Relay-Version: version B 2.10 5/3/83; site utzoo.UUCP Posting-Version: version B 2.10.3 4.3bsd-beta 6/6/85; site ucbvax.BERKELEY.EDU Path: utzoo!decvax!ittatc!dcdwest!sdcsvax!ucbvax!ailist From: KWH@AI.AI.MIT.EDU (Ken Haase) Newsgroups: mod.ai Subject: Re: Technology Review article Message-ID: <860206084600.3.KWH@SID.AI.MIT.EDU> Date: Thu, 6-Feb-86 08:46:00 EST Article-I.D.: SID.860206084600.3.KWH Posted: Thu Feb 6 08:46:00 1986 Date-Received: Sun, 9-Feb-86 00:58:36 EST References: Sender: daemon@ucbvax.BERKELEY.EDU Organization: The ARPA Internet Lines: 53 Approved: ailist@sri-ai.arpa From: Ken Haase Date: 3 Feb 86 14:25:24 GMT From: vax135!miles@ucbvax.berkeley.edu (Miles Murdocca) Subject: Re: Technology Review article To: AIList@SRI-AI The [Technology Review] article was written by the Dreyfuss brothers, who are famous for making bold statements that AI will never meet the expectations of the people who fund AI research. They make the claim that people do not learn to ride a bike by being told how to do it, but by a trial and error method that isn't represented symbolically. They use this argument and a few others such as the lack of a representation for emotions to support their view that AI researchers are wasting their sponsors' money by knowingly heading down dead-ends. I don't think the Dreyfus brothers accuse AI researches of knowingly heading down dead-ends. They just claim that most of ``what people do'' cannot be captured by the ``abstracted representations'' of nearly all current AI research. I don't agree with this claim, but can't deny that we (in AI) may be all wrong about our central hypothesis. We just have to make our hypothesis clear and explicit. I think that most high level intellectual processes have effective symbolic representations (and I'm working to find out what such representations might be). That is an explicit hypothesis of my research. On the other hand, I do not think that there is anything like a symbolic representation of ``how to ride a bike''. What happens in such cases is that our intellect ``trains'' the animal that is the rest of us to ride the bicycle. As I recall ["Machine Learning", Michalski et al, Ch 1], there are two basic forms of learning: 'knowledge acquisition' and 'skill refinement'. The Dreyfuss duo seems to be using a skill refinement problem to refute the work going on in knowledge acquisition. The distinction between the two types of learning was recognized by AI researchers years ago, and I feel that the Dreyfuss two lack credibility since they fail to align their arguments with the taxonomy of the field. The alchemists could have made the same argument against arguments for the periodic table; what the Dreyfus brothers are arguing for is the need for just such a ``paradigm shift'' in cognitive science. The fact that this shift will disrupt the foundations of most current AI technology (most of which is not well proven anyway) should not effect scientific judgements at all (though, pessimistically, it certainly will). In any case, the dichotomy between skill refinement and knowledge acquisition is even suspect; outside of rote learning of facts, most gained knowledge is gained by appropriating the knowledge as skills (in a broad sense of skills, which includes responses, perceptual skills, etc). Ken