>Pat Hayes wrote:
>> >Does it really involve breakdown and analysis?
>>>
>>>I can program a mainframe to view a rubber ball
>>>and calculate a space time coordinate where a
>>>robotic arm can intercept the ball and catch it
>>>after one bounce. I would have to feed in
>>>numbers for constant effect of gravity, mass of
>>>ball, air resistance, durometer reading of ball,
>>>initial trajectory and velocity etc.
>>>
>>>How is it a dog can catch the ball but does not
>>>process any of this? I think there is something
>>>more than simple calculations.
>>
>> There is a very simple algorithm for catching a
>> ball, which was worked out by the psychologist
>> Gibson. Assume the ball is in the air coming
>> roughly towards you (if not, give up). Look at
>> the ball and keep it in the center of your
>> vision. If the ball is moving left, run left; if
>> right, run right; if upwards, run back; if
>> downwards, run forward. Try to keep the ball
>> stationary in your field of view. When it gets
>> close enough, catch it. You don't need to do all
>> the simulation with numbers. Neither does the
>> robot.
>>
>> Now, this is a very basic algorithm, and dogs,
>> people and robots can all do better than this;
>> but this is the basic technique.
>
>
>Jeff Hawkins, in his book "On Intelligence", the book Frank mentioned (01)
I wonder why this potboiler gets so much
publicity. This is really an extremely poor book.
I was sent it to review, and turned the offer
down on the grounds that it was impossible to
review without slandering the author. It displays
ignorance and arrogance on almost every page in
roughly equal proportions. There are no new ideas
in it. The central "insight", that the cortex is
basically performing the same computation
everywhere, is a suggestion which certainly goes
back at least to Valentino Braitenberg in the
1960s, and probably before that. The only part
that one should read carefully is the repeated
observation, by the author, that he knows nothing
at all about AI or neuroscience. (02)
>, uses
>the dog catching a ball example. (03)
Its been familiar to AI and psychology
(particularly Gibsonian psychologists) for 40
years. This idea that computers must be doing
numerical-style simulations in order to act in
the world is a trope that only someone completely
ignorant of actual AI work could possibly take
seriously. Check out the ideas of an 'affordance'
and a 'heuristic'. (04)
> He offers another example as well. I will
>adapt it to this conversation:
>
>You might claim that a mainframe computer could calculate the new position
>of each of four individuals sitting on a waterbed after a fifth one climbs
>on board. You would have to feed it numbers about the weight of each of the
>five participants, the force of gravity, the stretchiness of the plastic,
>etc. (05)
That is one approach. Or, you might try using AI
techniques. Qualitiative physical reasoning for
example could tell you a lot without using a
single number. (06)
>Or you could come along and say there is a very basic algorithm for
>adjusting the position of people on a waterbed when an additional person
>climbs on. As the new weight depresses the plastic in the location of the
>fifth person, simultaneously move water out from under the new person and
>put it under the four that are already there, etc.
>
>Hawkins' point, as I understand it, is that neither calculation nor
>algorithms are needed to explain how a waterbed adjusts to changing
>conditions. (07)
This really is kind of stupidly obvious. (08)
>But further, that they are not necessary to explain how dogs or
>people learn how to catch balls. (09)
Where does he claim that? (010)
>And he goes even further, he claims that
>this ability is due to the way brains, the cortex in particular, are
>constructed and operate, as it is with the waterbed. (011)
Taken literally, that is clearly false. The brain
doesn't have an internal *physical* model of all
the things that brains can think about. No
account like this can possibly account for the
generality or the plasticity of neural
functioning. But in any case, I don't think that
he does claim this, in fact. (012)
> But he doesn't stop
>there, he also claims that progress in getting machines to due similar
>tasks has been severely hampered by the erroneous belief that algorithms and
>calculation could somehow reproduce the functionality of the cortex. (013)
About which he knows virtually nothing. And, by
the way, he does not claim that the cortex isn't
performing computations at all: in fact, he gives
a sketch of the IT process he thinks it is
performing. His idea here is about 50 years old
also, and has been investigated thoroughly, and
is known to be incomplete or wrong. (014)
>I think this is relevant to ontology and logic both when it comes to the
>ability to choose and interpret symbols to use to identify the things about
>which the ontology and logic are about. (015)
I don't think this book is relevant to anything
except the size of its author's ego. (016)
Pat (017)
>
>John Black
>www.kashori.com
>
>
>> Pat Hayes
>>
>>>Duane
>>>
>>>
>>>On 8/6/07 1:57 PM, "Kathryn Blackmond Laskey" <klaskey@xxxxxxx> wrote:
>>>
>>>XML, RDF, OWL, etc. are all human
>>>interpretations of trying to break down data
>>>representation in such a way that they mimic
>>>what the brain can do, in a highly simplified
>>>manner, in hopes of allowing machines to perform
>>>the basic work that a brain can.
>>>
>>>
>>>Flying birds gave humans an existence proof that
>>>flight was possible. However, attempts were
>>>unsuccessful to build flying machines that
>>>worked the way birds do. Eventually we achieved
>>>flight by understanding aerodynamics and
>>>propulsion well enough to build machines that
>>>worked, even though the mechanism was quite
>>>different from that used by birds. Similar
>>>remarks apply to cameras, and to information
>>>processing systems.
>>>
>>>K
>>>
>>>
>>>
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