Path: utzoo!utgpu!jarvis.csri.toronto.edu!mailrus!tut.cis.ohio-state.edu!rutgers!mcnc!xanth!lll-winken!uunet!portal!cup.portal.com!dan-hankins
From: dan-hankins@cup.portal.com (Daniel B Hankins)
Newsgroups: comp.ai
Subject: Re: Symbolic Connectionism? (was Re: What IS a symbol?)
Message-ID: <17548@cup.portal.com>
Date: 24 Apr 89 04:43:42 GMT
References: <11313@bcsaic.UUCP> <17467@cup.portal.com> <5418@cs.Buffalo.EDU>
Organization: The Portal System (TM)
Lines: 61

In article <5418@cs.Buffalo.EDU> lammens@sunybcs.uucp (Jo Lammens) writes:

>As far as I know (correct me if I'm wrong), all neural network topologies
>today are hand-crafted, and there is no prospect for any sort of
>general-purpose topology.

     Okay.  I have seen abstracts and conference topics for (a) random
connections and (b) connection generating systems based on biological
models.

     I'll grant that *most* ANN systems in use are fixed-topology, where
the topology is designed in advance.  I personally don't find them very
interesting - I'm into generality.


>In other words, based on what the 'programmer' [...] has in mind [...]
>a certain topology is chosen (number of layers, number of neurons, number
>of connections, type of input/activation/output functions, and what have
>you).

     The number of units, connections, and types of functions really are
not part of the topology... but they are often chosen by the programmer in
response to a particular problem setup.  Again, I consider this cheating,
and am interested in more general-purpose systems.

>Then the external symbols [...] are assigned to [...] input and/or output
>values for the net. Then the net sets off, merrily manipulating its
>'symbols' [...] until the programmer/builder decides that the net
>converges successfully, or not.
>This decision is based on the EXTERNAL INTERPRETATIONS of the net's
>'symbols', which are no more intrinsically related to the activation
>levels etc. than the meaning of words are related to their form, or the
>meaning of other symbols to the form of the symbols (leaving the
>inevitable exceptions like pictograms etc out of consideration). So where
>is the qualitative difference? The technique used to learn the system how
>to manipulate the symbols is different, but the relation between the
>symbols and their external interpretation is not. This is not a plea for
>or against neural networks.

     You're writing here of supervised learning, which is again the most
common form used (because it's the easiest to work with, both
computationally and mathematically).  I still think it's cheating, because
it isn't the way biological systems do it.  Nobody supervises directly the
learning of bio networks.  I think that many do this because it makes the
network converge faster on 'the solution'.

     When I write of connectionist systems that can achieve intelligence
(or combinations of connectionist and symbolic), I am thinking of more
biologically accurate approaches - non-back-propagation, self-organizing
topology, and unsupervised learning.

     I agree that the sort of systems you are thinking of are more like
traditional AI approaches, in that they embody the _programmer's_
symbol-referent associations rather than generating their own.

     However, that is not the be-all and end-all of connectionist systems.
Sorry if I misled anyone into thinking I was talking about garden-variety
connectionist systems such as are in use.


Dan Hankins