IMHO too much hubris about how we reason logically and formally led this top-down approach. " Our opinion, and that of the knowledge representation community,

IMHO too much hubris about how we reason logically and formally led this top-down approach. " Our opinion, and that of the knowledge representation community, is that it is better to provide computer programs with common sense concepts, suitably formalized " - McCarthy. The recent successes with deep neural nets arriving at seeming reason, or a statistical approximation to it, by complex hierarchical statistical inference, bottom up from the real data. Word2Vec suggests that neural networks come up with different representations of the data than symbolic ai but ones that have much utility semantically. High Level logic, semantics and representations become as transformations and vector mathematics in the word2vec context space. Symbolic AI was probably the wrong way to look for strong general AI because that is not the way meat brains really reason. High level logic is often a justification or proof of something that jumped into ones thought intuitively as if from nowhere. McCarthy's work is reasoning about reasoning, real reasoning as practiced by animals is probably more like the sort of heirarchical, fuzzy, stochastic society of processes neural nets use to jump to conclusions. Both Turing and von Neumann saw that mechanical computing could be performed in two distinct ways either logically and sequentially or statistical and evolutionary. All through AI there has been a dichotomy between embodied robotics, evolutionary statistical methods, and raw data versus hand engineered, sanitised, abstracted symbolic logic. A hybrid approach may yield great results.