I think choosing an architecture for AGI is a much more important problem than choosing a language. But there are some things we already know about AGI. First, AGI requires a vast amount of knowledge, and therefore a vast amount of computation. Therefore, at least part of the AGI will have to be implemented in a fast (perhaps parallel) language. Second, if you plan on having a team of programmers do the work (rather than all by yourself) then you will have to choose a widely known language.
Early work in AI used languages like Lisp or Prolog to directly express knowledge. Now we all know (except at Cycorp) that this does not work. There is too much knowledge to code directly. You will need a learning algorithm and training and test data. The minimum requirement for AGI is a language model, which requires about 10^9 bits of information (based on estimates by Turing and Landauer, and the amount of language processed by adulthood). When you add vision, speech, robotics, etc., it will be more. We don't know how much, but if we use the human brain as a model, then one estimate is the number of synapses (about 10^13) multiplied by the access rate (10 Hz) = 10^14 operations per second. But these numbers are really just guesses. Perhaps they are high, but people have been working on computational shortcuts for the last 50 years without success. My work is in data compression, which I believe is an AI problem. (You might disagree, but first see my argument at http://cs.fit.edu/~mmahoney/compression/rationale.html ). Whether or not you agree, compression, like AGI, requires a great deal of memory and CPU. Many of the top compressors ranked in my benchmark are open source, and of those, the top languages are C++ followed by C and assembler. I don't know of any written in Java, C#, Python, or any interpreted languages, or any that use relational databases. AGI is amenable to parallel computation. Language, vision, speech, and robotics all involve combining thousands of soft constraints. This requires vector operations. The fastest way to do this on a PC is to use the parallel MMX and SSE2 instructions (or a GPU) that are not accessible in high level languages. The 16-bit vector dot product that I implemented in MMX as part of the neural network used in the PAQ compressor is 6 times faster than optimized C. Fortunately you do not need a lot of assembler, maybe a couple hundred lines of code to do most of the work. AGI is still an area of research. Not only do you need fast implementations so your experiments finish in reasonable time, but you will need to change your code many times. Train, test, modify, repeat. Your code has to be both optimized and structured so that it can be easily changed in ways you can't predict. This is hard, but unfortunately we do not know yet what will work. -- 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
