Path: utzoo!attcan!utgpu!jarvis.csri.toronto.edu!rutgers!njin!princeton!udel!wuarchive!wugate!uunet!motcid!schultz
From: schultz@cell.mot.COM (Rob Schultz)
Newsgroups: comp.ai
Subject: Re: Building a brain
Summary: shortening the machine education period
Keywords: hardware brain
Message-ID: <246@carmine9.UUCP>
Date: 18 Oct 89 18:57:57 GMT
References: <14079@well.UUCP> <10175@venera.isi.edu>
Distribution: comp
Organization: Motorola Inc. - Cellular Infrastructure Div., Arlington Heights, IL 60004
Lines: 58

In article <10175@venera.isi.edu>, smoliar@vaxa.isi.edu (Stephen Smoliar) writes:
> In article <14079@well.UUCP> nagle@well.UUCP (John Nagle) writes:
> > [edited]
> >       So, to get the raw CPU power of a brain, we need about 5000 boards,
> >or 227 standard VME cages, or 76 racks, or about 150 linear feet of cabinetry.
> >
> >       I've worked in mainframe installations bigger than that.
> >
> >       If we knew how to solve the architecture problems, we could build
> >the hardware.  
> >
> There is one small difficulty which was observed by David Waltz in his
> contribution to the DAEDALUS issue on artificial intelligence, "The Prospects
> for Building Truly Intelligent Machines."  Even if you DO get the architecture
> right (and other readers of this bulletin board have been skeptical about
> that), you may face the prospect that "educating" your device may take
> something on the order of ten or twenty years.  After all, if you duplicate
> human hardware, you should not be surprised if you get human performance.

I see several methods for shortening this process to a (nearly?) manageable
level:

    1.  Memory/retention.  Presumably, an intelligent machine will not
        forget information it has learned. (This assumes we do not model
        the system after ourselves :-)) Therefore, the machine would not
        have to waste time re-learning something it should already know.

    2.  Countinuous Input.  Such a machine will not require sleep, nourishment,
        or any other such distractions. So, instead of losing 8 to 15 hours 
        out of every 24, it should be able to receive continuous input of
        information.

    3.  Input Speed.  Information may be input directly in electronic form, 
        thus reducing or even eliminating the time required to translate/digest
        information. This leads to several interesting possibilities:

        a.  partition the information, and have each of several machines
            digest it. The information could then be distributed among
            the machines.

        b.  "clone" the intelligence. Once the information is learned, it
            can be duplicated into other machines. Thus we have a way to 
            mass-produce intelligences, and completely bypass the learning
            process.

    4.  Restricted Domain.  If we decide to create function-specific machines,
        we can restrict the domain of information to the required function.
        For example, if a system is to be a medical diagnosis/treatment
        prescription system, it would have to learn little or nothing abou
        meteorology. Of course, this does not help us with a general-purpose
        system, but we can't have everything, eh? :-)
    

-- 
Thanks -          uunet!motcid!schultz
     rms          Rob Schultz, Motorola General Systems Group
312 / 632 - 7597  1501 W Shure Dr, Arlington Heights, IL  60004
"Kicking the terminal doesn't hurt the monsters (or the bugs)."