I've read every comment in this thread and its filled mostly with peoples self congratulatory intellectual views. Nobody, not even Robin Hansen himself has given a good, detailed argument as to why the current progress in Machine learning will stop.
An AI winter doesn't mean that progress stops. It means that businesses and the general public become disillusioned by AI's or ML's failure to live up to the popular hype, and stop throwing so much money at it. 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.
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.
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.
The "AI winter" as I know it was around 1988. I've still vintage issues of "AI Expert" and other publications of the time (which I kept for their great linocut-style artwork, and because they were expensive items here around).
Back then it wasn't so much about ML (aka "Deep Learning") but anything Prolog, expert systems, artificial neural nets and their generalizations, and Lisp.
Putting 'AI' on a startups prospectus will do it no harm. It may help sway lazy investors, or at least make the company appear more cutting edge.
Same goes for the investors, they want to be seen to be investing in cutting edge, buzzword compliant startups.
It is just human nature, following the herd is always going to be the safest option.
Have you ever played one of those strategy games with a tech tree? Research A and it lets you research B, C, and D; research C and D and it lets you research E; etc?
That's based on the way discoveries in the real world build on eachother. And in the real world, the research tree seems to be "lumpy".
Think "agricultural revolution", "industrial revolution", etc. Something new comes available, and everyone rushes to pick off all the new low-hanging fruit. Eventually the easiest gains are all taken, and people lose interest and move to other things. And as people keep picking away more slowly at the more difficult/involved things, eventually someone will find something that -- probably combined with some completely different existing knowledge -- opens up another new field. And it repeats.
Right now we're in the "low-hanging fruit" phase of (1) computers that are powerful enough to run neural networks, combined with (2) feedback algorithms that allow networks with lots of layers to learn effectively. Sooner or later the gains will get a bit tougher as we understand the field better, and then research will slow even further as many researchers find something else new and shiny -- and with better returns -- to focus on.
I'm not so sure about that. Places like Deepmind are not satisfied with simply having AI that does straight forward pattern matching problems (Though that's very important). They are moving into more complex problems like transfer learning, reinforcement learning and unsupervised learning for more complex, real world problems solving. They also seem to be making good progress on this as well.