I did my Ph.D. 28 years ago and Rod was on my thesis committee. At the time the big debate was over whether robots should plan or react (with Rod, of course, being at the vanguard of the "react" contingent). My thesis basically said: they should do both. 30 years on it feels like things have come full circle and we are once again arguing over what AI architectures should look like. I think the answer now is the same as it was then: all extreme positions are wrong. We are not going to build AI simply by throwing more computing power and larger training sets at the One True Algorithm, neither are we going to do it by hand-coding everything. Human brains are the messy result of millions of years of evolution. I see no reason to believe that successful AI will be any less messy.
Well, what use is there for AI solutions that require humans to do everything? Of course the procedures that only require computer's time will add more value.<p>But that doesn't mean that they'll solve more problems, or solve them better. It just means that if some expensive developers are competing on the market with a cheap GPU cluster, it's obvious who will win. At some point I imagine they will stop competing for solving the same problems, things are just not that mature yet.
It's a useful debate to be having -- although it is somewhat endless and undecidable in absolute terms, in the way that fundamental philosophical debates are.<p>I think about robotics as an example of a problem domain that seems resistant to pure-learning approaches. I'm thinking about the whole perception-planning-execution problem, in which a map of the world, and a notion of a goal, are key modeling objects.<p>It's really hard to think about how a robotic system that doesn't maintain a map could work intelligently to accomplish navigation goals, for instance. A map seems to imply some kind of model. And if the world (the map) isn't static, the modeling problem seems to get even more urgent.