Table of Contents:
- a. Pattern Recognizers
- b. Learning, Recognition and Thinking
- c. Top to Bottom Communication in Pattern Recognizers
- d. Positive and Negative Signals in Pattern Recognizers
- e. Establishing New Pattern Recognizers, Laying Down Redundant Ones and Replacing Unused Ones
- f. The Ubiquity and Reach of the Pattern Recognition Scheme
- a. Vector Quantization
- b. Hidden Markov Models (and Hierarchical Hidden Markov Models)
- c. Genetic Algorithms
- d. Training a Computer to Recognize Speech (and More)
- a. An Introduction to Watson
- b. Watson and the Turing Test
- a. The Human Connectome Project
- b. The Blue Brain Project
- c. Educating a Simulated Human Brain
- d. The Exponential Growth of Information-Based Technologies
- a. Beyond Human Intelligence
- b. Merging with Our Machines
When IBM’s Deep Blue defeated humanity’s greatest chess player Gary Kasparov in 1997 it marked a major turning point in the progress of artificial intelligence (AI). A still more impressive turning point in AI was achieved in 2011 when another creation of IBM named Watson defeated Jeopardy! phenoms Ken Jennings and Brad Rutter at their own game. As time marches on and technology advances we can easily envision still more impressive feats coming out of AI. And yet when it comes to the prospect of a computer ever actually matching human intelligence in all of its complexity and intricacy, we may find ourselves skeptical that this could ever be fully achieved. There seems to be a fundamental difference between the way a human mind works and the way even the most sophisticated machine works—a qualitative difference that could never be breached. Famous inventor and futurist Ray Kurzweil begs to differ
To begin with—despite the richness and complexity of human thought—Kurzweil argues that the underlying principles and neuro-networks that are responsible for higher-order thinking are actually relatively simple, and in fact fully replicable. Indeed, for Kurzweil, our most sophisticated AI machines are already beginning to employ the sample principles and are mimicking the same neuro-structures that are present in the human brain.
Beginning with the brain, Kurzweil argues that recent advances in neuroscience indicate that the neocortex (whence our higher-level thinking comes) operates according to a sophisticated (though relatively straightforward) pattern recognition scheme. This pattern recognition scheme is hierarchical in nature, such that lower-level patterns representing discrete bits of input (coming in from the surrounding environment) combine to trigger higher-level patterns that represent more general categories that are more abstract in nature. The hierarchical structure is innate, but the specific categories and meta-categories are filled in by way of learning. Also, the direction of information travel is not only from the bottom up, but also from the top down, such that the activation of higher-order patterns can trigger lower-order ones, and there is feedback between the various levels. (The theory that sees the brain operating in this way is referred to as the Pattern Recognition Theory of the Mind or PRTM).
As Kurzweil points out, this pattern recognition scheme is actually remarkably similar to the technology that our most sophisticated AI machines are already using. Indeed, not only are these machines designed to process information in a hierarchical way (just as our brain is), but machines such as Watson (and even Siri, the voice recognition software available on the iPhone), are structured in such a way that they are capable of learning from the environment. For example, Watson was able to modify its software based on the information it gathered from reading the entire Wikipedia file. (The technology that these machines are using is known as the hierarchical hidden Markov model or HHMM, and Kurzweil was himself a part of developing this technology in the 1980’s and 1990’s.)
Given that our AI machines are now running according to the same principles as our brains, and given the exponential rate at which all information-based technologies advance, Kurzweil predicts a time when computers will in fact be capable of matching human thought—right down to having such features as consciousness, identity and free will (Kurzweil’s specific prediction here is that this will occur by the year 2029).
What’s more, because computer technology does not have some of the limitations inherent in biological systems, Kurzweil predicts a time when computers will even vastly outstrip human capabilities. Of course, since we use our tools as a natural extension of ourselves (sometimes figuratively, but also literally), this will also be a time when our own capabilities will vastly outstrip our capabilities of today. Ultimately, Kurzweil thinks, we will simply use the markedly superior computer technology to replace our outdated neurochemistry (as we now replace a limb with a prosthetic), and thus fully merge with our machines (a state that Kurzweil refers to as the singularity). In the end, the author maintains, the singularity will stretch to all corners of the universe. This is the argument that Kurzweil makes in his new book How to Create a Mind: The Secret of Human Thought Revealed.
Here is Ray Kurzweil introducing and discussing his new book:
What follows is a full executive summary of How to Create a Mind: The Secret of Human Thought Revealed by Ray Kurzweil.
PART I: HUMAN INTELLIGENCE AND THE NEOCORTEX
Section 1: An Introduction to Intelligence, the Neocortex and Hierarchical Thinking
1. Intelligence, the Neocortex and Hierarchical Thinking
Cognitive intelligence includes many aspects (from object recognition, to memory, to abstract thinking, to imagination and creativity among others), but when we strip it down to its most basic level we may think of intelligence quite simply as the ability to process, store and manipulate information (loc. 117). In functional terms, it is what allows an individual to learn from their environment, to predict what will come next, and to adjust accordingly (loc. 1152). The human species is far above all others when it comes to intelligence, of course; but it is not the case that we enjoy a total monopoly here. Indeed, many other species show some ability to store and manipulate information, and other mammals in particular even demonstrate a fair bit of sophistication in this regard—to the point where they too are able to learn from and adjust to their environment in complex ways.
The reason why mammals (including humans) excel in terms of intelligence has to do with a very special brain structure that is unique to our class and which is called the neocortex (loc. 121). The neocortex is a relatively recent evolutionary add-on that sits atop the older, more primitive structures of the brain (loc. 559) (As shown below).
The neocortex is responsible for virtually all of our higher-level thinking, and, as Kurzweil explains, it is particularly important in intelligence because it allows mammals to think hierarchically (loc. 121, 558). Essentially, hierarchical thinking allows us to understand individual things as part of a larger superstructure, and to understand these larger superstructures as part of still larger superstructures, all of the way up to higher and higher levels of abstraction. Here is Kurzweil to explain: “the mammalian brain has a distinct aptitude not found in any other class of animal. We are capable of hierarchical thinking, of understanding a structure composed of diverse elements arranged in a pattern, representing that arrangement with a symbol, and then using that symbol as an element in a yet more elaborate configuration. This capability takes place in a brain structure called the neocortex” (loc. 124). Elsewhere, Kurzweil writes that “we do know the neocortex is responsible for our ability to deal with patterns of information and to do so in a hierarchical fashion. Animals without a neocortex (basically nonmammals) are incapable of understanding hierarchies” (loc. 557).
Hierarchical thinking is particularly important and powerful for two reasons. To begin with, as Kurzweil points out, nature itself is in many ways arranged hierarchically (loc. 147, 558). For example, “trees contain branches; branches contain leaves; leaves contain veins [etc.]” (loc. 147). Thinking hierarchically therefore allows us an especially rich and accurate representation of the world around us—which we can then exploit to advance our ends (loc. 557). Second, as we go further up the levels of the hierarchies, our thoughts become more and more abstract and complex. And as our thinking becomes increasingly complex it affords us an increasing range of mental capabilities with which to understand and react to the environment (loc. 125) (more on these capabilities in a moment).
In this sense, then, we can well see why the neocortex and its hierarchical thinking evolved in the first place; for it allows an individual to be very flexible and to adapt to its environment very quickly, which ultimately helps it to survive and reproduce. As Kurzweil explains, “once biological evolution stumbled on a neural mechanism capable of hierarchical learning, it found it to be immensely useful for evolution’s one objective, which is survival… The salient survival advantage of the neocortex was that it could learn in a matter of days” (loc. 1150).
Being able to learn quickly is a distinct advantage no matter what the circumstances are, of course, but it is especially valuable in times when the environment is changing rapidly. It is for this reason, scientists believe, that mammals were able to survive and flourish during the Cretaceous period, while the dinosaurs (who did not enjoy this advantage) were reduced to extinction (loc. 1156).
Now, while all mammals have a neocortex, they differ to a significant degree in just how large and developed it is. For example, “in rodents it is the size of a postage stamp and is smooth” (loc. 563). In primates, on the other hand, the neocortex is a fair bit larger, and is also “folded over the top of the rest of the brain with deep ridges, grooves, and wrinkles to increase its surface area” (loc. 563). In our species the neocortex is particularly large, as it “constitutes the bulk of the human brain, accounting for 80 percent of its weight” (loc. 563).
As the neocortex increases in size and complexity it becomes capable of achieving higher and higher levels of abstraction. These higher levels of abstraction lead not just to quantitative improvements in intelligence, but to qualitative differences that translate into new capabilities. For example, the size of the human neocortex affords us two important capabilities not found anywhere else in the animal kingdom. The first is the ability to build new ideas on top of old ones (thereby ever-increasing their complexity); and the second is the ability to use our scaffolded ideas to build increasingly complex tools (loc. 129). As Kurzweil explains, “through an unending recursive process we are capable of building ideas that are ever more complex. We call this vast array of recursively linked ideas knowledge. Only Homo sapiens have a knowledge base that itself evolves, grows exponentially, and is passed down from one generation to another. Our brains gave rise to yet another level of abstraction, in that we have used the intelligence of our brains plus one other enabling factor, an opposable appendage—the thumb—to manipulate the environment to build tools. These tools represented a new form of evolution, as neurology gave rise to technology” (loc. 129).
In addition to these impressive capabilities, the neocortex is (as mentioned above) also responsible for virtually all of our higher-level thinking. As the author explains, the human neocortex “is responsible for sensory perception, recognition of everything from visual objects to abstract concepts, controlling movement, reasoning from spatial orientation to rational thought, and language—basically, what we regard as ‘thinking’” (loc. 559). These capabilities are very diverse and wide ranging; but again, Kurzweil’s contention is that they are all built off of the same capacity, and that is the capacity to process information hierarchically (loc. 173, 204-208, 231). Essentially, then, while human intelligence may appear to be extraordinarily complex, it can actually be reduced to a very simple and straightforward ability adapted to many different types of phenomenon, and in many different ways (loc. 208, 231).
Section 2. The Human Neocortex: The Structure of the Neocortex and the Pattern Recognition Theory of Mind
2. The Structure of the Neocortex: Uniform and Hierarchical
The idea that all of the many wonders of the neocortex can be reduced to a single type of thought process (hierarchical thinking) is lent credence by the structure of the neocortex itself. This proves to be the case because the neocortex is remarkably uniform in structure throughout, and this structure is itself hierarchical in nature.
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