[nl]

Artificial neural networks ignore neurophysiology, so even when they work, they tell us very little about how the brain works.

That is completely wrong. People like Geoff Hinton spend most of their time thinking about how the brain works (indeed, his background is cognitive psychology). The "convolution" part of convolution neural networks is designed to mimic how the optic nerve interfaces with the brain.

I keep hearing claims that symbolic AI is the wrong approach for anything, and that it failed. Yet there were quite a few successes (expert systems, discovery learning, common sense reasoning, for example) before sources of funding dried up.

The funding dried up because they ran into the limits of what is possible.

[DonaldFisk]

No equivalent of error backpropagation has ever been found in real neurons, and it's biologically implausible. So ANNs are almost certainly using a different learning mechanism from the one used in the brain. Even single neurons are quite complex and very little of this complexity is present in neural networks.

The visual system (retina, lateral geniculate nucleus, visual cortex) was fairly well understood well before ANNs were developed. A few uncontroversial ideas (e.g. that cells take their inputs from neighbouring cells in the previous layer) were adopted for use in ANNs.

I was around at the time of, and affected by, the AI winter. There was certainly no consensus among those working in AI that they had got as far as they could. Work stopped when funding was cut, often for political reasons.

The most mature area at the time, apparently ripe for commercialization, was expert systems. However, it was very hard to commercialize them: customers couldn't think of any suitable applications, and when they could, they couldn't spare the time of their experts.

Finally, the main reason for the AI winter was probably that AI was unable to live up to the grossly inflated expectations, simply because the expectations were grossly inflated. This seems to be happening again, with neural networks.

[colllectorof]

> I was around at the time of, and affected by, the AI winter. There was certainly no consensus among those working in AI that they had got as far as they could. Work stopped when funding was cut, often for political reasons.

I wasn't around, but I got curious about symbolic systems after listening to MIT's AI course[1]. Did some reading about the subject. The impression I got matches what you describe.

It's ridiculous how many people here dogmatically recite statements about failures of symbolic systems without (apparently) knowing anything about how those systems were used and what they achieved. If you listen to the comments, it sounds as if research on symbolic systems only ever produced crude, useless toys. That was certainly my impression before I took some time to actually look into it. A bit of straightforward Googling can show that it's a gross misrepresentation of history. For example, MIT's lecture on knowledge engineering [2] has some really interesting info on this subject.

[1] http://ocw.mit.edu/courses/electrical-engineering-and-comput...

[2] http://ocw.mit.edu/courses/electrical-engineering-and-comput...

[nl]

I've done symbolic AI work. It's great within limits. Deep learning on its own isn't the complete solution either, but statistics and learning are more important than symbolics for achieving breakthrough performance.

I'd invite you to read "The Master Algorithm" to understand exactly how they failed the first time and how they aren't the route forward: https://en.m.wikipedia.org/wiki/The_Master_Algorithm

[nl]

Hinton, "How the brain does back-propegation": https://youtu.be/kxp7eWZa-2M?t=38m13s

[colllectorof]

If you want to be convincing, give us links to actual neurology research, no to Hinton "explaining away" objections of actual neuroscientists to his suppositions about human brain by making more suppositions. It's pretty obvious that he made up his mind decades ago and isn't going to be critical of his own theories.

[colllectorof]

I mean, really, this is quite an argument:

1) You have a working system. You know only bits and pieces of how it works.

2) You build a crude model of the system. It kinds of sucks at doing the stuff the System is doing well.

3) People over several decades apply tons and tons of task-specific optimizations and modifications to your model. Those modifications have nothing to do with the original system, but because of them the model finally achieves good performance at some tasks.

4) You use the hype generated by #3 to claim that you were right all along and that your model captures the essential aspects of the original system.

5) When people point out that your model works in ways that clearly don't match the original system, you make a claim that it's the original system that approximates your model, not the other way around. Without any observations of the original system supporting your claim.

[nl]

There's plenty around, eg: http://journals.plos.org/plosone/article?id=10.1371/journal....

[colllectorof]

I don't see how the paper you're linking to support Hinton's supposition. It's a study of topology, it doesn't aim to show that biological neural networks learn via backpropagation.

[colllectorof]

> People like Geoff Hinton spend most of their time thinking about how the brain works (indeed, his background is cognitive psychology).

If that was as significant a factor as you make it sound, the progress in artificial neural networks would be closely tied to the progress of neurology. So where are all the citations of neurology and cognitive psychology papers in recent AI/ANN research?

[nl]

Because we are so far off being able to simulate biological systems it is easier to do other things.

There is some work in this though, but often going the other way: see for example http://news.discovery.com/tech/robotics/brain-dish-flies-pla... and even more extreme http://www.nature.com/articles/srep11869