Re: AI isn't cheap (was Re: Real vs. simulated environments (was Re: [agi] draft for comment.. P.S.))
/aixigentle.htm 3. Legg, Shane, (2006), Is There an Elegant Universal Theory of Prediction?, Technical Report IDSIA-12-06, IDSIA / USI-SUPSI, Dalle Molle Institute for Artificial Intelligence, Galleria 2, 6928 Manno, Switzerland. http://www.vetta.org/documents/IDSIA-12-06-1.pdf -- Matt Mahoney, [EMAIL PROTECTED] --- On *Sat, 9/6/08, Steve Richfield [EMAIL PROTECTED]* wrote: From: Steve Richfield [EMAIL PROTECTED] Subject: Re: AI isn't cheap (was Re: Real vs. simulated environments (was Re: [agi] draft for comment.. P.S.)) To: agi@v2.listbox.com Date: Saturday, September 6, 2008, 2:58 PM 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 RD 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
Re: AI isn't cheap (was Re: Real vs. simulated environments (was Re: [agi] draft for comment.. P.S.))
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 RD 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
Re: AI isn't cheap (was Re: Real vs. simulated environments (was Re: [agi] draft for comment.. P.S.))
Steve, where are you getting your cost estimate for AGI? Is it a gut feeling,
or something like the common management practice of I can afford $X so it will
cost $X?
My estimate of $10^15 is based on the value of the world economy, US $66
trillion per year and increasing 5% annually over the next 30 years, which is
how long it will take for the internet to grow to the computational power of
10^10 human brains (at 10^15 bits and 10^16 OPS each) at the current rate of
growth, doubling every couple of years. Even if you disagree with these numbers
by a factor of 1000, it only moves the time to AGI by a few years, so the cost
estimate hardly changes.
And even if the hardware is free, you still have to program or teach about
10^16 to 10^17 bits of knowledge, assuming 10^9 bits of knowledge per brain [1]
and 1% to 10% of this is not known by anyone else. Software and training costs
are not affected by Moore's law. Even if we assume human level language
understanding and perfect sharing of knowledge, the training cost will be 1% to
10% of your working life to train the AGI to do your job.
Also, we have made *some* progress toward AGI since 1965, but it is mainly a
better understanding of why it is so hard, e.g.
- We know that general intelligence is not computable [2] or provable [3].
There is no neat theory.
- From Cyc, we know that coding common sense is more than a 20 year effort.
Lenat doesn't know how much more, but guesses it is maybe between 0.1% and 10%
finished.
- Google is the closest we have to AI after a half trillion dollar effort.
1. Landauer, Tom (1986), “How much do
people remember? Some estimates of the quantity of learned
information in long term memory”, Cognitive Science (10) pp.
477-493.
2. Hutter, Marcus (2003), A Gentle
Introduction to The Universal Algorithmic Agent {AIXI}, in
Artificial General Intelligence, B. Goertzel and C. Pennachin
eds., Springer. http://www.idsia.ch/~marcus/ai/aixigentle.htm
3. Legg, Shane, (2006), Is There an
Elegant Universal Theory of Prediction?, Technical Report
IDSIA-12-06, IDSIA / USI-SUPSI, Dalle Molle Institute for Artificial
Intelligence, Galleria 2, 6928 Manno, Switzerland.
http://www.vetta.org/documents/IDSIA-12-06-1.pdf
-- Matt Mahoney, [EMAIL PROTECTED]
--- On Sat, 9/6/08, Steve Richfield [EMAIL PROTECTED] wrote:
From: Steve Richfield [EMAIL PROTECTED]
Subject: Re: AI isn't cheap (was Re: Real vs. simulated environments (was Re:
[agi] draft for comment.. P.S.))
To: agi@v2.listbox.com
Date: Saturday, September 6, 2008, 2:58 PM
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
Re: Real vs. simulated environments (was Re: [agi] draft for comment.. P.S.)
Matt, FINALLY, someone here is saying some of the same things that I have been saying. With general agreement with your posting, I will make some comments... On 9/4/08, Matt Mahoney [EMAIL PROTECTED] wrote: --- On Thu, 9/4/08, Valentina Poletti [EMAIL PROTECTED] wrote: Ppl like Ben argue that the concept/engineering aspect of intelligence is independent of the type of environment. That is, given you understand how to make it in a virtual environment you can then tarnspose that concept into a real environment more safely. This is probably a good starting point, to avoid beating the world up during the debugging process. Some other ppl on the other hand believe intelligence is a property of humans only. Only people who haven't had a pet believe such things. I have seen too many animals find clever solutions to problems. So you have to simulate every detail about humans to get that intelligence. I'd say that among the two approaches the first one (Ben's) is safer and more realistic. The issue is not what is intelligence, but what do you want to create? In order for machines to do more work for us, they may need language and vision, which we associate with human intelligence. Not necessarily, as even text-interfaced knowledge engines can handily outperform humans in many complex problem solving tasks. The still open question is: What would best do what we need done but can NOT presently do (given computers, machinery, etc.). So far, the talk here on this forum has been about what we could do and how we might do it, rather than about what we NEED done. Right now, we NEED resources to work productively in the directions that we have been discussing, yet the combined intelligence of those here on this forum is apparently unable to solve even this seemingly trivial problem. Perhaps something more than raw intelligence is needed? But building artificial humans is not necessarily useful. We already know how to create humans, and we are doing so at an unsustainable rate. I suggest that instead of the imitation game (Turing test) for AI, we should use a preference test. If you prefer to talk to a machine vs. a human, then the machine passes the test. YES, like what is it that our AGI can do that we need done but can NOT presently do? Prediction is central to intelligence. If you can predict a text stream, then for any question Q and any answer A, you can compute the probability distribution P(A|Q) = P(QA)/P(Q). This passes the Turing test. More importantly, it allows you to output max_A P(QA), the most likely answer from a group of humans. This passes the preference test because a group is usually more accurate than any individual member. (It may fail a Turing test for giving too few wrong answers, a problem Turing was aware of in 1950 when he gave an example of a computer incorrectly answering an arithmetic problem). Unfortunately, this also tests the ability to incorporate the very misunderstandings that presently limit our thinking. We need to give credit for compression algorithms that cleans up our grammar, corrects our technical errors, etc., as these can probably be done in the process of better compressing the text. Text compression is equivalent to AI because we have already solved the coding problem. Given P(x) for string x, we know how to optimally and efficiently code x in log_2(1/P(x)) bits (e.g. arithmetic coding). Text compression has an advantage over the Turing or preference tests in that that incremental progress in modeling can be measured precisely and the test is repeatable and verifiable. If I want to test a text compressor, it is important to use real data (human generated text) rather than simulated data, i.e. text generated by a program. Otherwise, I know there is a concise code for the input data, which is the program that generated it. When you don't understand the source distribution (i.e. the human brain), the problem is much harder, and you have a legitimate test. Wouldn't it be better to understand the problem domain while ignoring human (mis)understandings? After all, if humans need an AGI to work in a difficult domain, it is probably made more difficult by incorporating human misunderstandings. Of course, humans state human problems, so it is important to be able to semantically communicate, but also useful to separate the communications from the problems. I understand that Ben is developing AI for virtual worlds. This might produce interesting results, but I wouldn't call it AGI. The value of AGI is on the order of US $1 quadrillion. It is a global economic system running on a smarter internet. I believe that any attempt to develop AGI on a budget of $1 million or $1 billion or $1 trillion is just wishful thinking. 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
AI isn't cheap (was Re: Real vs. simulated environments (was Re: [agi] draft for comment.. P.S.))
--- 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], natural language processing and language translation [2], models of human decision making [3], automatic theorem proving [4,8,10], natural language databases [5], game playing programs [9,13], optical character recognition [14], handwriting and speech recognition [15], and important theoretical work [16,17,18]. Since then we have had mostly just incremental improvements. Big companies like Google and Microsoft have strong incentives to develop AI and have billions to spend. Maybe the problem really is hard. 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/?member_id=8660244id_secret=111637683-c8fa51 Powered by Listbox: http://www.listbox.com
Re: [agi] draft for comment.. P.S.
That's if you aim at getting an AGI that is intelligent in the real world. I think some people on this list (incl Ben perhaps) might argue that for now - for safety purposes but also due to costs - it might be better to build an AGI that is intelligent in a simulated environment. Ppl like Ben argue that the concept/engineering aspect of intelligence is *independent of the type of environment*. That is, given you understand how to make it in a virtual environment you can then tarnspose that concept into a real environment more safely. Some other ppl on the other hand believe intelligence is a property of humans only. So you have to simulate every detail about humans to get that intelligence. I'd say that among the two approaches the first one (Ben's) is safer and more realistic. I am more concerned with the physics aspect of the whole issue I guess. --- 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=8660244id_secret=111637683-c8fa51 Powered by Listbox: http://www.listbox.com
Real vs. simulated environments (was Re: [agi] draft for comment.. P.S.)
--- On Thu, 9/4/08, Valentina Poletti [EMAIL PROTECTED] wrote: Ppl like Ben argue that the concept/engineering aspect of intelligence is independent of the type of environment. That is, given you understand how to make it in a virtual environment you can then tarnspose that concept into a real environment more safely. Some other ppl on the other hand believe intelligence is a property of humans only. So you have to simulate every detail about humans to get that intelligence. I'd say that among the two approaches the first one (Ben's) is safer and more realistic. The issue is not what is intelligence, but what do you want to create? In order for machines to do more work for us, they may need language and vision, which we associate with human intelligence. But building artificial humans is not necessarily useful. We already know how to create humans, and we are doing so at an unsustainable rate. I suggest that instead of the imitation game (Turing test) for AI, we should use a preference test. If you prefer to talk to a machine vs. a human, then the machine passes the test. Prediction is central to intelligence. If you can predict a text stream, then for any question Q and any answer A, you can compute the probability distribution P(A|Q) = P(QA)/P(Q). This passes the Turing test. More importantly, it allows you to output max_A P(QA), the most likely answer from a group of humans. This passes the preference test because a group is usually more accurate than any individual member. (It may fail a Turing test for giving too few wrong answers, a problem Turing was aware of in 1950 when he gave an example of a computer incorrectly answering an arithmetic problem). Text compression is equivalent to AI because we have already solved the coding problem. Given P(x) for string x, we know how to optimally and efficiently code x in log_2(1/P(x)) bits (e.g. arithmetic coding). Text compression has an advantage over the Turing or preference tests in that that incremental progress in modeling can be measured precisely and the test is repeatable and verifiable. If I want to test a text compressor, it is important to use real data (human generated text) rather than simulated data, i.e. text generated by a program. Otherwise, I know there is a concise code for the input data, which is the program that generated it. When you don't understand the source distribution (i.e. the human brain), the problem is much harder, and you have a legitimate test. I understand that Ben is developing AI for virtual worlds. This might produce interesting results, but I wouldn't call it AGI. The value of AGI is on the order of US $1 quadrillion. It is a global economic system running on a smarter internet. I believe that any attempt to develop AGI on a budget of $1 million or $1 billion or $1 trillion is just wishful thinking. -- 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/?member_id=8660244id_secret=111637683-c8fa51 Powered by Listbox: http://www.listbox.com
Re: [agi] draft for comment.. P.S.
I think I have an appropriate term for what I was trying to conceptualise. It is that intelligence has not only to be embodied, but it has to be EMBEDDED in the real world - that's the only way it can test whether information about the world and real objects is really true. If you want to know whether Jane Doe is great at sex, you can't take anyone's word for it, you have to go to bed with her. [Comments on the term esp. welcome). --- 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=8660244id_secret=111637683-c8fa51 Powered by Listbox: http://www.listbox.com
