Matt,
On 9/6/08, Matt Mahoney <[EMAIL PROTECTED]> wrote:
> Steve, where are you getting your cost estimate for AGI?
1. I believe that there is some VERY fertile but untilled ground, which if
it is half as good as it looks, could yield AGI a LOT cheaper than other
higher estimates. Of course if I am wrong, I would probably accept your
numbers.
2. I believe that AGI will take VERY different (cheaper and more valuable)
forms than do other members on this forum.
Each of the above effects are worth several orders of magnitude in effort.
> Is it a gut feeling, or something like the common management practice of
> "I can afford $X so it will cost $X"?
Given sufficient motivation and flexibility as to time and deliverable, this
isn't a bad way to go. "You can have it good, quick, and cheap - choose any
two." If you drop "cheap" and choose the other two, then you can get there.
> 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.
Here, you are doing the same thing that you were accusing me of - working
from available resources. I believe that present AGI efforts are SO
misdirected (and actively interfered with) that unless something changes,
humanity will never ever produce a true AGI. However, with those changes, I
believe that really *useful* computational intelligence is just a few years
away, though an AGI, that everyone here on this forum would wholeheartedly
agree without reservation is an AGI, is probably 30 years away, as you
guesstimated. However, my time guesstimate is based on the time needed to do
some really basic research that remains undone.
> 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.
You are starting to appreciate what I have said on this forum several times,
that if you do a realistic estimate of the software maintenance costs of an
AGI population, that it approaches the efforts of the entire human race. In
short, there is nothing to be gained by having AGIs, because while they may
do our menial work, we will be working full time just to keep them running.
> Also, we have made *some* progress toward AGI since 1965, but it is
> mainly a better understanding of why it is so hard,
I agree with this statement, though I arrived at this conclusion a little
differently than you. I will now make some critical comments that indirectly
support this same conclusion...
> e.g.
>
> - We know that general intelligence is not computable [2] or provable [3].
> There is no "neat" theory.
In recent decades, new forms of "logic" have emerged (e.g. Game Theory) that
are NOT incremental improvements over "general" intelligence. Our
evolutionary biological and social development has NOT allowed for this
possibility, providing a HUGE opportunity for machine intelligence. The AGI
goals of those here indirectly seek to dispense with this advantage while
apparently seeking to construct new artificial mouths to feed. Hence, I see
your statement to simply be further proof that AGI is a rather questionable
goal compared with a more rigorous form of machine intelligence.
> - 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.
Of course, the BIG value is in UNcommon sense. For example, someone saying
"I had no choice but to..." is really saying that they have constrained
their thinking, probably to the point of failing to even recognize the
existence of the optimal course of action. Common sense would be to accept
the truth of their statement. UNcommon sense would be to recognize the error
contained in their statement.
> - Google is the closest we have to AI after a half trillion dollar
> effort.
You really should see my Dr. Eliza demo.
Steve Richfield
================
> 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. 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
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