Don’t get me wrong, I’ve been following the progress here for many years now, and it’s clear the neocortex is doing something we ought to be mimicking in AI. So I’m not a skeptic by any means.
I don’t disagree with anything you’ve said Matt, but I have a slightly different perspective on why different things are popular. By my characterisation, the reason people are on the bandwagon with mainstream ML techniques is because they work so well you can’t ignore them. If you want to solve a problem in vision, you use a convolutional net, and if you have enough data it just works.
HTM doesn’t solve the problems people are interested in solving in 98% of AI-related fields, and there’s a simple reason why. 98% of AI problems do not involve time. So to refer back to the rocket example, I think that we’ve got a rocket that can escape the atmosphere but cannot yet achieve orbit, and we’re in an environment where people just want better cars.
The one major area in which time really can’t be ignored is a small but fast-growing field. It’s robotics. People have acknowledged for a long time in robotics that the traditional AI paradigm of time-agnostic input → output doesn’t scale. The physical world has temporal continuity, and that property is arguably more important than any other property. The need for temporal algorithms is growing, and one of these days there’ll be a killer application that can’t be solved well by any other means, and that’s when people will be forced to come on board.
So to summarise, rocket theory and engineering still needs a lot of work, but I’m sure people will start using the rocket once they actually need to go to space.