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I'd bet people outside the field are going to be disillusioned before long as the marketing BS falls apart. Even years ago, I was already getting questions from
by 6gvONxR4sf7o 5y ago
I'd bet people outside the field are going to be disillusioned before long as the marketing BS falls apart. Even years ago, I was already getting questions from friends that surprised me in how detached their perceptions are from the reality of state of the art ML. Eventually, they'll realize that no, we're not building superhuman AI today or soon.
However, there's a ton of cool shit happening and tons of stuff already powering industry $$$. Some of this works because of mismatched expectations, like companies who are trying to sell magic. They will fail as their customers stop buying their products that fail spectacularly 2% of the time, but really can't fail 2% of the time.
But there's tons of room in the market for stuff that fails spectacularly 2% of the time. For that reason, I don't see a "real" reckoning coming. People's expectations are too high, sure. But the reality is still Good Enough™ for a lot of problems and getting better.
- rscho 5y agoThe main issue is that people have unrealistic expectations in domains that are personally relevant, medicine being an iconic example. So, there there might still be a 'reckoning' of sorts, even if Walmart applies AI successfully behind the scenes to power non-critical user-facing stuff.
- 6gvONxR4sf7o 5y agoOh for sure. I guess I could have worded it better (not just scare quoted "real"), but it seems like a public expectations reckoning will happen, but not a reckoning that really affects practitioners who aren't 'script-kiddies' for lack of a better term. (side note, does anyone know a less insulting way to refer to the group people call 'script-kiddies' that still gets the same point across?)
- CityOfThrowaway 5y agoTo your side note: “technicians” is a reasonable label here. They know how to use a technique, but aren’t themselves capable of creating one.
- popcorncowboy 5y agoTrue Scotsmen. (I mean your practitioners.. everyone else is of course, not a true scotsman).
- 573e91b4e282b2 5y agoI don't believe that there is a polite way to say "These people have no idea what they're actually doing, they're applying other people's logic blindly, and if anything goes wrong they're stuck." If that's not what you meant to communicate, you should probably explain more what you mean by 'script kiddie'.
- ashtonkem 5y agoThis appears to be the pattern of AI development overall: booms and busts of hype and disillusionment that leave behind useful tools that most don’t consciously consider to be remnants of a previous AI bubble.
- mdoms 5y agoThe same will happen with deep learning networks because if we're honest no serious researcher would really consider this "AI" either. It's just a big self-correcting linear algebra machine. Researchers will keep using that "AI" label for as long as it gets them research grants or selling products.
- bopbeepboop 5y agoWhy do you think DNNs are unrelated to “AI”? They develop an internal language[*] through experience in which they model the world, and make decisions based upon that language; what more do you want from them? [*] You can interpret that “linear algebra machine” as the Euclidean VM modeling some latent type theory; Curry-Howard + algebra-geometry correspondences connect systems of difference equations to type theories.
- WanderPanda 5y agoI wouldn't call it a "linear algebra machine" if the central point (of NNs) is that they are highly non-linear (the deeper the more non-linear, roughly)
- flir 5y agoWhen it works, it's no longer AI. I know I'm paraphrasing someone smarter than me there. (Edit: Larry Tesler maybe)
- Jensson 5y agoPeople believe that achieving X means the agent will be intelligent and can thus do Y, Z, A, B, C etc. But then someone builds an agent that can do X, but it can't do anything else. So people don't view it as intelligent, since it doesn't do any of all the other things people associated with being able to do X. So it has nothing to do with being used to computers solving new problems, it is that we now realize that solving those problems doesn't really require the level of intelligence we thought it would. Of course solving new problems is great, but it isn't nearly as exciting as being able to create intelligent agents. Edit: And it isn't just hobbyists making this mistake, you see big AI researchers often make it as well and they have for a long time. You will find tons of articles of AI researchers saying something along the lines of "Now we solved X, therefore solving A, B, C, D, E, F is just around the corner!", but they basically never deliver on that. Intelligence was harder than they thought.
- Jugurtha 5y ago>Some of this works because of mismatched expectations, like companies who are trying to sell magic. We were asked to look into a company that had an, I kid you not, "AI-powered problem solving platform". The suggestions it spit out in their demo for agriculture were things like "What if the earth was upside down", and I read it like "Maaaaan, what if, like, just imagine like, the earth wasn't like below, but it was like, above... Duuuuude. Just imagine.". i.e: you could get these recommendations with a few dollars worth of haschish.
- vorpalhex 5y agoThe modern mechanical turk. Pay someone to be very stoned and to sit in a box made to look like a supercomputer. Have it occasionally print a prompt for the "AI" to reflect on. Add a pitch deck and a blockchain and you have a startup with the right buzzword cloud.
- Jugurtha 5y agoHere Comes Another Bubble: https://youtube.com/watch?v=I6IQ_FOCE6I https://youtube.com/watch?v=I6IQ_FOCE6I
- Animats 5y agoBut there's tons of room in the market for stuff that fails spectacularly 2% of the time. That's the key insight. It's why the commercial successes of AI have been primarily in advertising, marketing, and investing.
- ssivark 5y agoThat’s an interesting insight. What are the long term societal implications of enabling/inducing (Jevons paradox) activities which are not “important enough” to need 100% correctness?
- Jensson 5y ago> commercial successes of AI have been primarily in advertising, marketing, and investing. Which is why people say that it has few useful applications. People don't care if those areas become more effective. Another important application is mass surveillance, which people would also argue isn't a good thing.
- worik 5y agoInvesting? Is there any evidence that any AI is better than index buy and hold? I do not think there is.
- tomComb 5y agoThe only thing consistently better than that is insider info, which is very common and quite reliable.
- NumberCruncher 5y agoThere was a saying before 2008: "How do you start an AI based FOREX trading fund? You pretend to be the russian prodigy developing the algo, while I do the insider trading".
- credit_guy 5y agoCan I propose another insight? AI sometimes works and works spectacularly and does not fail in 2% of the cases, or even in 0.02%. Just look at iPhone photos. But that AI application require deep domain knowledge + deep AI (or, well, ML, whatever) knowledge. You can't simply throw sklearn at an arbitrary problem and expect to get great results.
- ChefboyOG 5y agoThere certainly are tasks where a 2% failure rate is fine, but even more importantly, where we see ML/DL having the biggest impact today is in regards to complex system-type problems, where there is often a less-than-discrete notion of failure. Looking at apps we use every day, almost all of them owe some core feature to ML/DL. ETA prediction, translation, search, spam filtering, speech synthesis, autocomplete, recommendation engines, fraud detection—and that's not even touching the world of computer vision behind nearly every popular photo app. A key understanding gap in the general public's knowledge of ML is that people think AI === Skynet, and they've therefore been lied to about the field's progress and impact, when in reality, they probably interface with a dozen pieces of technology that are built on top of recent breakthroughs in ML/DL.
- nly 5y agoSpam filtering is mostly still based on Bayesian filtering, which is simple probability theory over a set of matchers.
- sli 5y agoI worked for a startup for a while that, late in its life, decided it needed to use AI in their product. Then that turned into needing ML in the product. Then it turned out it was just the owners trying to market the product as AI and ML powered when half of the product was the frontend used to configure the decision engine. The manually-configured decision engine was being sold as AI and ML, with the terms used interchangeably. I was actually a little surprised when it didn't work out, but not that surprised.
- m0zg 5y agoPrecisely. AI outside the beaten-to-death academic problems and datasets is a vast and completely un-plowed field with enormous potential. And unlike last time, there are some sub-fields of this that do actually work this time (computer vision, signal processing, speech recognition/synthesis, some NLP tasks, etc) and beat older baselines, often by a mile. There is a lot of froth, but there's quite a bit of substance as well, and the field is moving faster than any other I have ever experienced.
- snidane 5y agoHuman level AI in 2021 is still "tree climbing with one's eyes on the moon" (Hubert Dreyfus).
- thomasahle 5y agoMight as well climb to the top just to be sure.
- giardini 5y agoCertainly as long as there is another grant or VC investment within reach!
- Jensson 5y agoIs there really an AI that can climb trees? That is pretty complicated, I've just seen stuff that crawls on the ground.
- user_named 5y agoML is just as much marketing as AI. The machine isn't actually learning.