read "Towards a Mathematical Theory of Cortical Micro-circuits," as reported in previous entries. Then again sometimes I have trouble taking up novel ideas. But my impression so far is that neural networks are not operating on a probability basis. The closest I can get, so far, to that kind of model is a signal-mixing basis, where analog functions might represent Pearl's probabilies. Nor do I see how the probability network models can cope with invariants. The word invariant is not found in the index to Probablistic Reasoning. I have higher hopes for a Tensor model, even though my own work on that is very preliminary. [I am reminded of the two seemingly totally different mathematical methods used in early quantum mechanics, which then were proven to be equivalants.]
In From Neuron, I have been reading astonishing details about how synapses and single nerve cells work, including how experiments were conducted, in mind-numbing detail. I am just getting to where how neurons and sets of neurons have been shown to operate, with the first example being neurons that sense the stretching of muscles. Again, I am pointed to tensors, which can be used to represent how multiple muscles representing various degrees of freedom of motion can lead to a coherent knowledge of where a body part is in three-dimensional, euclidean-modeled space.
More oddly, Neuron has basically nothing about Hebbian learning. True, the book is dated 1984. But did no one even try to find a physical basis for Hebbian learning as of that date? If you know of a definitive paper that appears to prove a biochemical mechanism for Hebbian learning, let me and my readers know.
I intended to explain what I was reading, in suitable chunks, in this blog. But it is easier to just keep reading at this point, rather than writing about details that I am not even sure are important yet. I keep finding I have to go back to basics. Today, worrying about tensors, muscles, feedback, and a neuron-level learning model, I am revisiting a two-neuron learning model that, oddly, I first worked on back in the 1980's. If anything comes of it, I'll let you know here.
Regarding Probabilistic Reasoning, so far I have seen a lot of interesting work on the problem of combining probability calculations with logic. I just finished the section on Markov Networks and am about to read up on Bayesian Networks. My problem so far is that I don't see any advantages to marrying anything I've seen of probabilistic reasoning to Jeff Hawkin's theory of predictive memory. Despite having read "Towards a Mathematical Theory of Cortical Micro-circuits," as reported in previous entries. Then again sometimes I have trouble taking up novel ideas. But my impression so far is that neural networks are not operating on a probability basis. The closest I can get, so far, to that kind of model is a signal-mixing basis, where analog functions might represent Pearl's probabilies. Nor do I see how the probability network models can cope with invariants. The word invariant is not found in the index to Probablistic Reasoning. I have higher hopes for a Tensor model, even though my own work on that is very preliminary. [I am reminded of the two seemingly totally different mathematical methods used in early quantum mechanics, which then were proven to be equivalants.]
In From Neuron, I have been reading astonishing details about how synapses and single nerve cells work, including how experiments were conducted, in mind-numbing detail. I am just getting to where how neurons and sets of neurons have been shown to operate, with the first example being neurons that sense the stretching of muscles. Again, I am pointed to tensors, which can be used to represent how multiple muscles representing various degrees of freedom of motion can lead to a coherent knowledge of where a body part is in three-dimensional, euclidean-modeled space.
More oddly, Neuron has basically nothing about Hebbian learning. True, the book is dated 1984. But did no one even try to find a physical basis for Hebbian learning as of that date? If you know of a definitive paper that appears to prove a biochemical mechanism for Hebbian learning, let me and my readers know.
I intended to explain what I was reading, in suitable chunks, in this blog. But it is easier to just keep reading at this point, rather than writing about details that I am not even sure are important yet. I keep finding I have to go back to basics. Today, worrying about tensors, muscles, feedback, and a neuron-level learning model, I am revisiting a two-neuron learning model that, oddly, I first worked on back in the 1980's. If anything comes of it, I'll let you know here.
Saturday, November 14, 2009
Review of Towards a Mathematical Theory of Cortical Micro-circuits
This is a report on my first reading of "Towards a Mathematical Theory of Cortical Micro-circuits" [Dileep George & Jeff Hawkins, PLoS Computational Biology, October 2009, Volume 5, Issue 10]. There were some bits of it that I had to treat as black boxes, but on the whole I found it comprehensible.
The core concern of the paper is to map a particular attempt to use computers to implement the Memory-Prediction framework of human intelligence to what is known about how nerve cells actually function in the cortex of the human brain. The specific machine methodology is called Hierarchical Temporal Memory (HTM). The article begins by explaining how HTM operates, then proceeds to map HTM processes to the cortex.
HTM structures are called nodes, but it should be noted that the nodes have extensive internal structure. They can have multiple inputs and outputs. They can have multiple "coincidence pattern matrices" and Markov chain processes. There are four major equations sets necessary for calibrating the nodes. I suggest you take a look at the HTM Technology Overview if you have not already. In particular, the authors assume the readers understand how Markov chains work, which is not obvious in this context.
It should be noted that HTM nodes are typically found in a hierarchy of nodes, and that information can move both up and down the hierarchy. The bottom of the hierarchy would typically correspond to sensory input, the top of the hierarchy to conceptual modes.
In fact, I found it easier to understand how the HTM worked internally when the authors started explaining the details in terms of neuron processes. The Markov chain likelihood circuit (Figure 4), in addition to mapping neurons to an aspect of an HTM node, makes it clearer that a Markov chain, in this context, is a set of information that has (or could have) predictive value. Markov chains are learned expectations about probabilities that events will occur in a time sequence.
This is a good example of a premise of the Memory-Prediction framework. The Markov chains are a sort of memory, yet they also are used to process information to make predictions. In computer science we tend to separate out memory from logical operations, but in the brain they are mostly inseparable.
As to the nervous system side of the mirror, I was not surprised to see a lot of the more complex work being performed by pyramidal cells. This neuron type appears to be quite differentiated from the more ordinary neurons that mainly relay information between muscles or sensory cells and the brain. Their very size and location in the cortex (particularly in layers 2, 3, and 5) should arose anyone's curiosity. Whether they really play the roles assigned to them here is not generally known, but hypotheses are made that should be testable.
The paper also covers the use of HTMs to recognize visual objects. Since much of the work done by Numenta and others using HTMs is in this field, I won't comment further here except to say that I found the work on the subjective contour effect particularly intriguing (example of subjective contours: Kanizsa triangle).
Merely categorizing visual objects is a more difficult problem that was originally thought by computer scientists working on Artificial Intelligence (AI). It is not learning about the successes with HTMs with pattern recognition that excites me. It is the way that it is done, and the promise that implies. I want to understand how the brain works in detail, but the ultimate goal is to understand intelligence, awareness, and understanding itself. The way HTMs process information, including the flowing of information both up and down the hierarchy, seems like a necessary condition for attacking these problems of higher intelligence. I don't think anyone thinks HTMs will prove to be entirely sufficient, but they seem like a good starting place for further physical and philosophical investigations.
The core concern of the paper is to map a particular attempt to use computers to implement the Memory-Prediction framework of human intelligence to what is known about how nerve cells actually function in the cortex of the human brain. The specific machine methodology is called Hierarchical Temporal Memory (HTM). The article begins by explaining how HTM operates, then proceeds to map HTM processes to the cortex.
HTM structures are called nodes, but it should be noted that the nodes have extensive internal structure. They can have multiple inputs and outputs. They can have multiple "coincidence pattern matrices" and Markov chain processes. There are four major equations sets necessary for calibrating the nodes. I suggest you take a look at the HTM Technology Overview if you have not already. In particular, the authors assume the readers understand how Markov chains work, which is not obvious in this context.
It should be noted that HTM nodes are typically found in a hierarchy of nodes, and that information can move both up and down the hierarchy. The bottom of the hierarchy would typically correspond to sensory input, the top of the hierarchy to conceptual modes.
In fact, I found it easier to understand how the HTM worked internally when the authors started explaining the details in terms of neuron processes. The Markov chain likelihood circuit (Figure 4), in addition to mapping neurons to an aspect of an HTM node, makes it clearer that a Markov chain, in this context, is a set of information that has (or could have) predictive value. Markov chains are learned expectations about probabilities that events will occur in a time sequence.
This is a good example of a premise of the Memory-Prediction framework. The Markov chains are a sort of memory, yet they also are used to process information to make predictions. In computer science we tend to separate out memory from logical operations, but in the brain they are mostly inseparable.
As to the nervous system side of the mirror, I was not surprised to see a lot of the more complex work being performed by pyramidal cells. This neuron type appears to be quite differentiated from the more ordinary neurons that mainly relay information between muscles or sensory cells and the brain. Their very size and location in the cortex (particularly in layers 2, 3, and 5) should arose anyone's curiosity. Whether they really play the roles assigned to them here is not generally known, but hypotheses are made that should be testable.
The paper also covers the use of HTMs to recognize visual objects. Since much of the work done by Numenta and others using HTMs is in this field, I won't comment further here except to say that I found the work on the subjective contour effect particularly intriguing (example of subjective contours: Kanizsa triangle).
Merely categorizing visual objects is a more difficult problem that was originally thought by computer scientists working on Artificial Intelligence (AI). It is not learning about the successes with HTMs with pattern recognition that excites me. It is the way that it is done, and the promise that implies. I want to understand how the brain works in detail, but the ultimate goal is to understand intelligence, awareness, and understanding itself. The way HTMs process information, including the flowing of information both up and down the hierarchy, seems like a necessary condition for attacking these problems of higher intelligence. I don't think anyone thinks HTMs will prove to be entirely sufficient, but they seem like a good starting place for further physical and philosophical investigations.
Friday, October 23, 2009
On Bayesian Belief Propagation
Towards a Mathematical Understanding of Cortical Micro-circuits [Dileep George & Jeff Hawkins, PLoS Computational Biology, October 2009, Volume 5, Issue 10] assumes some knowledge of Bayesian Belief Propagation. I was hoping the article would explain this topic as it went along, but apparently, to AI specialists, this is a well-known area. I found the Wikipedia entry, belief propagation, jumps into mathematical formulism rather quickly. Some words of explanation without math symbols would probably help many people who are interested in machine understanding; that is my goal here. If some of you Bayesian belief propagation experts think I have it wrong, or want to add anything, feel free to add comments. I have ordered a copy of the classic work on the subject, Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference
, and so hope to be more fluent in this area at a later date.
Bayesian Belief Propagation (BBP) was formulated by Judea Pearl, who later authored Probabilistic Reasoning in Intelligent Systems. It appears to be a system for determining unknown probabilities within certain types of complex systems. Let's start with a review of a very simple probability model.
Much of early probability work was formulated in terms of a pair of ordinary gambling dice, six-sided cubes with faces numbered one through six. Thrown many times, they make a nice distribution pattern of numbers two through twelve. It can be shown with a little math and logic that there are percentages that are expected for each of these numbers. If you make a large number of dice throws, say more than 100, and you get results that differ by very much from the expected distribution of results, you may be looking at one of two things. It is possible that you have a distribution skewed purely by chance. On the other hand, you might want to investigate whether your dice and throwing systems are physically constructed to be truly random in the results they give.
Now imagine a black box, say the size of a shoe box. If you shake the box and then stop shaking, it gives you a number. In fact, imagine a room with a variety of such boxes. You shake them, keeping tracks of the resulting numbers for each. If one box consistently gives numbers between 2 and 12 inclusive, and the frequencies for each number match the pattern that we believe two dice would give, then we can conclude that the internal mechanism of the box, whatever it is, has a probability distribution that is created by the same logic that creates the dice probability distribution. There might even be a tiny pair of nano-dice inside.
In computer programming we tend to think in purely causal mechanics. If something does not happen as expected, we have a bug in our program. But in the case of living groups of biological neurons we cannot (so far) look at the inner mechanics of each neuron while watching how the whole group operates. However, if we can control the inputs and read the outputs of a system, then, if we can do the math, we might figure out the probabilities of individual neurons firing given known inputs from the other neurons in the system.
Formally, BBP allows us to calculate the probability characteristics for nodes we can't observe directly, if we can observe enough of the rest of the system. A node does not need to be a neuron. In an AI system, a node is a processing unit with inputs, outputs, or both, that connect it to the system of nodes in question. In our case the nodes are part of a Bayesian network, also known as a belief network. Communication (or messaging) within the network is in terms of probabilities. (See also Bayesian probability)
Think of a network of three people, where each of the people represents a node. Let there be a piece of paper we will call the message (it is blank; the paper is the message, in this case). Let each person have a single die and the rule: if you have the message, throw the die. If it comes up a one, a two, or a three, pass the message to the left. Otherwise, pass it to the right. If we watch the game progress, the message moves around or back and forth in a random pattern. However, it is not a Bayesian network, because it is cyclical. Bayesian networks are not cyclic. Messages more only in one direction.
Now start a series of games in which the message is always handed to person 1 (node 1). Person 1 throws the die and passes the message to person 2 if the result is 3 or less, and to person 3 if the result is 3 or more. Person 2 also throws a die and simply holds the message if the result is 3 or less, but passes if to person 3 if the result is 3 or more. When person 3 gets a message the game is over.
It is a simple game and a tad boring, but if we focus on the propagation time, the number of die throws from the beginning of a game until the end of a game, we can see a set of variations. Over the long run they should form a probability distribution of the times.
Now change the rules slightly. Say person 2 forwards the message if the die comes out 2 or higher. The range of possible results does not change, but the probability distribution will.
Now suppose you know about this type of game, but you can't see the game because it is in a tent. You see a message handed in, and can time how long it is before the message is handed back out by person 3.
By keeping track of the times, you can get the probability distribution. That should tell you the exact rules of the game being played, including the probability that person 1 will give the message directly to person 3, and how long person 2 might keep the message before passing it.
In more complex networks, you need to pin down the probabilities at more of the nodes, if you want to be able to characterize the probability rules for an unknown node.
Which is what the BBP algorithm is supposed to do. It calculates the probabilities for an unobservable node based on the observed nodes.
, and so hope to be more fluent in this area at a later date.Bayesian Belief Propagation (BBP) was formulated by Judea Pearl, who later authored Probabilistic Reasoning in Intelligent Systems. It appears to be a system for determining unknown probabilities within certain types of complex systems. Let's start with a review of a very simple probability model.
Much of early probability work was formulated in terms of a pair of ordinary gambling dice, six-sided cubes with faces numbered one through six. Thrown many times, they make a nice distribution pattern of numbers two through twelve. It can be shown with a little math and logic that there are percentages that are expected for each of these numbers. If you make a large number of dice throws, say more than 100, and you get results that differ by very much from the expected distribution of results, you may be looking at one of two things. It is possible that you have a distribution skewed purely by chance. On the other hand, you might want to investigate whether your dice and throwing systems are physically constructed to be truly random in the results they give.
Now imagine a black box, say the size of a shoe box. If you shake the box and then stop shaking, it gives you a number. In fact, imagine a room with a variety of such boxes. You shake them, keeping tracks of the resulting numbers for each. If one box consistently gives numbers between 2 and 12 inclusive, and the frequencies for each number match the pattern that we believe two dice would give, then we can conclude that the internal mechanism of the box, whatever it is, has a probability distribution that is created by the same logic that creates the dice probability distribution. There might even be a tiny pair of nano-dice inside.
In computer programming we tend to think in purely causal mechanics. If something does not happen as expected, we have a bug in our program. But in the case of living groups of biological neurons we cannot (so far) look at the inner mechanics of each neuron while watching how the whole group operates. However, if we can control the inputs and read the outputs of a system, then, if we can do the math, we might figure out the probabilities of individual neurons firing given known inputs from the other neurons in the system.
Formally, BBP allows us to calculate the probability characteristics for nodes we can't observe directly, if we can observe enough of the rest of the system. A node does not need to be a neuron. In an AI system, a node is a processing unit with inputs, outputs, or both, that connect it to the system of nodes in question. In our case the nodes are part of a Bayesian network, also known as a belief network. Communication (or messaging) within the network is in terms of probabilities. (See also Bayesian probability)
Think of a network of three people, where each of the people represents a node. Let there be a piece of paper we will call the message (it is blank; the paper is the message, in this case). Let each person have a single die and the rule: if you have the message, throw the die. If it comes up a one, a two, or a three, pass the message to the left. Otherwise, pass it to the right. If we watch the game progress, the message moves around or back and forth in a random pattern. However, it is not a Bayesian network, because it is cyclical. Bayesian networks are not cyclic. Messages more only in one direction.
Now start a series of games in which the message is always handed to person 1 (node 1). Person 1 throws the die and passes the message to person 2 if the result is 3 or less, and to person 3 if the result is 3 or more. Person 2 also throws a die and simply holds the message if the result is 3 or less, but passes if to person 3 if the result is 3 or more. When person 3 gets a message the game is over.
It is a simple game and a tad boring, but if we focus on the propagation time, the number of die throws from the beginning of a game until the end of a game, we can see a set of variations. Over the long run they should form a probability distribution of the times.
Now change the rules slightly. Say person 2 forwards the message if the die comes out 2 or higher. The range of possible results does not change, but the probability distribution will.
Now suppose you know about this type of game, but you can't see the game because it is in a tent. You see a message handed in, and can time how long it is before the message is handed back out by person 3.
By keeping track of the times, you can get the probability distribution. That should tell you the exact rules of the game being played, including the probability that person 1 will give the message directly to person 3, and how long person 2 might keep the message before passing it.
In more complex networks, you need to pin down the probabilities at more of the nodes, if you want to be able to characterize the probability rules for an unknown node.
Which is what the BBP algorithm is supposed to do. It calculates the probabilities for an unobservable node based on the observed nodes.
Saturday, October 17, 2009
Bottom Up Machine Understanding
The first paragraph of "Towards a Mathematical Theory of Cortical Micro-circuits" [Dileep George & Jeff Hawkins, PLoS Computational Biology, October 2009, Volume 5, Issue 10] states:
Understanding the computational and informtion processing roles of cortical circuitry is one of the oustanding problems in neuroscience. ... the data are not sufficient to derive a computational theory in a purely bottom-up fashion
My own cortex, probably not wanting to give itself a headache by proceeding too rapidly into what looks like a dense and difficult paper, immediately drifted off into thoughts on deriving a computational theory in a purely bottom-up fashion.
The closer we get to the physical bottom, the easier it seems to be to understand that the project might work. Suppose we could model an entire human brain at the molecular level. We imagine a scanner that can tell us, for a living person who we admit is intelligent and conscious, where each molecule is to a sufficient degree of exactitude. We also would have a computational system for the rules of molecular physics, and appropriate inputs and outputs.
Unless you believe that the human mind is not material (a dualist or idealist philosophic view), such a molecular-detail model should run like a human brain. At first it should think (and talk and act, to the extent the outputs allow) exactly like the person whose brain was scanned.
However, that does not mean scientists would understand how the brain works, or how a computational machine could exhibit understanding. Reproducing a phenomena and understanding a phenomena are not the same thing. The advantage of such a molecular computional brain model would be that we could run experiments on it in a way that could not be done on human beings or even on other mammals. We could start inputting and tracing data flows. We could interrupt and view the model in detail at any time. We could change parameters and isolate subsystems. Perhaps, further in the future, such a model could even be constructed without having to scan a human brain for the initial state.
At present, for a bottoms-up approach that might actually be workable in less than a decade, we would probably want to do a neuron-by-neuron model (probably with all the non-neural supporting cells in the brain as well). However, a lot of new issues arise even at this seemingly low level, even if we presume we have some way to scan into the model all of the often-complicated axon to dendrite paths and synapses. If learning is indeed based on synapse strength (Hebbian hypothesis), we would need both a good model for synapse dynamics and a detailed original state of synapses. This would require modeling the synapses themselves at the molecular level, or perhaps one-level up at the some molecular aggregate level. In effect it would not be possible to model an adult brain that has exhibited intelligence and understanding. We would need to start with a baby brain and hope that it would go through a pattern of neural information development similar to that of a human child.
A complete neural-level model would be much easier to test intelligence hypothesese on than a molecular-level model. It would not in itelf indicate that we understand how humans understand the world. By running the model in two parallel instances (with identical starting states), but with selected malfunctions, we could probably isolate most of the sub-systems required for intelligence. This should help us build a comprehensible picture of how intelligence operates once it is established and of how it can be constructed by neural circuits from whatever the raw ingredients of intelligence turn out to be.
Despite our lack of such complete bottom-up models, I don't think it is too early to try to reconstruct how the brain works, or how to make machines intelligent. The paper outlines the HTM approach to this subject. HTM was based on a great deal of prior work in neuroscience and in modeling neural aggregates with computers. Often in science success has come from the combination of bottom-up and top-down approaches. Biological species, and fossil species, were long catalogued and studied before Darwin's theory of evolution revealed the connections between species. Darwin did not invent the concept of evolution of species, or of inherited traits, which many scientists already believed were shown by the fossil record and living organisms. He added the concept of natural selection, and suddenly evolution made sense. The whole picture popped into view in a way that any intelligent human being could see.
Understanding the computational and informtion processing roles of cortical circuitry is one of the oustanding problems in neuroscience. ... the data are not sufficient to derive a computational theory in a purely bottom-up fashion
My own cortex, probably not wanting to give itself a headache by proceeding too rapidly into what looks like a dense and difficult paper, immediately drifted off into thoughts on deriving a computational theory in a purely bottom-up fashion.
The closer we get to the physical bottom, the easier it seems to be to understand that the project might work. Suppose we could model an entire human brain at the molecular level. We imagine a scanner that can tell us, for a living person who we admit is intelligent and conscious, where each molecule is to a sufficient degree of exactitude. We also would have a computational system for the rules of molecular physics, and appropriate inputs and outputs.
Unless you believe that the human mind is not material (a dualist or idealist philosophic view), such a molecular-detail model should run like a human brain. At first it should think (and talk and act, to the extent the outputs allow) exactly like the person whose brain was scanned.
However, that does not mean scientists would understand how the brain works, or how a computational machine could exhibit understanding. Reproducing a phenomena and understanding a phenomena are not the same thing. The advantage of such a molecular computional brain model would be that we could run experiments on it in a way that could not be done on human beings or even on other mammals. We could start inputting and tracing data flows. We could interrupt and view the model in detail at any time. We could change parameters and isolate subsystems. Perhaps, further in the future, such a model could even be constructed without having to scan a human brain for the initial state.
At present, for a bottoms-up approach that might actually be workable in less than a decade, we would probably want to do a neuron-by-neuron model (probably with all the non-neural supporting cells in the brain as well). However, a lot of new issues arise even at this seemingly low level, even if we presume we have some way to scan into the model all of the often-complicated axon to dendrite paths and synapses. If learning is indeed based on synapse strength (Hebbian hypothesis), we would need both a good model for synapse dynamics and a detailed original state of synapses. This would require modeling the synapses themselves at the molecular level, or perhaps one-level up at the some molecular aggregate level. In effect it would not be possible to model an adult brain that has exhibited intelligence and understanding. We would need to start with a baby brain and hope that it would go through a pattern of neural information development similar to that of a human child.
A complete neural-level model would be much easier to test intelligence hypothesese on than a molecular-level model. It would not in itelf indicate that we understand how humans understand the world. By running the model in two parallel instances (with identical starting states), but with selected malfunctions, we could probably isolate most of the sub-systems required for intelligence. This should help us build a comprehensible picture of how intelligence operates once it is established and of how it can be constructed by neural circuits from whatever the raw ingredients of intelligence turn out to be.
Despite our lack of such complete bottom-up models, I don't think it is too early to try to reconstruct how the brain works, or how to make machines intelligent. The paper outlines the HTM approach to this subject. HTM was based on a great deal of prior work in neuroscience and in modeling neural aggregates with computers. Often in science success has come from the combination of bottom-up and top-down approaches. Biological species, and fossil species, were long catalogued and studied before Darwin's theory of evolution revealed the connections between species. Darwin did not invent the concept of evolution of species, or of inherited traits, which many scientists already believed were shown by the fossil record and living organisms. He added the concept of natural selection, and suddenly evolution made sense. The whole picture popped into view in a way that any intelligent human being could see.
Wednesday, October 14, 2009
Markov Chains for Brain Models
I got an email from Numenta today, telling me that Dileep George and Jeff Hawkins had a paper, "Towards a Mathematical Theory of Cortical Micro-circuits", published in the PLoS Computational Biology journal. Part of the abstract states:
“we describe how Bayesian belief propagation in a spatio-temporal hierarchical model, called
Hierarchical Temporal Memory (HTM), can lead to a mathematical model for cortical circuits. An HTM node is abstracted using a coincidence detector and a mixture of Markov chains.”
HTM is the model being used by Numenta and others to crack to machine understanding problem by making an abstract, computable model of the human cortex (brain). I figured the article would explain “Bayesian belief propagation” which parses into three words I understand. I knew I had run into the term “Markov chains” before, in probability theory and elsewhere. I thought I would just refresh my memory about good old Andrey Markov with Wikipedia. But the Markov chains article at Wikipedia was tough going, jumping into the math without clearly explaining it in English. The definition there was comprehensible: “a random process where all information about the future is contained in the present state.” That implies there is a set of rules, expressed as probabilities, so that if you know the current state of a system you can predict its future state probabilities. It neatly combines determinism with indeterminism. For a first impression, I found the Examples of Markov Chains article more helpful.
I thought I might find a better explanation on my book shelf. There was nothing in my Schaum’s Statistics, but my next guess (thank you, cortex) Schaum's Outline of Operations Research
by Richard Bronson and Govindasami Naadimuthu, has an entire chapter, “Finite Markov Chains,” starting on page 369.
Markov chains are a subset of Markov processes. Markov processes consist of a set of objects with a set of states for the objects. At each point in time each object must be in one of the states. As time progresses, “the probability that an object moves from one state to another state in one time period depends only on the two states,” the current state and the probabilities for the future states given the current state.
For example, in the board game Monopoly each “square” or piece of real estate is part of the set of objects. The two dice create a probability structure, the standard one for two dice. The probability that, at any point, a player will transition to the same point, the forward adjacent point, or any point beyond the 12th point forward is zero, because the dice can only roll numbers between 2 and 12. However, more typically there would be a set of states and a probability for the transition to each of the states, including the transition of remaining in the current state.
If there are N possible states, and you know all the probabilities for transitions, you can show this and manipulate it mathematically with a matrix P, the stochastic or transition matrix. There would be N rows and columns in P. As in all probability theory, in any row the probabilities add up to 1.
N=2 is the simplest meaningful number of states. Let’s call them black and white. It is really only interesting if the transitions are different. Say 90% of the time black transitions to white, but only 20% of the time white transitions to black. Then the matrix P would be
.1 .9
.8 .2
There is some ability to predict the future in a statistical way if you have a Markov chain like this. Without doing any math, you would expect over time that if you have only one object, it would probably be white. If it is black, probably the next state of the sequence flips to white, but if it is white, probably the next state will be white. If you have a large number of objects, you can know the likelihood that a given percentage of them will be white or black. Yet you can’t know for sure. Even the least likely state, all objects black, can occur at any time if each black stays black but each white transitions to black.
You can see that Markov chains might be used to represent the states of neurons or of collections of neurons. In my simple examples above, each object influences only its own future state. But in real systems often future state probabilities are determined by the states of a collection of objects. It could be a collection of transistors, or a collection of nerve cells, or HTM nodes.
“we describe how Bayesian belief propagation in a spatio-temporal hierarchical model, called
Hierarchical Temporal Memory (HTM), can lead to a mathematical model for cortical circuits. An HTM node is abstracted using a coincidence detector and a mixture of Markov chains.”
HTM is the model being used by Numenta and others to crack to machine understanding problem by making an abstract, computable model of the human cortex (brain). I figured the article would explain “Bayesian belief propagation” which parses into three words I understand. I knew I had run into the term “Markov chains” before, in probability theory and elsewhere. I thought I would just refresh my memory about good old Andrey Markov with Wikipedia. But the Markov chains article at Wikipedia was tough going, jumping into the math without clearly explaining it in English. The definition there was comprehensible: “a random process where all information about the future is contained in the present state.” That implies there is a set of rules, expressed as probabilities, so that if you know the current state of a system you can predict its future state probabilities. It neatly combines determinism with indeterminism. For a first impression, I found the Examples of Markov Chains article more helpful.
I thought I might find a better explanation on my book shelf. There was nothing in my Schaum’s Statistics, but my next guess (thank you, cortex) Schaum's Outline of Operations Research
by Richard Bronson and Govindasami Naadimuthu, has an entire chapter, “Finite Markov Chains,” starting on page 369.
Markov chains are a subset of Markov processes. Markov processes consist of a set of objects with a set of states for the objects. At each point in time each object must be in one of the states. As time progresses, “the probability that an object moves from one state to another state in one time period depends only on the two states,” the current state and the probabilities for the future states given the current state.
For example, in the board game Monopoly each “square” or piece of real estate is part of the set of objects. The two dice create a probability structure, the standard one for two dice. The probability that, at any point, a player will transition to the same point, the forward adjacent point, or any point beyond the 12th point forward is zero, because the dice can only roll numbers between 2 and 12. However, more typically there would be a set of states and a probability for the transition to each of the states, including the transition of remaining in the current state.
If there are N possible states, and you know all the probabilities for transitions, you can show this and manipulate it mathematically with a matrix P, the stochastic or transition matrix. There would be N rows and columns in P. As in all probability theory, in any row the probabilities add up to 1.
N=2 is the simplest meaningful number of states. Let’s call them black and white. It is really only interesting if the transitions are different. Say 90% of the time black transitions to white, but only 20% of the time white transitions to black. Then the matrix P would be
.1 .9
.8 .2
There is some ability to predict the future in a statistical way if you have a Markov chain like this. Without doing any math, you would expect over time that if you have only one object, it would probably be white. If it is black, probably the next state of the sequence flips to white, but if it is white, probably the next state will be white. If you have a large number of objects, you can know the likelihood that a given percentage of them will be white or black. Yet you can’t know for sure. Even the least likely state, all objects black, can occur at any time if each black stays black but each white transitions to black.
You can see that Markov chains might be used to represent the states of neurons or of collections of neurons. In my simple examples above, each object influences only its own future state. But in real systems often future state probabilities are determined by the states of a collection of objects. It could be a collection of transistors, or a collection of nerve cells, or HTM nodes.
Tuesday, October 6, 2009
Synaptic Transmission Puzzle
"Over the past twenty years, a wide variety of modes of synaptic transmission have been discovered, in addition to simple, chemically mediated increases in permeability leading to excitation and inhibiation. Such modes of transmission include electrical exictation and inhibition, combined chemical-electrical synapses, chemical synapted changes produced by reductions in memberane permeability, and prolonged synaptic potentials mediated by chemical reactions in the postsynaptic cell."
-- page 208, From Neuron to Brain, Second Edition, by Stephen W. Kuffler, John G. Nicholls, and A. Robert Martin; 1984, Sinauer Associates, Inc., Suderland, Massachusetts
In Jeff Hawkins' On Intelligence, he presents a predictive-memory model for human intelligence which he believes can serve as a basis for intelligent machines [which I call machine understanding to emphasize that the machines would not merely be mimicking surface features of human intelligence, as is the case with, for example, expert systems]. I agree with him that while neuroscience and computing science have made great progress in understanding many details of the human brain, and in writing software, we need to have an overview that allows us to make progress on the centrals issues that interest us. Thus, in Hawkins' model, he does not worry about the exact nature of neural synapses.
But at the very least, we should be aware of how complicated human synapses are. This should allow us a greater freedom of thought when modeling the mechanics of machine understanding than if we simply assumed synapses all work alike. I suspect the Hebbian learning model for neurons would benefit from considering that the real world may complex, and in that complexity there may be keys to progress that we leave out with overly simple models.
What struck me most about the above paragraph about synapses, however, is the role played by evolution. Charles Darwin wrote about the process of species creation, but we have long grown used to the idea that evolution takes place on a molecular level. Each nerve cell, presumably, contains the full set of genes of the organism, but many different types of synapses are manifested. There must be controlling, blueprint genes that tell the cells which typese of synapses they are to form as they develop. In turn, we can expect that many different blueprints have been tried over the last four million years or so. The most successful gave their human organisms survival advantages.
It would be interesting to know how much synaptic variation exists in the current human population. Is this variation responsible, or partly responsible, for variations in basic intelligence capabilities of human beings?
This brings us back to the hard-wiring versus plastic debate. Human beings are very adaptable, as is shown by their ability to learn any of thousands of human languages as a child. We tend to think that we are very plastic and programmable creatures. But nerve transmission speeds and synaptic types are hard wired, as is the basic design of the brain. One might say we have the hard-wired capability to be flexible.
And that is what we aim to build into the new machines: hard wiring (a stable system with a design we understand and can reproduce) that is capable of showing the flexibility required to exhibit intelligence and understanding.
-- page 208, From Neuron to Brain, Second Edition, by Stephen W. Kuffler, John G. Nicholls, and A. Robert Martin; 1984, Sinauer Associates, Inc., Suderland, Massachusetts
In Jeff Hawkins' On Intelligence, he presents a predictive-memory model for human intelligence which he believes can serve as a basis for intelligent machines [which I call machine understanding to emphasize that the machines would not merely be mimicking surface features of human intelligence, as is the case with, for example, expert systems]. I agree with him that while neuroscience and computing science have made great progress in understanding many details of the human brain, and in writing software, we need to have an overview that allows us to make progress on the centrals issues that interest us. Thus, in Hawkins' model, he does not worry about the exact nature of neural synapses.
But at the very least, we should be aware of how complicated human synapses are. This should allow us a greater freedom of thought when modeling the mechanics of machine understanding than if we simply assumed synapses all work alike. I suspect the Hebbian learning model for neurons would benefit from considering that the real world may complex, and in that complexity there may be keys to progress that we leave out with overly simple models.
What struck me most about the above paragraph about synapses, however, is the role played by evolution. Charles Darwin wrote about the process of species creation, but we have long grown used to the idea that evolution takes place on a molecular level. Each nerve cell, presumably, contains the full set of genes of the organism, but many different types of synapses are manifested. There must be controlling, blueprint genes that tell the cells which typese of synapses they are to form as they develop. In turn, we can expect that many different blueprints have been tried over the last four million years or so. The most successful gave their human organisms survival advantages.
It would be interesting to know how much synaptic variation exists in the current human population. Is this variation responsible, or partly responsible, for variations in basic intelligence capabilities of human beings?
This brings us back to the hard-wiring versus plastic debate. Human beings are very adaptable, as is shown by their ability to learn any of thousands of human languages as a child. We tend to think that we are very plastic and programmable creatures. But nerve transmission speeds and synaptic types are hard wired, as is the basic design of the brain. One might say we have the hard-wired capability to be flexible.
And that is what we aim to build into the new machines: hard wiring (a stable system with a design we understand and can reproduce) that is capable of showing the flexibility required to exhibit intelligence and understanding.
Tuesday, September 15, 2009
How I Spent My Summer
Obviously I have not spent time on the blog lately. I had a busy time indexing a technology book, and when that was done was behind on many other things. I have also been doing a considerable amount of studying on issues of machine understanding, but delayed my project of going through On Intelligence by Jeff Hawkins and explicating it. I have read it twice now; I am not ready for a third read. I did look at the web sites associated with the Hawkin's model, and will report on them when I have a chance to study them in more detail.
I am reading, or wading through, Neuron to Brain by Stephen W. Kuffler et al. The chapter on the structure of the visual cortex was near the front, which made interesting compare and contrast reading with On Intelligence. But I did not make the effort to write up my thoughts, and that must now wait for a vacation from other duties.
I have become better at tensor analysis, which had the added benefit of making Einstein's "The Foundation of the General Theory of Relativity" readable. I don't think tensors are going to be good models for neural networks, but the concept of invariance is well-developed with tensors and it seems central to the topic of maching understanding. By thinking about the structure of neurons in the brain, and invariance, I hope to at least exercise my mind in the vicinity of the problem.
Today I am fascinated by the possible application of metrics, inner-product spaces, and invariant angles to the question of how human babies construct their mental world. I was struck by the important of coordinating tactile senses with vision and auditory sensation. The main apparatus for this is the human arm. Excluding the hand, the arm consists of two big angles, one at the shoulder, the other at the elbow. There are a lot of degrees of freedom therein. To grab objects requires a lot of coordination. The angles are going to vary with muscle tone, which is controlled by the motor areas of the brain. But the general space should be an inner-product space. Changes of coordinates, say if the coordinate system was directed from the eyes, would not change the angles, lengths, or positions of the hands.
It makes me think about how flexible the human mind can be, and then contrast with how rigid people's thoughts can be. Once we learn about Euclidean space we tend to think we are walking around in it, with the ground as a two-dimensional plane. The idea that a coordinate system could be anchored by an imaginary line perpendicular to a line connnected the eyes strikes us as peculiar. Yet we use that coordinate system constantly, perhaps more than any other.
I am reading, or wading through, Neuron to Brain by Stephen W. Kuffler et al. The chapter on the structure of the visual cortex was near the front, which made interesting compare and contrast reading with On Intelligence. But I did not make the effort to write up my thoughts, and that must now wait for a vacation from other duties.
I have become better at tensor analysis, which had the added benefit of making Einstein's "The Foundation of the General Theory of Relativity" readable. I don't think tensors are going to be good models for neural networks, but the concept of invariance is well-developed with tensors and it seems central to the topic of maching understanding. By thinking about the structure of neurons in the brain, and invariance, I hope to at least exercise my mind in the vicinity of the problem.
Today I am fascinated by the possible application of metrics, inner-product spaces, and invariant angles to the question of how human babies construct their mental world. I was struck by the important of coordinating tactile senses with vision and auditory sensation. The main apparatus for this is the human arm. Excluding the hand, the arm consists of two big angles, one at the shoulder, the other at the elbow. There are a lot of degrees of freedom therein. To grab objects requires a lot of coordination. The angles are going to vary with muscle tone, which is controlled by the motor areas of the brain. But the general space should be an inner-product space. Changes of coordinates, say if the coordinate system was directed from the eyes, would not change the angles, lengths, or positions of the hands.
It makes me think about how flexible the human mind can be, and then contrast with how rigid people's thoughts can be. Once we learn about Euclidean space we tend to think we are walking around in it, with the ground as a two-dimensional plane. The idea that a coordinate system could be anchored by an imaginary line perpendicular to a line connnected the eyes strikes us as peculiar. Yet we use that coordinate system constantly, perhaps more than any other.
Wednesday, July 8, 2009
Numenta
While I am getting up to speed (a slow process, given my other commitments), there is no reason for someone who is interested in this topic to zoom to the cutting edge. A good place for a jump start, after you have read On Intelligence, is the Numenta web site. You can also check out the OnIntelligence.org web site, which sponsors some forums on Hawkins's predictive memory model of intelligence and has some links to related materials. Today I'll just describe what is at the Numenta site.
If you have not read On Intelligence, in particular Chapter 6, "How the Cortex Works," you can still get a lot out of reading materials at the Numenta site. However, I would advise reading the book first, old fashioned as that may sound.
You might be ready to jump right into "Hierarchical Temporal Memory Concepts, Theory and Terminology" by Jeff Hawkins and Dileep George. You are going to want to read it at some point. It is focused on HTMs (Hierarchical Temporal Memory technology). This is an attempt to implement the model of the human brain cortex, but in data-processing rather than biological terms. It is also the main technology being developed at Numenta.
On the other hand you may want to look at the HTM Technology Overview first. It is short and sweet, and also has a link to Problems That Fit HTMs. Of course if the HTM actually is a good model for the human cortex, HTMs should do well at problems that people (and often other mammals) do well at, but that computers, even using AI (artificial intelligence) techniques are not good at.
If you are a hands-on person, you could skip the theory and start with one of the software development kits. One is Vision Software, the other is NuPIC which now includes Vision. However, the NuPIC web page has a link to technical resources that would be important if you get serious about working with HTMs.
I'll write more about Numenta, just as I'll be writing more commentary on machine understanding and intelligence in general. I will be approaching HTMs from a critical perspective: do they implement a predictive memory model; is this the best implementation; and is the model really central to biological intelligence and machine understanding.
If you have not read On Intelligence, in particular Chapter 6, "How the Cortex Works," you can still get a lot out of reading materials at the Numenta site. However, I would advise reading the book first, old fashioned as that may sound.
You might be ready to jump right into "Hierarchical Temporal Memory Concepts, Theory and Terminology" by Jeff Hawkins and Dileep George. You are going to want to read it at some point. It is focused on HTMs (Hierarchical Temporal Memory technology). This is an attempt to implement the model of the human brain cortex, but in data-processing rather than biological terms. It is also the main technology being developed at Numenta.
On the other hand you may want to look at the HTM Technology Overview first. It is short and sweet, and also has a link to Problems That Fit HTMs. Of course if the HTM actually is a good model for the human cortex, HTMs should do well at problems that people (and often other mammals) do well at, but that computers, even using AI (artificial intelligence) techniques are not good at.
If you are a hands-on person, you could skip the theory and start with one of the software development kits. One is Vision Software, the other is NuPIC which now includes Vision. However, the NuPIC web page has a link to technical resources that would be important if you get serious about working with HTMs.
I'll write more about Numenta, just as I'll be writing more commentary on machine understanding and intelligence in general. I will be approaching HTMs from a critical perspective: do they implement a predictive memory model; is this the best implementation; and is the model really central to biological intelligence and machine understanding.
Tuesday, June 23, 2009
Theories of the Brain
As a summary, what Jeff Hawkins says on page 2 of On Intelligence seems accurate to me and note this was published in 2004:
All of my experience led me to the same conclusion. It is not that some scientists, computer software engineers, and philosophers have not looked at the problems that On Intelligence addresses. In retrospect, however, most of them made the same fundamental mistake. Characterize it as trying to replicate behavior instead of creating intelligence that replicates human intelligence and therefore human behavior. And one reason most people made the same mistake was because there was no good model of the brain available from the neuroscientists.
I have on my bookshelf The Formal Mechanics of Mind by Stephen N. Thomas. The inside cover blurb says "The author's intention is to provide an analysis of the nature of the mind, and of our knowledge of it—an analysis that solves or avoids longstanding philosophical problems and that fits well with the results of psychological and neuropsychological investigations of mental phenomena." It is a good book. Published in 1978, it is one of the best I had read before On Intelligence.
On the other hand we have the computer programming AI people. They did some pretty good work, but it is a cautionary tale. Most AI worked with very simple models of objects. AI did best, or appeared to do best, when dealing with certain higher human mental functions, like formal logic. But the brain does not appear to operate with formal logic. Humans invented formal logic. It may (or may not) serve as a basis for a theory of mathematics, as Bertrand Russel, A. N. Whitehead, Ludwig Wittgenstein, and John von Neuman attempted to show.
We will, of course, be using mathematics to understand "real" intelligence. Yet there is almost no math in On Intelligence. Before we can develop a math, or apply an existing branch of mathematics, to this problem we have to understand both the true nature of the problem and how the brain implements the solution, both structurally and dynamically.
I also have on my bookshelf, among other books on neural networks, Parallel Distributed Processing by James L. McClelland et. al. I wrote some computer programs to implement some of the models presented in the book. That was back in the late 1980's. The intelligence problem, then, seemed to be solvable in a piecemeal manner. Difference types of neural networks could be combined and improved until something approaching intelligence emerged. But my own attempts at building an intelligence model went nowhere, and I got involved in trying to save some redwood forests. The forests were clearcut despite my efforts, and I'll take Hawkins' word for it when he said that the neural network model had not made substantial progress in the 1990's. Again, neural network software and hardware became very good at certain things, notably pattern recognition. But while intelligent systems (humans) can recognize patters, apparently pattern recognition is not in itself the basis of intelligence.
Next up: Real Intelligence versus AI
Yet we have no productive theories about what intelligence is or how the brain works as a whole. Most neurobiologists don't think much about overall theories of the brain because they're engrossed in doing experiments to collect more data about the brain's many subsystems. And although legions of computer programmers have tried to make computers intelligent, they have failed. I believe they will continue to fail as long as they keep ignoring the differences between computers and brains.Of course later Hawkins discusses in some detail both neuroscience and attempts at creating human-like machine behavior with what we will call the old AI, or Artificial Intelligence.
All of my experience led me to the same conclusion. It is not that some scientists, computer software engineers, and philosophers have not looked at the problems that On Intelligence addresses. In retrospect, however, most of them made the same fundamental mistake. Characterize it as trying to replicate behavior instead of creating intelligence that replicates human intelligence and therefore human behavior. And one reason most people made the same mistake was because there was no good model of the brain available from the neuroscientists.
I have on my bookshelf The Formal Mechanics of Mind by Stephen N. Thomas. The inside cover blurb says "The author's intention is to provide an analysis of the nature of the mind, and of our knowledge of it—an analysis that solves or avoids longstanding philosophical problems and that fits well with the results of psychological and neuropsychological investigations of mental phenomena." It is a good book. Published in 1978, it is one of the best I had read before On Intelligence.
On the other hand we have the computer programming AI people. They did some pretty good work, but it is a cautionary tale. Most AI worked with very simple models of objects. AI did best, or appeared to do best, when dealing with certain higher human mental functions, like formal logic. But the brain does not appear to operate with formal logic. Humans invented formal logic. It may (or may not) serve as a basis for a theory of mathematics, as Bertrand Russel, A. N. Whitehead, Ludwig Wittgenstein, and John von Neuman attempted to show.
We will, of course, be using mathematics to understand "real" intelligence. Yet there is almost no math in On Intelligence. Before we can develop a math, or apply an existing branch of mathematics, to this problem we have to understand both the true nature of the problem and how the brain implements the solution, both structurally and dynamically.
I also have on my bookshelf, among other books on neural networks, Parallel Distributed Processing by James L. McClelland et. al. I wrote some computer programs to implement some of the models presented in the book. That was back in the late 1980's. The intelligence problem, then, seemed to be solvable in a piecemeal manner. Difference types of neural networks could be combined and improved until something approaching intelligence emerged. But my own attempts at building an intelligence model went nowhere, and I got involved in trying to save some redwood forests. The forests were clearcut despite my efforts, and I'll take Hawkins' word for it when he said that the neural network model had not made substantial progress in the 1990's. Again, neural network software and hardware became very good at certain things, notably pattern recognition. But while intelligent systems (humans) can recognize patters, apparently pattern recognition is not in itself the basis of intelligence.
Next up: Real Intelligence versus AI
Thursday, June 18, 2009
On Passions and Machine Understanding
Today I started reading On Intelligence by Jeff Hawkins for the third time. The first time I read the book I was impressed. The second time I read the book I believe I understood the more difficult parts. This time I am reading the book with two agendas: commenting on it in-depth, and developing my own ideas, complete with a mathematical framework, for machine understanding.
On page 1 of the prologue Jeff writes:
Hopefully my interest in literature and writing, history, gardening, politics, and art will bring something to this enterprise. If nothing else, I can chronicle the field's development. And I hope to range into any topic that could be helpful in the endeavor, not just neuroscience and computer technology.
On page 1 of the prologue Jeff writes:
I suffer from passion attention deficit disorder (PADD). I am of the same temporal generation as Jeff Hawkins, a child of the space, computer, and nuclear age. I cannot even remember when I first wondered how people can be intelligent and conscious. Such wonder was in the air of the science fictions novels I read as a child. But I have other passions as well. So I have engaged in a variety of pursuits over the decades, only once in a while returning to the contemplation of machine understanding and its related issues of biology, philosophy, and technology.But I have a second passion that predates my interest in computers, one I view as more important. I am crazy about brains. I want to understand how the brain works, not just from a philosophical perspective, not just in a general way, but in a detailed nuts and bolts engineering way.
Hopefully my interest in literature and writing, history, gardening, politics, and art will bring something to this enterprise. If nothing else, I can chronicle the field's development. And I hope to range into any topic that could be helpful in the endeavor, not just neuroscience and computer technology.
Wednesday, April 8, 2009
The Cortex, Dogs, and Changes of Coordinates
I finished my second read-though of On Intelligence by Jeff Hawkins several weeks ago. I got a lot out of this reading, and even was able to follow the details of Chapter 6 which eluded me the first time. Despite good intentions I neglected to write down my much-provoked thoughts until now. I have been spending most of that time indexing the new edition of Windows Internals
by Mark Russinovich et. al., which is interesting in an entirely different way: one sees the products of human intelligence, but it is obvious that there is no danger of a Windows operating system of the current style becoming intelligent or conscious, ever.
In addition to following Jeff's suggestions about noticing how my own mind works, I have been thinking about these matters while watching my dog, Hugo. Let's say he represents mammals in general. He may not have the big old cortex that Jeff admires so much, but he seems to be constantly using his little one to make predictions. Hugo has to make a lot of decisions, and he often freezes in place while making them. Come when called? Maybe, maybe not. A treat in hand might just mean being captured and taken indoors, or left out of a car ride. To make such decisions, I believe, Hugo has to predict outcomes.
Like most dogs, Hugo likes to chase thrown toys. He has come to associate arm movements with probable outcomes. He knows if you are throwing in a particular direction, and begins his run in that direction without waiting for the toy to be released. He expects the toy to appear in front of him. If it does not, he looks back. Will I go ahead and throw the toy past him, or throw it in another direction.
Do this a few times, and he stops dashing as soon as my arm is moving. He waits to see if and where where I actually throw the toy.
I also believe Hugo has a construct of the world very similar to our human construct. He navigates the real world with an ease that can only come from having an internal map of the world. He understands the three-dimensional nature of the world, and in particular that obstacles like a tree or a house can have space behind them.
All this means is that if we want to build cortex like machine designs, we can do a lot without having to recreate a human brain. A car as smart as Hugo could go anywhere it wants on roads without smashing into other cars. This reminds me of science fiction stories where human brains are disembodied and plugged directly into space ships.
So maybe our first goal should be to create animal-brain equivalents and see what can be done with them.
I just happen to be reviewing the branch of mathematics that deals with changes of coordinates. I've always wanted to understand quantum physics and general relativity better, so I occasionally break open a math book, because at some point you must do the math to know what the smart guys are talking about. It is clear to me that human brains, and probably mammal brains too, are pretty good at changes of coordinates. The fact that we construct a mental map of the world and so easily map visual, audio, and tactile coordinates to it and back again is pretty remarkable.
I am again about to review how invariants are treated in tensor mathematics. Maybe that has nothing to do with the ability of the cortex to navigate the world, but it just might. Our brains certainly are good at creating invariant memories and comparing them to real world experiences.
Using the cortex to analyze the cortex: now that is a wonder.
by Mark Russinovich et. al., which is interesting in an entirely different way: one sees the products of human intelligence, but it is obvious that there is no danger of a Windows operating system of the current style becoming intelligent or conscious, ever.In addition to following Jeff's suggestions about noticing how my own mind works, I have been thinking about these matters while watching my dog, Hugo. Let's say he represents mammals in general. He may not have the big old cortex that Jeff admires so much, but he seems to be constantly using his little one to make predictions. Hugo has to make a lot of decisions, and he often freezes in place while making them. Come when called? Maybe, maybe not. A treat in hand might just mean being captured and taken indoors, or left out of a car ride. To make such decisions, I believe, Hugo has to predict outcomes.
Like most dogs, Hugo likes to chase thrown toys. He has come to associate arm movements with probable outcomes. He knows if you are throwing in a particular direction, and begins his run in that direction without waiting for the toy to be released. He expects the toy to appear in front of him. If it does not, he looks back. Will I go ahead and throw the toy past him, or throw it in another direction.
Do this a few times, and he stops dashing as soon as my arm is moving. He waits to see if and where where I actually throw the toy.
I also believe Hugo has a construct of the world very similar to our human construct. He navigates the real world with an ease that can only come from having an internal map of the world. He understands the three-dimensional nature of the world, and in particular that obstacles like a tree or a house can have space behind them.
All this means is that if we want to build cortex like machine designs, we can do a lot without having to recreate a human brain. A car as smart as Hugo could go anywhere it wants on roads without smashing into other cars. This reminds me of science fiction stories where human brains are disembodied and plugged directly into space ships.
So maybe our first goal should be to create animal-brain equivalents and see what can be done with them.
I just happen to be reviewing the branch of mathematics that deals with changes of coordinates. I've always wanted to understand quantum physics and general relativity better, so I occasionally break open a math book, because at some point you must do the math to know what the smart guys are talking about. It is clear to me that human brains, and probably mammal brains too, are pretty good at changes of coordinates. The fact that we construct a mental map of the world and so easily map visual, audio, and tactile coordinates to it and back again is pretty remarkable.
I am again about to review how invariants are treated in tensor mathematics. Maybe that has nothing to do with the ability of the cortex to navigate the world, but it just might. Our brains certainly are good at creating invariant memories and comparing them to real world experiences.
Using the cortex to analyze the cortex: now that is a wonder.
Thursday, January 8, 2009
Welcome to Machine Understanding
I have been interested in the idea of intelligent, understanding, conscious machines (computers and robots) since I was a child. How could a fan of science fiction books and movies not be? At some point I started wondering how the human mind itself could be intelligent, understanding, and conscious. During my life I have periodically returned to that theme.
A relatively recent wake-up call for me was On Intelligence by Jeff Hawkins. I read the book last year and I am re-reading it now. So most of these early blog entries are inspired by statements from the book. I am more than half-way through the book, well into the nuts-and-bolts of Chapter 6, so I will probably both comment on what I am reading and go back to various passages in the front half of the book.
A few years ago my article Indexing Books: Lessons in Language Computations was published by the Key Words, the Bulletin of the American Society for Indexing. I don't want to discourage you from reading the article, but let me sum it up: if a machine can't read with comprehension, it can't create a high-quality index for a text. Most people think using a computer to generate a good index of a text should be an easy accomplishment.
I chose the term "machine understanding" over AI, artificial intelligence, because I agree with Jeff that the AI field has mainly been a failure. Perhaps I should have called it "human understanding," since we don't really have a good understanding of why humans can understand things, exhibit intelligence, and are conscious.
This blog is mainly for me to keep a record of my thoughts, but if anyone else stumbles across it I hope I can introduce them to the ideas of people who work in this fascinating field.
A relatively recent wake-up call for me was On Intelligence by Jeff Hawkins. I read the book last year and I am re-reading it now. So most of these early blog entries are inspired by statements from the book. I am more than half-way through the book, well into the nuts-and-bolts of Chapter 6, so I will probably both comment on what I am reading and go back to various passages in the front half of the book.
A few years ago my article Indexing Books: Lessons in Language Computations was published by the Key Words, the Bulletin of the American Society for Indexing. I don't want to discourage you from reading the article, but let me sum it up: if a machine can't read with comprehension, it can't create a high-quality index for a text. Most people think using a computer to generate a good index of a text should be an easy accomplishment.
I chose the term "machine understanding" over AI, artificial intelligence, because I agree with Jeff that the AI field has mainly been a failure. Perhaps I should have called it "human understanding," since we don't really have a good understanding of why humans can understand things, exhibit intelligence, and are conscious.
This blog is mainly for me to keep a record of my thoughts, but if anyone else stumbles across it I hope I can introduce them to the ideas of people who work in this fascinating field.
Subscribe to:
Posts (Atom)
