Peter,
I'm afraid that your question cannot be answered as it is. AI is
highly fragmented, which not only means that few project is aiming at
the whole field, but also that few is even covering a subfield as you
listed. Instead, each project usually aims at a special problem under
a set of
- Comprehensive (common-sense) knowledge-bases and/or ontologies
Cyc/OpenCyc, Wordnet, etc. but there seems to be no good way for applications
to use this information and no good alternative to hand coding knowledge.
- Inference engines, etc.
- Adaptive expert systems
A dead end. There has
On 10/19/06, Matt Mahoney wrote:
- NLP components such as parsers, translators, grammar-checkers
Parsing is unsolved. Translators like Babelfish have progressed little since
the 1959
Russian-English project. Microsoft Word's grammar checker catches some mistakes
but is clearly not AI.
Hi Peter,
I think in all of the categories you listed, thereshould be a lot ofprogress, but they will hit a ceiling because of the lack of an AGI architecture.
It is very clear that vision requires AGI to be complete. So does NLP. In vision, many objects require reasoning to recognize.NLP also
- Original Message
From: BillK [EMAIL PROTECTED]
To: agi@v2.listbox.com
Sent: Thursday, October 19, 2006 11:43:46 AM
Subject: Re: [agi] SOTA
On 10/19/06, Matt Mahoney wrote:
- NLP components such as parsers, translators, grammar-checkers
Parsing is unsolved. Translators like
Matt Mahoney wrote:
From: BillK [EMAIL PROTECTED]
Parsing is unsolved. Translators like Babelfish have progressed little since
the 1959
Russian-English project. Microsoft Word's grammar checker catches some mistakes
but is clearly not AI.
I think the problem will eventually be solved.
On 10/19/06, Richard Loosemore [EMAIL PROTECTED] wrote:
Sorry, but IMO large databases, fast hardware, and cheap memory ain't
got nothing to do with it.
Anyone who doubts this get a copy of Pim Levelt's Speaking, read and
digest the whole thing, and then meditate on the fact that that book is
(Excellent list there, Matt)Although Pei Wang makes a good point that the fragmentation of AI does make it difficult to compare projects, it is interesting+ to note the huge differences in the movements in different narrow-AI fields.
As has already been mentioned, it is interesting+ to compare the
