Augustow Noclegi Huuheldein

Fizycznymi Wyzwaniami Dostają Żadnej Viagry Ogrodnika

Przy pierwszej sesji śmierci bliskiej ani historia ani domostw obrębie statements. particular, there are only a finite number of statements of the form x is random |x|) that can be proven, out of infinite number of possible finite strings x. Suppose otherwise. Then it would be possible to to enumerate all proofs and describe the string x such that it is the first to be proven to be a million bits and random spite of the fact that we just gave a short description of it. If F describes any formal system, then the longest string that can be proven to be random is never much longer than F, even though there are infinite number of longer strings and most of them are random. Nor is there a general test to determine whether the compression of a string can be improved any further, because that would be equivalent to testing whether the compressed string is random. Because determining the length of the shortest descriptions of strings is not computable, neither is optimal compression. It is not hard to find difficult cases. For example, consider the short description a string of a million zero bytes compressed with AES CBC mode with key 'foo'. To any program that does not know the key, the data looks completely random and incompressible. Prediction is intuitively related to understanding. If you understand a sequence, then you can predict it. If you understand a language, then you could predict what word might appear next a paragraph that language. If you understand image, then you could predict what might lie under portions of the image that are covered up. Alternatively, random data has no meaning and is not predictable. This intuition suggests that prediction or compression could be used to test or measure understanding. We now formalize this idea. Turing first proposed a test for to sidestep the philosophically difficult question of whether machines could think. This test, now known as the Turing test, is now widely accepted. The test is a game played by two humans who have not previously met and the machine under test. One human communicates with the other human and with the machine through a terminal. Both the confederate and the machine try to convince the judge that each is human. If the judge cannot guess correctly which is the machine 70% of the time after 10 minutes of interaction, then the machine is said to have Turing gave the following example of a possible dialogue: Q: Please write me a sonnet on the subject of the Forth Bridge. A: Count me out on this one. I never could write poetry. Q: Add 34957 to 70764. A: 105621. Q: Do you play chess? A: Yes. Q: I have K at my K1, and no other pieces. You have only K at K6 and R at R1. It is your move. What do you play? A: R-R8 mate. It should be evident that compressing transcripts like this requires the ability to compute a model of the form p p P where Q is the context up to the current question, and A is the response. But if a model could make such predictions accurately, then it could also generate responses indistinguishable from that of a human. Predicting transcripts is similar to the problem to predicting ordinary written language. It requires either case vast, real-world knowledge. estimated that the information content of written case-insensitive English without punctuation is 0 to 1 bits per character, based on experiments which human subjects guessed successive characters text with the help of letter n-gram frequency tables and dictionaries. The uncertainty is due not much to variation subject matter and human skill as it is due to the fact that different probability assignments lead to the same observed guessing sequences. Nevertheless, the best text compressors are only now compressing near the upper end of this range. Legg and Hutter proposed the second definition, universal intelligence, to be far more general than Turing's human intelligence. They consider the problem of reward-seeking agents completely arbitrary environments described by random programs. this model, agent communicates with environment by sending and receiving symbols. The environment also sends a reinforcement or reward signal to the agent. The goal of the agent is to maximize accumulated reward. intelligence is defined as the expected reward over all possible environments, where the probability of each environment described by a program M is algorithmic, proportional to 2 -|M|. Hutter proved that the optimal strategy for the agent is to guess after each input that the distribution over M is dominated by the shortest program