On 2/17/2011 9:45 PM, John F. Sowa wrote:
> I really don't understand that complaint:
>> ... what you were trying to do in your first line is to put the
>> naming issue firmly in the hand of the AI community.
>> Sorry, too old a trick to allow to pass without comment.
> Following is the opening of my previous note, in which I responded
> to Peter B's comment that "artificial intelligence is neither."
>> That's a quibble about a name. Many people, including me, have stated
>> such quibbles from time to time, but they're irrelevant. They're as
>> pointless as the behaviorists who objected to the name 'psychology'
>> because it implies an unobservable psyche.
> Notice that I explicitly dismissed Peter's complaint as irrelevant.
> That's too obvious to be a trick.
Words are important. They set expectations whether we personally agree
with them or not. (03)
Example: The near universal avoidance of racist language in American
politics. Well, with some exceptions from time to time. (04)
The same racist policies (at least in the view of some) continue, but
the overt racist language isn't used. (05)
Peter's complaint is not irrelevant. (06)
Your dismissal of Peter's complaint leaves the "artificial intelligence"
camp in charge of the term. (07)
It may be that in terms of language usage that it is a losing battle,
but that does not mean it is irrelevant.
>> Looking at "accomplishments," the fine folks at IBM designed a black
>> box system (to outsiders) that answers questions on a game show.
>> Gee, why am I not impressed?
> Actually, there is sufficient documentation available that anybody
> knowledgeable about AI and NLP who had access to a team of 25 people,
> a supercomputer with 2820 cores, four years time, and a few million
> dollars to pay salaries could reproduce something as good or better.
>> Had the team published its NLP processing algorithms, which were no
>> doubt innovative, that would raise all boats.
> I'm sure there was some innovation in algorithms, but nothing that
> talented AI experts couldn't invent just as easily as they could copy.
> For anybody who would like to reproduce their approach, I suggest
> starting by reading David Ferrucci's slides from 2006 about UIMA
> (the Unstructured Information Management Architecture):
> Following is the talk that explains the slides:
> For more about UIMA, which IBM released to open source under the care
> of the Apache Foundation, type "UIMA" to Google.
> And following is Ferrucci's recent talk about the DeepQA project for
> building and extending that foundation for Jeopardy:
> For further documentation, see the following technical report
> by Ferrucci et al. while they were two years into the project:
> At the end of this note is an excerpt from a note I sent to
> Ontolog Forum yesterday with further discussion about Watson,
> based on an article about it published in AI Magazine.
> Given the above information, what I summarize below, and the
> money and hardware available to Ferrucci's team, quite a few
> good AI groups around the world could build a system that would
> perform as well or better than Watson.
Research progress isn't based upon being able to replicate the work of
others, given enough time and money. (09)
I have understood it, even within AI, to be a matter of building on the
work of others. (010)
The absence of formal publication has been noticed by others. (011)
Do you agree/disagree that has been the case? (012)
Hope you are looking forward to a great weekend! (013)
> -------- Original Message --------
> Subject: Re: [ontolog-forum] IBM Watson& source code
> Date: Wed, 16 Feb 2011 11:29:38 -0500
> From: John F. Sowa
> > From what little I've seen it does rather seem to be a triumph
> > of statistics over semantics.
> Statistics certainly plays a big role, but the relative role
> depends on what you mean by 'semantics'. I would say that Watson
> represents a triumph of 1980s style of NLP, and I'd summarize
> the influences in one line:
> Michael McCord + Roger Schank + statistics + supercomputer
> For McCord's influence, I'll quote the following passage from the
> article in AI Magazine:
> Page 11, http://www.stanford.edu/class/cs124/AIMagzine-DeepQA.pdf
> > The DeepQA approach encourages a mixture of experts at this
> > stage, and in the Watson system we produce shallow parses,
> > deep parses (McCord 1990), logical forms, semantic role labels,
> > coreference, relations, named entities, and so on, as well as
> > specific kinds of analysis for question answering.
> In the 1980s, McCord had written an excellent parser in Prolog
> with a good grammar of English. In the 1990s, he rewrote the
> parser in C for better performance. Apparently, that's the
> "deep" parser they use for Watson.
> Also in the 1980s, Roger Schank made the claim that axioms in
> logic are irrelevant, but large volumes of background knowledge
> are essential for language understanding. He also said that
> machine learning is essential for NLP, since every text says
> something new that must be added to the background knowledge.
> Unfortunately, the technology at the time was too slow, and
> the available resources of machine-readable texts were woefully
> inadequate to support Schank's claims.
> Watson also uses ideas developed in the 1990s and later, but
> one could say that the Watson-style of semantics is a high-speed
> implementation of a Schankian style of NLP. The crucial technology
> that Schank did not have is a supercomputer plus huge volumes of
> preprocessed material indexed and accessible via a relational DB.
> I certainly won't downplay the importance of statistics, which
> are essential for many aspects of Watson: evaluation of what
> is relevant, estimating the confidence in an answer, and most
> especially for techniques of machine learning. But learning
> is also one of the aspects that Schank emphasized years ago.
> In short, Roger Schank's emphasis on informal methods of
> processing and using large volumes of background knowledge,
> case-based reasoning, and machine learning are much closer
> to what Watson is doing than any logic-based method --
> either Richard Montague's formal logic for NLP or Lenat's
> enormous formal ontology for Cyc.
> Yet Watson does use some logic, however. It's just not the
> main focus. Statistics and heuristics are more important.
> > As Watson turns out to be very successful this leads again to the
> > question which role ontological categories play...
> Watson does use WordNet and other lexical resources. That is
> important for selectional constraints on permissible combinations,
> but Wordnet and similar resources have very few axioms.
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