On 2/19/07, Bo Morgan <[EMAIL PROTECTED]> wrote:
On Mon, 19 Feb 2007, John Scanlon wrote: ) Is there anyone out there who has a sense that most of the work being ) done in AI is still following the same track that has failed for fifty ) years now? The focus on logic as thought, or neural nets as the ) bottom-up, brain-imitating solution just isn't getting anywhere? It's ) the same thing, and it's never getting anywhere. Yes, they are mostly building robots and trying to pick up blocks or catch balls. Visual perception and motor control for solving this task was first shown in a limited context in the 1960s. You are correct that the bottom up approach is not a theory driven approach. People talk about mystical words, such as Emergence or Complexity, in order to explain how their very simple model of mind can ultimately think like a human. Top-down design of an A.I. requires a theory of what abstract thought processes do. ) The missing component is thought. What is thought, and how do human ) beings think? There is no reason that thought cannot be implemented in ) a sufficiently powerful computing machine -- the problem is how to ) implement it. Right, there are many theories of how to implement an AI. I wouldn't worry too much about trying to define Thought. It has different definitions depending on the different problem solving contexts that it is used. If you focus on making a machine solve problems, then you might see some part of the machine you build will resemble your many uses for the term Thought. ) Logical deduction or inference is not thought. It is mechanical symbol ) manipulation that can can be programmed into any scientific pocket ) calculator. Logical deduction is only one way to think. As you say, there are many other ways to think. Some of these are simple reactive processes, while others are more deliberative and form multistep plans, while still others are reflective and react to problems in actual planning and inference processes. ) Human intelligence is based on animal intelligence. No. Human intelligence has evolved from animal intelligence. Human intelligence is not necessarily a simple subsumption of animal intelligence. ) The world is continuous, spatiotemporal, and non-descrete, and simply is ) not describable in logical terms. A true AI system has to model the ) world in the same way -- spatiotemporal sensorimotor maps. Animal ) intelligence. Logical parts of the world are describable in logical terms. We think in many different ways. Each of these ways uses different representations of the world. We have many specific solutions to specific types of problem solving, but to make a general problem solver we need ways to map these representations from one specific problem solver to another. This allows alternatives to pursue when a specific problem solver gets stuck. This type of robust problem solving requires reasoning by analogy.
I hope my ignorance does not bother this list too much. Regarding what or what may not be done through logical inference and other expressive enough symbolic approaches; given unlimited resources would it not be possible to implement an UTM with at most a finite overhead which in turn yields that any algorithm running on an UTM could also run on expressive enough symbolic systems, whether they "learn" or not? I do not argue that it is not inefficient, both for running and implementation speed. It's even so that the logical inference in such a case may be reduced entirely and proven to be more efficiently obviously, than to implement the system direcly on certain systems. I do not think however that such a strict and not well-formulated position is rationally justified since it's not clear (at least not to me) that the logical inference may be efficiently reduced for every algorithm expressed in the logical language. Just rambling and unrelated but perhaps the brain's operations do not even allow for UTMs since they are not so clear and there might not be appropriate transformations and if assume the Turing-Church thesis we might find that there are problems that artificial components may solve that humans cannot even given unlimited resources. Perhaps not very likely since we can simulate the process of an UTM by hand and even the errors may be corrected given enough time. ) Ask some questions, and I'll tell you what I think.
People always have a lot to say, but what we need more of are working algorithms and demonstrations of robust problem solving. ----- 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
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