The arguments Eliezer describes are made, and his reactions are fair. But really the actual research community "grew out" of most of this stuff a while back. CYC and the "common sense" efforts were always a sideshow (in terms of research money and staff, not to mention results). Neural networks were a metonym for statistical learning for a while, then serious researchers figured out they needed to address statistical learning explicitly. Etc.

Admittedly there's always excessive enthusiasm for the current hot thing. A few years ago it was support vector machines, I'm not sure what now.

I recognize there's some need to deflate popular misconceptions, but there's also a need to move on and look at current work.

Eliezer, I'd be *very* interested in your comments on (what I regard as) the best current work. Examples for you to consider would be Sebastian Thrun, Andrew Ng (both in robotics at Stanford), Chris Manning (linguistics at Stanford), and the papers in the last couple of NIPS conferences (the word "Neural" in the conference title is just a fossil, don't have an allergic reaction).

As an entertaining side note, here's an abstract for a poster for NIPS '08 (happening tomorrow) that addresses the crossover between AI and ems:

A Bayesian Approach for Extracting State Transition Dynamics from Multiple Spike Trains

Neural activity is non-stationary and varies across time. Hidden Markov Models (HMMs) have been used to track the state transition among quasi-stationary discrete neural states. Within this context, an independent Poisson model has been used for the output distribution of HMMs; hence, the model is incapable of tracking the change in correlation without modulating the firing rate. To achieve this, we applied a multivariate Poisson distribution with a correlation term for the output distribution of HMMs. We formulated a Variational Bayes (VB) inference for the model. The VB could automatically determine the appropriate number of hidden states and correlation types while avoiding the overlearning problem. We developed an efficient algorithm for computing posteriors using the recursive relationship of a multivariate Poisson distribution. We demonstrated the performance of our method on synthetic data and a real spike train recorded from a songbird.

This is a pretty good example of what I meant by "solving engineering problems" and it should help the ems program "cut corners".

I should mention that the NIPS '08 papers aren't on line yet, but all previous conferences do have the papers, tutorials, slides, background material, etc. on line. For example here's last year.