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AI has, in the past, failed repeatedly. The shadow cast by this failure falls over all proposals for new AI projects. The question is always asked: "Why won't your project fail, like all the other projects? Why did the previous projects fail? Does your theory of general intelligence explain the previous failures while predicting success for your own efforts?" Actually, anyone can explain away previous failures and predict success; all you have to do is assert that some particular new characteristic is the One Great Idea, necessary and sufficient to intelligence. The real question is whether a new approach to AI makes the failure of previous efforts seem massively inevitable, the predictable result of historical factors; whether the approach provides a theory of previous failures that is satisfyingly obvious in retrospect, makes earlier errors look like natural mistakes that any growing civilization might make, and thus "swallows" the historical failures in a new theory which leaves no dangling anxieties.
Okay. I won't go quite that far. Still, AI has an embarassing tendency to predict success where none materializes, to make mountains out of molehills, and to assert that some simpleminded pattern of suggestively-named LISP tokens completely explains some incredibly high-level thought process. Why?
Consider the symbol your mind contains for 'light bulb'. In your mind, the sounds of the spoken words "light bulb" are reconstructed in your auditory cortex. A picture of a light bulb is loaded into your visual cortex. Furthermore, the auditory and visual cortices are far more complex, and intelligent, than the algorithm your computer uses to play sounds and MPEG files. Your auditory cortex has evolved specifically to process incoming speech sounds, with better fineness and resolution than it displays on other auditory tasks. Your visual cortex does not simply contain a 2D pixel array. The visual cortex has specialized processes that extract David Marr's "two-and-a-half dimensional world" - edge detection, corner interpretation, surfaces, shading, movement - and processes that extract from this a model of 3D objects in a 3D world. "About 50 percent of the cerebral cortex of primates is devoted exclusively to visual processing, and the estimated territory for humans is nearly comparable." (MITECS, "Mid-Level Vision".)
In the semantic net or Physical Symbol System of classical AI, a light bulb would be represented by an atomic LISP token named light-bulb.
| NOTE: | I say "LISP tokens", not "LISP symbols", despite convention and accepted usage. Calling the lowest level of the system "symbols" is a horrifically bad habit. |
Some of the problem may be explained by history; back when AI was being invented, in the 1950s and 1960s, researchers had tiny little machines that modern pocket calculators would sneer at. These early researchers chose to believe they could succeed with "symbols" composed of small LISP structures, cognitive "processes" with the complexity of one subroutine in a modern class library. They were wrong, but the need to believe produced approaches and paradigms that sank AI for decades.
Previous AI has been conducted under the Physicist's Paradigm. The development of physics over the past few centuries - at least, the dramatic, stereotypical part - has been characterized by the discovery of simple equations that neatly account for complex phenomena. In physics, the task is finding a single bright idea that explains everything. Newton took a single assumption (masses attract each other with a force equal to the product of the masses divided by the square of the distance) and churned through some calculus to show that, if an apple falls towards the ground at a constant acceleration, then this explains why planets move in elliptical orbits. The search for a similar fits-on-a-T-Shirt unifying principle to fully explain a brain with hundreds of cytoarchitecturally distinct areas has wreaked havoc on AI.
"Heuristics are compiled hindsight; they are judgemental rules which, if only we'd had them earlier, would have enabled us to reach our present state of achievement more rapidly." (Douglas Lenat, 1981.) The heuristic learned from past failures of AI might be titled "Necessary, But Not Sufficient". Whenever neural networks are mentioned in press releases, the blurb always includes the phrase "neural networks, which use the same parallel architecture found in the human brain". Of course, the "neurons" in neural networks are usually nothing remotely like biological neurons. But the main thing that gets overlooked is that it would be equally true (not very) to say that neural networks use the same parallel architecture found in an earthworm's brain. Regardless of whether neural networks are Necessary, they are certainly Not Sufficient. The human brain requires millions of years of evolution, thousands of modules, hundreds of thousands of adaptations, on top of the simple bright idea of "Hey, let's build a neural network!"
The Physicist's Paradigm lends itself easily to our need for drama. One great principle, one bold new idea, comes along to overthrow the false gods of the old religion... and set up a new bunch of false gods. As always when trying to prove a desired result from a flawed premise, the simplest path involves the Laws of Similarity and Contagion. For example, the "neurons" in neural networks involve associative links of activation. Therefore, the extremely subtle and high-level associative links of human concepts must be explained by this low-level property. Similarly, any instance of human deduction which can be written down (after the fact) as a syllogism must be explained by the blind operation of a ten-line-of-code process - even if the human thoughts blatantly involve a rich visualization of the subject matter, with the results yielded by direct examination of the visualization rather than formal deductive reasoning.
In AI, the one great simple idea usually operates on a low level, in accordance with the Physicist's Paradigm. Reasoning from similarity of surface properties is used to assert that high-level cognitive phenomena are explained by the low-level phenomenon, which (it is claimed) is both Necessary and Sufficient. This cognitive structure is a full-blown fallacy; it contains the social drama (one brilliant idea, new against old) and the rationalization (reasoning by similarity of surface properties, sympathetic magic) necessary to bear any amount of emotional weight. And that's how AI research goes wrong.
There are several ways to avoid making this class of mistake. One is to have the words "Necessary, But Not Sufficient" tattooed on your forehead. One is an intuition of causal analysis that says "This cause does not have sufficient complexity to explain this effect." One is to be instinctively wary of attempts to implement cognition on the token level. (One is learning enough evolutionary psychology to recognize and counter ideology-based thoughts directly, but that's moving off-topic...)
One is introspection. Human introspection currently has a bad reputation in cognitive science, looked on as untrustworthy, unscientific, and easy to abuse. This is totally true. Still, you can't build a mind without a working model. It is necessary to know, intuitively, that classical-AI propositional logic - syllogisms, property inheritance, et cetera - is inadequate to explain your deduction that dropping an anvil on a car will break it. You should be able to see, introspectively, that there's more than that going on. You can visualize an anvil smashing into your car's hood, the metal crumpling, and the windshield shattering. (1). Clearly visible is vastly more mental material, more cognitive "stuff", than classical-AI propositional logic involves.
The revolt against the Physicist's Paradigm can be formalized as the Law of Pragmatism:
| The Law of Pragmatism |
| Any form of cognition which can be mathematically formalized, or which has a provably correct implementation, is too simple to contribute materially to intelligence. |
The key words are "contribute materially". An architecture can be necessary to thought without accounting for the substance of thought. The Law of Pragmatism says that if a neural network's rules are simple enough to be formalized mathematically, than the substance of any intelligent answers produced by that network will be attributable to the specific pattern of weightings. If the pattern of weightings is created by a mathematically formalizable learning method, then the substance of intelligence will lie, not in the learning method, but in the intricate pattern of regularities within the training instances.
We can't be certain that the Law of Pragmatism will hold in the future, but it's definitely a heuristic in the Lenatian sense; if only we'd known it in the 1950s, so much error could have been avoided. The Law of Pragmatism is one of the tools used to determine whether an idea is Necessary, But Not Sufficient. (3).
GISAI proposes a mind which contains modules vaguely analogous to human sensory modalities (auditory cortex, visual cortex, etc.). This does not mean that you can design any old system which can be described as "containing modular sensory modalities" and then dash off a press release about how your company is building an AI containing modular sensory modalities. That's the trophy mentality I was talking about earlier. A modular, modality-based system is Necessary, But Not Sufficient; it is also necessary to have the right modules, in the right sensory modalities, using the right representation and the right intuitions to process the right base of experience to produce the right concepts that support the right thoughts within the right larger architecture.
When you think of a light bulb, the syllables and phonemes of "light bulb" are loaded into your auditory cortex; if you're a visual person, a generic picture of a light bulb - the default exemplar - appears in your visual cortex. Let's suppose that some AI has reasonably sophisticated analogues of the auditory cortex and visual cortex, capable of perceiving higher-level features as well as the raw binary data. This is clearly necessary; is it sufficient to understand light bulbs in the same way as a human?
No. Not even close. When you hear the phrase "triangular light bulb", you visualize a triangular light bulb.
| NOTE: | Please halt, close your eyes, and visualize a triangular light bulb. Please? Pretty please with sugar on top? |
How do these two symbols combine? You know that light bulbs are fragile; you have a built-in comprehension of real-world physics - sometimes called "naive" physics - that enables you to understand fragility. You understand that the bulb and the filament are made of different materials; you can somehow attribute non-visual properties to pieces of the three-dimensional shape hanging in your visual cortex. If you try to design a triangular light bulb, you'll design a flourescent triangular loop, or a pyramid-shaped incandescent bulb; in either case, unlike the default visualization of "triangle", the result will not have sharp edges. You know that sharp edges, on glass, will cut the hand that holds it.
Look at all that! It requires a temporal, four-dimensional understanding of the light bulb. It requires an appreciation, a set of intuitions, for cause and effect. It requires that you be capable of spotting a problem - a conflict with a goal - which requires means for representing conflicts, and cognitive reflexes derived from a goal system.
Look at yourself "looking at all that". It requires introspection, reflection, self-perception. It requires an entire self-sensory modality - representations, intuitions, cached reflexes, expectations - focused on the mind doing the thinking.
For you to read this paragraph, and think about it, requires a stream of consciousness. For you to think about light bulbs implies that you codified your past experiences of actual light bulbs into the representation used by your long-term memory. The visual image of the light bulb, appearing in your visual cortex, implies that a default exemplar for "light bulb" was abstracted from experience, stored under the symbol for "light bulb", and triggered by that symbol's auditory tag of 'light bulb'. And this exemplar can even be combined with the learned symbol for "triangle". You have formed an adjective, "triangular", consisting of characteristics which can be applied to modify the visual and design substance of the light-bulb concept. For you to visualize a light-bulb smashing, with an accompanying tinkling noise, requires synchronization of recollection and reconstruction across multiple sensory modalities.
I've mentioned many features in the last paragraphs; none of them are emergent. None of them will magically pop into existence on the high level "if only the simple low-level equation can be found". In a human, these features are complex functional adaptations, generated by millions of years of evolution. For an AI, that means you sit down and write the code; that you change the design, or add design elements (special-purpose low-level code that directly implements a high-level case is usually a Bad Thing), specifically to yield the needed result.
In short, the design in GISAI is simply far larger, as a system architecture, than any design which has been previously attempted. It's large enough to resemble systems of the complexity described in the 471 articles in The MIT Encyclopedia of the Cognitive Sciences. (4). You'll appreciate this better after reading the rest of the document, of course, but when you have done so, I expect that seed AI will look too different from past failures for one to reflect on the other. Fish and fowl, apples and oranges, elephants and typewriters. There is still the possibility that any given seed AI project will fail, or even that seed AI itself will fail - but if so, it will fail for different reasons.
Introspection, like evolutionary reasoning, is an incredibly powerful tool. Like evolutionary reasoning, it takes practice, talent, and self-awareness to use it on a professional level - to reliably distinguish between post facto and "pre facto" (2) reasoning, or between original thought and cached thought.
Some people, maybe even a majority of readers, may not have needed to visualize the car smashing before deducing that it would break - or, rather, accepting that the sentence "Dropping an anvil on a car will break it" is true - or, rather, continuing to read without noticing that the sentence was false.
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