Artificial intelligence: Riders on a swarm

I'm not sure if you truncated that excerpt on purpose or not, but the sentence that follows it indicates that the author does actually know what the TSP is.

> In particular, Dr Dorigo was interested to learn that ants are good at choosing the shortest possible route between a food source and their nest. This is reminiscent of a classic computational conundrum, the travelling-salesman problem. Given a list of cities and their distances apart, the salesman must find the shortest route needed to visit each city once.

Absolutely---it's a matter of degree, and judgments thereof are bound to be subjective. That said, I'd still claim that the EC community, broadly taken, appears particularly prone to tribalism.

I'm far from a zealot on the "theoretical foundation" question, and it's possible to point to fields that have been, perhaps, even harmed by their possession of such a foundation (eg, reinforcement learning). That said, you need at least one or the other---theory or methodology. You mention DPB, and it's a good example: a lot of value was gained from one line of research centered on that single benchmark, despite its flaws. Pull together a curated suite of such problems, release a common implementation, get it widely used, and the field would benefit immensely. Some equivalent of Caruana's grand comparison, from the supervised ML world, could also do much good.

In any case, it's an interesting topic, but we could go back and forth on it forever. GAs and their progeny (including ACO and NEAT) are fun to consider and lend themselves to accessible explanations, so they get a fair amount of press. Whether or not that's justified is something we'll likely disagree on---but I wanted to push back a bit against the implication, intended or not, that they're where the action is in AI today.

Very interesting article. The Hard AI problem is one of the new frontiers of science.

If you're interested in this topic, I highly suggest you check out the Radiolab episode on emergence.

The episode doesn't focus on AI, per se, but it does talk a lot about how many individual things (ants, fireflies, bees) are not intelligent on their own, but do appear intelligent as a collective whole.

Here's the link: http://www.wnyc.org/shows/radiolab/episodes/2005/02/18

I agree -- artificial intelligence modeled on human brains hasn't been explored nearly enough to label it a failure. In addition to advances in artificial neural networks, there has been a lot of progress in more realistic models of the brain (specifically the neocortex). Artificial neural networks started the interest in "brain-like" modeling, but for a while, most neural networks were about as "brain-like" as a 100 component circuit is "computer-like"$ But unlike classical ANNs, new research such as HyperNEAT is exciting because it might reveal the mechanisms by which the wiring of the brain evolved into a structure capable of carrying out robust learning mechanisms.

Equally exciting is the research going on in figuring out the specifics of the learning mechanisms themselves. Research groups like the Redwood Center for Theoretical Neuroscience in Berkeley and the Center for Biological and Computational Learning at MIT (and many others) are making progress in understanding neocortical function and companies such as Numenta$$ are developing cortical learning algorithms based on specific mechanisms observed in the neocortex.

$ This example is from Jeff Hawkins's book "On Intelligence"

$$ Disclosure - I am working there this summer.

Thanks, enjoyed your response.

Your "tackling nastier problems" point is well-taken, and one reason I have a great deal of sympathy for work in these research areas.

I think that you're painting an overly-rosy picture of the benchmark situation, but you're right in that I was likely too critical. The attention paid to synthetic fitness landscapes, for example, is also a positive sign that that many researchers do care about issues of comparison and analysis.

(That said, I obviously believe that the field can and must do much better---and, since it relies so heavily on empirical evidence, its need for consistent methodology is somewhat higher.)