Natural language requires more than the words on the page in the real world.
Of course that didn't work.

Cyc also is trying to store knowledge about a super complicated world in
simplistic forms and also requires more data to get right.

Vision and other sensory interpretaion, on the other hand, do not require
more info because that is where the experience comes from.

On Jun 28, 2010 8:52 PM, "Matt Mahoney" <matmaho...@yahoo.com> wrote:

David Jones wrote:
> I also want to mention that I develop solutions to the toy problems with
the re...
A little research will show you the folly of this approach. For example, the
toy approach to language modeling is to write a simplified grammar that
approximates English, then write a parser, then some code to analyze the
parse tree and take some action. The classic example is SHRDLU (blocks
world, http://en.wikipedia.org/wiki/SHRDLU ). Efforts like that have always
stalled. That is not how people learn language. People learn from lots of
examples, not explicit rules, and they learn semantics before grammar.

For a second example, the toy approach to modeling logical reasoning is to
design a knowledge representation based on augmented first order logic, then
write code to implement deduction, forward chaining, backward chaining, etc.
The classic example is Cyc. Efforts like that have always stalled. That is
not how people reason. People learn to associate events that occur in quick
succession, and then reason by chaining associations. This model is built
in. People might later learn math, programming, and formal logic as rules
for manipulating symbols within the framework of natural language learning.

For a third example, the toy approach to modeling vision is to segment the
image into regions and try to interpret the meaning of each region. Efforts
like that have always stalled. That is not how people see. People learn to
recognize visual features that they have seen before. Features are made up
of weighted sums of lots of simpler features with learned weights. Features
range from dots, edges, color, and motion at the lowest levels, to complex
objects like faces at the higher levels. Vision is integrated with lots of
other knowledge sources. You see what you expect to see.

The common theme is that real AGI consists of a learning algorithm, an
opaque knowledge representation, and a vast amount of training data and
computing power. It is not an extension of a toy system where you code all
the knowledge yourself. That doesn't scale. You can't know more than an AGI
that knows more than you. So I suggest you do a little research instead of
continuing to repeat all the mistakes that were made 50 years ago. You
aren't the first person to do these kinds of experiments.


-- Matt Mahoney, matmaho...@yahoo.com


------------------------------
*From:* David Jones <davidher...@gmail.com>
*To:* agi <agi@v2.listbox.com>
*Sent:* Mon, June 28, 2010 4:00:24 PM


Subject: Re: [agi] A Primary Distinction for an AGI


I also want to mention that I develop solutions to the toy problems with the
real problems in mind....

On Mon, Jun 28, 2010 at 3:56 PM, David Jones <davidher...@gmail.com> wrote:

> >
> > That does not have to be the case. Yes, you need to know what problems
> you might have in more co...
>
>
> > On Mon, Jun 28, 2010 at 3:41 PM, Russell Wallace <
> russell.wall...@gmail.com> wrote:
>
>> >>
>> >> On Mon, Jun 28, 2010 at 4:54 PM, David Jones <davidher...@gmail.com>
>> wrote:
>> >> > But, that's w...
>>
>> >>
>> >>
>> >> -------------------------------------------
>> >> agi
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