Richard, Despite your statement to the contrary --- despite your "FURY" --- I did get your point. Not everybody beside Richard Loosemore is stupid.
I understand there have been people making bold promises in AI for over 40 years, and most of them have been based on a gross under estimation of the problem. For example, in 1969 Minsky was claiming with then current minicomputers AI would surpass human intelligence within several years. But in 1970, after my year long special study my senior year at Harvard, in which I read a long reading list Minsky gave me, I came to the conclusion that Minsky projection seemed rediculous. I believed human level thinking required deep experiential knowledge (now called grounding) and that I seriously doubted anybody could make a human level AI without hardware capable of storing many terabytes of memory, and the ability to access significant portions of that memory multiple times a second -- a level of hardware that is still not available, and that only recently been approximated at a cost of many tens of millions of dollars. To date, I am unaware of anyone approaching AGI with the type of hardware that I have felt for much of the last 38 years would be necessary for human level AGI. So don't accuse me of being one of those who has been shown to have been making false AI promises, because the hardware my predictions have been based on has never yet been available to AI researchers. Since 1970 I have thought that if multiple teams had really powerful hardware of the type you can buy now for several million dollars (but which would have cost 20 to 30 times as much just a decade ago), although this hardware was not capable of human level performance, it would enable very rapid progress in AI in just ten or twenty years. But that was before I became aware of the advances in brain science and AI that have been made in the last decade or two, advances that have radically improved and clarified my understanding of they type of computation architectures needed for various mind functions. Now, we actually have good ideas how to address almost all of the known functions of the mind that we would want an AGI to have. For people like Ben, Joscha Bach, Sam Adams, myself, and multiple others IT IS NOT THAT WE --- as you claim --- "JUST HAVE THIS BELIEF THAT IT WILL WORK." --- We have much more. WE HAVE REASONABLY GOOD EXPLANATIONS FOR HOW TO PERFORM ALMOST ALL OF THE MENTAL FUNCTIONS OF THE HUMAN MIND THAT WE WANT AGI'S TO HAVE. It not as if these explanations are totally nailed down, at least in my mind. (They may be much better nailed down in Ben's, Joscha Bach's, and Sam Adams's.) But I have an idea at a high level how each of them could be made to work. This is relatively new, at least for me. They are complex multi-level arguments so they cannot be conveyed briefly. Ben has probably done a better job of putting his ideas in writing, and his recent post in this thread promises that relatively shortly he will provide them in much more detail. One example of some of the new reasons for confidence that we are learning how to design AGI is shown in the amazing success of the Serre-Poggio system descrived at http://cbcl.mit.edu/projects/cbcl/publications/ps/MIT-CSAIL-TR-2006-028.pdf. This paper show the tremendous advances that have been made in automatically learning hierarchical memory, and the power such memory provides in machine perceptions. This is not belief. This a significantly automatically learned system that works amazingly well for the rapid feed forward part of visual object recognition. Another reason for optimism is Hintons new work described in papers such as "Modeling image patches with a directed hierarchy of Markov random fields" by Simon Osindero and Geoffrey Hinton and the Google Tech Talk at http://www.youtube.com/watch?v=AyzOUbkUf3M. Hinton has shown how to automatically learn hierarchical neural nets that have 2000 hidden nodes in one layer, 500 in the next, and 1000 in the top layer. In the past it would have been virtually impossible to train a neural net with so many hidden nodes, but Hinton's new method allows rapid largely automatic training of such large networks, enabling in the example show, surprisingly good handwritten numeral recognition. Yet another example of the power of automatic learning is shown by impressive success of Hecht-Nielsen confabulation system in generating a second sentence that reasonably follows from first, as if it had been written by a human intelligence, withoug any attempt to teach the rules of grammar or any explicit semantic knowledge. The system learns from text corpora. You may say this is narrow AI. But it all has general applicability. For example, the type of hierarchical memory with max-pooling shown in Serre's paper shows is an extremely powerful paradigm that addresses some of the most difficult problems in AI, including robust non-literal matching. Such hierarchical memory can be modified to perform a lot of tasks for which many people in AI still think there is no method for solving, such as complex context-appropriate inferencing. Hinton's paper shows that neural net learning is suddenly much more powerful than it has been before. And Hecht-Neilsen's paper shows another powerful form or neural net-like learning and computing that scales well. The convergence of such much more sophisticated software approaches and the much more powerful hardware necessary to actually build minds that use them is much more than just a belief. Today, for $33K you can buy a system I talked about in my email which started this thread. It has 126Gbytes of RAM and roughly 160Million random RAM access/second. This is enough power to start building small toy AGI mind that could show limited generalized learning, perception, inferencing, planning, behaviors, attention focusing, and behavior selection, i.e., something like Ben's pet brains. The $850K system would allow substantially more sophisticated demonstrations of artificial minds to be created. This combination of much more sophisticated understandings for how to build AGI's, combined with much more powerful hardware is something new. And, much, much more powerful hardware should be arriving in about 6 years when multi-level chips with mesh-networked, massively-mutli-cored processors, and 8 or more layers of memory connected to the processors with many thousands of though silicon vias, and with hundreds of high speed channels to external memory and other such multi-level chips will hopefully become routinely available. Richard, a lot has changed since the '70, '80, '90s, and early '00s --- and if you do see it --- that's your problem. Ed Porter -----Original Message----- From: Richard Loosemore [mailto:[EMAIL PROTECTED] Sent: Saturday, June 28, 2008 4:14 PM To: [email protected] Subject: Re: [agi] WHAT SORT OF HARDWARE $33K AND $850K BUYS TODAY FOR USE IN AGI Ed Porter wrote: > I do not claim the software architecture for AGI has been totally solved. > But I believe that enough good AGI approaches exist (and I think Novamente > is one) that when powerful hardware available to more people we will be able > to relatively quickly get systems up and running that demonstrate the parts > of the problems we have solved. And that will provide valuable insights and > test beds for solving the parts of the problem that we have not yet solved. You are not getting my point. What you just said was EXACTLY what was said in 1970, 1971, 1972, 1973 ......2003, 2004, 2005, 2006, 2007 ...... And every time it was said, the same justification for the claim was given: "I just have this belief that it will work". Plus ca change, plus c'est la meme fubar. > With regard to your statement "the problem is understanding HOW TO DO IT" > --- > WE DO UNDERSTAND HOW TO DO IT --- NOT ALL OF IT --- AND NOT HOW TO MAKE IT > ALL WORK TOGETHER WELL AUTOMATICALLY --- BUT --- GIVEN THE TYPE OF HARDWARE > EXPECTED TO COST LESS THAN $3M IN 6 YEARS --- WE KNOW HOW TO BUILD MUCH OF > IT --- ENOUGH THAT WE COULD PROVIDE EXTREMELY VALUABLE COMPUTERS WITH OUR > CURRENT UNDERSTANDINGS. You do *not* understand how to do it. But I have to say that statements like your paragraph above are actually very good for my health, because their humor content is right up there in the top ten, along with Eddie Izzard's Death Star Canteen sketch and Stephen Colbert at the 2006 White House Correspondents' Association Dinner. So long as the general response to the complex systems problem is not "This could be a serious issue, let's put our heads together to investigate it", but "My gut feeling is that this is just not going to be a problem", or "Quit rocking the boat!", you can bet that nobody really wants to ask any questions about whether the approaches are correct, they just want to be left alone to get on with their approaches. History, I think, will have some interesting things to say about all this. Good luck anyway. Richard Loosemore ------------------------------------------- agi Archives: http://www.listbox.com/member/archive/303/=now RSS Feed: http://www.listbox.com/member/archive/rss/303/ Modify Your Subscription: http://www.listbox.com/member/?&; Powered by Listbox: http://www.listbox.com ------------------------------------------- agi Archives: http://www.listbox.com/member/archive/303/=now RSS Feed: http://www.listbox.com/member/archive/rss/303/ Modify Your Subscription: http://www.listbox.com/member/?member_id=8660244&id_secret=106510220-47b225 Powered by Listbox: http://www.listbox.com
