--- William Pearson <[EMAIL PROTECTED]> wrote: > Matt mahoney: > "I propose prediction as a general test of understanding. For example, > do you understand the sequence 0101010101010101 ? If I asked you to > predict > the next bit and you did so correctly, then I would say you understand > it." > > What would happen if I said, "I don't have time for silly games, > please stop emailing me". Would you consider that I understood it?
If it was a Turing test, then probably yes. But a Turing test is not the best way to test for intelligence. Ben Goertzel once said something like "pattern recognition + goals = AGI". I am generalizing pattern recognition to prediction and proposing that the two components can be tested separately. For example, a speech recognition system is evaluated by word error rate. But for development it is useful to separate the system into its two main components, an acoustic model and a language model, and test them separately. A language model is just a probability distribution. It does not have a goal. Nevertheless, the model's accuracy can be measured by using it in a data compressor whose goal (implicit in the encoder) is to minimize the size of the output without losing information. The compressed size correlates well with word error rate. Such testing is useful because if the system has a poor word error rate but the language model is good, then the problem can be narrowed down to the acoustic model. Without this test, you wouldn't know. I propose compression as a universal goal for testing the predictor component of AI. More formally, if the system predicts the next symbol with probability p, then that symbol has utility log(p). AIXI provides a formal justification for this approach. In AIXI, an agent and an environment (both Turing machines) exchange symbols interactively. In addition, the environment signals a numeric reward to the agent during each cycle. The goal of the agent is to maximize the accumulated reward. Hutter proved that the optimal (but uncomputable) strategy of the agent is to guess at each step that the environment is modeled by the shortest Turing machine consistent with the interaction so far. Note that this strategy is independent of the goal implied by the reward signal. -- Matt Mahoney, [EMAIL PROTECTED] ------------------------------------------- agi Archives: http://www.listbox.com/member/archive/303/=now RSS Feed: http://www.listbox.com/member/archive/rss/303/ Modify Your Subscription: http://www.listbox.com/member/?member_id=8660244&id_secret=101455710-f059c4 Powered by Listbox: http://www.listbox.com
