Epstein Secrets

at they tend to favor or ignore. Surely (ding!)?, any man who could beat a woman at being perceived to be a woman would be an intelligent agent. What Turing did not foresee is the power of deep-learning AI to acquire this wealth of information in an exploitable form without having to understand it. Turing

the CIRL framework, one can formulate and solve the off-switch problem—that is, the problem of how to prevent a robot from disabling its off-switch. (Turing may rest easier.) A robot that’s uncertain about human preferences actually benefits from being switched off, because it understands that the human w

Age is also the Dysinformation Age. What can we do? We need to rethink our priorities with the help of the passionate but flawed analyses of Wiener, Weizenbaum, and the other serious critics of our technophilia. A key phrase, it seems to me, is Wiener’s almost offhand observation, above, that “these machines

at they tend to favor or ignore. Surely (ding!)?, any man who could beat a woman at being perceived to be a woman would be an intelligent agent. What Turing did not foresee is the power of deep-learning AI to acquire this wealth of information in an exploitable form without having to understand it. Turing

the CIRL framework, one can formulate and solve the off-switch problem—that is, the problem of how to prevent a robot from disabling its off-switch. (Turing may rest easier.) A robot that’s uncertain about human preferences actually benefits from being switched off, because it understands that the human w

Age is also the Dysinformation Age. What can we do? We need to rethink our priorities with the help of the passionate but flawed analyses of Wiener, Weizenbaum, and the other serious critics of our technophilia. A key phrase, it seems to me, is Wiener’s almost offhand observation, above, that “these machines

ere? A dim, honest, slow-multiplying human? Impossible to tell. The distinction between machine and man blurred a bit. Buried in the very premise of Turing’s test was an assumption about what machines could do, and how they might do it. “The idea behind digital computers may be explained by saying that t

systems we are using. In 1965 the MIT computer scientist Joseph Weizenbaum found himself, somewhat unexpectedly, considering this same problem. 175 Weizenbaum had written a primitive computer program to perform what is now known as natural language processing, essentially a bit of code designed to translate