[bvan]

Yikes, I’m old. There was a lot of NN work and a lot of books available on NN’s back in the mid and late 90’s. ‘Soft computing’ was the all-encompassing term for NN, genetic algorithms, AI, expert systems, fuzzy logic, ALife and all sorts of nascent computational areas back then. I still have a bunch of issues to the monthly AI Expert magazine one could buy at a decent magazine stand. Small data-sets were definitely a limiting factor as well as limited computer power. I remember certain applied fields did embrace NN’s early on, like some civil engineers and hydrologists, who were finding some use for them. At the U of Toronto, I considered doing a PhD with a biologist who was using them to investigate vision (and got help from Hinton). Physiology was one area where you could generate “long” time-series in a relatively short period of time. Those were still the days when Intel 286/386/486 and lowly Pentium machines were still common currency. Computer scientists at the time didn’t yet have clear break-through commercial applications which would have attracted crazy funding. A lot of theory, little real actions.

[p_l]

Let's not forget that especially early 1990s are still in shock from AI Winter and there's essentially no funding.

[m-i-l]

>"Small data-sets were definitely a limiting factor as well as limited computer power."

Not just small data-sets and limited computer power, but also very few libraries to help you out - although you could download something like xerion from ftp.cs.toronto.edu and join their email list, it was generally a case of retyping examples or implementing algorithms from printed textbooks. And it was all in C, presumably for performance reasons, while most of the symbolic AI folks came from Lisp or Prolog backgrounds.