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.
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