Relay-Version: version B 2.10 5/3/83; site utzoo.UUCP Path: utzoo!utgpu!water!watmath!clyde!rutgers!seismo!mnetor!yetti!unicus!craig From: craig@unicus.UUCP Newsgroups: sci.bio,sci.med,comp.ai,sci.misc Subject: Taking AI models and applying them to biology... Message-ID: <622@unicus.UUCP> Date: Fri, 5-Jun-87 21:38:26 EDT Article-I.D.: unicus.622 Posted: Fri Jun 5 21:38:26 1987 Date-Received: Sun, 7-Jun-87 10:32:39 EDT Reply-To: craig@unicus.UUCP (Craig D. Hubley) Organization: Unicus Software Inc., Toronto, Ont. Lines: 101 Xref: utgpu sci.bio:344 sci.med:2036 comp.ai:456 sci.misc:270 Forgive the wide cross posting, net.gods, but I am interested in gathering an opinion from biological and artifical intelligence people on a model that arises from AI but has (possibly) biological implications: Foreword or WHY I'M WRITING THIS. -------------------------------- I was semi-surprised in recent months to discover that cognitive psychology, far from developing a bold new metaphor for human thinking, has (to a degree) copied at least one metaphor from third-generation computer science. This description of the human memory system, though cloaked in vaguer terms, corresponds more or less one-to-one with the traditional computer architecture we all know and love. To wit: - senses have "iconic" and "echo" memories analogous to buffers. - short term memory holds information that is organized for quick processing, much like main storage in a computing system. - long term memory holds information in a sort of semantic association network where large related pieces of information reside, similar to backing or "archived" computing storage. At least this far, this theory appears to owe a lot to computer science. Granted, there is lots of empirical evidence in favour, but we all know how a little evidence can go far too far towards developing an analogy. What I think we may need are good parallel connectionist computing models for the social sciences to copy, rather than these old ones that we are beginning to fuse and modify and discard. After all, engineering can construct and test artifacts much quicker than psychologists can. And investigate their insides and their performance as well... The Point or WHAT I'M THINKING ABOUT ------------------------------------ Single cells are constructed according to instructions resident in their own DNA. When their reproductive process fails, they die, become cancerous, etc... In computing terms, a self-reproducing program messes up the code and therefore fails to function (it does not reproduce). Or, it may continue to reproduce a flawed cell (cancer...). But a biological mechanism such as, say, a muscle or a brain is a massively parallel system consisting of many many redundant cells, each of which is capable of performing (at least almost) the same function. So many many parts would have to fail before the effect was enough to endanger the system as a whole. That is, it degrades gracefully. This effect has been observed in parallel sensing systems, which use several low-resolution phased fields that redundantly cover the same area. Removing one such field results in a loss of resolution, but not utter failure to detect a stimulus. Details in Geoffrey Hinton and others... (Byte AI issue, 1985?) At some point of degradation, the whole parallel system will collapse. Or an aged human being will die of a cold. The Question or WHAT DO YOU THINK? ---------------------------------- Apparently, all human organ weights begin to decline shortly after puberty. The cumulative effect of this seeming reduction of resources isn't felt so strongly until middle-age, when we become more susceptible to disease. So far, this is just a statement of the nature of parallel systems. But does it hold up as a theory of aging? - Is mitosis sufficiently prone to failure to account for organ decline? - Statistically, one would expect exponential distribution for failure of single cells, the rate dependent on mitosis failure, and perhaps modified by other cell-killing factors - Does organ failure, medically, occur at the point where a parallel processing system, mathematically, would fail? I've heard that mammal cells appear to suffer a "hard" reproductive limit of 52 mitosis operations, and that meiosis "resets this counter" to 0. - any comment on this, bio-med types? Is it true? - Would a theory assuming a simple variable or random "counter" in each cell limiting its reproductive span better explain aging (programmed cells...) It doesn't seem so... regardless of the origin of the failure, the observed degradation of the system as a whole would still follow this pattern. The upshot of this is that a potentially useful life science model may have just materialized in artificial intelligence. The main flaw that I can see in it is that a cell is complex mechanism in and of itself, and so the success/failure of each might be subject to many factors in parallel as well. That is, it might not fail the way a short subroutine would were it copied badly, which is the gist of this. But then one might find a lower level where the parts were sufficiently monolithic that the analogy held. This seems to kick the butt of the good old 'Entropy' theory... cop-out. Incompentent nineteenth century philosophers leaned heavily on entropy. Comments? Flames? The name of a good shrink? Musing, Craig.