Part of that too, is that I think people now realize that narrow AI is sufficient to create tremendous value, and that it doesn't necessarily matter if the "AGI breakthrough" happens anytime soon or not.
It's hard to be sure, but I don't see the kind of collapse that happened in the past happening anytime soon.
We haven't stopped buying tulips, trains, stocks, technology, or real estate. And just as well, we haven't stopped using symbolic AI, single-layer neural nets, ensemble models, expert systems, or logic programming languages. The new topological enhancements to Neural Nets won't ever go away either...but that doesn't mean we won't see a drop in investment once the general public realizes that your neural nets aren't going to learn how to do double-entry accounting any time soon. The AI winter isn't characterized by the technology going away, it is characterized by lofty idealism being shattered and investment dropping back to reflect reality.
Right, but that doesn't match the way I feel the term "AI Winter" has been used. Now I could be mis-interpreting things, but I've always looked at an "AI Winter" as a period of underinvestment, created as an over-reaction to the mechanics you're referring to.
but that doesn't mean we won't see a drop in investment once the general public realizes that your neural nets aren't going to learn how to do double-entry accounting any time soon.
Right, but again, I don't think most people use "AI Winter" to mean a simple "drop in investment". If it were something that straightforward, there would be no need for the "Winter" metaphor.
And that's why I say there may indeed be a drop... an "AI Fall" if you will, that still represents a pull-back of sorts, but perhaps just a less pronounced and extended pullback like we've seen in the past.
less pronounced and extended pullback like we've seen in the past.
should read:
less pronounced and extended pullback than we've seen in the past.
Now it feels to me we are in a design winter again: there are still a lot of designers on staff, but they are relegated to a "touch up" role... They have been moved ahead of implementation (which is good) but they are expected to "touch up" concepts generated by management in a matter of days. Very similar to when designers would get a few days to clean up a UI that was already implemented.
Sort of off topic, but just and example that widespread use and business value doesn't mean something can't fall back into a winter of sorts.
>On the other hand, if you bet that stocks will go down, you have some compensating psychic rewards. For one thing, occasionally stocks will go down, and you will be praised for your prescience in predicting the crash, and the people who were long will be mocked for their complacency. How smart you will feel!
>For another thing, even if stocks haven’t gone down, you get to borrow psychically, as it were, against that future moment of glory. You can just go around sort of saying “this is unsustainable and eventually stocks will go down and I will be praised for my prescience,” and people will be surprisingly willing to say “yes that’s correct, I admire your hypothetical prescience.” Particularly since the 2008 financial crisis, financial markets—and financial media—have a strongly entrenched narrative of prescient bears and complacent bulls, a widespread sense that any rising market is suspect and that the cynical view is always the smart one.
It's just weird to me there's always people looking to figure out a way to call the top on absolutely everything.
NNets are already a disappointment to nearly any applied researcher that isn't sitting on petabytes of data. It won't be long before the CEOs realize it and put their research funding somewhere else.
Who are "people" in this context? Are you talking about the general public, or misinformed journalists who are writing about things they don't understand? Because from what I've seen, by and large, the researchers working on Deep Learning are not claiming that DL (alone) is sufficient to achieve AGI, nor do they posit that AGI is anywhere close to reality. Read, for example, Martin Ford's book Architects of Intelligence[1] and note how the various researchers interviewed talk about AGI. That list, BTW, includes all three of LeCun, Hinton, and Bengio, as well as many others.
Now if you talk to the actual researchers/engineers when they're not afraid of hiding behind their NDAs (basically a friend at a bar), these people will talk about the challenges and how much work there is to do and so on. But the company's public stance, and the many billions of dollars chasing this area, is that it's just around the corner.
The point is, there is a lot of hype and it's easy to understand why. Elon Musk is happy to proclaim that Tesla is already selling the hardware for full self-driving vehicles, and it's just the regulators holding him back (which is absurd, to put it kindly). This is the same person saying AI is a bigger threat than nuclear weapons. So arguably the largest tech celebrity isn't even talking about when, he's saying it's here and we need to be prepared. This is the kind of person shaping the public's opinion.
But the money chasing these investments are mostly from private VC firms and tech companies, not public research grants. Once investors realize AI just isn't good enough for self-driving cars right now they won't want to keep throwing billions of dollars behind it. And that's when we arrive at a winter. Even Alphabet is starting to get weird about raising its stock price, so not even the tech giants are immune from this.
There are many other verticals that follow the same story, I was just using the most obvious example of an industry that has billions flowing into it despite nobody having a real product on the promise of advancements in AI and "the time is now!" But eventually (my guess is next year, but I'm old enough to know it's impossible to time these things) the hype will come crashing down to reality and that is when funding will dry up.
1) Some scrappy researcher on a low budget develops a new AI technology that shows promise in a specific area.
2) More researchers take that idea and successfully apply it more broadly.
3) Massive investment in research happens, pushing PhD candidates into extracting every possible nuance out of that technology.
4) Researchers, encouraged by the broad success, begin to think we've found the one true key in the quest towards general intelligence. Suddenly all the researchers are Neats. The research funding is now mostly businesses instead of governments and non-profits. They grow overconfident that it can be used for virtually anything, funding everything in sight.
5) Starry-eyed futurists start lionizing the founders of the new revolution as geniuses, publishing flowery bullshit about how the world will be forever changed and how the General AI revolution is only 5-10 years away.
6) Surprise! It actually can't be used that broadly. Improvements stall, and CEOs start to realize that they can't just dump data in and get money out. It becomes a commercial disappointment, triggering disinvestment.
7) The Neats, disappointed that their glamorous and simplistic theory of general intelligence, start to disappear. Newspapers begin to make fun of all of the visionaries, comparing them to the people that said flying cars were 5-10 years away.
8) A handful of Scruffies take over the now low-funding AI winter, working hard on minimal budgets until a new breakthrough is found and we return back to #1.
This article is showing that we're solidly within stage 5, and we're already seeing signs of stage 6. When everybody is buying, it's time to sell.