Deconstructing AI

Where to begin?

Imagine that you want to colonize Alpha Centauri. Your task is daunting, but it's conceptually fairly straightforward: you're gonna need propulsion systems, life support, radiation shielding, power sources, etc. Some of those individual problems may be very hard, but it's clear what they are, and in every case, there's a pretty obvious starting point. If you want to spent $10B on the project, you can do a reasonable job of hiring people, building teams, and getting started. You may or may not succeed, but you know how to get started.

AI is different, and therein lies one of the fundamental problems with developing AGI.

Although lots of people have opinions, the truth is that none of us have anything more than guesses as to what technologies or components are needed to build an AGI, let alone how to put them all together. About the only certainty is that faster computers and better development tools are helpful. Beyond that, who knows? Here's a very incomplete list of some possible technologies that might or might not be useful: HTM, pattern recognition, NLP, neural networks, Bayes nets, large atom tables, predicate logic parsers, large databases of common knowledge, neuron and brain simulation, etc....

There are a number of plausible AGI designs floating around, each with a list of component technologies and a high-level plan for gluing them together into a working whole. The problem is that both the high-level plans and the component technologies vary wildly from one design to another.

On a related note, if you want to colonize Alpha Centauri, there are a number of obvious incremental steps. Launching a successful moon shot will almost certainly be helpful. Establishing a Mars colony will get you tangibly closer to your goal. With AI, the incremental steps are not obvious. Does the DARPA Grand Challenge get us any closer to an AGI, or is it purely a distraction? Does working on Machine Translation teach us anything at all?

Smart, well-informed people have different opinions, but I think that I probably speak for the majority when I say that as far as I can tell, pretty much all current narrow AI work contributes little or nothing to an AGI effort in the foreseeable future. Many of them are useful in their own right, and many of them contribute to our general scientific knowledge, but none of them are obvious stepping stones toward the ultimate goal of AGI.

Does all of this mean that AGI is a currently intractable problem? Maybe. Peter Norvig make a good case at this weekend's Singularity Summit II that working on AGI doesn't currently make sense because the underlying science simply isn't sufficiently advanced yet. Other smart people feel that AGI has simply been neglected, and that the problem is tractable by the right team.

I think we can all agree, though, that AGI is at the very least an incredibly daunting challenge.