Progress in machine learning has never been at a snails pace. What has happened at times is that the futurists of the world stop making breathless pronouncements about machine learning, and that creates a public perception that things are going slowly.
Case in point: Deep learning really isn't anything revolutionary or new. I first started noticing papers about stuff that now falls under the "deep learning" catchphrase about 15 years ago. Not because it started then, but because that's when I started reading that sort of thing. During that time, there's been relatively constant, steady progress being made. But you wouldn't know it unless you had been following the literature, which not many people do. And all of this happened at a time when everyone was convinced that neural nets were dead and support vector machines were the way of the future.
As far as Robin Hansen's skepticism about deep learning getting us to true artificial intelligence, meh. A technology doesn't need to make Ray Kurzweil's eyes roll back in his head for it to be useful.
If we can successfully emulate the brain, it seems we would have necessarily acquired the knowledge needed to build models that are very powerful without having to exactly mimic the brain.
Robin Hansen is notoriously skeptical about the possibility that Deep Learning can make real gains. He for some reason thinks brain emulation is more likely to make large progress in AI.
>An AI winter doesn't mean that progress stops.
It doesn't completely stop, but progress would be at a snails pace.
> The hype then dies down. Research continues, though, until enough progress is made that machine learning starts to produce results that excite the public again, and the cycle goes into another hype phase.
I think we as a community may need to take a good long look at the hype cycle theory and be skeptical it has any merit.