Sure they can, there has to be a finite amount of this information received and passed, it may be large but still finite. So if a human moves his arm because you told them to, you can measure and look at the arm and say: Why did his arm move? The scientist could look down at the instruments and say, because this X set of nerves fired here in this muscle, and you could work your way up and trace that back to the brain. just because its Hard, or large doesnt make it impossible. For the AI, it actually gives us better results, because we can stop and query the AI specificilly at every level. Why did your AI move your arm: Because my motor received this signal, becuase it recieved this signal, because I reasoned that, ..... because you told me to. Further, a useful AI will need to differentiate which levels of diagnistic reasoning you want to display. This as well may be large, for some items, but they are also weighted, so some are more important, so we can have it show us the more important ones in each layer, until we see something that looks out. Internally changing that to keep everything else consistent is a bit of a harder task, but still possible.
James Ratcliff In your argument for transparency, you assume that individual pieces of knowledge can be isolated. Prove it. In the brain, knowledge is distributed. We make decisions by integrating many sources of evidence from all parts of the brain. [1] 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] ----- Original Message ---- From: Mark Waser To: [email protected] Sent: Thursday, November 16, 2006 9:57:40 AM Subject: Re: [agi] A question on the symbol-system hypothesis > The knowledge base has high complexity. You can't debug it. You can examine it and edit it but you can't verify its correctness. While the knowledge base is complex, I disagree with the way in which you're attempting to use the first sentence. The knowledge base *isn't* so complex that it causes a truly insoluble problem. The true problem is that the knowledge base will have a large enough size and will grow and change quickly enough that you can't maintain 100% control over the contents or even the integrity of it. I disagree with the second but believe that it may just be your semantics because of the third sentence. The question is what we mean by "debug". If you mean remove all incorrect knowledge, then the answer is obviously "yes, we can't remove all incorrect knowledge" because odd sequences of observed events and incomplete knowledge means that globally incorrect knowledge *is* the correct deduction from experience. On the other hand, we certainly should be able to debug how the knowledge base operates, make sure that it maintains an acceptable degree of internal integrity, and responds correctly when it detects a major integrity problem. The *process* and global behavior of the knowledge base is what is important and it *can* be debugged. Minor mistakes and errors are just the cost of being limited in an erratic world. > An AGI with a correct learning algorithm might still behave badly. No! An AGI with a correct learning algorithm may, through an odd sequence of events and incomplete knowledge, come to an incorrect conclusion and take an action that it would not have taken if it had perfect knowledge -- BUT -- this is entirely correct behavior, not bad behavior. Calling it bad behavior dramatically obscures what you are trying to do. > You can't examine the knowledge base to find out why. No, no, no, no, NO! If you (or the AI) can't go back through the causal chain and explain exactly why an action was taken, then you have created an unsafe AI. A given action depends upon a small part of the knowledge base (which may then depend upon ever larger sections in an ongoing pyramid) and you can debug an action and see what lead to an action (that you believe is incorrect but the AI believes is correct). > You can't manipulate the knowledge base data to fix it. Bull. You should be able to correctly come across a piece of incorrect knowledge that lead to an incorrect decision. You should be able to find the supporting knowledge structures. If the knowledge is truly incorrect, you should be able to provide evidence/experiences to the AI that leads it to correct the incorrect knowledge (or, you could just even just tack the correct knowledge in the knowledge base, fix it so that it temporarily can't be altered, and run your integrity repair routines -- which, I contend, any AI that is going to go anywhere must have). > At least you can't do these things any better than manipulating the inputs > and observing the outputs. No. I can find structures in the knowledge base and alter them. I would prefer not to. I would strongly prefer that it take the form of a conversation where I "ask" the AGI what it's reasoning was, where it answers, where I point out where I believe it's knowledge is incorrect and provide proof, and where it can then alter its own knowledge base appropriately. > The reason is that the knowledge base is too complex. In theory you could > do these things if you lived long enough, but you won't. For practical > purposes, the AGI knowledge base is a black box. No. I disagreed with your previous statement and I disagree with the reason. The knowledge base is not that complex. It is that large. And the AI should not be a black box *at all*. You should be able to examine any given piece to any given level of detail at will -- you just can't hold all of it in mind at once. Yes, that will lead to circumstances where it surprises you -- but we're not looking for 100% predictability. We're looking for an intelligence with *bounded* behavior. > You need to design your goals, learning algorithm, data set and test > program with this in mind. Prove to me that the AGI knowledge base is a black box and I will. However, you have already told me that I "can examine it and edit it" -- so what the heck do *you* mean by a black box? > Trying to build transparency into the data structure would be pointless. > Information theory forbids it. Bull, information theory does not forbid transparency into the data structure. Prove this and you would invalidate a huge swath of AGI research. What makes you say this? I believe that this is the core of your argument and would like to see *any* sort of evidence/argument to support this grandiose claim. > I am sure I won't convince you, so maybe you have a different explanation > why 50 years of building structured knowledge bases has not worked, and > what you think can be done about it? Hmmm. Let's see . . . . Codd's paper was published in 1970, so the first fifteen years were devoted to getting to that point. And SQL wasn't even commercially available until Oracle was released in 1979, so we're down to only about half that time. Cyc didn't start until 1984 after Machine Learning started in the early 1980s. Many people took horrible detours into neural networks while a lot of the rest were forced to constrain their systems by the limited computing ability available to them (I remember spending thousands of dollars per month running biochemistry simulations on a Vax that I was able to easily run on a PC less than five years later). In the past twenty years, people have continued to make financially-proven progress in applications like genome databases. It looks to me like 50 years of building structured knowledge bases has worked and that were getting better at it all the time and also that we can do more and more as space and computing power is getting cheaper and cheaper and languages and techniques are getting more and more powerful. What hasn't worked *yet* is self-structuring databases and we're learning more all the time . . . . So *prove* to me why information theory forbids transparency of a knowledge base. Mark P.S. Yes, yes, I've seen that Google article before where the author believes that he "proves" that "Google is currently storing multiple copies of the entire web in RAM." Numerous debunking articles have also come out including the facts that Google does not store HTML code, that what is stored *is* stored in compressed form, and --from Google, itself -- that it does *not* store sections that do not include "new instances of significant terms". But I can certainly understand your personal knowledge base deriving the "fact" that "Google DOES keep the searchable part of the Internet in memory" if that article is all that you've seen on the topic (though one would have hoped that an integrity check or a reality check would have prompted further evaluation -- particularly since the article itself mentions that that would require an unreasonably/impossibly large amount of RAM.) ----- Original Message ----- From: "Matt Mahoney" To: Sent: Wednesday, November 15, 2006 6:41 PM Subject: Re: [agi] A question on the symbol-system hypothesis Mark Waser wrote: >Are you conceding that you can predict the results of a Google search? OK, you are right. You can type the same query twice. Or if you live long enough you can do it the hard way. But you won't. >Are you now conceding that it is not true that "Models that are simple >enough to debug are too simple to scale."? OK, you are right again. Plain text is a simple way to represent knowledge. I can search and edit terabytes of it. But this is not the point I wanted to make. I am sure I expressed it badly. The point is there are two parts to AGI, a learning algorithm and a knowledge base. The learning algorithm has low complexity. You can debug it, meaning you can examine the internals to test it and verify it is working the way you want. The knowledge base has high complexity. You can't debug it. You can examine it and edit it but you can't verify its correctness. An AGI with a correct learning algorithm might still behave badly. You can't examine the knowledge base to find out why. You can't manipulate the knowledge base data to fix it. At least you can't do these things any better than manipulating the inputs and observing the outputs. The reason is that the knowledge base is too complex. In theory you could do these things if you lived long enough, but you won't. For practical purposes, the AGI knowledge base is a black box. You need to design your goals, learning algorithm, data set and test program with this in mind. Trying to build transparency into the data structure would be pointless. Information theory forbids it. Opacity is not advantagous or desirable. It is just unavoidable. I am sure I won't convince you, so maybe you have a different explanation why 50 years of building structured knowledge bases has not worked, and what you think can be done about it? And Google DOES keep the searchable part of the Internet in memory http://blog.topix.net/archives/000011.html because they have enough hardware to do it. http://en.wikipedia.org/wiki/Supercomputer#Quasi-supercomputing -- Matt Mahoney, [EMAIL PROTECTED] ----- This list is sponsored by AGIRI: http://www.agiri.org/email To unsubscribe or change your options, please go to: http://v2.listbox.com/member/?list_id=303 This list is sponsored by AGIRI: http://www.agiri.org/email To unsubscribe or change your options, please go to: http://v2.listbox.com/member/?list_id=303 ----- This list is sponsored by AGIRI: http://www.agiri.org/email To unsubscribe or change your options, please go to: http://v2.listbox.com/member/?list_id=303 _______________________________________ James Ratcliff - http://falazar.com New Torrent Site, Has TV and Movie Downloads! http://www.falazar.com/projects/Torrents/tvtorrents_show.php --------------------------------- Sponsored Link Mortgage rates near 39yr lows. $310,000 Mortgage for $999/mo - Calculate new house payment ----- This list is sponsored by AGIRI: http://www.agiri.org/email To unsubscribe or change your options, please go to: http://v2.listbox.com/member/?list_id=303
