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Why are deep learning technologists so overconfident?

97 points by leonidasv 4y ago

coldtea 4y ago
Because technologists these days are first and foremost salesmen
- jfghi 4y ago
  Agreed. The billions of dollars thrown at anything claiming “AI” is quite disappointing. It’s a small branch of statistics the same way that statistics is a small branch of math. At this point, even a rolling average is labeled as AI.
  - blitzar 4y ago
    The billions of dollars thrown at anything claiming “AI” but would be lukcy to be high school level statistics under the hood is disappointing There fixed it for you ... However, that is the cycle of society. 1990's all you had to do wat put '.com' in your company name and you were a paper millionaire overnight. 2000's "Social, 2010's mobile first. Metaverse, AI and beyond.
  - wiz21c 4y ago
    Out of curiosity, what are some other, powerful, statistical branches besides those used in "data sciences" ? (honest question, I just completed a MSc in data sciences but I must admit I don't know much besides that)
    - jfghi 4y ago
      I’d recommend generalized linear models which are a subset of linear mixed models. These are the driving force for financial services. The model building process for these translates well to other types of models. Decision trees and random forests show up too. I think data science is great in that it exposes one to programming and technology moreso than a focus in math or stats, I was mostly complaining about the marketing I’ve seen surrounding AI. I recall listening to a few mba consultants claiming that they could improve a business process significantly by using ML even though there was sparse inaccurate data available and they had no modeling background. Overall I think AI models are interesting and often useful (although not sure what fits under the umbrella), but am tired of the hype, strong opinions, and amount of claimed expertise.
  - bee_rider 4y ago
    > It’s a small branch of statistics the same way that statistics is a small branch of math. Is this sarcastic or something? Math understanding is a fractal, the closer you look at a branch the bigger it gets. Statistics in particular seems to have countless real world applications.
    - jfghi 4y ago
      Not sarcastic, looking at statistical modeling there is a vast library of modeling techniques and research of which “AI” is a small component. In my experience, non-AI models tend to outperform the AI ones in many applications. This is different than how AI is marketed.
  - deltasevennine 4y ago
    Statistics is the basis of science. Probability is one of the axioms of reality. Probability just works and is assumed to work and nobody knows why. It is fundamentally the reason why Entropy always goes up. It is much more then a small branch of math given it's foundational relation to the real world. Intuitively AI is much more just a branch of math. We are getting closer and closer to understand theoretically how our own brains work. The philosophical connections are profound and you are using language categories to limit your thinking.
  - zmgsabst 4y ago
    Deep learning is statistics weighted graphs, but graphs are a way to encode all of mathematics. Which is why things like manifolds and topology are useful for analyzing our models, because all of the ways to encode mathematics are equivalent and you can transport ideas between them.
  - Banana699 4y ago
    This is as ridiculous as claiming that a neural network is going to make Skynet. First, statistics is not a small branch of math, proability theory is (not a small branch by the way, not by a long shot, just a branch, one of the fundamental and very large ones). Statistics is the art and science of dealing with data, it uses probability theory like physics uses math, but is far more than probability theory just like physics is far more than math. Second, Neural nets and machine learning in general is an extremly powerful way of solving problems of certain structure and form, and it solves problems not encountered in statistics. Claiming it's a small branch of statistics is like claiming that the entirety of computer science is a small branch of logic.
    - jfghi 4y ago
      What are the top 3 things accomplished and implemented by the “extremely powerful” neural networks and machine learning? How significant are these top 3 things? Have you ever built a production model for a large company or have a tier 1 phd? If not, what are your qualifications for labeling a claim ridiculous.
      - Banana699 4y ago
        >What are the top 3 things accomplished and implemented by the “extremely powerful” neural networks and machine learning? Off the top of my head and in no particular order : 1- Playing human games at superhuman performance from raw pixels 2- Recognizing objects and people in images at human or better performance 3- Recognizing human voice in real world noisy environments and parsing it into text at human or better performance >How significant are these top 3 things? They generate billions of dollars of profit and beat human knowledge and expertise that took thousands\millions of years to craft and\or evolve with nothing but a GPU and a lot of electricity. >Have you ever built a production model for a large company or have a tier 1 phd? No. >what are your qualifications for labeling a claim ridiculous. I have a brain that can spot ridiculous hyperbolic claims, and experience-backed knowledge with neural networks and machine learning that can explain with examples why they are ridiculous and hyperbolic. If you do have the qualifications you request, start with using them to support your claims. Here's a small challenge that should pose no difficulty to you. Show me, with derivations and citations, how an RNN is "just" an obscure statistical model. Which statistician first published or applied it ? Is it taught in a statistics course you know of ? Which ones ? Are research statisticians publishing on it right now or ever ? In what sense is it part of the science of statistics like, say, the normal distribution is ?
  - elcomet 4y ago
    I don't understand this criticism. What matter are the results, not the technology used. If your company is capable of solving a new problem that is worth billion of dollars, then does it matter that it uses basic statistics ? Of course if they overpromise, then reality will catch them and the company will fail.. but this has nothing to do with statistics.
- causi 4y ago
  Exactly. Remember a couple years ago when just saying "blockchain" would get money thrown at you?
- martindbp 4y ago
  No, it appears the "technologists" here on HN (such as yourself) are now people who are techno-pessimists through and through, don't see the big deal in new breakthroughs, aren't excited about technology at all really, can't extrapolate a trend a few years into the future, and would rather quit their job and do wood working. Is there another forum where all the hackers went, I think I want to go somewhere else. Edit: I take the part about wood working, I think that's a reasonable response to the way today's tech jobs make us feel. I know many people who'd like to quit and just work on side-projects (or wood working), but they don't hate technology itself, that's the difference.
  - szundi 4y ago
    Subscribed to the answers to this comment.
  - logisticseh 4y ago
    Discord servers.
    - api 4y ago
      What are your favorites?
  - JohnHaugeland 4y ago
    - martindbp 4y ago
      I've been on HN since about 2012 under various accounts, mostly lurking
  - trention 4y ago
    >can't extrapolate a trend a few years into the future It's better to not extrapolate S-curves into exponential functions when you don't know what you are talking about, yes. A good advice for both DL researchers and "techno-optimists" who haven't seen unsubstantiated hype they didn't jump on yet.
- rvz 4y ago
  Or more accurately, grifters. The very same ones did it with 'open-source' (hijacking 'free-software' and creating companies that develop closed-source products). The same happened with the Web (and has been ruined by the ads industry thanks to Big Tech), IoT (with phones being the worst IoT device), Blockchains (web3 being yet another hyped up narrative duping retail investors), and now AI once again (with Deep Learning) is already creating a new grift after the release of Stable Diffusion. All together have been a total disaster towards privacy and are quite frankly dystopian creations and tools for the technologist grifter.
blitzar 4y ago
They are talking up their own chances of success for the $$$'s. When they tweet / do a puff piece that a PR person has lined up in the news of course they will say that their one true technology is going to solve world hunger and bring about world peace. Why are <insert language, database, framework, technology etc> so overconfident?
ipnon 4y ago
The adage that we overestimate technology in the short term and we underestimate it in the long term holds. There is no way we all have a personal AGI assistants that drive our cars and work jobs for us this decade. At the end of this century we will all have this technology, barring any one of the possible apocalyptic catastrophes besetting us at present. The question is whether this advance is possible without some irrational exuberance today. We need the cult of AI to inspire us to think it’s even possible in the first place.
- arise 4y ago
  There's also the AI adage that you don't get to the moon by building successively larger ladders... all this deep learning stuff is great and will unlock amazing value (esp if we fund open data+models), but we run the risk of exhausting consumer and government patience if we keep promising the world with this single family of techniques.
  - TremendousJudge 4y ago
    Not sure if the analogy holds. Human brains work on the same principle as lizard brais, no change in strategy was needed to go from lizard intelligence to human intelligence. I'm not saying that DL holds this promise though.
    - russdill 4y ago
      - Zamiel_Snawley 4y ago
        Be kind. Don't be snarky. Have curious conversation; don't cross-examine. Please don't fulminate. Please don't sneer, including at the rest of the community.[1] [1] https://news.ycombinator.com/newsguidelines.html https://news.ycombinator.com/newsguidelines.html
    - deleted 4y ago
      [deleted]
ocimbote 4y ago
Because they have no idea what they're doing so they compensate for that with extra confidence. :shrug:
- TheRealPomax 4y ago
  counter argument: they know exactly what they're doing, but no one with actual money is going to fund them if they talk about their work in realistic terms instead of boasting and promising the moon, next week. Academic budgets are one of the biggest jokes in science, the real money comes from investment capitalists, and goes to those who make the biggest promises, the loudest.
  - xcambar 4y ago
    So, worse than not knowing, it's plain lies? The counter argument makes no one look good. Who's to blame: The game for pushing the gamers or the gamers that keep the game alive?
    - TheRealPomax 4y ago
      If you're asking people to pick just one, you're being far too generous in placing blame.
Jevon23 4y ago
They delivered technology that everyone would have said was sci-fi ~5 years ago. I think some confidence is deserved at this point. I personally remain skeptical of the claims that the singularity is just around the corner though.
SeanLuke 4y ago
I don't argue with the general claim of this article, but the following statements are just wrong: > Deep learning entered the sphere of mainstream awareness less than a decade ago, but the science is ancient. This 1986 paper in Nature contains almost all of the core technical innovations that make it work well. (The term deep learning hadn’t been coined yet and neural networks was used instead). > However, the dataset sizes and compute resources that were available in the 1980s weren’t enough to demonstrate the effectiveness of deep learning, and other machine learning techniques like support vector machines took center stage. I think there are two glaring errors here. First, multilayer error backpropagation wasn't developed in 1986. It was developed in the early 1970s, but was quashed by the elimination of funding after the Perceptrons papers and book. It was rediscovered several times but didn't take hold until the PDP group (Rummelhart et al) pushed things through with enough gravitas. Second, and this is the big one: there were plenty of reasonably large datasets, and frankly plenty of sufficiently large compute resources in the late 1980s and certainly the 1990s to do this research. That wasn't the problem. The problem was that neural networks were largely restricted to two layers due to the vanishing gradient problem, so they had to be fat rather than deep, which wasn't good. That's the whole point of the name deep learning -- by overcoming issues which prevented deep networks, researchers were able to do the stuff they can now. That didn't come until later.
- jeremysalwen 4y ago
  The story your telling was the mainstream story from when "deep learning" first arose, using Boltzmann machines for pretraining, etc. But then the field exploded, more compute power was directed at the problem, and people discovered that yes, in fact basic MLPs with SGD work well with enough compute.
  - sdenton4 4y ago
    But the point is that there were super important algorithm advanced that allowed training bigger models. It wasn't all hardware... A big part of it was getting more eyes on the problems (via hype over imagenet results circa 2012) to figure out how to solve them.
- 4y ago
sinenomine 4y ago
>snake oil >our book Ctrl+F "scaling" gives 0 results across this essay Deep learners have their scaling laws which seem to hold across many orders of magnitude of model size and compute spent on training. Critics sometimes have to resort to building their argumentation along pretty esoteric tangents to avoid naming this elephant in the room. Instead of denying scaling, why shouldn't we make it work for our society's benefit? A new generation of thinkers already proposes a whole research program for life science, designed from the scaling and transfer learning fundamentals: https://markov.bio/biomedical-progress/ https://markov.bio/biomedical-progress/
prometheus76 4y ago
Here's my cynical take on the state of the art for 99% of AI projects: 1. take some training data. 2. Do some statistical analysis on it and find relevant variables for the variable you want to predict. 3. Build a model to predict the variable. 4. Put it into production. 5. Run model and get predictions. 6. Notice predictions are becoming less reliable 7. Retrain the model on new data. 8. GOTO Step 5.
PeterisP 4y ago
Is there actual overconfidence? I think that if just in 2020 we did a survey about whether the capabilities of (for example) the image generation systems discussed in other today's threads would materialize at 2022, then more than half of deep learning technologists would have said that it's not going to happen so fast, demonstrating underconfidence instead of overconfidence. For another example, when people in NLP put up tasks like the GLUE dataset, a reasonable expectation was that it would be a meaningful benchmark for some time, but it turned out that the previously too hard tasks became too easy just a year or so later - again, demonstrating underconfidence instead of overconfidence. IMHO the overconfidence appears when people listen to PR of various companies overpromising products, not to the actual technologists doing research on the technology.
- frgtpsswrdlame 4y ago
  Maybe the problem is trying to understand this in terms of confidence and not in terms of accuracy. In fact, we might say that some are underconfident, some are overconfident but almost all are inaccurate in predicting the form and/or details of progress. In that case we don't have to care about the technologists who thought the recent wave of image generation would take 10+ years AND we don't have to care about the people who think evil AI will bury us under paperclips in the next 30.
- logisticseh 4y ago
  I agree. There are two different sets: 1. Actual deep learning researchers, actively doing research. I rarely hear nonsense about impending AGI or mass technological unemployment from these folks. They might express tepid concern about the ethics of various applications of deep learning and tepidly point out that some technological disruption in employment is possible, but in both cases with an emphasis on tepid. And who, if anything, tend to under-estimate progress. (Self-driving is still very far off and also much further along than I thought it would be 9 years ago. I turned down jobs at self-driving cos because I didn't believe they would make enough progress to even have an ADAS product, but I was clearly wrong and if I could do it over I'd work on self-driving from 2013 onwards even though L5 is still a long way out.) 2. Deep learning fan-boys, for lack of a better term. The rationalist community in particular has a sub-community of tech-adjacent folks who aren't publishing in major conferences every cycle or running research labs but do talk a lot about AGI/UBI. IMO it's not that dissimilar from climate science or even in the extreme the existence of aliens. Scientists with a lot of real expertise will sort of tepidly talk you through the full complexity. And then some "true believers" who aren't actually expert will sort of run to the extremes of anti-natalism or aliens among us. If that makes sense.
wikitopian 4y ago
We have a stock market investment bubble. Deep learning, AI, metaverse, blockchain, quantum computing, self driving cars, and this very website's whole shtick all revolve around fumbling for an answer that isn't there. "Technology" is set for a long winter of commoditization and consolidation. Technology as a standalone sector will become sleepy with all the excitement being in every other sector of the economy leveraging cheaper and better technology for their own benefit.
- echelon 4y ago
  That's one way to look at it. Another is that AI/ML will replace illustrators, musicians, vocalists, and the like. That "metaverse" will take over gaming and lead to powerful new filmmaking techniques. I'm building something in this space and I can tell you that these technologies are powerful because people who aren't "traditionally skilled" are using these tools to create amazing content and excel. This stuff is going to eat the world. Not sure about the other domains you mentioned, but I'm sure they have ardent believers too.
  - wikitopian 4y ago
    VR does not add enough value to counteract the UI problem that most of the people who try to use it experience nausea, vomiting, and visceral aversion. Minecraft is plenty cool and my four year old is making amazing stuff in it. But increasing the resolution, frame rate, and immersiveness of that experience is decidedly evolutionary, not revolutionary. And there was already no money in music or graphics design before the machines showed up, so I'm not sure how that's going to justify the investment bubble.
    - echelon 4y ago
      The tech stack isn't just VR/AR, but includes the scale and affordability of rendering, increasingly wide range of sensor input types, spatial computing, mocap, photogrammetry etc. https://youtu.be/RR_3eMIP6YE https://youtu.be/RR_3eMIP6YE https://youtu.be/QM8qOoYK3Fs https://youtu.be/QM8qOoYK3Fs (mildly NSFW) https://youtu.be/2-1G8JUWoG8 https://youtu.be/2-1G8JUWoG8 (same techniques but narrative) Pretty soon everyone is going to have access to world class creative tools that give them the same capabilities that previously only studios with institutional capital had. This is the creative decade.
deleted 4y ago
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nmca 4y ago
I find it more illuminating to look at resolved prediction markets for concrete benchmarks; many of which have resolved faster and more positively than expected. https://bounded-regret.ghost.io/ai-forecasting-one-year-in/ https://bounded-regret.ghost.io/ai-forecasting-one-year-in/ I guess we will have to see what happens, but starting an anti deep learning blog after the staggering progress of the last six months seems pretty wild...
- rafaelero 4y ago
  The beauty of all is that it actually seems we are underestimating current methods of AI. The noise (especially in this community) is very high though, so I suggest everyone just look at the experts predictions from the year before and compare with what we have achieved.
seydor 4y ago
I don't know man, everything so far seems to show that it scales frighteningly well. Maybe the skepticism over DL is wildly exagerrated?
cirgue 4y ago
Deep learning technologists != people that write articles about machine learning making various professions obsolete. I don’t think anyone that works with neural networks professionally is drinking the kool-aid. Don’t get me wrong, deep learning is an incredible technology, but not in ways that it’s made out to be in the press. Like a lot of other technologies, it’s real value is in solving really boring problems at a scale. But that doesn’t make for good content so we end up with a bunch of articles about self driving cars and nothing about using deep learning to take something like 3d photogrammetry from “passable for artistic purposes” to “industrially valuable.”
somenameforme 4y ago
Because the field saw exponential progress which has started stalling out only relatively recently. And so now there are two possible explanations: 1) We're reaching near the end of where the current domain can take us. 2) We're making some sort of mistake or otherwise missing something that's the key to unlocking continued exponential progress. It's always natural, especially for those working within a domain, to assume it's #2. Unfortunately it seems our universe has this habit of always kicking us back to #1 just before the completely game breaking stuff starts happening.
kalkin 4y ago
"We won’t waste your time arguing with these claims" Err, actually, if you have something substantive to say, please do? I would really love to be persuaded that unaligned AGI is not something I need to worry about in my kids' lifetimes. I'd much prefer to believe more strongly in my ability to set them up to thrive! But when skeptics repeatedly don't seem to have anything to say beyond sneering, my anxiety is not alleviated.
- nradov 4y ago
  I would really love to be persuaded that an alien invasion is not something I need to worry about in my kids' lifetimes. Come on. We can't even build an AGI equivalent to a cockroach yet. Let's get real and stop panicking about things that are 100% hypothetical.
  - kalkin 4y ago
    I'm not sure what you mean by an AGI equivalent to a cockroach, but let's take it for granted that cockroaches can do things AIs still can't. AIs can also do a lot of things that cockroaches can't, including plenty that until recently seemed far out of reach (translation, superhuman Go play, illustration). Why would we expect intelligence to get smoothly better along a total ordering before it's dangerous? Human capabilities don't work like that. Re 100% hypothetical: this seems like a pretty useless heuristic. Climate change, the health impact of leaded gas, and the nuclear bomb were all hypothetical, until they weren't. For that matter, if something isn't at least a little bit hypothetical there's no point in worrying at all; it's a done deal! The alien invasion analogy seems like it suggests the heuristic you're actually using is "wild scifi thing so not worrying". This is a better option than "still hypothetical so not worrying" but still doesn't seem reliable.
    - nradov 4y ago
      We have a lot of dangerous technologies which exist today and are either actively killing people, or could if they are actually used. It's silly to worry about something which doesn't exist, and for which we don't even have a clear path to build. It's like someone in 15th century Milan seeing Leonardo da Vinci's design for a helicopter (aerial screw) and worrying about attack helicopter gunships coming to destroy their city.
blueblisters 4y ago
To borrow an analogy from cognitive science, deep learning has pretty much mastered System 1 thinking in independent domains (image, language, audio) and scaling laws suggest we have a clear path to self-supervised multi modal models (video+audio+text) by applying more data and compute. We will likely miss critical world knowledge about dynamics, contact forces, taste, feel etc. until we figure out a way to efficiently collect datasets for these modalities. What’s less certain is the path to System 2 thinking that is required for true AGI. We have yet to find evidence that deep learning is enough for AI to learn planning and conscious thinking.
jrm4 4y ago
This feels related to the idea that there is mostly no such thing as "general intelligence." Rather, there's a diverse field of mental/cognitive skills. Many may correlate (or even..causate?) but I believe this is why e.g. AI is overhyped.
AtNightWeCode 4y ago
I think the problem is that most of the people working in this field does not seem to have basic knowledge about statistics or how to evaluate statistics. They somehow think they can cheat it. And they over evaluate their own results. The death of Google Translate was caused by GT started to learn from itself. Same thing will be happening to self-driving cars and so on. Five years ago, some garbage predictors said developers would become obsolete. I tell you, the amount of work that needs to be done by real people at real companies in the software biz has never been higher.
andreilys 4y ago
In our experience, even deep learning technologists at the top of their game are surprised when we point out that deep learning (or any other form of AI) struggles when tasked with predicting the future. It seems to do a pretty good job of predicting future stock market performance. But of course the authors have an axe to grind and choose to view the world through their narrow filter, probably because they want to promote their book and tap into the "tech is bad" trope thats going on in the wider culture. Most significantly, AGI hypers have succeeded in directing a large amount of funding toward the fanciful goal of ensuring that this supposedly imminent AGI will be aligned with humanity’s interests. Crucially, much of this community believes that we already have all the innovations we need to reach AGI; we just need to throw more hardware at the neural networks we already have. The problem with this argument is that we have no idea what the "warning signs" of imminent AGI is. It could take 2-3 key breakthroughs or it could take a few hundred. Whatever the case is, we should think about investing in a fire alarm system before theres a fire.
- andrepd 4y ago
  > It seems to do a pretty good job of predicting future stock market performance. Does it? Care to elaborate on that?
  - andreilys 4y ago
    Well the obvious example is RenTech's Medallion fund which consistently out-performs human stock traders. The Medallion Fund has employed mathematical models to analyze and execute trades, many of them automated. The firm uses computer-based models to predict price changes. These models are based on analyzing as much data as can be gathered, then looking for non-random movements to make predictions. [1] Renaissance Technologies' Medallion fund, which from 1988 to 2018 clocked annualized returns of 66%. After fees, those annualized returns were still remarkable, at 39%.[2] [1] https://wealthofgeeks.com/medallion-fund/#:~:text=Algorithmic%20Trading,random%20movements%20to%20make%20predictions https://wealthofgeeks.com/medallion-fund/#:~:text=Algorithmi.... [2]https://money.usnews.com/investing/funds/articles/top-hedge-funds-this-year#:~:text=Perhaps%20the%20most%20enigmatic%20and,still%20remarkable%2C%20at%2039%25 https://money.usnews.com/investing/funds/articles/top-hedge-....
gwbas1c 4y ago
Uh, the nag to harvest email addresses was weird. It completely covered the article. I almost closed the tab without reading the article. IMO: Get rid of it. (Or use a normal style that doesn't nag for an email address until you've scrolled a bit, and keeps the page visible while the nag is open.) Why? People don't always read an article when they open a page. (I certainly don't.) By the time I saw the page I was totally lost from the context of what the page was, and the only reason why I didn't close the article was because the title on the Hacker News adjacent tab looked interesting.
_nalply 4y ago
Deep learning has two main deficiencies: 1. They depend on pre-existing data. In other words they might not create something new. 2. They need a lot of hand-holding for learning. Children have an innate instinct to learn, and deep learning is not (yet) there.

97 points by leonidasv 4y ago

coldtea 4y ago

Because technologists these days are first and foremost salesmen

jfghi 4y ago
Agreed. The billions of dollars thrown at anything claiming “AI” is quite disappointing. It’s a small branch of statistics the same way that statistics is a small branch of math. At this point, even a rolling average is labeled as AI.
- blitzar 4y ago
  The billions of dollars thrown at anything claiming “AI” but would be lukcy to be high school level statistics under the hood is disappointing There fixed it for you ... However, that is the cycle of society. 1990's all you had to do wat put '.com' in your company name and you were a paper millionaire overnight. 2000's "Social, 2010's mobile first. Metaverse, AI and beyond.
- wiz21c 4y ago
  Out of curiosity, what are some other, powerful, statistical branches besides those used in "data sciences" ? (honest question, I just completed a MSc in data sciences but I must admit I don't know much besides that)
  - jfghi 4y ago
    I’d recommend generalized linear models which are a subset of linear mixed models. These are the driving force for financial services. The model building process for these translates well to other types of models. Decision trees and random forests show up too. I think data science is great in that it exposes one to programming and technology moreso than a focus in math or stats, I was mostly complaining about the marketing I’ve seen surrounding AI. I recall listening to a few mba consultants claiming that they could improve a business process significantly by using ML even though there was sparse inaccurate data available and they had no modeling background. Overall I think AI models are interesting and often useful (although not sure what fits under the umbrella), but am tired of the hype, strong opinions, and amount of claimed expertise.
- bee_rider 4y ago
  > It’s a small branch of statistics the same way that statistics is a small branch of math. Is this sarcastic or something? Math understanding is a fractal, the closer you look at a branch the bigger it gets. Statistics in particular seems to have countless real world applications.
  - jfghi 4y ago
    Not sarcastic, looking at statistical modeling there is a vast library of modeling techniques and research of which “AI” is a small component. In my experience, non-AI models tend to outperform the AI ones in many applications. This is different than how AI is marketed.
- deltasevennine 4y ago
  Statistics is the basis of science. Probability is one of the axioms of reality. Probability just works and is assumed to work and nobody knows why. It is fundamentally the reason why Entropy always goes up. It is much more then a small branch of math given it's foundational relation to the real world. Intuitively AI is much more just a branch of math. We are getting closer and closer to understand theoretically how our own brains work. The philosophical connections are profound and you are using language categories to limit your thinking.
- zmgsabst 4y ago
  Deep learning is statistics weighted graphs, but graphs are a way to encode all of mathematics. Which is why things like manifolds and topology are useful for analyzing our models, because all of the ways to encode mathematics are equivalent and you can transport ideas between them.
- Banana699 4y ago
  This is as ridiculous as claiming that a neural network is going to make Skynet. First, statistics is not a small branch of math, proability theory is (not a small branch by the way, not by a long shot, just a branch, one of the fundamental and very large ones). Statistics is the art and science of dealing with data, it uses probability theory like physics uses math, but is far more than probability theory just like physics is far more than math. Second, Neural nets and machine learning in general is an extremly powerful way of solving problems of certain structure and form, and it solves problems not encountered in statistics. Claiming it's a small branch of statistics is like claiming that the entirety of computer science is a small branch of logic.
  - jfghi 4y ago
    What are the top 3 things accomplished and implemented by the “extremely powerful” neural networks and machine learning? How significant are these top 3 things? Have you ever built a production model for a large company or have a tier 1 phd? If not, what are your qualifications for labeling a claim ridiculous.
    - Banana699 4y ago
      >What are the top 3 things accomplished and implemented by the “extremely powerful” neural networks and machine learning? Off the top of my head and in no particular order : 1- Playing human games at superhuman performance from raw pixels 2- Recognizing objects and people in images at human or better performance 3- Recognizing human voice in real world noisy environments and parsing it into text at human or better performance >How significant are these top 3 things? They generate billions of dollars of profit and beat human knowledge and expertise that took thousands\millions of years to craft and\or evolve with nothing but a GPU and a lot of electricity. >Have you ever built a production model for a large company or have a tier 1 phd? No. >what are your qualifications for labeling a claim ridiculous. I have a brain that can spot ridiculous hyperbolic claims, and experience-backed knowledge with neural networks and machine learning that can explain with examples why they are ridiculous and hyperbolic. If you do have the qualifications you request, start with using them to support your claims. Here's a small challenge that should pose no difficulty to you. Show me, with derivations and citations, how an RNN is "just" an obscure statistical model. Which statistician first published or applied it ? Is it taught in a statistics course you know of ? Which ones ? Are research statisticians publishing on it right now or ever ? In what sense is it part of the science of statistics like, say, the normal distribution is ?
- elcomet 4y ago
  I don't understand this criticism. What matter are the results, not the technology used. If your company is capable of solving a new problem that is worth billion of dollars, then does it matter that it uses basic statistics ? Of course if they overpromise, then reality will catch them and the company will fail.. but this has nothing to do with statistics.
causi 4y ago
Exactly. Remember a couple years ago when just saying "blockchain" would get money thrown at you?
martindbp 4y ago
No, it appears the "technologists" here on HN (such as yourself) are now people who are techno-pessimists through and through, don't see the big deal in new breakthroughs, aren't excited about technology at all really, can't extrapolate a trend a few years into the future, and would rather quit their job and do wood working. Is there another forum where all the hackers went, I think I want to go somewhere else. Edit: I take the part about wood working, I think that's a reasonable response to the way today's tech jobs make us feel. I know many people who'd like to quit and just work on side-projects (or wood working), but they don't hate technology itself, that's the difference.
- szundi 4y ago
  Subscribed to the answers to this comment.
- logisticseh 4y ago
  Discord servers.
  - api 4y ago
    What are your favorites?
- JohnHaugeland 4y ago
  - martindbp 4y ago
    I've been on HN since about 2012 under various accounts, mostly lurking
- trention 4y ago
  >can't extrapolate a trend a few years into the future It's better to not extrapolate S-curves into exponential functions when you don't know what you are talking about, yes. A good advice for both DL researchers and "techno-optimists" who haven't seen unsubstantiated hype they didn't jump on yet.
rvz 4y ago
Or more accurately, grifters. The very same ones did it with 'open-source' (hijacking 'free-software' and creating companies that develop closed-source products). The same happened with the Web (and has been ruined by the ads industry thanks to Big Tech), IoT (with phones being the worst IoT device), Blockchains (web3 being yet another hyped up narrative duping retail investors), and now AI once again (with Deep Learning) is already creating a new grift after the release of Stable Diffusion. All together have been a total disaster towards privacy and are quite frankly dystopian creations and tools for the technologist grifter.

blitzar 4y ago

They are talking up their own chances of success for the $$$'s. When they tweet / do a puff piece that a PR person has lined up in the news of course they will say that their one true technology is going to solve world hunger and bring about world peace. Why are <insert language, database, framework, technology etc> so overconfident?

ipnon 4y ago

The adage that we overestimate technology in the short term and we underestimate it in the long term holds. There is no way we all have a personal AGI assistants that drive our cars and work jobs for us this decade. At the end of this century we will all have this technology, barring any one of the possible apocalyptic catastrophes besetting us at present. The question is whether this advance is possible without some irrational exuberance today. We need the cult of AI to inspire us to think it’s even possible in the first place.

arise 4y ago
There's also the AI adage that you don't get to the moon by building successively larger ladders... all this deep learning stuff is great and will unlock amazing value (esp if we fund open data+models), but we run the risk of exhausting consumer and government patience if we keep promising the world with this single family of techniques.
- TremendousJudge 4y ago
  Not sure if the analogy holds. Human brains work on the same principle as lizard brais, no change in strategy was needed to go from lizard intelligence to human intelligence. I'm not saying that DL holds this promise though.
  - russdill 4y ago
    - Zamiel_Snawley 4y ago
      Be kind. Don't be snarky. Have curious conversation; don't cross-examine. Please don't fulminate. Please don't sneer, including at the rest of the community.[1] [1] https://news.ycombinator.com/newsguidelines.html https://news.ycombinator.com/newsguidelines.html
  - deleted 4y ago
    [deleted]

ocimbote 4y ago

Because they have no idea what they're doing so they compensate for that with extra confidence. :shrug:

TheRealPomax 4y ago
counter argument: they know exactly what they're doing, but no one with actual money is going to fund them if they talk about their work in realistic terms instead of boasting and promising the moon, next week. Academic budgets are one of the biggest jokes in science, the real money comes from investment capitalists, and goes to those who make the biggest promises, the loudest.
- xcambar 4y ago
  So, worse than not knowing, it's plain lies? The counter argument makes no one look good. Who's to blame: The game for pushing the gamers or the gamers that keep the game alive?
  - TheRealPomax 4y ago
    If you're asking people to pick just one, you're being far too generous in placing blame.

Jevon23 4y ago

They delivered technology that everyone would have said was sci-fi ~5 years ago. I think some confidence is deserved at this point. I personally remain skeptical of the claims that the singularity is just around the corner though.

SeanLuke 4y ago

I don't argue with the general claim of this article, but the following statements are just wrong: > Deep learning entered the sphere of mainstream awareness less than a decade ago, but the science is ancient. This 1986 paper in Nature contains almost all of the core technical innovations that make it work well. (The term deep learning hadn’t been coined yet and neural networks was used instead). > However, the dataset sizes and compute resources that were available in the 1980s weren’t enough to demonstrate the effectiveness of deep learning, and other machine learning techniques like support vector machines took center stage. I think there are two glaring errors here. First, multilayer error backpropagation wasn't developed in 1986. It was developed in the early 1970s, but was quashed by the elimination of funding after the Perceptrons papers and book. It was rediscovered several times but didn't take hold until the PDP group (Rummelhart et al) pushed things through with enough gravitas. Second, and this is the big one: there were plenty of reasonably large datasets, and frankly plenty of sufficiently large compute resources in the late 1980s and certainly the 1990s to do this research. That wasn't the problem. The problem was that neural networks were largely restricted to two layers due to the vanishing gradient problem, so they had to be fat rather than deep, which wasn't good. That's the whole point of the name deep learning -- by overcoming issues which prevented deep networks, researchers were able to do the stuff they can now. That didn't come until later.

jeremysalwen 4y ago
The story your telling was the mainstream story from when "deep learning" first arose, using Boltzmann machines for pretraining, etc. But then the field exploded, more compute power was directed at the problem, and people discovered that yes, in fact basic MLPs with SGD work well with enough compute.
- sdenton4 4y ago
  But the point is that there were super important algorithm advanced that allowed training bigger models. It wasn't all hardware... A big part of it was getting more eyes on the problems (via hype over imagenet results circa 2012) to figure out how to solve them.
4y ago

sinenomine 4y ago

>snake oil >our book Ctrl+F "scaling" gives 0 results across this essay Deep learners have their scaling laws which seem to hold across many orders of magnitude of model size and compute spent on training. Critics sometimes have to resort to building their argumentation along pretty esoteric tangents to avoid naming this elephant in the room. Instead of denying scaling, why shouldn't we make it work for our society's benefit? A new generation of thinkers already proposes a whole research program for life science, designed from the scaling and transfer learning fundamentals: https://markov.bio/biomedical-progress/ https://markov.bio/biomedical-progress/

prometheus76 4y ago

Here's my cynical take on the state of the art for 99% of AI projects: 1. take some training data. 2. Do some statistical analysis on it and find relevant variables for the variable you want to predict. 3. Build a model to predict the variable. 4. Put it into production. 5. Run model and get predictions. 6. Notice predictions are becoming less reliable 7. Retrain the model on new data. 8. GOTO Step 5.

PeterisP 4y ago

Is there actual overconfidence? I think that if just in 2020 we did a survey about whether the capabilities of (for example) the image generation systems discussed in other today's threads would materialize at 2022, then more than half of deep learning technologists would have said that it's not going to happen so fast, demonstrating underconfidence instead of overconfidence. For another example, when people in NLP put up tasks like the GLUE dataset, a reasonable expectation was that it would be a meaningful benchmark for some time, but it turned out that the previously too hard tasks became too easy just a year or so later - again, demonstrating underconfidence instead of overconfidence. IMHO the overconfidence appears when people listen to PR of various companies overpromising products, not to the actual technologists doing research on the technology.

frgtpsswrdlame 4y ago
Maybe the problem is trying to understand this in terms of confidence and not in terms of accuracy. In fact, we might say that some are underconfident, some are overconfident but almost all are inaccurate in predicting the form and/or details of progress. In that case we don't have to care about the technologists who thought the recent wave of image generation would take 10+ years AND we don't have to care about the people who think evil AI will bury us under paperclips in the next 30.
logisticseh 4y ago
I agree. There are two different sets: 1. Actual deep learning researchers, actively doing research. I rarely hear nonsense about impending AGI or mass technological unemployment from these folks. They might express tepid concern about the ethics of various applications of deep learning and tepidly point out that some technological disruption in employment is possible, but in both cases with an emphasis on tepid. And who, if anything, tend to under-estimate progress. (Self-driving is still very far off and also much further along than I thought it would be 9 years ago. I turned down jobs at self-driving cos because I didn't believe they would make enough progress to even have an ADAS product, but I was clearly wrong and if I could do it over I'd work on self-driving from 2013 onwards even though L5 is still a long way out.) 2. Deep learning fan-boys, for lack of a better term. The rationalist community in particular has a sub-community of tech-adjacent folks who aren't publishing in major conferences every cycle or running research labs but do talk a lot about AGI/UBI. IMO it's not that dissimilar from climate science or even in the extreme the existence of aliens. Scientists with a lot of real expertise will sort of tepidly talk you through the full complexity. And then some "true believers" who aren't actually expert will sort of run to the extremes of anti-natalism or aliens among us. If that makes sense.

wikitopian 4y ago

We have a stock market investment bubble. Deep learning, AI, metaverse, blockchain, quantum computing, self driving cars, and this very website's whole shtick all revolve around fumbling for an answer that isn't there. "Technology" is set for a long winter of commoditization and consolidation. Technology as a standalone sector will become sleepy with all the excitement being in every other sector of the economy leveraging cheaper and better technology for their own benefit.

echelon 4y ago
That's one way to look at it. Another is that AI/ML will replace illustrators, musicians, vocalists, and the like. That "metaverse" will take over gaming and lead to powerful new filmmaking techniques. I'm building something in this space and I can tell you that these technologies are powerful because people who aren't "traditionally skilled" are using these tools to create amazing content and excel. This stuff is going to eat the world. Not sure about the other domains you mentioned, but I'm sure they have ardent believers too.
- wikitopian 4y ago
  VR does not add enough value to counteract the UI problem that most of the people who try to use it experience nausea, vomiting, and visceral aversion. Minecraft is plenty cool and my four year old is making amazing stuff in it. But increasing the resolution, frame rate, and immersiveness of that experience is decidedly evolutionary, not revolutionary. And there was already no money in music or graphics design before the machines showed up, so I'm not sure how that's going to justify the investment bubble.
  - echelon 4y ago
    The tech stack isn't just VR/AR, but includes the scale and affordability of rendering, increasingly wide range of sensor input types, spatial computing, mocap, photogrammetry etc. https://youtu.be/RR_3eMIP6YE https://youtu.be/RR_3eMIP6YE https://youtu.be/QM8qOoYK3Fs https://youtu.be/QM8qOoYK3Fs (mildly NSFW) https://youtu.be/2-1G8JUWoG8 https://youtu.be/2-1G8JUWoG8 (same techniques but narrative) Pretty soon everyone is going to have access to world class creative tools that give them the same capabilities that previously only studios with institutional capital had. This is the creative decade.

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nmca 4y ago

I find it more illuminating to look at resolved prediction markets for concrete benchmarks; many of which have resolved faster and more positively than expected. https://bounded-regret.ghost.io/ai-forecasting-one-year-in/ https://bounded-regret.ghost.io/ai-forecasting-one-year-in/ I guess we will have to see what happens, but starting an anti deep learning blog after the staggering progress of the last six months seems pretty wild...

rafaelero 4y ago
The beauty of all is that it actually seems we are underestimating current methods of AI. The noise (especially in this community) is very high though, so I suggest everyone just look at the experts predictions from the year before and compare with what we have achieved.

seydor 4y ago

I don't know man, everything so far seems to show that it scales frighteningly well. Maybe the skepticism over DL is wildly exagerrated?

cirgue 4y ago

Deep learning technologists != people that write articles about machine learning making various professions obsolete. I don’t think anyone that works with neural networks professionally is drinking the kool-aid. Don’t get me wrong, deep learning is an incredible technology, but not in ways that it’s made out to be in the press. Like a lot of other technologies, it’s real value is in solving really boring problems at a scale. But that doesn’t make for good content so we end up with a bunch of articles about self driving cars and nothing about using deep learning to take something like 3d photogrammetry from “passable for artistic purposes” to “industrially valuable.”

somenameforme 4y ago

Because the field saw exponential progress which has started stalling out only relatively recently. And so now there are two possible explanations: 1) We're reaching near the end of where the current domain can take us. 2) We're making some sort of mistake or otherwise missing something that's the key to unlocking continued exponential progress. It's always natural, especially for those working within a domain, to assume it's #2. Unfortunately it seems our universe has this habit of always kicking us back to #1 just before the completely game breaking stuff starts happening.

kalkin 4y ago

"We won’t waste your time arguing with these claims" Err, actually, if you have something substantive to say, please do? I would really love to be persuaded that unaligned AGI is not something I need to worry about in my kids' lifetimes. I'd much prefer to believe more strongly in my ability to set them up to thrive! But when skeptics repeatedly don't seem to have anything to say beyond sneering, my anxiety is not alleviated.

nradov 4y ago
I would really love to be persuaded that an alien invasion is not something I need to worry about in my kids' lifetimes. Come on. We can't even build an AGI equivalent to a cockroach yet. Let's get real and stop panicking about things that are 100% hypothetical.
- kalkin 4y ago
  I'm not sure what you mean by an AGI equivalent to a cockroach, but let's take it for granted that cockroaches can do things AIs still can't. AIs can also do a lot of things that cockroaches can't, including plenty that until recently seemed far out of reach (translation, superhuman Go play, illustration). Why would we expect intelligence to get smoothly better along a total ordering before it's dangerous? Human capabilities don't work like that. Re 100% hypothetical: this seems like a pretty useless heuristic. Climate change, the health impact of leaded gas, and the nuclear bomb were all hypothetical, until they weren't. For that matter, if something isn't at least a little bit hypothetical there's no point in worrying at all; it's a done deal! The alien invasion analogy seems like it suggests the heuristic you're actually using is "wild scifi thing so not worrying". This is a better option than "still hypothetical so not worrying" but still doesn't seem reliable.
  - nradov 4y ago
    We have a lot of dangerous technologies which exist today and are either actively killing people, or could if they are actually used. It's silly to worry about something which doesn't exist, and for which we don't even have a clear path to build. It's like someone in 15th century Milan seeing Leonardo da Vinci's design for a helicopter (aerial screw) and worrying about attack helicopter gunships coming to destroy their city.

blueblisters 4y ago

To borrow an analogy from cognitive science, deep learning has pretty much mastered System 1 thinking in independent domains (image, language, audio) and scaling laws suggest we have a clear path to self-supervised multi modal models (video+audio+text) by applying more data and compute. We will likely miss critical world knowledge about dynamics, contact forces, taste, feel etc. until we figure out a way to efficiently collect datasets for these modalities. What’s less certain is the path to System 2 thinking that is required for true AGI. We have yet to find evidence that deep learning is enough for AI to learn planning and conscious thinking.

jrm4 4y ago

This feels related to the idea that there is mostly no such thing as "general intelligence." Rather, there's a diverse field of mental/cognitive skills. Many may correlate (or even..causate?) but I believe this is why e.g. AI is overhyped.

AtNightWeCode 4y ago

I think the problem is that most of the people working in this field does not seem to have basic knowledge about statistics or how to evaluate statistics. They somehow think they can cheat it. And they over evaluate their own results. The death of Google Translate was caused by GT started to learn from itself. Same thing will be happening to self-driving cars and so on. Five years ago, some garbage predictors said developers would become obsolete. I tell you, the amount of work that needs to be done by real people at real companies in the software biz has never been higher.

andreilys 4y ago

In our experience, even deep learning technologists at the top of their game are surprised when we point out that deep learning (or any other form of AI) struggles when tasked with predicting the future. It seems to do a pretty good job of predicting future stock market performance. But of course the authors have an axe to grind and choose to view the world through their narrow filter, probably because they want to promote their book and tap into the "tech is bad" trope thats going on in the wider culture. Most significantly, AGI hypers have succeeded in directing a large amount of funding toward the fanciful goal of ensuring that this supposedly imminent AGI will be aligned with humanity’s interests. Crucially, much of this community believes that we already have all the innovations we need to reach AGI; we just need to throw more hardware at the neural networks we already have. The problem with this argument is that we have no idea what the "warning signs" of imminent AGI is. It could take 2-3 key breakthroughs or it could take a few hundred. Whatever the case is, we should think about investing in a fire alarm system before theres a fire.

andrepd 4y ago
> It seems to do a pretty good job of predicting future stock market performance. Does it? Care to elaborate on that?
- andreilys 4y ago
  Well the obvious example is RenTech's Medallion fund which consistently out-performs human stock traders. The Medallion Fund has employed mathematical models to analyze and execute trades, many of them automated. The firm uses computer-based models to predict price changes. These models are based on analyzing as much data as can be gathered, then looking for non-random movements to make predictions. [1] Renaissance Technologies' Medallion fund, which from 1988 to 2018 clocked annualized returns of 66%. After fees, those annualized returns were still remarkable, at 39%.[2] [1] https://wealthofgeeks.com/medallion-fund/#:~:text=Algorithmic%20Trading,random%20movements%20to%20make%20predictions https://wealthofgeeks.com/medallion-fund/#:~:text=Algorithmi.... [2]https://money.usnews.com/investing/funds/articles/top-hedge-funds-this-year#:~:text=Perhaps%20the%20most%20enigmatic%20and,still%20remarkable%2C%20at%2039%25 https://money.usnews.com/investing/funds/articles/top-hedge-....

gwbas1c 4y ago

Uh, the nag to harvest email addresses was weird. It completely covered the article. I almost closed the tab without reading the article. IMO: Get rid of it. (Or use a normal style that doesn't nag for an email address until you've scrolled a bit, and keeps the page visible while the nag is open.) Why? People don't always read an article when they open a page. (I certainly don't.) By the time I saw the page I was totally lost from the context of what the page was, and the only reason why I didn't close the article was because the title on the Hacker News adjacent tab looked interesting.

_nalply 4y ago

Deep learning has two main deficiencies: 1. They depend on pre-existing data. In other words they might not create something new. 2. They need a lot of hand-holding for learning. Children have an innate instinct to learn, and deep learning is not (yet) there.