Most other scientists dismissed neural networks?
Is there some history I am unaware of as that doesn't seem true. Did the article want to push the idea of the lone rogue thinker a bit too much?
Neural Networks weren't really thought of very highly until CNNs started winning image recognition competitions in the early 2010's.
I think most people had the feeling that they were interesting tools to learn how the brain worked, but too slow and opaque to be practical statistical tools. I've been following Hinton for a while (because of Hinton and Shallice 1991), and my understanding is that it was really hard for him to get funding especially when he was just starting out.
The fact that so much of the work from the mid-80's to 2000 came from just a few labs should tell you how hard it was to get funding for that kind of research.
If you look through papers from that era I don't think you'll find that's true at all. You could fairly say that only a few of the prominent labs from the first neural-net boom lasted long enough to still be prominent labs now in the second neural-net boom (though even then there are several: Hinton, Bengio, Schmidhuber, LeCun, etc.). Since they're still around doing interviews and putting out new papers, understandably their work has a higher profile now than that of people who aren't in the field anymore. But there have been a ton of others over the years too, just many of them from the first wave have moved on or retired by now.
Especially in the '90s the field was hot and reasonably large (and it was pretty easy to get funding, too). If you look at e.g. the NIPS 1992 proceedings, it's definitely not just a handful of labs: https://papers.nips.cc/book/advances-in-neural-information-p...
I agree, I don't mean to imply that Hinton did all of the work on NN's, but he certainly contributed a lot to the field, even as the popularity waxed and waned.
Even if you just click on a few of the names in your link, you can see most of those authors submitted ~1-6 papers. Hinton, Bengio, and Jordan have submitted >50 each. I wouldn't say that NN's were exactly thought of as a joke before 2011, but from the people I talked to about them, they weren't considered very promising as practical statistical systems.
When I did my M.S. reasearch in 2010 SVMs were far more popular than neural networks. Deep learning was just over the horizon and most people had given up on it being useful for learning real world problems. We had proven that NNs could learn anything given enough data and the right structure, but the hard part in real life was having enough data and finding the correct structure. So SVMs seemed more robust with available datasets, and most people used those. There were/are several other popular methods, like nearest neighbors and tree-based methods, and NNs just weren't as sexy as they seemed in the late 80's.
It’s not a super long book and an excellent, eye opening read even 50 years later.
If you took half the apps built today and tried to run them on compatible hardware from 20 years ago, they would all flop.
Without the understanding of modern computing, one would have dismissed all such inventions as useless wastes of time.