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Re: [ontolog-forum] Neuro-ontology, Onto-neurology, and the Semantic Web

To: "Pat Hayes" <phayes@xxxxxxx>
Cc: "[ontolog-forum] " <ontolog-forum@xxxxxxxxxxxxxxxx>
From: "John Black" <JohnBlack@xxxxxxxxxxx>
Date: Tue, 7 Aug 2007 23:49:48 -0400
Message-id: <103b01c7d96f$2b52ea80$6601a8c0@KASHORI001>
Pat Hayes wrote:
> >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
>
> I wonder why this potboiler    (01)

Oh come on, do you really think Jeff Hawkins needs book royalty money? 
Surely not.    (02)

>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.    (03)

"slandering", is maliciously making *FALSE* statements about someone. Was 
that your intent?    (04)

> 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.    (05)

So what? FOL is even older. Is there something wrong with working with 
existing ideas like that? I don't think so.    (06)

 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.
>
>>, uses
>>the dog catching a ball example.
>
> 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'.    (07)

But I think he is also objecting to ridicuouly over-simplified, but pompouly 
stated and stupidly named 'basic algorithms' such as the one you laid out 
here for catching a ball. I feel certain that no dog, human, or robot has 
ever or will ever catch anything with such trivial nonsense. And it was just 
this kind of exagerated, hyperbolic claim about how easy it would be for AI 
solve such problems that led to first to extreme enthusiasm and later bitter 
disappointment with the whole AI enterprise.    (08)

>>  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.
>
> 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.
>
>>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.
>
> This really is kind of stupidly obvious.    (09)

And yet people still stupidly claim that when biological organisms with 
brains solve difficult problems they must be doing some sort of computation 
or following some 'basic algorithm'. So maybe its not so obvious and 
deserves repeating.    (010)

>>But further, that they are not necessary to explain how dogs or
>>people learn how to catch balls.
>
> Where does he claim that?    (011)

How about on page 68, when he says, "The answer is the brain doesn't 
"compute" the answers to problems; it retrieves the answers from memory. 
....The entire cortex is a memory system. It isn't a computer at all.....Let 
me show, through an example, the difference between computing a solution to 
a problem and using memory to solve the same problem.  Consider the task of 
catching a ball. Someone throws a ball to you, you see it traveling toward 
you, and  in less than a second you snatch it out of the air. This doesn't 
seem too difficult - until you try to program a robot arm to do the same. As 
many a graduate student has found out the hard way, it seems nearly 
impossible......And although a computer might be programmed to successfully 
solve this problem, the one hundred step rule tells us that a brain solves 
it in a different way. It uses memory......The memory of how to catch a ball 
was not programmed into your brain; it was learned over years of repetitive 
practice, and it is solved, not calculated, in your neurons."    (012)

>>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.
>
> 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.    (013)

He makes no such claim, so stop indulging yourself, you're demolishing only 
your own strawman. The idea is that the structure and function of the 
cortex, combined with worldly experiences, transforms the cortex in ways 
that increase skills. Skill and knowledge are the inevitable consequences of 
the cortex encountering experiences. They are learned, not programmed. 
Skills are effective memories, not algorithms. Knowledge is accurate 
prediction based on memories, not a set of axioms. Memories happen to brains 
exposed to experiences the way deformations happen to a waterbed when sat 
on. And Hawkins accounts for plasticity as well.    (014)

>>  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.
>
> 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.    (015)

I'm not convinced you really know what his ideas are. I'm not sure you 
were/are capable of giving them a fair consideration. I get a sense that you 
may have an ax to grind. But if not, then how is his "old" idea incomplete 
or wrong. And what is wrong with old ideas?    (016)

>
>>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.
>
> I don't think this book is relevant to anything except the size of its 
> author's ego.    (017)

This is a classic angry Hayesism, and thus easy to dismiss, but I still 
disagree.    (018)

As used by humans to think and to communicate, ontology and logic, like 
language, are about worlds, real or imagined. But as Randall Shulz said in 
another post, "...I suppose the symbol grounding problem is still with 
us...". Computers can store, transmit, and perform inferences on ontological 
symbols and logical sentences, but there doesn't appear to me to have been 
much progress in two other acts essential to intelligence: one, selecting 
the most effective symbol to use to represent things experienced in the 
world, and two, selecting what things in the world symbols are most 
effectively interpreted to be about. These are the skills of naming and 
interpreting names.    (019)

John    (020)



> Pat
>
>>
>>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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>     (021)



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