Matt, I heartily disagree with your view as expressed here, and as stated to my by heads of CS departments and other "high ranking" CS PhDs, nearly (but not quite) all of whom have lost the "fire in the belly" that we all once had for CS/AGI.
I DO agree that CS is like every other technological endeavor, in that almost everything that can be done as a PhD thesis has already been done. but there is a HUGE gap between a PhD thesis scale project and what that same person can do with another few more millions and a couple more years, especially if allowed to ignore the naysayers. The reply is a even more complex than your well documented statement, but I'll take my best shot at it, time permitting. Here, the angel is in the details. On 9/5/08, Matt Mahoney <[EMAIL PROTECTED]> wrote: > > --- On Fri, 9/5/08, Steve Richfield <[EMAIL PROTECTED]> wrote: > >I think that a billion or so, divided up into small pieces to fund EVERY > >disparate approach to see where the "low hanging fruit" is, would go a > >LONG way in guiding subsequent billions. I doubt that it would take a > >trillion to succeed. > > Sorry, the low hanging fruit was all picked by the early 1960's. By then we > had neural networks [1,6,7,11,12], ... but we STILL do not have any sort of useful *unsupervised* NN, the equivalent of which seems to be needed for any good AGI. Note my recent postings about a potential "theory of everything" that would most directly hit unsupervised NN, providing not only a good way of operating these, but possibly the provably best way of operating. natural language processing and language translation [2], My Dr. Eliza is right there and showing that useful "understanding" out of precise context is almost certainly impossible. I regularly meet with the folks working on the Russian translator project, and rest assured, things are STILL advancing fairly rapidly. Here, there is continuing funding, and I expect that the Russian translator will eventually succeed (they already claim success). models of human decision making [3], These are curious but I believe them to be an emergent properties of processes that we don't understand at all, so they have no value other than for testing of future systems. Note that "human decision making" does NOT generally include many advanced sorts of logic that simply don't occur to ordinary humans, which is where an AGI could shine. Hence, understanding the human but not the non-human processes is nearly worthless. automatic theorem proving [4,8,10], Great for when you already have the answer - but what is it good for?! natural language databases [5], Which are only useful if/when the provably false presumption is true that NL "understanding" is generally possible. game playing programs [9,13], Note relevant for AGI. optical character recognition [14], Only recently have methods emerged that are truly font-independent. This SHOULD have been accomplished long ago (like shortly after your 1960 reference), but no one wanted to throw significant money at it. I nearly launched an OCR company (Cognitext) in 1981, but funding eventually failed * because* I had done the research and had a new (but *un*proven) method that was truly font-independent. handwriting and speech recognition [15], ... both of which are now good enough for AI interaction (e.g. my Gracie speech I/O interface to Dr. Eliza), but NOT good enough for general dictation. Unfortunately, the methods used don't seem to shed much light on how the underlying processes work in us. and important theoretical work [16,17,18]. Note again my call for work/help on what I call "computing's theory of everything" leveraging off of principal component analysis. Since then we have had mostly just incremental improvements. YES. This only shows that the support process has long been broken. and NOT that there isn't a LOT of value that is just out of reach of PhD-sized projects. Big companies like Google and Microsoft have strong incentives to develop AI Internal politics at both (that I have personally run into) restrict expenditures to PROVEN methods, as a single technical failure spells doom for the careers of everyone working on them. Hence, their R&D is all D and no R. and have billions to spend. Not one dollar of which goes into what I would call genuine "research". Maybe the problem really is hard. ... and maybe it is just a little difficult. My own Dr. Eliza program has seemingly unbelievable NL-stated problem solving capabilities, but is built mostly on the same sort of 1960s technology you cited. Why wasn't it built before 1970? I see two simple reasons: 1. Joe Weizenbaum, in his *Computer Power and Human Reason,* explained why this approach could never work. That immediately made it impossible to get any related effort funded or acceptable in a university setting. 2. It took about a year to make a demonstrable real-world NL problem solving system, which would have been at the outer reaches of a PhD or casual personal project. I have a similar story for processor architecture which I have discussed here. It appears possible to build processors that are ~10,000 times faster on the same fabrication equipment, but that the corporate cultures at Intel and others makes this impossible. >From my vantage point, the fallen fruit has already been picked up, but the best fruit is still on the tree and waiting to be picked. The lowest-hanging branches may have been cleared, but if you just stand on your tiptoes, most of it is right there. Steve Richfield ================ > References > > 1. Ashby, W. Ross (1960), Design for a Brain, 2'nd Ed., London: Wiley. > Describes a 4 neuron electromechanical neural network. > > 2. Borko, Harold (1967), Automated Language Processing, The State of the > Art, New York: Wiley. Cites 72 NLP systems prior to 1965, and the 1959-61 > U.S. government Russian-English translation project. > > 3. Feldman, Julian (1961), "Simulation of Behavior in the Binary Choice > Experiment", Proceedings of the Western Joint Computer Conference 19:133-144 > > 4. Gelernter, H. (1959), "Realization of a Geometry-Theorem Proving > Machine", Proceedings of an International Conference on Information > Processing, Paris: UNESCO House, pp. 273-282. > > 5. Green, Bert F. Jr., Alice K. Wolf, Carol Chomsky, and Kenneth Laughery > (1961), "Baseball: An Automatic Question Answerer", Proceedings of the > Western Joint Computer Conference, 19:219-224. > > 6. Hebb, D. O. (1949), The Organization of Behavior, New York: > Wiley. Proposed the first model of learning in neurons: when two neurons > fire simultaneously, the synapse between them becomes stimulating. > > 7. McCulloch, Warren S., and Walter Pitts (1943), "A logical calculus of > the ideas immanent in nervous activity", Buletin of Mathematical Biophysics > (5) pp. 115-133. > > 8. Newell, Allen, J. C. Shaw, H. A. Simon (1957), "Empirical Explorations > with the Logic Theory Machine: A Case Study in Heuristics", Proceedings of > the Western Joint Computer Conference, 15:218-239. > > 9. Newell, Allen, J. C. Shaw, and H. A. Simon (1958), "Chess-Playing > Programs and the Problem of Complexity", IBM Journal of Research and > Development, 2:320-335. > > 10. Newell, Allen, H. A. Simon (1961), "GPS: A Program that Simulates Human > Thought", Lernende Automaten, Munich: R. Oldenbourg KG. > > 11. Rochester, N., J. J. Holland, L. H. Haibt, and Wl L. Duda (1956), > "Tests on a cell assembly theory of the action of the brain, using a large > digital computer", IRE Transactions on Information Theory IT-2: pp. 80-93. > > 12. Rosenblatt, F. (1958), "The perceptron: a probabilistic model for > information storage and organization in the brain", Psychological Review > (65) pp. 386-408. > > 13. Samuel, A. L. (1959), "Some Studies in Machine Learning using the Game > of Checkers", IBM Journal of Research and Development, 3:211-229. > > 14. Selfridge, Oliver G., Ulric Neisser (1960), "Pattern Recognition by > Machine", Scientific American, Aug., 203:60-68. > > 15. Uhr, Leonard, Charles Vossler (1963) "A Pattern-Recognition Program > that Generates, Evaluates, and Adjusts its own Operators", Computers and > Thought, E. A. Feigenbaum and J. Feldman eds, New York: McGraw Hill, pp. > 251-268. > > 16. Turing, A. M., (1950) "Computing Machinery and Intelligence", Mind, > 59:433-460. > > 17. Shannon, Claude, and Warren Weaver (1949), The Mathematical Theory of > Communication, Urbana: University of Illinois Press. > > 18. Minsky, Marvin (1961), "Steps toward Artificial Intelligence", > Proceedings of the Institute of Radio Engineers, 49:8-30. > > > -- Matt Mahoney, [EMAIL PROTECTED] > > > > ------------------------------------------- > agi > Archives: https://www.listbox.com/member/archive/303/=now > RSS Feed: https://www.listbox.com/member/archive/rss/303/ > Modify Your Subscription: > https://www.listbox.com/member/?&; > Powered by Listbox: http://www.listbox.com > ------------------------------------------- agi Archives: https://www.listbox.com/member/archive/303/=now RSS Feed: https://www.listbox.com/member/archive/rss/303/ Modify Your Subscription: https://www.listbox.com/member/?member_id=8660244&id_secret=111637683-c8fa51 Powered by Listbox: http://www.listbox.com
