JFS: I believe that solving the problem of perception and action at
the level of birds is *more critical* to general intelligence
than getting a computer to prove theorems or parse NL sentences.
John
+1
I don’t mean to change the subject, but I agree that “solving the problem of perception and action” is a very important goal. I also agree that AI has not, over its sixty five years of discussion and thought provoking experiments, had very deep successes. IMHO, the major gains in computational flexibility have come from the practical day to day problems that AI has helped with.
It’s clear that people see what we expect to see. So forecasting expectations is a way to forecast future events a few seconds or tenths of seconds prior to experiencing the actual scene that is expected.
-Rich
Sincerely,
Rich Cooper
EnglishLogicKernel.com
Rich AT EnglishLogicKernel DOT com
9 4 9 \ 5 2 5 - 5 7 1 2
-----Original Message-----
From: ontolog-forum-bounces@xxxxxxxxxxxxxxxx [mailto:ontolog-forum-bounces@xxxxxxxxxxxxxxxx] On Behalf Of John F Sowa
Sent: Saturday, January 03, 2015 8:20 AM
To: ontolog-forum@xxxxxxxxxxxxxxxx
Subject: Re: [ontolog-forum] A No-Go Result For Human-Level Machine Intelligence
Let's not engage in blanket no-go debates on this list. If there's
a doubtful claim in a publication, then question that point, not
the author's competence.
There are many reasons why strong AI (as claimed by some of the
pioneers) has not succeeded. The question whether any digital
device can attain human-level intelligence is still open.
In fact, there's still an open question whether a digital device
can achieve the animal-level intelligence of a bird or a beaver.
See the slide (copy below) about the bird-nest problem.
I believe that solving the problem of perception and action at
the level of birds is *more critical* to general intelligence
than getting a computer to prove theorems or parse NL sentences.
John
_________________________________________________________________
From slide 5 of http://www.jfsowa.com/talks/micai.pdf
BIRD NEST PROBLEM
Robots can perform many tasks with great precision.
But they don’t have the flexibility to handle unexpected shapes:
* They can’t wash dishes the way people do — with an open-ended
variety of shapes and sizes.
* And they can’t build a nest in an irregular tree with irregular
twigs, straw, and moss.
If a human guides a robot through a complex task with complex
material, the robot can repeat the same task in the same way.
But it doesn’t have the flexibility of a bird, a beaver, or a human.
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