With the research directions and methodology that dominate AI now, I don't look forward to anything significantly new or better. To me, the core technology of

With the research directions and methodology that dominate AI now, I don't look forward to anything significantly new or better. To me, the core technology of my startup looks better than anything I've seen in AI, but my technology is from some of my original research in applied math based on some advanced pure/applied math prerequisites. I'm not calling my work AI, but maybe some AI people would. E.g., my work does well with the meaning of content. Just how my applied math does that has nothing to do with anything I've seen in current AI. My view is that the current directions of AI are essentially out of gas -- there will continue to be new applications of the existing techniques, but new techniques are not promising. IMHO, for new techniques for AI we need to do much better with (A) applied math and/or (B) implementations of relatively direct borrowing from natural intelligence, e.g., at the level of a kitty cat and working up from there. E.g., for the math, stochastic optimal control can look brilliant, even prescient, and has had the basics on the shelves of the research libraries for decades.