I agree absolutely. I believe that future AI's will be educated in much the same way we educate children. Perhaps less education will be needed - possibly we could start them off at a higher age bracket for example. But ultimately I'm certain the first "strong" AI's will be educated/supervised to some degree.
My point was more that perhaps we're a bit too focused on the wrong metric for success. The current criteria set is {positives, negatives, false positives, false negatives}, and we try to optimise for high/low degrees of one or the other in order to determine whether a particular approach is successful or not.
What is then overlooked, is that perhaps we don't need to have a near-perfect positive rate, but instead achieve an acceptably-incorrect false positive or false negative rate. Where the answer may be wrong, but it's not too far wrong. Much like a human might pin a country like India in the wrong place on the map, but wouldn't ever put it in the middle of the Indian ocean.
In summation: Perhaps the key for computers to appear intelligent, is not to be perfectly correct, but to be not too disastrously incorrect.
My point was more that perhaps we're a bit too focused on the wrong metric for success. The current criteria set is {positives, negatives, false positives, false negatives}, and we try to optimise for high/low degrees of one or the other in order to determine whether a particular approach is successful or not.
What is then overlooked, is that perhaps we don't need to have a near-perfect positive rate, but instead achieve an acceptably-incorrect false positive or false negative rate. Where the answer may be wrong, but it's not too far wrong. Much like a human might pin a country like India in the wrong place on the map, but wouldn't ever put it in the middle of the Indian ocean.
In summation: Perhaps the key for computers to appear intelligent, is not to be perfectly correct, but to be not too disastrously incorrect.