Rich, (01)
Some comments: (02)
JFS
>> About a dozen years ago, I was talking with Mike Genesereth,
>> who said "Lenat is probably the only one who doesn't know that
>> Cyc has failed." (03)
RC
> That is the kind of thinking that all of us show in one form or
> another. We seem stuck in our structured ways after the first four
> decades or six, unable to overthrow the past beliefs and institute
> new untried ones. (04)
Genesereth has been one of the strongest proponents of classical
logic-based AI. He has been teaching at Stanford for years in
close collaboration with the same people (McCarthy, Feigenbaum,
Fikes, etc.) as Lenat. Any success stories from Cyc would have
provided more attention (and funding) for all kinds of projects
that used logic-based AI. But Mike G. was being realistic. I
would qualify his comment, but I certainly couldn't refute it. (05)
In my 1984 book, I tried to take a balanced view of the strengths
and limitations of logic-based systems. My view then (and with
more input since then) has been that logic-based systems are
important, especially for applications to comp. sci., but that
NLP systems must include logic-based approaches as a proper subset: (06)
1. Large numbers of applications in computer systems, database
systems, programming systems, and hardware/software design,
require a foundation in formal logic. (07)
2. Natural languages can be used in very precise ways (for
example, along the lines of controlled NLs), but they
can also be used in very scruffy, very informal ways. (08)
3. The overwhelming volume of NL speech and documents use
highly informal, often ungrammatical, and "innovative"
language. (I'm using "innovative" as a neutral term
for what many people would call "incorrect".) (09)
3. I also agree with the comment by Alan Perlis that you
can't translate informal language to formal language by
any formal algorithm. (010)
4. I believe that you can interpret highly informal language
by computer, but that you need to use huge amounts of
background knowledge (i.e., extralinguistic information)
to do so. (011)
5. Point #4 is acknowledged by classical logic-based AI projects
such as Cyc. But they assume that you need a long gestation
period that depends on hand-coded logical representations
(e.g., formal ontologies and knowledge bases). (012)
6. The scruffies, such as Roger Schank, disputed that claim
from the early days (1960s). But they didn't have the
facilities for acquiring, storing, and using such large
volumes of information. (013)
7. The hardware today is more than adequate to store and
process the huge volumes of information needed to support
point #6. One example is the IBM Watson project, but
there are other projects that have achieved comparable
success with more modest hardware resources. The
VivoMind applications I summarized are among them. (014)
> I don't see much of anything discussed about Cyc past the
> precursors I mentioned anywhere in the public literature;
> I'm not referring to tutorials about Cyc, but about analyses. (015)
For the research publications, see (016)
http://cyc.com/cyc/technology/pubs (017)
For free downloads of the ontology and supporting software: (018)
http://opencyc.org/ (019)
I believe that there are many useful applications of Cyc and OpenCyc,
but I also believe that a different architecture is necessary to
achieve something that could be called natural language understanding. (020)
That is what I have been discussing in talks, publications, and emails. (021)
John (022)
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