Path: utzoo!attcan!uunet!wuarchive!cs.utexas.edu!tut.cis.ohio-state.edu!purdue!tippy!sawmill!mdbs!zed From: zed@mdbs.uucp (Bill Smith) Newsgroups: comp.ai Subject: Re: What Has Traditional AI Accomplished? Message-ID: <1990Oct27.203010.1378@mdbs.uucp> Date: 27 Oct 90 20:30:10 GMT References: <69609@lll-winken.LLNL.GOV> <1990Oct15.143325.26044@unislc.uucp> <1990Oct16.135631.6444@cbnewsj.att.com> <69929@lll-winken.LLNL.GOV> <3740@media-lab.MEDIA.MIT.EDU> Reply-To: zed@mdbs.UUCP (Bill Smith) Organization: mdbs, Inc. Lines: 206 Growth is slow, but it builds sturdy foundations. Voices are weak yet they overcome evil. Society is concrete but it is a fluid home for members. Progress is finite yet it touches the infinite. wws, sept 12 1988 ============================================================================== In article <3740@media-lab.MEDIA.MIT.EDU> minsky@media-lab.media.mit.edu (Marvin Minsky) writes: >In article <69929@lll-winken.LLNL.GOV> loren@tristan.llnl.gov (Loren Petrich) writes: > >> The simplicity of the basic algorithms keep making me wonder >>why NN's did not take off earlier -- the basic code for one takes up >>only a couple pages of Fortran or C. Try writing one yourself. I guess >>that (in)famous book by Minsky and Papert, _Perceptrons_, with its >>seemingly airtight theoretical arguments, is what had squelched the >>field for so long. > >DAMMIT. Try reading the book. What happened was that the field had >already flattened out, because, although Perceptrons could learn to >recognize certain patterns, they seemed unable to learn some other >kinds of patterns. The book explicitly analyzes "three layer nets" -- >input layer / coefficients / hidden layer / coefficients / and single >neuron output. But, in fact, most theorems apply to unrestricted multilayer, >loop-free nets. This does not seem to be well-known. I assumed it >was obvious. That's right. Curse at what you don't understand. The Jews did it in the first century A.D. I guess it makes sense that they've continued to do it till today. > >Since no one has found any errors in those "seemingly airtight >theoretical arguments", you should try to understand what point you're >missing! It seems strange that I should have to do explain this in >comp.ai, at this late date. "Perceptrons" explained that it will be >hard for such nets to recognize, for example, certain kinds of >group-invariant recognitions, without duplicating hardware for every >element of the group. "Group invariant?!?!!!" Do you even *know* the mathematical definition of a Group? A Group is a set of elements and 1 binary operator that is closed under that operation. Such a simple idea, yet it couldn't be more complicated in reality. "Duplicating Hardware!?!???" What about recycling, saving the environment and life, happiness and the American Way? Have you ever heard of a crisis in faith that is shaking the morally dead of the earth? But, since they are dead, it don't matter that they will all die (permanently) within a year or 10. > > EXAMPLE: in a simple 100 x 100 square retina, recognize all the > images that could be reasonably described as depicting "A SQUARE > INSIDE A CIRCLE". What a good idea! The greeks loved it. Especially Alpha Chi Rho. > >Loren and others are absolutely right, in that the 80's showed that ML >(multilayer) nets could be made to learn many useful patterns. >"Perceptrons" was concerned with patterns that MLs couldn't learn, not >ones they could!!!!!!!!!! And as such it is a complete work of unadulterated academia and should be flushed down a toilet, except that the toilet would back up, a plumber would have to be called and the department head would sure have some funny looks to make about the whole funny incident. It would be so funny, and he would be such a perfect academic that he wouldn't get the joke. Herman Rubin should try this on his lunch hour October 30, 1990 as an April Fools Joke (postponed) If he get's fired, well, it just as well, because there are better things awaiting him. > >So no collection of exciting stories of MLs learning things counters >the problems with what they can't learn -- like those distance >invariant relationships between parts of images. Oh get a life! > >In many cases, "successful" applications of MLs depend on >pre-processing a picture image, by first normalizing it in size, and >then centering it, before presenting it to the ML. Fine - but don't >tell people that this refutes the Minsky-Papert theorems. Instead, >now try todo that "circle-ionside-square" problem! And then realize >that many real-world problems require multiple normalizations, which >cannot be pre-computed until you have picked out the sub-patterns. Theorems are, by definition incomplete. If a theorem were complete, It would explain why God is gay. > >In that connection, there is wisdom in Thomas G Edwards' remarks in ><6664@jhunix.HCF.JHU.EDU>: > > ... Cascade-Correlation is a NN algorithm which is able to solve > many problems which were difficult for homogenous NNs to solve. ... > I see a future where inductive learning by small homogeneous NNs > is used in combination with more traditional AI type goal building. > Cascade-Correlation is a step in that direction. Divide-and-conquer > of traditional AI is combined with the easy inductive learning of > traditional NNs. Of course, the trick is to couch this in a > connectionist framework to continue to allow for fast parallel > computation. > Quoting scripture will get you a dinner of your works when the revolution comes. Not only that, but it will be so well seasoned and tenderly baked that you will even enjoy the aroma of the decaying ink. >Divide-and-conquer is surely needed for circle-inside-square. Note >that we still don't nkow how the brain does it. I don't "nkow" how the brain does it either. I nkow all information in binary decisions: yes/no up/down in/out live/die Q-bit/R-bit cubit/metre happy/sade (say, the Marquis was a knower of things you don't even imagine) Oh what I would nkow to conquer you assholes so that they could be divided into perfect lattices of neural networks able to calculate Pi at a speed that makes the Cray computers look like the stone tablets of Sumerians that they are. You didn't know that Cray is Iraqi like the rest of the civilized world, did you? > >Get with it, guys! Of course there are many exciting things that can >be done with ML networks. A good deal of the brain is made of them. >And there is a lot that require non-ML networks, and a lot of the >brain is non-ML. Instead of bashing "Perceptrons", you should use it >as a model, and try to find more general statements about what ML and >other networks can do, and what are their limitations. Fortunately, I can do just fine without a brain. I've managed to beat it into submission with an awl and meat hook. Ok, so I made that part up. What will you do? Censor me? > >What we don't need are intemperate remarks like those in >, who seems to >deliberately misinterpret everything I have said in this group and >other places. I don't know why he's so angry at me. I am angry too. I don't blame him. An asshole is an asshole. Pure and simple. and you are just one of them. > >For example, in one message to this group I said: > > "... Where is the "traditional, symbolic, AI in the brain"? The > answer seems to have escaped almost everyone on both sides of this > great and spurious controversy! The 'traditional AI' lies in the > genetic specifications of those functional interconnections: the bus > layout of the rel A large, perhaps messy software is there before your > eyes, hiding in the gross anatomy. Some 3000 "rules" about which > sub-NN's should do what, and under which conditions, as dictated by > the results of computations done in other NNs...." > >Pollack replied, with this weird objection > > "I have to admit this is definitely a novel version of the > homunculus fallacy: If we can't find him in the brain, he must be > in the DNA! Of all the data and theories on cellular division and > specialization and on the wiring of neural pathways I have come > across, none have indicated that DNA is using means-ends analysis." > >And then, he proceeded to make the same points that I have been >making, as though it were different from what I was saying: > > "Certainly, connectionist models are very easy to decimate when > offered up as STRONG models of children learning language, of real > brains, of spin glasses, quantum calculators, or whatever. That is > why I view them as working systems which just illuminate the > representation and search processes (and computational theories) which > COULD arise in natural systems. There is plenty of evidence of > convergence between representations found in the brain and backprop or > similar procedures despite the lack of any strong hardware equivalence > (Anderson, Linsker); constrain the mapping task correctly, and local > optimization techniques will find quite similar solutions. > >It is the same thing again. Yes, you can find things nets do, but >it's like bad statistics in which you don't describe what you're >testing for until after the experiment is done. Let's see an ML solve >circle-in-square. Let's see one of Pollack's massively parallel >parsers solve circel in square. Without any "strong hardware" >pre-figuring of the network. In fact, Pollack's next paragraph begins >with Oh, so you are a statistiction. You have a brilliant, but short career ahead of you. > > "Furthermore, the representations and processes discovered by >connectionist models may have interesting scaling properties and can >be given plausible adaptive accounts." > >Is he angry at me because the required scaling properties for human >visual perception are not among those posessed by the NN models he >advocates? I don't know, by there must be some reason for his rage? >He finishes with, > > "On the other hand, I take it as a weakness of a theory of > intelligence, mind or language if, when pressed to reveal its > origin, shows me a homunculus, unbounded nativism, or some > evolutionary accident with the same probability of occurrence as God. > >Is this a paraphrase of the beginning of "Society of Mind", or does >Pollack think it is opposing it. Come on Jordan. We're on the same >side. Yet you have been writing the most hostile and savage reviews >of my work. What's the deal here? Know I nkow that you are on different sides. Jordan is good. You are not. Hebrews are not the children of God. They are an angry, stiff necked race that God chose as his Own only because He is Himself a Hebrew. An asshole is a great thing to be. Lucky you are that you are one and don't just have one. (propriety limits my further comments on this subject.) God (Yes, I am lying)(Obviously) Don't forget to pray for the 6,000,000 who were saved by God from the reign of Harry Truman, King of Japan. From LBJ, a Texan of profound self-esteem in his own mind. From King Nixon, the man who kept the Cambodians enslaved and murdered each of them (personally and without regret) From King Ronald, clown for a day, King of Japan. King George IV of Kennebunkport, whom God loves and will allow to amend for his valiant service in the military. Death is too good for "men" such as these. They must, They must, They must, They must make amends.