I will try to answer several posts here.
First, I said that there is no knowledge that you can demonstrate verbally that cannot also be learned verbally. For simple cases, this is easy to show. If you test for knowledge X by asking question Q, expecting answer A, then you can train a machine "the answer to Q is A". I realize for many practical cases that there could be many questions about Q and you can't anticipate them all. In other words, X could be a procedure or algorithm for generating answers from an intractably large set of questions. For example, X could be the rules for addition or playing chess. In this case, you could train the machine by giving it the algorithm in the form of natural language text (here is how you play chess...).
Humans possess a lot of knowledge that cannot be demonstrated verbally. Examples: how to ride a bicycle, how to catch a ball, what a banana tastes like, what my face looks like. The English language is inadequate to convey such knowledge fully, although some partial knowledge transfer is possible (I have brown hair). Now try to think of questions to test for the parts of the knowledge that cannot be conveyed verbally. Sure, you could ask what color my hair is. Try to ask a question about knowledge that cannot be conveyed verbally to the machine at all. If you can't convey this knowledge to the machine, it can't convey it to you.
An important question is: how much information does a machine need to pass the Turing test? The machine only needs knowledge that can be verbally tested. Information theory says that this quantity cannot exceed the entropy of the training data plus the algorithmic complexity (length of the program) of the machine prior to training. From my argument above, all of the training data can be in the form of text. I estimate that the average adult has been exposed to about 1 GB of speech (transcribed) and writing since birth. This is why I chose 1 GB for the large text benchmark. I do not claim that the Wikipedia data is the *right* text to train an AI system, but I think it is the right amount, and I believe that the algorithms we would use on the right training set would be very similar to the ones we would use on this training set.
Second, on lossy vs. lossless compression. It would be a good demonstration of AI if we could compress text using lossy techniques and uncompress to different text that had the same meaning. We can already do this at a simple level, e.g. swapping spaces and linefeeds, or substituting synonyms, or swapping the order of articles. We can't yet do this in the more conceptual way that humans could, but I think that a lossless model could demonstrate this capability. For example, an AI-level language model would recognize the similarity of "I ate a Big Mac" and "I ate at McDonalds" by compressing the concatenated pair of strings to a size only slightly larger than either string compressed by itself. This ability could then be used to generate conceptually similar strings (in O(n) time as I described earlier).
Third, on AIXI, this is a mathematically proven result, so there is no need to test it experimentally. The purpose of the Hutter prize is to encourage research in human intelligence with regard to verbally expressable knowledge, not the more general case. The general case is known to be undecidable, or at least intractable in environments controlled by a finite state machine.
AIXI requires the assumption that the environment be computable by a Turing machine. I think this is reasonable. People actually do behave like rational agents. If they didn't, we would not have Occam's razor.
Here is an example: you draw 100 marbles from an urn. All of them are red. What do you predict will be the color of the next marble? Answer this way: what is the shortest program you could write that outputs 101 words, where the first 100 are "red"?
Fourth, a program that downloads the Wikipedia benchmark violates the rules of the prize. The decompressor must run on a computer without a network connection. Rules are here:
http://cs.fit.edu/~mmahoney/compression/textrules.html
-- Matt Mahoney, [EMAIL PROTECTED]
First, I said that there is no knowledge that you can demonstrate verbally that cannot also be learned verbally. For simple cases, this is easy to show. If you test for knowledge X by asking question Q, expecting answer A, then you can train a machine "the answer to Q is A". I realize for many practical cases that there could be many questions about Q and you can't anticipate them all. In other words, X could be a procedure or algorithm for generating answers from an intractably large set of questions. For example, X could be the rules for addition or playing chess. In this case, you could train the machine by giving it the algorithm in the form of natural language text (here is how you play chess...).
Humans possess a lot of knowledge that cannot be demonstrated verbally. Examples: how to ride a bicycle, how to catch a ball, what a banana tastes like, what my face looks like. The English language is inadequate to convey such knowledge fully, although some partial knowledge transfer is possible (I have brown hair). Now try to think of questions to test for the parts of the knowledge that cannot be conveyed verbally. Sure, you could ask what color my hair is. Try to ask a question about knowledge that cannot be conveyed verbally to the machine at all. If you can't convey this knowledge to the machine, it can't convey it to you.
An important question is: how much information does a machine need to pass the Turing test? The machine only needs knowledge that can be verbally tested. Information theory says that this quantity cannot exceed the entropy of the training data plus the algorithmic complexity (length of the program) of the machine prior to training. From my argument above, all of the training data can be in the form of text. I estimate that the average adult has been exposed to about 1 GB of speech (transcribed) and writing since birth. This is why I chose 1 GB for the large text benchmark. I do not claim that the Wikipedia data is the *right* text to train an AI system, but I think it is the right amount, and I believe that the algorithms we would use on the right training set would be very similar to the ones we would use on this training set.
Second, on lossy vs. lossless compression. It would be a good demonstration of AI if we could compress text using lossy techniques and uncompress to different text that had the same meaning. We can already do this at a simple level, e.g. swapping spaces and linefeeds, or substituting synonyms, or swapping the order of articles. We can't yet do this in the more conceptual way that humans could, but I think that a lossless model could demonstrate this capability. For example, an AI-level language model would recognize the similarity of "I ate a Big Mac" and "I ate at McDonalds" by compressing the concatenated pair of strings to a size only slightly larger than either string compressed by itself. This ability could then be used to generate conceptually similar strings (in O(n) time as I described earlier).
Third, on AIXI, this is a mathematically proven result, so there is no need to test it experimentally. The purpose of the Hutter prize is to encourage research in human intelligence with regard to verbally expressable knowledge, not the more general case. The general case is known to be undecidable, or at least intractable in environments controlled by a finite state machine.
AIXI requires the assumption that the environment be computable by a Turing machine. I think this is reasonable. People actually do behave like rational agents. If they didn't, we would not have Occam's razor.
Here is an example: you draw 100 marbles from an urn. All of them are red. What do you predict will be the color of the next marble? Answer this way: what is the shortest program you could write that outputs 101 words, where the first 100 are "red"?
Fourth, a program that downloads the Wikipedia benchmark violates the rules of the prize. The decompressor must run on a computer without a network connection. Rules are here:
http://cs.fit.edu/~mmahoney/compression/textrules.html
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