Re: Cool visual illusion


"feedbackdroids" <feedbackdroids@xxxxxxxxx> wrote in message
news:1133978771.420788.205900@xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx
>
> Wolf Kirchmeir wrote:
>> feedbackdroids wrote:
>> > Wolf Kirchmeir wrote:
>> [...]
>> >>Try building a circuit/network that exhibits classical Pavlovian
>> >>conditioning, for example. How do you do that? I don't know, I'm not an
>> >>EE. But it looks to me like such a circuit/network is good deal more
>> >>complex than anything built sofar (eg, the letter-recognition
>> >>networks.)
>> >>How do you have the network "associate" two inputs? Ie, how do you
>> >>program the response system so that after a sufficient number of dual
>> >>inputs from radically different sensors the network exhibits the same
>> >>respone(s) for either input in isolation?
>> >
>> > I thought this might be your answer, but what you describe is no real
>> > help. For one thing, an ant is not a symbol and language-processing
>> > human.
>>
>> True, but it is one smart cookie compared to the most sophisticated
>> robots that have been built sofar. They are also trainable.
>
>
>
> What you've done with your answers is to show exactly what has been
> pointed out many times. Beh provides some help at the highest-most
> levels of the List, but not much towards building specific AI
> solutions. We already know this.
>
> If you think, as you do, that
>
>> That's why I assert that making a bacterium-level learner is apparently
>> beyond us at this time.
>
> then what will help? That's the real question. Techniques of "classical
> conditioning" have already been shown to not get very far.

No, fool, what is the case is that we lack the capacity to implement
classical conditioning. C'mon, Mr. Physiology, build a robot that implements
classical conditioning in all its complexity. Or do you want to give another
"lecture" on the 30+ modules involved in seeing?




So, more of
> the same will magically solve the problem? This approach can solve some
> simple problems, is all, and has been tried many times already.
>
>
>
>>
>> > In addition, people have already been doing what you describe
>> > since the time of Grey Walter and his tortoises, in 1948 or so. CORA =
>> > conditioned reflex analogue. Simple variations on Brooks' subsumption
>> > also do this. Most AI'ers would dismiss this level of intelligence as a
>> > toy. People already have "many" schemes to do association,
>> > letter-processing, etc. It's not that hard.
>>
>> These machine don't come close to implementing conditioning (for
>> example.) Letter processing is not AI - it's just another form of
>> pattern recognition (not pattern _learning_, as I understand it). And so
>> on.
>>
>> > It is well illustrated from past work that simple learners will NOT do
>> > the real job of AI. The search-space [problem-space] is just too
>> > enormous for simple naiive learning approaches to handle. Many people
>> > have already tried this route, and some like Curt and Bill.M still try
>> > it.
>>
>> The devices you have alluded to are not "simple learners". They aren't
>> even at the level of bacteria, which can be trained to change their
>> responses to temperature gradients, pH gradients, etc. IOW, the people
>> who built these machines did not (and do not) have a clear understanding
>> of what they mean by learning, let alone what they mean by intelligence.
>>
>> They devices might be useful in building a learning machine though. EG,
>> you might be able to use several CORA modules, plus assorted circuitry
>> and software to implement a single conditioned reflex.
>>
>> > What evolution has given living organisms is all manner of hardwired
>> > and/or modifiable sensory pre-processors which drastically limit the
>> > search-space. This is why such organisms are able to survive, not just
>> > because they can learn, which many do not even do to any great extent.
>>
>> Ah, I think I see a blind spot. I don't know of any organism that
>> doesn't learn. That's one of the features of an organism: its ability to
>> modify its responses to its environment. To say that what a worm does is
>> not "learning to any great extent" is only possible if you don't in fact
>> know what a worm can learn. It's is not simple learner: I don't know of
>> any device or program that comes close to being able to do what a worm
>> can do.
>>
>> I'm sure that you will accept that learning = changing behaviour. But I
>> suspect that you haven't assimilated the full import of that equation,
>> and are still hung up on symbol processing etc. I suspect further that
>> you give too much weight to the kind of learning that's hard for humans
>> to do, and don't give enough weight to the learning that "comes
>> naturally" (which a lot of people don't even want to call learning, in
>> my experience.) I know it took me a long time to get rid of the bias
>> that all educated people have: that what they did in school and college
>> is what constitutes the bulk of their learning, and/or is the model of
>> learning.
>>
>> I'll give you a simple example: some snails can be "habituated" to
>> continue moving forward into a hostile (say slightly too acid)
>> environment. This is clearly a type of learning: its normal behaviour is
>> to move away from lower pH. Yet the snail can be trained to move towards
>> it.
>>
>> Clearly, some aspects of its physiology must change to allow it to
>> change its behaviour. Does this change occur in the sensors of pH? In
>> the internal chemical responses to pH, eg, some change in enzyme
>> production? In the neurons that trigger avoidance when low pH is sensed?
>> Or what? I don't know. I'm sure one could find out; I suspect that
>> someone has already found out. Now suppose that the change in behaviour
>> is mediated by a change in the chemistry of the sensors. Does that
>> discovery change your concept of "learning?" Why/not?
>>
>> > I, for one, spent several years working with back-prop nets - naiive
>> > generic learners - and they are just too limited in what they can
>> > solve.
>>
>> Precisely. That's my point. They aren't even as smart as bacteria, as
>> far as I can tell (the last time I read a technical description was
>> about 5 years ago, so my impressions are getting rather hazy.) OTOH, I
>> suspect a bacterium might be simulated by a network of such networks
>> (and probably a few other types of NN, too.)
>>
>> > The one thing that behaviorists fail to comprehend, and factor in to
>> > any great degree when arguing nature-vs-nurture, is the ENORMOUS extent
>> > to which the innate [genetically-specified] mechanisms in the nervous
>> > system reduce the problems that the learning systems have to deal with.
>> > For every organism, genetics specifies overall organization, types of
>> > sensory preprocessors, general environmental situations the orgranism
>> > can effectively deal with, types of learning mechanism [if present],
>> > specific architecture of said learning mechanisms, available behavioral
>> > repertoires, on and on.
>>
>> I think them big numbers are a major discombobulation factor for you.
>> They don't faze me at all. And I don't think nothing you say above
>> invalidates EAB's contribution to the enterprise. If anything, it
>> sharpens an understanding of what EAB can contribute: a better concept
>> of how the "problem space" as you call it, is reduced to manageable
>> proportions. Because that's what EAB studies: the "problem space" a she
>> is actually encountered by the organism.
>>
>> Alos, EAB IMO has shown that, for most of us, our concepts of "learning"
>> are not subtle and complex enough.
>>
>> > Without all this underlying infrastructure, simple naiive learning gets
>> > you nowheres. There is a HUGE amount more to the systems than what you
>> > indicate. Basically, what you describe is a trivial approach which
>> > already has been shown for decades to not get us all that far. It's
>> > little more than reactive-robotics with a learning module.
>>
>> Well, I beg to disagree. What you seem to think of as "simple naive
>> learning" is neither. That's why I invited you to consider classical
>> conditioning: I expected you to notice that it actually is rather more
>> complex than "reactive robotics with a learning module." Glen then gave
>> you a load more data to show just how complex it really is.
>>
>> That's why I assert that making a bacterium-level learner is apparently
>> beyond us at this time.
>>
>> >>The behaviour will have to built-in, and hardwired to appear when input
>> >>A is sensed in the environment. Sofar, so good: that's a simple task.
>> >>Now add sensors that provide different inputs, B, C, etc, to which the
>> >>system does not respond. What changes in the response system must occur
>> >>so that it will (eventually) respond to B in the same way as it
>> >>responds
>> >>to A? Um, if we're simulating it, I suppose an array that counts input
>> >>instances would be part of the system; but how you decide what a
>> >>"sufficient number" of instances of A is? Hardwire it (ie, store that
>> >>number in an unwritable array)? What do you do to ensure that the
>> >>system
>> >>will associate Input C with Input B, so that a secondary Pavlovian
>> >>conditioning can occur? And what do you do to allow for the fact that
>> >>when a conditioned behaviour is extinguished, the number of
>> >>presentations to retrain it changes? And so on.
>> >>
>> >>It looks to me that if you think of the system's desired behaviours
>> >>(and
>> >>the desired changes in them) in EAB terms, you get a pretty strict
>> >>specification of "what the client wants." Too strict, perhaps?
>> >>
>> >>You will object that this is a long way from implementing Artifical
>> >>Intelligence. Quite so. But the reason for that is not that we don't
>> >>know how to build an intelligent structure, but that we don't know, let
>> >>alone agree on, what's meant by intelligent behaviour.
>> >>
>> >
>> >
>> >
>> > This last is BS. This is not the limitation with implementing AI.
>>
>> Do you mean my claim that "we don't know, let alone agree on what's
>> meant by intelligent behaviour"?
>>
>> >>So, leave the structure alone for the time being. Concentrate on
>> >>intelligent behaviour, and see whether you can specify that precisely
>> >>enough that you might be able to implement it on a machine.
>> >
>> > You can write software along this same approach using what are called
>> > "stub"-routines. Stub-routines don't actually do anything, except to
>> > simply output apparently-correct return values. People sometimes use
>> > these to satisfy the customer that they have something to show, when
>> > they still have most of the implementation work yet to do.
>> >
>> > It looks like it does something, but it just returns dummy values that
>> > look like they mean something. I also use these when writing code, in
>> > order to do high-level integration of code modules. Then, I go back and
>> > write the actual code inside the modules. Having a return value
>> > outputted involves writing 1 or 2 lines of code, whereas writing the
>> > actual functional code might take 100s or 1000s of lines.
>>
>> OK, I used to call them "empty routines", it seems terminology has
>> standardised since those long ago days. But I really don't see how that
>> relates to my claim about understanding intelligent behaviour, nor my
>> opinion that we can't as yet specify intelligent behaviour well enough
>> to know what we are actually trying to do.
>>
>> HTH
>


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