Peter Norvig wrote today at LinkedIn: “AGI is not solved, but it has arrived. Like how ENIAC was the first general purpose computer in 1945 (but computing was not “solved”) or like how powered flight arrived at Kitty Hawk in 1903 (but aviation was not “solved”)“. Read his article “Artificial General Intelligence Is Already Here“.
Setting aside the question of whether intelligence is always reliant on symbols and logic, there are reasons to question this claim about the inadequacy of neural nets and machine learning, because neural nets are so powerful at doing anything a computer can do. For example:
- Discrete or symbolic representations can readily be learned by neural networks and emerge naturally during training.
- Advanced neural net models can apply sophisticated statistical techniques to data, allowing them to make near-optimal predictions from the given data. The models learn how to apply these techniques and to choose the best technique for a given problem, without being explicitly told.
- Stacking several neural nets together in the right way yields a model that can perform the same calculations as any given computer program.
- Given example inputs and outputs of any function that can be computed by any computer, a neural net can learn to approximate that function. (Here “approximate” means that, in theory, the neural net can exceed any level of accuracy — 99.9% correct for example — that you care to state.)
In recent years, a great many tests have been devised for cognitive tasks associated with “intelligence,” “knowledge,” “common sense” and “reasoning.” These include novel questions that can’t be answered through memorization of training data but require generalization — the same proof of understanding we require of students when we test their understanding or reasoning using questions they haven’t encountered during study. Sophisticated tests can introduce novel concepts or tasks, probing a test-taker’s cognitive flexibility: the ability to learn and apply new ideas on the fly.