I don't think there is a simple answer to this problem. We observe very complex behavior in much simpler organisms that lack long term memory or the ability to learn. For example, bees are born knowing how to fly, build hives, gather food, and communicate its location.
The complexity of inductive bias is bounded by the complexity of your DNA, about 6 x 10^9 bits. This is probably too high by a few orders of magnitude, just as the number of synapses overestimates the complexity of AGI. Nevertheless, we risk repeating the error of GOFAI. Early AI researchers were led astray by the successes of explicitly coding knowledge into toy systems. Now we know to use statistical and machine learning techniques, but we may still be led astray by oversimplified models of inductive bias. Certain aspects of the cerebral cortex are highly uniform, which suggests a simple model. But the rest of the brain has a complex structure that is poorly understood. AGI might still be harder than we think. It has happened before. -- Matt Mahoney, [EMAIL PROTECTED] ----- Original Message ---- From: Ben Goertzel <[EMAIL PROTECTED]> To: [email protected] Sent: Tuesday, February 13, 2007 9:28:53 PM Subject: [agi] Enumeration of useful "genetic biases" for AGI Hi, In a recent offlist email dialogue with an AI researcher, he made the following suggestion regarding the "inductive bias" that DNA supplies to the human brain to aid it in learning: ***** What is encoded in the DNA may include a starting ontology (as proposed, with exasperating vaguess, by developmental psychologists, though much more complex than anything they have thought of) but the more important thing is an implicit set of constraints on ontologies that can be discovered by systematic 'scientific' investigation. So it might not work in an arbitrary universe, including some simulated universes,e.g. 'tileworld' universes. One such constraint (as Kant pointed out in 1780) is the assumption that everything physical happens in 3-D space and time. Another is the requirement for causal determinism (for most processes). There may also be constraints on kinds of information-processing entities that can be learnt about in the environment, e.g. other humans, other animals, dead-ancestors, gods, spirits, computer games, .... The major, substantive, ontology extensions have to happen in (partially ordered) stages, each stage building on previous stages, and brain development is staggered accordingly. ****** My response to him was that these "genetic biases" are indeed encoded in the Novamente design, but in a somewhat unsystematic and scattered way. For instance, in the Novamente system, -- the restriction to 3D space is implicit in the set of elementary predicates and procedures supplied to the system for preprocessing perceptual data on its way to abstract cognition -- the bias toward causal determinism is implicit in an inference control mechanism that specifically tries to build "PredictiveAttractionLink" relationships that embody likely causal relationships etc. I have actually never gone through the design with an eye towards identifying exactly how each important "genetic bias" of cognition is encoded in the system. However, this would be an interesting and worthwhile thing to do. Toward that end, it would be interesting to have a systematic list somewhere of the genetic biases that are thought to be most important for structuring human cognition. Does anyone know of a well-thought-out list of this sort. Of course I could make one by surveying the cognitive psych literature, but why reinvent the wheel? -- Ben G ----- This list is sponsored by AGIRI: http://www.agiri.org/email To unsubscribe or change your options, please go to: http://v2.listbox.com/member/?list_id=303 ----- This list is sponsored by AGIRI: http://www.agiri.org/email To unsubscribe or change your options, please go to: http://v2.listbox.com/member/?list_id=303
