It's Almost Here

Announcement

An effective and affordable treatment for amyotrophic lateral sclerosis (Lou Gehrig's disease) is right around the corner. A complete cure is not out of the question. That's all I can say for now.

Excuses, Excuses

One of the reasons that I cannot wrap my head around artificial intelligence at this time is that I am currently heavily involved in trying to find a cure or an effective treatment for my wife who suffers from ALS, also known as Lou Gehrig's disease. The good news is that I am now convinced that we are on the verge of a genuine breakthrough, not just for ALS but for other neurodegenerative diseases as well. The bad news is that AI will have to wait a little longer. One thing at a time. Hang in there.

Darn It!

What the Hell Is Wrong with Me?

The last couple of weeks, I've been feeling like I'm in a daze. I can't bring myself to make any important decision, as if something alien had taken a hold of me and slowed my brain to a crawl. I've been meaning to post the last two installments of my article, Secrets of the Holy Grail, but I can't bring myself to do it. It feels like I'm not in charge of my own free will, as if I have been drugged or something. My ears are ringing all the time. I don't know what's wrong with me but I can't stand it. If I were superstitious, I would say that someone cast an evil spell on me. I need more time to get over this.

The Perfect Brain: Another Nail in the Coffin of the Bayesian Brain

The Impending Crash of the Bayesian Bandwagon

Last August, I wrote a series of posts titled, The Myth of the Bayesian Brain. I argued against the prevailing notion in the AI community that the brain uses some kind of Bayesian statistics to make decisions. I argued that, internally, the brain always assumes that the world is perfect even if its sensory space is inherently noisy. The brain does this bit of magic by filling in any missing information and ignoring irrelevant noise. This cleansing process is essential to reasoning and planning. At least one other researcher (to my knowledge), computer scientist Judea Pearl, has been saying the same thing. Well, a story out of Princeton University points to a new study that corroborates what I have been saying. Essentially, Princeton University researchers found that, when we make an error, the brain's decision making system is not at fault. The system is flawless. The fault is invariably due to faulty sensory information. Here's an excerpt:

Previous measurements of brain neurons have indicated that brain functions are inherently noisy. The Princeton research, however, separated sensory inputs from the internal mental process to show that the former can be noisy while the latter is remarkably reliable, said senior investigator Carlos Brody, a Princeton associate professor of molecular biology and the Princeton Neuroscience Institute (PNI), and a Howard Hughes Medical Institute Investigator.

"To our great surprise, the internal mental process was perfectly noiseless. All of the imperfections came from noise in the sensory processes," Brody said.

The "great surprise" of Carlos Brody and his team is understandable, given their training within the current Bayesian paradigm. But it's never too late to jump off that silly wagon. I am not one to laugh and say, "I told you so". But I did, didn't I?

Coming Soon

I have decided to publish the last two installments of my article, Secrets of the Holy Grail, in the next several days or so. Hang in there.

See Also:

The Second Great AI Red Herring Chase
The Myth of the Bayesian Brain

Soul Searching Again

I apologize for the delay in posting Part III and IV of The Secrets of the Holy Grail series. I am seriously debating whether or not I should continue to publish this stuff at this time. Given the dangerous world that we live in, true machine intelligence is not something to be taken lightly.

When it comes out, AI will change the world drastically in a very short order, for good and bad. Scientific and technological know-how will no longer be concentrated within the so-called developed nations. Knowledge is power. There is no doubt that various groups will immediately use AI to gain a powerful economic and military advantage over others. This shit will get out of control real fast and this is why I am paranoid. We are either on the edge of a precipice or on the border of paradise. I hate it. I really do.

I am not claiming that I understand it all but I understand enough of it to know that the rest will mostly be about dotting the i's and crossing the t's. I also realize that this knowledge will come out sooner or later, with or without me. I just need a little more time to think about how I should reveal what I have found so far. I swear, sometimes I wish I was living in the stone age. Hang in there.

Secrets of the Holy Grail, Part II

Part I, II, III, IV

Abstract

In Part I, I gave a brief description of the brain's memory architecture. In this post, I explain how the brain does pattern learning and catches "thieves" in its sleep.

Winner Takes All vs the Bayesian Brain

Although it feels like I am preaching in the wilderness, I have been railing against the use of Bayesian statistics in machine learning for some time now. The idea that the brain reasons or recognizes objects by juggling statistics is ridiculous when you think about it. The brain actually abhors uncertainty and goes to great lengths to eliminate it. As computer scientist Judea Pearl put it not too long ago, "people are not probability thinkers but cause-effect thinkers."

Even though it is continually bombarded with noisy and incomplete sensory data, internally, the brain is strictly deterministic. It uses a winner-take-all mechanism in which sequences compete to fire and the winner is the one with the most hits. Once a winner is determined, the other competitors are immediately suppressed. The winning sequence is assumed to be perfect. To repeat, the brain is not a probability thinker. It learns every pattern and sequence that it can learn, anything that is more than mere random chance. Then it lets them compete for attention. Read The Myth of the Bayesian Brain for more on this topic.

Pattern Learning

The job of the pattern learner is to discover as many unique patterns in the sensory space as possible. Pattern learning consists of randomly connecting sensory inputs to pattern neurons and checking to see if they fire concurrently. However, keep in mind that a pattern neuron will fire when a majority of its input signals arrive concurrently.

The pattern learning rule can be stated thus:

In order to become permanent, an input connection must contribute to the firing of its pattern neuron at least X times in a row.

X is a number that depends on the desired learning speed of the system. In the human brain, X equals 10. With this rule, the brain can quickly find patterns in the sensory space. It is based on the observation that sensory signals are not always imperfect. Every once in a while, even if for a brief interval, they are perfect. This perfection is captured in pattern memory.

Catching Thieves

The pattern learning rule is simple and powerful but it suffers from a major flaw: it imposes no restrictions or boundaries on the growth of a pattern. Without proper boundaries, patterns become more and more complex and the simpler ones eventually disappear, crippling the system. Obviously, we need a way to prevent a pattern neuron from acquiring more complexity than its level within the hierarchy requires. The solution to the boundary problem consists of enforcing the boundary rule:

A branch may not have duplicate sensory input connections.

For example, in the illustration below, pattern neuron A behaves as if it were connected directly to sensors a, b, c, d, and e.

Suppose sensor c was connected (dotted red line) to pattern neuron C. This would mean that pattern neuron A would have two duplicate inputs from sensor c, one via C and the other via D. This is forbidden by the boundary rule. The younger of the two is called a thief because it took something that does not belong to it. By purging thieves from all branches and levels of the pattern hierarchy, the growth of every pattern is automatically limited to a degree of complexity commensurate with its level in the hierarchy. The power of the boundary rule is betrayed by its simplicity. It prevents runaway pattern growth while facilitating the discovery of every possible unique pattern in the sensory space. The boundary rule is indispensable to pattern learning and works for any type of sensory patterns, not just visual.

Note: As far as I know, the boundary rule is not in any books. Please make copies of this page on your computer. This is intended to serve as "prior art" in the public domain, i.e., it cannot be patented. :-D

The brain cannot eliminate thieves while it is awake because it must test fire all untested connections. This could cause problems during waking hours. An intelligent machine, by contrast, is not so limited. During learning, a computer program can examine a branch on the fly to see if a new connection is a thief.

Coming up

In Part III, I will show how learning occurs in sequence memory and how to catch a liar. Coming soon.

See Also

The Myth of the Bayesian Brain
The Holy Grail of Robotics
Raiders of the Holy Grail
Jeff Hawkins Is Close to Something Big

Secrets of the Holy Grail, Part I

Part I, II, III, IV

Abstract

According to brain and machine learning expert, Jeff Hawkins, goal-directed behavior is the holy grail of intelligence and robotics. He believes that the best way to solve the intelligence puzzle is to emulate the brain. Hawkins is right, of course. There is no question that we can learn everything we need to know about intelligence by studying the brain. The only problem is that some of the answers are so deeply buried in an ocean of complexity that a hundred years of painstaking research could not uncover them. In this multi-part article, I will describe some of the amazing secrets of the brain before revealing the surprising source of my knowledge (no, it's not the brain, sorry).

Liars and Thieves

Let me come right out with a bold statement: nobody can rightfully claim to understand the brain’s perceptual learning mechanism without also knowing exactly what the brain does during sleep and why. Sure, we know what neuroscientists and psychologists have told us, that the brain uses sleep to consolidate recent memories, whatever that means. Unfortunately, that is pretty much the extent of their knowledge on the subject. Hawkins doesn't know either, although he should. That is, assuming he wants to stay in this business. It turns out that the brain performs at least two essential functions while we are asleep: it purges liars (bad predictors) from sequence memory and eliminates thieves (redundant connections) from pattern memory.

Note: I will explain my choice of the liars and thieves metaphors in an upcoming post.

Without these frequent purges, the brain would get confused and eventually stop working. But why is that, you ask? That, my astute and inquisitive friend, is one of the secrets of the holy grail, which is why you must read the rest of the article. But before I can answer your question, I must first say a few things about how memory is organized.

Pattern Memory

I did not always think so but the brain has two types of hierarchical memories: pattern memory and sequence memory. My original objection was that a pattern hierarchy cannot do invariant object recognition. That was before I realized that it doesn't have to; that's the purpose of sequence memory. Pattern memory is a hierarchy of pattern detectors that send their output signals directly to sequence memory. A pattern is a transient group of sensory signals that occur together often and a pattern detector or neuron is best viewed as a complex event sensor. Pattern detectors (red-filled circles) can have an indefinite number of inputs.

A hierarchy makes sense for several reasons. First, it gives us a very compact storage structure because of the inherent reuse of lower level patterns. Second, and just as importantly, it provides a way to automatically limit the boundaries of patterns. This, in turn, makes it possible to discover all possible patterns in the sensory space. I'll have more to say on this later.

A peculiar but critical aspect of pattern memory is that the time it takes an incoming signal to propagate through the hierarchy must be very fast. The cortex uses electric synapses to do this. The end result is that signal propagation through the hierarchy appears instantaneous to the rest of the brain. And the reason for this has to do with timing integrity. For instance, if a high level neuron (A) fires, all the pattern neurons in the branch below A in the hierarchy are assumed to have fired concurrently with A.

Sequence Memory

It would be accurate to say that sequence memory is the seat of intelligence. It is used for many functions such as recollection, prediction, attention, invariant object recognition, reasoning, goal-directed motor behavior and adaptation. Sequence memory contains sequences of patterns organized hierarchically just like pattern memory. Note that, in the diagram below, the pattern hierarchy is shown as a single flat layer (red circles). This is because sequence memory (yellow circles) does not see pattern memory as a hierarchy. That is to say, the system must act as if sensory signals could travel through pattern hierarchy instantaneously. Otherwise, pattern detection timing would be askew.

One of the more interesting design characteristics of sequence memory is that a sequence detector has a maximum of seven nodes or inputs. Why seven? For one, it explains the capacity of what psychologists call short-term or working memory. Second, it is a compromise that aims to minimize energy usage while maximizing the breadth of focus. As it turns out, the brain can focus on only one branch of sequence memory at a time. A branch should be seen as a grouping mechanism that represents a single object or concept. No need to look any further. The branch is the mechanism of both attention and invariant object recognition.

What is even more interesting from the point of view of invariant object recognition is that multiple sequences may and do share patterns. In fact, every complex recognized object in memory consists of multiple, tightly intertwined sequences. This will become clearer later.

Coming up

In Part II, I will explain how learning occurs in pattern memory and how to catch a thief.

See Also

The Holy Grail of Robotics
Raiders of the Holy Grail
Jeff Hawkins Is Close to Something Big