On 11/7/06, James Ratcliff [EMAIL PROTECTED] wrote: Yan, Do you have a version of the book layout that is all on one page, or PDF or anything?
I would like ot print the whole thing off and look over it in more detail. Also lots of broken links, run a link checker, the GO link on the front
Yes. All of the above.We have already heard the statement from all around I believe, and seen the results that show that one single algorithm is just not goign to work, and its unreasonable to think it would. So then its really down to breaking up the parts, defining them precisely, and
Matt: To parse English you have to know that pizzas have pepperoni, that demonstrators advocate violence, that cats chase mice, and so on. There is no neat, tidy algorithm that will generate all of this knowledge. You can't do any better than to just write down all of these facts. The data is not
Hi,
About
But a simple example is
ate a pepperoni pizza
ate a tuna pizza
ate a VEGAN SUPREME pizza
ate a Mexican pizza
ate a pineapple pizza
I feel this discussion of sentence parsing and interpretation is
taking a somewhat misleading direction, by focusing on examples that
are in fact very
http://www.physorg.com/news82190531.html
Rabinovich and his colleague at the Institute for Nonlinear Science at the
University of California, San Diego, Ramon Huerta, along with Valentin
Afraimovich at the Institute for the Investigation of Optical Communication
at the University of San Luis
My plan has both A with B and D examplesand Ben: So, I feel much of the present discussion on NLP interpretation isbypassing the hard problem, which is enabling an AGI system to learnthe millions or billions of commonsense (probabilistic) rules relatingto basic relationships like with_tool, which
So, how to get all this probabilistic commonsense knowledge (which in
humans is mostly unconscious) into the AGI system?
a-- embodied learning
b-- exhaustive education through NLP dialogue in very simple English
c-- exhaustive education through dialogue in some artificial language
like Lojban++
Ben Jef wrote:
As I see it, the present key challenge of artificial intelligence
is to develop a fast and frugal method of finding fast and frugal
methods,
Ben However, this in itself is not possible. There can be a fast
Ben method of finding fast and frugal methods, or a frugal method of
Kevin wrote:
http://www.physorg.com/news82190531.html
Rabinovich and his colleague at the Institute for Nonlinear Science at the
University of California, San Diego, Ramon Huerta, along with Valentin
Afraimovich at the Institute for the Investigation of Optical Communication
at the
Eric wrote:
The challenge is to find a methodology
for producing fast enough and frugal enough code, where that
methodology is practicable. For example, as a rough upper bound,
it would be practicable if it required 10,000 programmer years and
1,000,000 PC-years (i.e a $3Bn budget).
(Why should
Eric Baum wrote:
As I and Jef and you appear to agree, extant Intelligence
works because it exploits structure *of our world*; there is
and can be (unless P=NP or some such radical and unlikely
possibility) no such thing as as General Intelligence that
works in all worlds.
I'm going to
About
http://www.physorg.com/news82190531.html
Rabinovich and his colleague at the Institute for Nonlinear Science at the
University of California, San Diego, Ramon Huerta, along with Valentin
Afraimovich at the Institute for the Investigation of Optical Communication
at the University of
Back in 1987, during my M.Sc., I invented the term 'dynamic relaxation'
to describe a quasi-neural system whose dynamics were governed by
multiple relaxation targets that are changing all the time. So the idea
of having a multi-lobe attractor, or structured, time-varying
attractors, is not
Richard wrote:
What Rabinovich et al appear to do is to buy some mathematical
tractability by applying their idea to a trivially simple neural model.
That means they know a lot of detail about a model that, if used for
anything realistic (like building an intelligence) would *then* beg so
many
I don't know if the Novamente baby is going to be anything like a human baby. If it is, this article might be of interest. Design methodologies for central pattern generators: an application to crawling humanoids
http://birg2.epfl.ch/publications/fulltext/righetti06d.pdfAlso for some more
Eric Baum wrote:
(Why should producing a human-level AI be cheaper than decoding the
genome?)
Because the genome is encrypted even worse than natural language.
--
Eliezer S. Yudkowsky http://singinst.org/
Research Fellow, Singularity Institute for Artificial
Eliezer Eric Baum wrote:
(Why should producing a human-level AI be cheaper than decoding the
genome?)
Eliezer Because the genome is encrypted even worse than natural
Eliezer language.
(a) By decoding the genome, I meant merely finding the sequence
(should have been clear in context), which
Ben Goertzel [EMAIL PROTECTED] wrote:
I am afraid that it may not be possible to find an initial project that is both
* small
* clearly a meaningfully large step along the path to AGI
* of significant practical benefit
I'm afraid you're right. It is especially difficult because there is a long
I think that natural language and the human genome have about the same order of
magnitude complexity.
The genome is 6 x 10^9 bits (2 bits per base pair) uncompressed, but there is a
lot of noncoding DNA and some redundancy. By decoding, I assume you mean
building a model and understanding the
Eric Baum wrote:
Eliezer Eric Baum wrote:
(Why should producing a human-level AI be cheaper than decoding the
genome?)
Eliezer Because the genome is encrypted even worse than natural
Eliezer language.
(a) By decoding the genome, I meant merely finding the sequence
(should have been clear in
James,Many of the solutions you describe can use information gathered from statistical models, which are opaque. I need to elaborate on this, because I think opaque models will be fundamental to solving AGI. We need to build models in a way that doesn't require access to the internals. This
Fully decoding the human genome is almost impossible. Not only is there the
problem of protein folding, which I think even supercomputers can't fully
solve, but the purpose for the structure of each protein depends on
interaction with the incredibly complex molecular structures inside cells.
I will be out of the office until November 15. Please call 501-682-1115 if you
need immediate assistance.
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