I don't think we can say the AI Winter is here until these large funding rounds stop.
ML works incredibly well on even lightly-structured data (which includes most financial data streams). Agree it hasn’t lived up to the AI hype, but that doesn’t mean what it can deliver on isn’t useful.
"I don't think the winter depends on the amount that somebody makes today on AI, rather on how much people are expecting to make in the future. If these don't match, there will be a winter. My take is that there is a huge bet against the future. And if DL ends up bringing just as much profit as it does today, interest will die very, very quickly."
Let's posit that DL does just that, bring in the same value (as a whole system) as non-DL.
Inside such a system, there is a business advantage to implementation of DL, and there is a concentration of value around those with the largest dataset.
This means that DL systems will still be very valuable in that they are a competitive advantage, but only so much as they are difficult to implement independently.
The value differentiator then becomes the data itself. I think this points to a value proposition for data providers. GDPR and such seem to be quite correctly identifying and correcting for this risk.
There's so much hype around "AI" right now that I wonder if we're headed to a winter where you need very strong business justification to get funding to apply those existing tools. It seems like anyone with a problem to solve is spending millions to throw machine learning at it, just to see if it will stick. That's not sustainable.
Not exactly. "AI Winter" refers the historical periods that followed unsustainable hype that surrounds AI methods prior to those times. Skepticism of AI during the last winter was so strong that "AI" kinda became a dirty word and people invented euphemisms like "machine learning" to avoid the stigma.