Again, you're quoting me on that. Yes, 'not used much in data science' is a valid argument that it's not one of the biggest breakthroughs in data science.

And if you want to discuss the topic (while blanket criticizing people for not knowing what they're talking about) at least get the father of deep network's name right: it's Geoff Hinton, not George.

2. Model X for intelligence being highly technical or cool in some mathematical way is not scientific validation.

3. Google hiring X is not the same as X's model being successful in the industry. I have long switched over to DuckDuckGo for technical queries.

Anyways, what AI people should first address always is point number 2.

AI has always jumped from one cool thing to the next without answering whether that cool thing has any scientific basis.

Don't bring another AI winter ;)

It is always cool to see excitement over research in AI! (As long as it does not drown out other competitive approaches which might bear fruit in the long run.)

That's exactly what I am complaining about.

2. Model X for intelligence being highly technical or cool in some mathematical way is not scientific validation.

I never said that, it just narrows the amount of people that can comment on it with merit.

3. Google hiring X is not the same as X's model being successful in the industry.

It was just a side-comment.

How is one to scientifically validate against something that can't even be defined?

First of all that is completely wrong even for simple things like image recognition (try building a face recognizer which works under all possible conditions).

But more starkly consider the following question:

Is Geoff Hinton a machine?