Artificial intelligence: Riders on a swarm
economist.com
Artificial intelligence: Riders on a swarm
1–10 of 19 posts
Re: Artificial intelligence: Riders on a swarm
#2"The purposeful collective activity of ants and other social insects does, indeed, look intelligent on the surface. An illusion, presumably. "
No more an illusion than the individual intelligence/consciousness we humans experience.
Re: Artificial intelligence: Riders on a swarm
#3Good companion resource to the article: http://en.wikipedia.org/wiki/Ant_colony_optimization
Re: Artificial intelligence: Riders on a swarm
#4> The search for artificial intelligence modelled on human brains has been a dismal failure.
No, it hasn't. There have been huge strides made in artificial neural networks in the last decade. One example is the HyperNEAT algorithm [1], which uses an indirect encoding enabling it to evolve networks with millions of connections. There's an entire conference on Neural Information Processing Systems (NIPS), which is considered one of the most prestigious publication venues in AI.
This article is complete garbage. Ant colony optimization has been around for decades. It's great for routing and similar tasks where you need to find the best path and be able to handle breakdowns in that path. However, there is no basis for making the leap that human brains function like ant colonies.
[1] Stanley et. al. A Hypercube-Based Encoding for Evolving Large-Scale Neural Networks. In: Artificial Life journal. Cambridge, MA: MIT Press, 2009. http://eplex.cs.ucf.edu/publications/2009/stanley.alife09.ht...
Re: Artificial intelligence: Riders on a swarm
#5Goedel, Escher, Bach applies yet again....
Re: Artificial intelligence: Riders on a swarm
#6Ray Kurzweil does not understand the ants and the bees.
Re: Artificial intelligence: Riders on a swarm
#7> The search for artificial intelligence modelled on human brains has been a dismal failure. No, it hasn't. There have been huge strides made in artificial neural networks in the last decade. One example is the HyperNEAT algorithm [1], which uses an indirect encoding enabling it to evolve networks with millions of connections. There's an entire conference on Neural Information Processing Systems (NIPS), which is cons…
Thank you, thank you, thank you for posting a link to all of these papers. There are so * many * cool * papers. I'm now fully absorbed learning about genetic algorithms searching for novelty instead of the direct objective. Fascinating!
Re: Artificial intelligence: Riders on a swarm
#8I posted this article almost a week ago :-(
Good article though.
Re: Artificial intelligence: Riders on a swarm
#9> The search for artificial intelligence modelled on human brains has been a dismal failure. No, it hasn't. There have been huge strides made in artificial neural networks in the last decade. One example is the HyperNEAT algorithm [1], which uses an indirect encoding enabling it to evolve networks with millions of connections. There's an entire conference on Neural Information Processing Systems (NIPS), which is cons…
To disagree on a side issue, it's been a long time since NIPS dealt much with classic ANNs (which never had much to do with human brains, in any case). Most of the action there, as in AI and ML at large these days---eg, the focus also at ICML and at other venues---is in statistical methods.
(On the other hand, neuroscience and explicitly biological neural modeling are exciting areas, reasonably well-represented at NIPS. Those topics, however, are almost entirely different from neural networks of the multilayer perceptron / [Hyper]NEAT varieties.)
But your criticism of the article seems accurate. ACO isn't new, and there's little evidence that it will solve any of the major outstanding problems in AI.
Less generously, however, I'd suggest that much of the research related to the family of population-based stochastic search methods, ACO, PSO, and HyperNEAT included, is prone to the same risk: the lack of a field-wide theoretical foundation, coupled with the absence of a field-wide standard methodology and benchmark set for empirical comparison (as opposed to, say, the situation in supervised learning), makes it temptingly easy for a particular researcher to believe too strongly in the capabilities of that researcher's pet algorithm. This situation seems to have balkanized the field (page through a recent GECCO proceedings, for example), and holds back wider progress.
That's not to say that HyperNEAT can't do great things. It's a fun approach, and Stanley et al are running far with it. But your boosterism of it, and the boosterism of ACO that you're objecting to, seem closely related.
(For contrast, I'd suggest, eg, the natural gradient work at IDSIA. It's unlikely to be the ultimate method, but may be a good model for solid research in this area.)
Re: Artificial intelligence: Riders on a swarm
#10>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.
Ugh.