18 ms·

The Prospect of an AI Winter

144 points by erwald 4y ago

macrolime 4y ago
Open AI Whisper was probably made to make transcripts of videos that will be used to train future AI models. The text in itself won't be that interesting, the magic happens once you essentially train three different token predictors, one that predicts image tokens (16x16 pixels) and then combine that to predict video frames, one that predicts audio tokens and one that predicts text tokens. Then you use cross-attention between these predictors. To train this model you first pre-train the text predictor, after that's done you continue training the text predictor from the transcribed videos, while combining it with the video predictor and audio predictor with cross-attention. Such a model will understand physics and actions in the real world much better than GPT-4, combined with all the knowledge from all the text on the internet it should turn out to be something quite interesting. I think there probably doesn't exist enough compute yet to train such a model on something like all of YouTube, but I wouldn't be surprised if GPT-5 is a first step in this direction.
- candiodari 4y ago
  But this can never work. Or it will just do one thing, and one thing only. All this does is summarize information produced by humans, to "extract value" from it. It cannot produce information itself. In other words: to make sure all the money goes to the plumber finding service, never to the plumber. These AIs serve to extract value from these summaries through "enshittification". https://pluralistic.net/2023/01/21/potemkin-ai/ https://pluralistic.net/2023/01/21/potemkin-ai/ It will massively increase the value of human interaction ... and the cost. It may even go so far that actual professionals start hiding from the internet, to hide from these models and avoid enshittification that way. We may have to start going back to a visiting a local cafe to find a plumber. I don't even think that is such a bad thing. Any AI that wants to actually learn more than just language (from summarization to bullshitting) will need to take actions in the real world and learn from them.
  - HarHarVeryFunny 4y ago
    > Any AI that wants to actually learn more than just language (from summarization to bullshitting) will need to take actions in the real world and learn from them. Well, that's one additional source of knowledge, but we haven't even got into other sources like voice, video, simulation data, etc (let alone the interesting stuff like financial market data, aggregate human behavior, etc, etc). GPT-4 is only just starting to support image data, and who knows what GPT-5, 6.. will bring. Robotic embodiment is maybe better thought of as robotics, not an necessity for advancing AI in dozens of useful ways. But of course not every AI needs to be an expert in every domain. If you want to build a robot then it'll need to learn via interaction, but there's are tons of applications for even these 1st-gen language-only systems - look at all the applications that people have already found for them, and what is starting to be done with LangChain and plugins.
thomastjeffery 4y ago
Speculation on the future of inference model tech... We can do better. All we have to do is be constructive when we write narratives about LLMs. Unfortunately, that's hard work: we basically have to start over. Why? Because every narrative we have today personifies LLMs. It's always a top-down perspective about the results, and never about how the actual thing works from the ground up. The reality is that we never left AI winter. Inference models don't make decisions or symbolically define subjects. LLMs infer patterns of tokens, and exhibit those patterns. They don't invent new patterns or new tokens. They rely entirely on the human act of writing: that is the only behavior they exhibit, and that behavior does not belong to the LLM itself. We should definitely stop calling them AI. That may be the category of pursuit, but it misleadingly implies itself to be a descriptive quality. I propose that we even stop calling them LLMs: they model tokens, which are intentionally misaligned with words. After tokenization, there is no symbolic categorization: no grammar definitions: just whatever patterns happen to be present between tokens. That means a pattern that is not language will still show up in the model. Such a pattern may be considered by humans after the fact to be exciting, like the famous Othello game board study, or limiting, like prompts that circumvent guardrails. The LLM can't draw any distinction between grammar-aligned, desirable, or undesirable patterns; yet that is exactly what most people expect to be possible after reading about a personified Artificially Intelligent Large Language Model. I would rather call them "Text Inference Models". Those are the clearest descriptors of what the thing itself is and does.
andsoitis 4y ago
If LLMs are so great at learning by themselves, why does OpenAI need to resort to the plug-in model? Is it because it can’t actually do logic (hence the Wolfram Alpha plugin)? Or is it because that’s the way it gets access to more data? Or both? https://openai.com/blog/chatgpt-plugins https://openai.com/blog/chatgpt-plugins
arisAlexis 4y ago
What? This is an article where author puts 5% of happening and others are commenting on his opinion?
virtual_nikola 4y ago
The other way is saying there is a gold rush coming
brucethemoose2 4y ago
I think the hardware/cost factor is also a business one, eg how dominant does Nvidia stay in the space. If they effectively shut out other hardware companies, that is going to slow scaling and price/perf reduction.
- pixl97 4y ago
  At some point it's going to be difficult to shut everyone else out. Intel tried this a long time ago, and while they maintained dominance, they were not able to shut out competitors completely, and experienced a lot of legal battles over it. Foreign nations aren't going to be happy about one mostly US company holding all the cards too.
totoglazer 4y ago
Another big concern will be regulatory. It seems unlikely a couple billion people whose livelihood is significantly impacted will just chill as it happens? I think it’s unlikely, but no less likely than the compute issues mentioned.
- knodi123 4y ago
  So the research and development will just move over to a neighboring country? This isn't like the manhattan project. There are lots of people who know how to make this stuff, and they don't need rare volatile elements - just consumer hardware.
  - pixl97 4y ago
    It depends if were talking 'Butlerian Jihad' levels of disruption here. And no, the big models require exabytes of processing power and time, so at least at the most extreme scales if nations started punching missiles in processor factories and data centers you'd slow top end AI projects way down.
    - knodi123 4y ago
      > It depends if were talking 'Butlerian Jihad' levels of disruption here. Yeah, but we weren't. We were talking about chatgpt and llms. Of course if the singularity arrives, all bets are off, all dominoes will topple, and the new world order will be hard to guess, if there even is one.
  - flangola7 4y ago
    If it seems dire enough lethal military force might be used. If Russia and China think the US is about to have AI capable of absolute total global domination they may launch a preemptive strike. Maybe hypersonic cruise missiles at datacenters, maybe a full EMP or nuclear launch. (Swap countries around as desired.)
- bloodyplonker22 4y ago
  Isn't this a "concern" whenever a new technology comes out? ie: the internet? Yet, due to how slowly government moves and how hard new technology is to understand for governments, it is barely a concern.
crop_rotation 4y ago
I think scaling limits and profitability are the only things that can stop the march of the AI. The utility is already there and even the current GPT4 utility is revolutionary.
HervalFreire 4y ago
It's different this time. Because this time AI is hugely more popular in the public and corporate sphere. The previous AI winters were more academic winters with few people pushing the envelope. I don't think compute is the issue. It's an issue with LLMs. Current LLMs are just a stepping stone for true AGI. I think there's enough momentum right now that we can avoid a winter and find something better through sheer innovation.
- pixl97 4y ago
  I think the difference is AI takes data and in the past we just didn't have much data. Now the vast majority of the worlds population has a cellphone and internet service, and use services that AI can improve/affect.
RandomLensman 4y ago
After the massive hype around generative AI, seems likely there will be an AI winter when the promised transformation in many business areas just doesn't happen as advertised.
- pixl97 4y ago
  I remember the hype cycle around this thing called the "internet" back in the day. People said it was going to take over the world, even though back then it was slow and kinda sucked. And then it did.
  - diceduckmonk 4y ago
    I remember a hype cycle called “BigData”. Companies now realize data science that big of a step to business analytics of the 90s.
  - lm28469 4y ago
    What about cryptos, VR, nosql, nfts, &c. ?
    - zachnwhite 4y ago
      thank you
  - RandomLensman 4y ago
    Do you remember all the failed predictions, too?
  - palata 4y ago
    > And then it did. After the dotcom bubble exploded. Can't we call that a winter?
    - pixl97 4y ago
      What metrics do you want to go by? At least by internet use and user growth the dotcom bubble still saw massive amounts of new user growth and online time by users. Was there a massive reduction in completely untenable .com's? But I'm not exactly sure if that's the definition of a winter, plenty of other internet based businesses did fine and kept growing in that time.
greatwave1 4y ago
Can anyone give some color on to what extent advancements in AI are limited by the availability of compute, versus the availability of data? I was under the impression that the size and quality of the training dataset had a much bigger impact on performance versus the sophistication of the model, but I could be mistaken.
- jacobn 4y ago
  Both matter, and returns fall off as you go further in one but not the other. The Chinchilla paper[0] established a simple scaling law for Large Language Models: model size and training tokens should grow at the same pace. Compute is then proportional to the product of model size & data quantity. That said, quality of data also matters a lot - OpenAI has had human labelers produce the data for their Reinforcement Learning from Human Feedback (RLHF), which has probably had a disproportionate impact on the success of ChatGPT compared to previous models, but that data is probably O(1%) of what they trained on. At this point I'm guessing OpenAI are limited by both data & compute. Rumor has it they're training the "next big thing" now and it won't finish until December. If they had more compute they could presumably finish sooner, and if they had more data they would presumably let it train longer. [0] https://arxiv.org/abs/2203.15556 https://arxiv.org/abs/2203.15556
  - pixl97 4y ago
    Also at this point, very few of the biggest players are going to tell us anything about which matters most and those fine tuning numbers can represent a huge strategic advantage. Forcing your competitors to spend billions in hardware and time can put you far ahead of them quickly, at least at our current rate.
- wsgeorge 4y ago
  AFAIK it's still an active area of research, and evidence from Meta AI [0] suggests that size and quality of data can let smaller (not necessarily less sophisticated) models do amazing things. But a lot of the advancements we're seeing right now are the result of more sophisticated models [1], and one person is doing some interesting work [2] around achieving transformer-level performance with other architectures. So it's not completely settled if more data is the answer. But it has a significant impact. [0] https://ai.facebook.com/blog/large-language-model-llama-meta-ai/ https://ai.facebook.com/blog/large-language-model-llama-meta... [1] https://en.wikipedia.org/wiki/Transformer_(machine_learning_model) https://en.wikipedia.org/wiki/Transformer_(machine_learning_... [2] https://github.com/BlinkDL/RWKV-LM https://github.com/BlinkDL/RWKV-LM
nuancebydefault 4y ago
So. The article starts with "I give it an estimate of 5 per cent chance..." and then explains: what if... Is this case really worth exploring? Or was the article written by a bored AI? I find it striking that there are still so many people downplaying the latest developments of AI. We all feel that we are at the verge of a next revolution on par or even greater than the emergence of the www, while some people just can't to seem to let it sink in.
- Workaccount2 4y ago
  It should be evident at this point that there are a lot of people who feel threatened by AI, and will click on anything that can give hope.
- JohnFen 4y ago
  > while some people just can't to seem to let it sink in. Just because people may have opinions different from yours doesn't mean they're denying reality. They just have a different opinion. The hard, cold truth is that nobody knows the future. Everybody is just guessing.
  - B1FF_PSUVM 4y ago
    > The hard, cold truth is that nobody knows the future. Probably, yes, especially after what happened to Cassandra. Still a fun counterfactual to develop. Lessee, you do know the future, but do not run your mouth off. Matter of fact, you pooh-pooh any such notion ...
  - nuancebydefault 4y ago
    Yes that is the truth,nobody knows the future. But we you see something coming right at us, why still so much doubt?
    - johnfn 4y ago
      A lot of people saw crypto coming right at us as well.
pixl97 4y ago
> Eden writes, “[Which] areas of the economy can deal with 99% correct solutions? My answer is: ones that don’t create/capture most of the value.” And >Take for example the sorting of randomly generated single-digit integer lists. These seem like very confused statements to me. For example, lets take banking. It's actually two (well far more) different parts. You have calculating things like interest rates and issues like 'sorting integers' like above. This is very well solved in simple software at extremely low energy costs. If you're having your AI model spend $20 trying to figure out if 45827 is prime, you're doing it wrong. The other half of banking is figuring out where to invest your money for returns. If you're having your AI read all the information you can feed it for consumer sentiment and passing that to other models, you're probably much closer to doing it right. And guess what, ask SVB about 99% correct correct solutions that do/don't capture value. Solutions that have correct answers are quickly commoditized and have little value in themselves. Really the most important statement is the last one, mostly the article is telling is the reasons why AI could fail, not that those reasons are very likely. >I still think an AI winter looks really unlikely. At this point I would put only 5% on an AI winter happening by 2030, where AI winter is operationalised as a drawdown in annual global AI investment of ≥50%. This is unfortunate if you think, as I do, that we as a species are completely unprepared for TAI.
- mirekrusin 4y ago
  Can you ever be prepared for TAI? What does it even mean?
blintz 4y ago
I'm not an expert, but I see the main threat to continued improvement as running out of high-quality data. LLM's are a cool thing you can produce only because there is a bunch of freely available high-quality text representing (essentially) the sum of human knowledge. What if GPT-4 (or 5, or 6) is the product of that, and then further improvements are difficult? This seems like the most likely way for improvement to slow or halt; the article cites synthetic data as a fix, but I'm suspicious that that could really work.
- brucethemoose2 4y ago
  Even just finetuning these models, they will pick up on the weirdest things a human wouldn't see, and artificial datasets will contain all kinds of invisible artifacts that further "inbreeding" is going to massively amplify. I am not speaking speculatively either. I have seen it happen finetuning ESRGAN on previous upscales that I even personally vetted as "good," and these generative models are way more sensitive than the old GANs.
- marcyb5st 4y ago
  Googler, but opinion are my own. More than that, I believe we will hit a ceiling when the impossibility of these models to incorporate causality becomes evident. Right now, LLMs are trained by predicting the next word given a context (the prompt). This approach, IMHO, gives a resemblance of cause/effect because the training data is made by humans and obviously we are able to express ourselves and reason in those terms. So we have a poor proxy for that which, also IMHO, partially explains why LLMs performance degrades when asked to solve novel problems (there was an entry few days ago about this).
  - akiselev 4y ago
    After seeing LangChain and the reasoning paper I think it's fairly obvious that we're just starting to scratch the surface of AI architectures, dictated largely by scalability of GPU resources. The LLMs we're playing with are at best the proof of concept of what will eventually be the message passing for the next generation of models.
anonzzzies 4y ago
AI winter will arrive if we don’t get the models to depend on ‘just’ more training data to get better. There is no more training data. We need models the same or better than gpt-4 but trained on roughly what an 18 year old >100 iq human would digest to get to that point. Which is vastly less than what gpt 4 gets fed. If advancing means ever larger and more expensive systems and ever more data, we will enter a cold winter soon.
- pixl97 4y ago
  There is nearly an unlimited amount of training data. As of so far models have been eating up text. We still have sound, image, video, temperature, gravimetrics, and other sources that we can feed multimodal models. And that's not even including training models from learning from the world itself.
  - anonzzzies 4y ago
    Sound, image and video we’ll ravish through (already happening); is temp and gravimetrics valuable? (I don’t know; it’s a question) Games might be a source too. But yes, the world itself is a good source. So maybe that won’t freeze it over; computing power/energy?
    - pixl97 4y ago
      >computing power/energy? Energy is "probably" not a big deal. If you're looking at long term oversupply from green energy sources, it's probably not hard to train with bursty, but very cheap power like this. Compute is currently the biggest limitation, and will remain so far a long time, as long as scaling continues.
  - akiselev 4y ago
    > gravimetrics Calling it now: humanity will seal its fate the day we hook up LIGO to ChatGPT and it gets corrupted by gravitational waves from the beginning of time when the Great Old Ones freely roamed the universe.
- stevenhuang 4y ago
mpsprd 4y ago
>“[Which] areas of the economy can deal with 99% correct solutions? My answer is: ones that don’t create/capture most of the value.” The entertainment industry disagrees with this. These systems are transformative for any creative works and in first world countries, this is no small part of the economy.
ahofmann 4y ago
I think the assumption that companies are willing to spend 10 billion dollars on AI training is unrealistic. Even the biggest companies would find such an investment to be a financial burden.
- pixl97 4y ago
  You're "probably" right, but it's not something I'm going to place bets on with the level of uncertainty. When you have some companies still sitting on war chests of tens of billions of dollars, and almost nothing to spend it on, not investing in AI is a risk in itself. If your competitor succeeds they may rapidly take parts of your market while you now attempt to reproduce their work. Also if these 10 billion dollar models are 'AGI' level, then your model pays for itself if you can find enough interesting work to throw at it.
- PeterisP 4y ago
  If they really believe that it will bring appropriate returns, a $10B investment is manageable. It is a significant burden, but even much larger investments have been made - e.g. Starlink satellite fleet on the private side, or the Large Hadron Collider from government funds.
chess_buster 4y ago
Write a counterpoint to the article posted. Your goal is to refute all claims by giving correct facts with references. Cite your sources. Make it 3 paragraphes. As a poem. In Klingon.
nico 4y ago
At the same time this AI revolution is happening, there is also a psychedelic revolution happening. When this happened in the 60s-70s, the psychedelic revolution was crushed by the government. And we entered an AI winter. I’m not implying causation. Just pointing out a curious correlation between the two things. I wonder what will happen now.
- DennisAleynikov 4y ago
  I really hope the government does not intervene in this process but I know for a fact they will want to.
  - nico 4y ago
    You are correct. Today on HNs front page there was an article talking about how the EU has already said they are going to regulate AI.
    - layer8 4y ago
      That was targeting pre-GPT AI. They’re now trying to figure out how GPT fits into their considerations.
      - nico 4y ago
        Which shows that governments are trying to control this thing already.
        layer8 4y ago
        It means they don't want to institute regulations that don't make sense for GPT-type AI. See https://digital-strategy.ec.europa.eu/en/policies/regulatory-framework-ai https://digital-strategy.ec.europa.eu/en/policies/regulatory... for the rationale and general approach.
superb-owl 4y ago
We're only just seeing expectations for the tech inflate now. VCs will probably pump money into LLM-related companies for at least a couple years, and it'll be a couple years after that before things really start to decline. It's late spring right now, a strange time to start forecasting winter.
Hizonner 4y ago
Well, I'm HOPING for that, but not RELYING on it...
- DennisAleynikov 4y ago
  this person has the right idea :) pray for a winter but prepare for a societal upheaval
endisneigh 4y ago
I think the AI winter will come, but not for why the author asserts (quality, reliability, etc.). I think the current crop of AI is good enough. It will happen because people will actually grow resentful of things that AI can do. I anticipate a small, yet growing segment of populations worldwide to start minimizing internet usage. This, will result in fewer opportunities for AI to be used and thus the lack of investment and subsequent winter.
numinary1 4y ago
Being old as dirt, my observation is that potential tech revolutions take ten years after the initial exuberance to be realized broadly, or three to five years to fizzle. Of those that fizzle, some were bad ideas and some were good ideas replaced by better ideas. FWIW
- lostmsu 4y ago
  This one started with GPT-2, IMHO. So 4 years into making.
- mirekrusin 4y ago
  And how long did it take for those tech revolutions to gain first 100M users?
  - KaiserPro 4y ago
    openai having 100million users seems a bit suspect to me. given that they need a _huge_ amount of GPU power to server that kind of traffic, I suspect that 100million seems out by an order of magnitude.
  - PaulDavisThe1st 4y ago
    Well, during the initial "tech" (broadly interpreted to mean computational processes that somehow involve the internet) revolutions, there were not 100M, nor even 1M users to be gained. And what is a "user" when it comes to ChatGPT and it's ilk? How does that compare with the definition of a user of, say, the web? Or of SMS ?
    - pixl97 4y ago
      With most other technologies before this there was the issue with rollout of physical infrastructure. And that is still true somewhat. All the big AI players want more AI processors and would take possession of as many as they could get. Buy the other side of physical infrastructure is already here. The internet, cellphones, and computers already exist in mass and can be utilized by AI now. We didn't need to build new highways for this. No new towers. No wires put in the ground. Those fields already exist and are ready for planting.
Havoc 4y ago
> reliability Humans are unreliable AF and we employ them just fine. Better reliability would certainly be nice but I don’t think it is strictly speaking necessary
gumby 4y ago
The "winter" analogy (I remember the AAAI when marvin made that comment) was to the so-called "nuclear winter" that was widely discussed at the time: a devestating pullback. It did indeed come to pass. I don't see that any time soon. I think the rather breathless posts (which I also remember from the 80s and apparently used to be common in the 60s when computers just appeared) will die down as the limits of the LLMs become more widely understood, and they become ubiquitous where they make sense.
- unsupp0rted 4y ago
  Unless of course the limits of LLMs are just outside the bounds of LLMs being able to write their own successors. In which case progress becomes unlimited and there’s never an AI winter again.
muyuu 4y ago
idk if there will be that much of a winter, but i would welcome it in the late 90s and early 2000s, neural network had a significant stigma for being dead ends and were unpromising grads were sent - people didn't want to go there because it was a self-fulfilled prophecy that if you went to research ANNs then you were a loser, and you were seen as such, and in academia that is all you need to be one but, in real life, they worked sure, not for everything because of hardware limitations among other things, but these things worked and they were a useful arrow in your quiver as everybody else just did whatever was fashionable at the time (simulated annealing, SVMs, conditional random fields, you name it) hype or no hype, if you know what you are doing and the stuff you do works, you will be okay
karmasimida 4y ago
There will never be another winter moving forward. ChatGPT as is, is already transformative. It CAN do human level reasoning really well. The only winter I can see, is the AI gets so good, there is little incentive to improve upon it.
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- throwbadubadu 4y ago
  The dissonance what people see in it is really gross.. maybe I'm too stupid, but session from simple to more complicated problems have all shown me: It is definitely not reasoning at all, it doesn't understand anything.. it is a different version of stackoverflow that sometimes gets you quicker to target, but for me even more often not. shrug
  - ux-app 4y ago
    have you read this[1]? >It is definitely not reasoning at all read the paper. GPT4 is the real deal. [1]https://arxiv.org/pdf/2303.12712.pdf https://arxiv.org/pdf/2303.12712.pdf
    - throwbadubadu 4y ago
      Sure I did. Did you try it yourself?
      - deleted 4y ago
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codelord 4y ago
IMHO the prospect of an AI winter is 0%. As someone who has done research in ML I think ML technology is moving forward much faster than we anticipated. ChatGPT shouldn’t work based on what we knew. It’s incredible that it works. Makes you think what other things that shouldn’t work we should scale up and see if they would work. And then there are things that we think they should work. Each new paper or result opens the door for many more ideas. And there are massive opportunities in applying what we already have to all industries. You can absolutely build high precision ML models. Using a transformer LM to sum numbers is dumb because the model makes little assumptions about the data by design, you can modify the architecture to optimize for this type of number manipulation or you can change the problem to generating code for summing values. In fact Google is using RL to optimize matmul implementations. That’s the right way of doing it.
- dragonwriter 4y ago
  > As someone who has done research in ML I think ML technology is moving forward much faster than we anticipated. The past AI winters have been preceded by periods of AI moving forward faster than anticipated. Its when the limits of easy advancement with the current approaches are reached without a new approach that allows continued rapid progress you get an AI winter.
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- RivieraKid 4y ago
  > ChatGPT shouldn’t work based on what we knew Why?
  - layer8 4y ago
    I guess because we still don’t understand why it works. We still have no basis on which to predict that it works as well as it does, except for the manifest fact that it does.
WheelsAtLarge 4y ago
I give it a 95% chance that an AI winter is coming. Winter in a sense that there won't be any new ways to move forward towards AGI. The current crop of AIs will be very useful but it won't lead to the scary AGI people predict. Reasons: 1) We are currently mining just about all the internet data that's available. We are heading towards a limit and the AIs aren't getting much better. 2) There's a limit to the processing power that can be used to assemble the LLM's and the more that's used the more it will cost. 3) People will guard their data more and will be less willing to share it. 4) The basic theory that got us to the current AI crop was defined decades ago and no new workable theories have been put forth that will move us closer to an AGI. It won't be a huge deal since we probably have decades of work to sort out what we have now. We need to figure out its impact on society. Things like how to best use it and how to limit its harm. Like they say,"interesting times are ahead."
- Uupis 4y ago
  > 3) People will guard their data more and will be less willing to share it. I recently came across this myself when writing a reply on another forum. A feeling of reluctance to attempt to contribute something maybe-useful in a public, 'minable' space. Almost feels like this has tarnished the 'magic' of Internet, of sharing information and knowledge. I'm not saying this is the appropriate reaction, but it is what it is.
  - HarHarVeryFunny 4y ago
    > I recently came across this myself when writing a reply on another forum. A feeling of reluctance to attempt to contribute something maybe-useful in a public, 'minable' space I used to be like this, but the reality is that ideas are a dime a dozen. Any idea that you or I (or anyone else) have had, have likely also been thought by thousands (or maybe add a few more 0's) of people. What makes things happen is people not ideas. Talk to any VC or people involved in startups - it's all about the team, not the ideas they are working on.
- chongli 4y ago
  5) AI-generated content will become more and more common over time. This will inevitably end up in the training sets of future AI. By recycling its own training material, AI will get less meaningful results over time.
javaunsafe2019 4y ago
I don’t even understand why we call models that predict text output to a question AI. For sure we will get a lot stuff automated with it in the near future but this is far away from anything real intelligent. It just doesn’t really understand and or feel things. It’s dead cause it just outputs data based on it’s model. Intelligence contains a will and chaos.
stephc_int13 4y ago
The current expectations around AI are extremely high and frankly quite a few of them are bordering into speculative territory. That said, I don't think we're going to see a new AI winter anytime soon, what we're seeing is already useful and potentially transformative with a few iterative improvements and infrastructure.
collaborative 4y ago
> cheap text data has been abundant The winter before the AI winter will consist in all the cheap data disappearing. What fun will it be to write a blog post so that it can be scraped by a bot and regurgitated without attribution? Dito for code Or, how will info sites survive without ad revenue? Last I checked bots don't consume ads When the internet winter comes, all remaining sites will be behind login screens and a strict ToS popup
zitterbewegung 4y ago
I think many technologies go from spring , summer and eventually winter. The last one focused on good old fashioned AI . The next one was big data with ML and this one is large language models.
HarHarVeryFunny 4y ago
Let's not forget that GPT-4 was finished over 6 months ago, with OpenAI now presumably well into 4.5 or 5, and Altman appearing confident on what's to come ... In the meantime we've got LangChain showing what's possible when you give systems like this a chance to think more than one step ahead ... I don't see an AI winter coming anytime soon... this seems more like an industry changing iPhone or AlexNet moment, or maybe something more. ChatGPT may be the ENIAC of the AI age we are entering.
karmasimida 4y ago
Previous AI winters are due to overpromise and no delivery: Big promise of what AI can do, but never able to actually even deliver a prototype of that in reality. ChatGPT, at least for GPT-4, can already be considered as someone coined, baby AGI. It is already practical and useful, so it HAS to be REAL. If it is already REAL, there is no need for another winter to ever come to reap the heads of liars. Instead AI will become applied technology, like cars, like chips. It will evolve continuously, and never go away.
- roflyear 4y ago
  It's not a babi agi. You can't teach it things. Often you ask it to not do something and it does anyway. Say don't use that function and it uses it in the example. It's crazy impressive but that's not 0.00001% of an AGI.
- deleted 4y ago
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- PaulDavisThe1st 4y ago
  How old were you when expert systems arrived on the scene?
blurbleblurble 4y ago
People love talking about AI winter.
christkv 4y ago
We are all ready using GPT 4 for a ton of BS documents we have to write for our planning permission and other semi legal paper work. My lawyer has been doing pretty much every public filing for civil cases and licenses assisted by GPT. So much bureaucracy could probably be removed by just having GPT validated permissions and manage the correctness of the submissions leaving a human to rubber stamp the final result if at all.
skybrian 4y ago
Sometimes unreliability can be worked around with human supervision. You wouldn't normally want to write a script that just chooses the first Google result, but that doesn't mean search engines aren't useful. The goal when improving a search engine is to put some good choices on the first page, not perfection, which isn't even well-defined. The AI generators work similarly. They're like slot machines, literally built on random number generators. If you don't like the result, try again. When you get something you like, you keep it. There are diminishing returns to re-running the same query once you got a good result, because most results are likely to be worse than what you have. Randomness can make games more fun at first. But I wonder how much of a grind it will be once the novelty wears off?
- Karrot_Kream 4y ago
  I think that will be the hard part of productionizing these models. Some domains absolutely need high reliability, like AVs, and applying these ML models is fraught because of how imprecise their outputs are. Problem domains where unreliable results are useful either through human mitigation (e.g. expert search engines, customer service chatbots) or because they're just not that reliable anyway (like image generation) will be the domains where these models will have sticking power.
- christkv 4y ago
  The fact that they produce valuable content is more a reflection of the mediocre output of many people. I hold it up against the response I would expect from a human and in a lot of cases I’m not sure the human wins.
  - pixl97 4y ago
    Yea, I see a lot of "AI models need to be perfect", but man, that is really neglecting that humans on average suck at a great many things.
  - skybrian 4y ago
    Often when you have a question, you don't have access to a qualified expert you could ask for help. So there's less competition than you might think.
beepbooptheory 4y ago
All this stuff can't transform anything if you can't afford to keep the computer on. Which is really, to me, the bigger/most convincing point in the thread this article links at the top. If there isn't a winter, will ChatGPT et al be able solve the energy crises they might be implicated in? Is there something in its magic text completion that can stop global warming? Coming famines? Is perhaps the fixation on these LLMs right now, however smart and full of Reason they are, not paying the fullest attention to the existential threats of our meat world, and how they might interfere with what ever speculative dystopia/utopia we can imagine at the moment?
ChatGTP 4y ago
AI Winter will likely come because we've not addressed climate change...instead of blowing billions / trillions on our survival, we're yet again blowing it on moonshots. We have the brains collectively, already to solve the problems should we want to, we don't because that's not where "the money" is. Silicon Valley Tech is already promising that AI will be the likely solution to climate change..., if there is any more disruption to the economy it's just going to yet again slow down mitigation steps for climate change, thus having negative affects on the amount of capital available for these projects. Printing money works, until it doesn't.
- CatWChainsaw 4y ago
  Humanity: Oh great AI, what is the solution to climate change? AI: Stop building me, I take up far too many resources and generate way too much heat.
boringuser1 4y ago
The reason why these types of claims are baseless is because of the key fact that if AI tech stopped progressing right now, it's already a game-changer once companies adopt.
stuckinhell 4y ago
I strongly disagree. ChatGPT is bleeding into everything. Midjourney is too damn good see the example below. The avengers if they had 90's actors is going viral. https://cosmicbook.news/avengers-90s-actors-ai-art https://cosmicbook.news/avengers-90s-actors-ai-art Also the avengers as a dark sci fi https://www.tiktok.com/@aimational/video/7186426442413215022 https://www.tiktok.com/@aimational/video/7186426442413215022 AI art and generative text is just astounding, and it's only getting better.
kromem 4y ago
I increasingly think we're underestimating what's ahead. Two years ago was an opinion piece from NIST on the impact optoelectronics would bring specifically to neural networks and AGI, and watching as nearly every major research institution has collectively raised probably half a billion for AI photonics plays through their VC partnerships or internal resource allocations on the promise of order of magnitude improvements much closer than something like quantum computing, I think we really haven't seen anything yet. We're probably just at the very beginning of this curve, not approaching its diminishing returns. And that's both very exciting and terrifying. After decades in tech (including having published a prediction over a decade ago that mid 2020s would see roles shift away from programming towards emergence of specialized roles for knowing how to ask AI to perform work in natural language) I think this is the sort of change so large and breaking from precedent we really don't know how to forecast it.
thelazydogsback 4y ago
> I put 5% on an AI winter happening by 2030 lol. 5%? - that's really laying it on the line
deleted 4y ago
[deleted]
atleastoptimal 4y ago
More so that there's a 5% chance there won't be a human winter in the next 5 years
mikewarot 4y ago
It is entirely possible that Moore's Law gets assassinated by supply chain destruction as deglobalization continues. There are too many single source suppliers in the chain up to EUV lithography. We may in fact be at peak IC.
- HarHarVeryFunny 4y ago
  People have been predicting the end of Moore's law for decades ... eventually they will be right, but it seems there are at least a few generations of process reduction and transistor design left before we start to hit any hard physical limits. Of course there hasn't been much increase in single core clock speeds or cost reduction for a while now (certainly nothing from Intel), but TSMC and Samsung do continue to make progress, with multiple future process generations under development. As far as AI, or more specifically training (and running) massive neural nets, it's not single core performance that matters at all - it's all about specialized matrix multiplication hardware whether NVidia's Tensor Cores or Google's TPUs, and more importantly the ability to bring many of these specialized units together to work on a single problem. Distributed NN training has been a solved problem for a long time, with things like GPT-4 being trained across 10,000 machines. This type of massive parallelism can continue to grow regardless of what is happening to Moore's law and single core performance.
  - mikewarot 4y ago
    I'm not suggesting a slowdown of innovation, but rather supply chain collapse which is a completely different kettle of fish. Our ability to make high end chips may plato due to a complete inability of AMSL to source all the parts for EUV steppers. Many of those components themselves have multi-national supply chains.
    - HarHarVeryFunny 4y ago
      Right, but what I was trying to say is that we're not really that dependent on cutting edge chips, at least not for AI development. Even if all we had was older pre-EUV fabs we could still train in parallel on as many less powerful chips as needed. I'm surprised that the US didn't consider tech self-reliance as a national security issue a long time ago.
happycube 4y ago
There'll be an AI Winter from a VC standpoint... but even in the 90's there was some (GOF)AI stuff still going on after that. There are too many actually useful things coming out of this for a true winter. And for there not to be a bubble.
nojvek 4y ago
There's a few things at play here. LLMs - OpenAI, Google Brain, Meta FAIR, HuggingFace and others are now routinely training models with the entire corpus of the internet in a few months. The models are getting larger and more efficient. Diffusion models - MidJourney, StableDiffusion, Dall-E and it's control net cousins - Trained on terabytes of images, almost entire corpus of internet. Same with voice and other multimodal models. The transformer algorithm is magical but we're just getting started. There are now multiple competing players who can drop millions of dollars on compute and have access to internet sized datasets. The compute, the datasets, the algorithms, the human reinforcement loops, all are getting better week over week. Millions of users are interacting with these systems daily. A large subset even paying for it. There is the gold rush.
_nalply 4y ago
I think, this time it is different. Of course, there's a bubble, but after that bubble pops, people will realize that current models are useful enough, even with their quirks. People all have quirks and mostly they get along, so they will accept quirks from machines. Anthropomorphizing machines will help accepting models. I know, this is dangerous, but I have this mental image: a doll in form of a seal baby with soft white fur with a Whisper model helping lonely handicapped people (note that I myself am a person with a disability, so don't cancel me, please). Or someone who technically is not very adept phoning for support and a Whisper model helping along and having a lot of time to chit-chat. And technically I think, something will happen in about five years. A new floating number format, the posit (https://spectrum.ieee.org/floating-point-numbers-posits-processor https://spectrum.ieee.org/floating-point-numbers-posits-proc...), is too useful to be ignored. It will take years because we need new hardware. Why do I think that posits are very useful? Posits could encode weights using very little storage (down to 6 bit per weight). Models perhaps need to be retrained using these weights because 6 bits are not precise. After all, you have only 64 different values. And I think with the new hardware supporting posits they will also have more memory for the weights. Cell phones will be able to run large and complex models efficiently. In other words, Moore's law is not dead yet. It just shifted to a different, more efficient computation implementation. When this happens, immediate feedback could become feasible. With immediate feedback we do another step to achieve AGI. I could imagine that people get delivered a partially trained model and then they have a personal companion helping them through life.
- wnkrshm 4y ago
  > then they have a personal companion helping them through life or a personal cop/political commissar for every citizen
  - _nalply 4y ago
    In this case people won't trust AI.

144 points by erwald 4y ago

Open AI Whisper was probably made to make transcripts of videos that will be used to train future AI models. The text in itself won't be that interesting, the magic happens once you essentially train three different token predictors, one that predicts image tokens (16x16 pixels) and then combine that to predict video frames, one that predicts audio tokens and one that predicts text tokens. Then you use cross-attention between these predictors. To train this model you first pre-train the text predictor, after that's done you continue training the text predictor from the transcribed videos, while combining it with the video predictor and audio predictor with cross-attention. Such a model will understand physics and actions in the real world much better than GPT-4, combined with all the knowledge from all the text on the internet it should turn out to be something quite interesting. I think there probably doesn't exist enough compute yet to train such a model on something like all of YouTube, but I wouldn't be surprised if GPT-5 is a first step in this direction.

candiodari 4y ago
But this can never work. Or it will just do one thing, and one thing only. All this does is summarize information produced by humans, to "extract value" from it. It cannot produce information itself. In other words: to make sure all the money goes to the plumber finding service, never to the plumber. These AIs serve to extract value from these summaries through "enshittification". https://pluralistic.net/2023/01/21/potemkin-ai/ https://pluralistic.net/2023/01/21/potemkin-ai/ It will massively increase the value of human interaction ... and the cost. It may even go so far that actual professionals start hiding from the internet, to hide from these models and avoid enshittification that way. We may have to start going back to a visiting a local cafe to find a plumber. I don't even think that is such a bad thing. Any AI that wants to actually learn more than just language (from summarization to bullshitting) will need to take actions in the real world and learn from them.
- HarHarVeryFunny 4y ago
  > Any AI that wants to actually learn more than just language (from summarization to bullshitting) will need to take actions in the real world and learn from them. Well, that's one additional source of knowledge, but we haven't even got into other sources like voice, video, simulation data, etc (let alone the interesting stuff like financial market data, aggregate human behavior, etc, etc). GPT-4 is only just starting to support image data, and who knows what GPT-5, 6.. will bring. Robotic embodiment is maybe better thought of as robotics, not an necessity for advancing AI in dozens of useful ways. But of course not every AI needs to be an expert in every domain. If you want to build a robot then it'll need to learn via interaction, but there's are tons of applications for even these 1st-gen language-only systems - look at all the applications that people have already found for them, and what is starting to be done with LangChain and plugins.

thomastjeffery 4y ago

Speculation on the future of inference model tech... We can do better. All we have to do is be constructive when we write narratives about LLMs. Unfortunately, that's hard work: we basically have to start over. Why? Because every narrative we have today personifies LLMs. It's always a top-down perspective about the results, and never about how the actual thing works from the ground up. The reality is that we never left AI winter. Inference models don't make decisions or symbolically define subjects. LLMs infer patterns of tokens, and exhibit those patterns. They don't invent new patterns or new tokens. They rely entirely on the human act of writing: that is the only behavior they exhibit, and that behavior does not belong to the LLM itself. We should definitely stop calling them AI. That may be the category of pursuit, but it misleadingly implies itself to be a descriptive quality. I propose that we even stop calling them LLMs: they model tokens, which are intentionally misaligned with words. After tokenization, there is no symbolic categorization: no grammar definitions: just whatever patterns happen to be present between tokens. That means a pattern that is not language will still show up in the model. Such a pattern may be considered by humans after the fact to be exciting, like the famous Othello game board study, or limiting, like prompts that circumvent guardrails. The LLM can't draw any distinction between grammar-aligned, desirable, or undesirable patterns; yet that is exactly what most people expect to be possible after reading about a personified Artificially Intelligent Large Language Model. I would rather call them "Text Inference Models". Those are the clearest descriptors of what the thing itself is and does.

andsoitis 4y ago

If LLMs are so great at learning by themselves, why does OpenAI need to resort to the plug-in model? Is it because it can’t actually do logic (hence the Wolfram Alpha plugin)? Or is it because that’s the way it gets access to more data? Or both? https://openai.com/blog/chatgpt-plugins https://openai.com/blog/chatgpt-plugins

arisAlexis 4y ago

What? This is an article where author puts 5% of happening and others are commenting on his opinion?

virtual_nikola 4y ago

The other way is saying there is a gold rush coming

brucethemoose2 4y ago

I think the hardware/cost factor is also a business one, eg how dominant does Nvidia stay in the space. If they effectively shut out other hardware companies, that is going to slow scaling and price/perf reduction.

pixl97 4y ago
At some point it's going to be difficult to shut everyone else out. Intel tried this a long time ago, and while they maintained dominance, they were not able to shut out competitors completely, and experienced a lot of legal battles over it. Foreign nations aren't going to be happy about one mostly US company holding all the cards too.

totoglazer 4y ago

Another big concern will be regulatory. It seems unlikely a couple billion people whose livelihood is significantly impacted will just chill as it happens? I think it’s unlikely, but no less likely than the compute issues mentioned.

knodi123 4y ago
So the research and development will just move over to a neighboring country? This isn't like the manhattan project. There are lots of people who know how to make this stuff, and they don't need rare volatile elements - just consumer hardware.
- pixl97 4y ago
  It depends if were talking 'Butlerian Jihad' levels of disruption here. And no, the big models require exabytes of processing power and time, so at least at the most extreme scales if nations started punching missiles in processor factories and data centers you'd slow top end AI projects way down.
  - knodi123 4y ago
    > It depends if were talking 'Butlerian Jihad' levels of disruption here. Yeah, but we weren't. We were talking about chatgpt and llms. Of course if the singularity arrives, all bets are off, all dominoes will topple, and the new world order will be hard to guess, if there even is one.
- flangola7 4y ago
  If it seems dire enough lethal military force might be used. If Russia and China think the US is about to have AI capable of absolute total global domination they may launch a preemptive strike. Maybe hypersonic cruise missiles at datacenters, maybe a full EMP or nuclear launch. (Swap countries around as desired.)
bloodyplonker22 4y ago
Isn't this a "concern" whenever a new technology comes out? ie: the internet? Yet, due to how slowly government moves and how hard new technology is to understand for governments, it is barely a concern.

crop_rotation 4y ago

I think scaling limits and profitability are the only things that can stop the march of the AI. The utility is already there and even the current GPT4 utility is revolutionary.

HervalFreire 4y ago

It's different this time. Because this time AI is hugely more popular in the public and corporate sphere. The previous AI winters were more academic winters with few people pushing the envelope. I don't think compute is the issue. It's an issue with LLMs. Current LLMs are just a stepping stone for true AGI. I think there's enough momentum right now that we can avoid a winter and find something better through sheer innovation.

pixl97 4y ago
I think the difference is AI takes data and in the past we just didn't have much data. Now the vast majority of the worlds population has a cellphone and internet service, and use services that AI can improve/affect.

RandomLensman 4y ago

After the massive hype around generative AI, seems likely there will be an AI winter when the promised transformation in many business areas just doesn't happen as advertised.

pixl97 4y ago
I remember the hype cycle around this thing called the "internet" back in the day. People said it was going to take over the world, even though back then it was slow and kinda sucked. And then it did.
- diceduckmonk 4y ago
  I remember a hype cycle called “BigData”. Companies now realize data science that big of a step to business analytics of the 90s.
- lm28469 4y ago
  What about cryptos, VR, nosql, nfts, &c. ?
  - zachnwhite 4y ago
    thank you
- RandomLensman 4y ago
  Do you remember all the failed predictions, too?
- palata 4y ago
  > And then it did. After the dotcom bubble exploded. Can't we call that a winter?
  - pixl97 4y ago
    What metrics do you want to go by? At least by internet use and user growth the dotcom bubble still saw massive amounts of new user growth and online time by users. Was there a massive reduction in completely untenable .com's? But I'm not exactly sure if that's the definition of a winter, plenty of other internet based businesses did fine and kept growing in that time.

greatwave1 4y ago

Can anyone give some color on to what extent advancements in AI are limited by the availability of compute, versus the availability of data? I was under the impression that the size and quality of the training dataset had a much bigger impact on performance versus the sophistication of the model, but I could be mistaken.

jacobn 4y ago
Both matter, and returns fall off as you go further in one but not the other. The Chinchilla paper[0] established a simple scaling law for Large Language Models: model size and training tokens should grow at the same pace. Compute is then proportional to the product of model size & data quantity. That said, quality of data also matters a lot - OpenAI has had human labelers produce the data for their Reinforcement Learning from Human Feedback (RLHF), which has probably had a disproportionate impact on the success of ChatGPT compared to previous models, but that data is probably O(1%) of what they trained on. At this point I'm guessing OpenAI are limited by both data & compute. Rumor has it they're training the "next big thing" now and it won't finish until December. If they had more compute they could presumably finish sooner, and if they had more data they would presumably let it train longer. [0] https://arxiv.org/abs/2203.15556 https://arxiv.org/abs/2203.15556
- pixl97 4y ago
  Also at this point, very few of the biggest players are going to tell us anything about which matters most and those fine tuning numbers can represent a huge strategic advantage. Forcing your competitors to spend billions in hardware and time can put you far ahead of them quickly, at least at our current rate.
wsgeorge 4y ago
AFAIK it's still an active area of research, and evidence from Meta AI [0] suggests that size and quality of data can let smaller (not necessarily less sophisticated) models do amazing things. But a lot of the advancements we're seeing right now are the result of more sophisticated models [1], and one person is doing some interesting work [2] around achieving transformer-level performance with other architectures. So it's not completely settled if more data is the answer. But it has a significant impact. [0] https://ai.facebook.com/blog/large-language-model-llama-meta-ai/ https://ai.facebook.com/blog/large-language-model-llama-meta... [1] https://en.wikipedia.org/wiki/Transformer_(machine_learning_model) https://en.wikipedia.org/wiki/Transformer_(machine_learning_... [2] https://github.com/BlinkDL/RWKV-LM https://github.com/BlinkDL/RWKV-LM

nuancebydefault 4y ago

So. The article starts with "I give it an estimate of 5 per cent chance..." and then explains: what if... Is this case really worth exploring? Or was the article written by a bored AI? I find it striking that there are still so many people downplaying the latest developments of AI. We all feel that we are at the verge of a next revolution on par or even greater than the emergence of the www, while some people just can't to seem to let it sink in.

Workaccount2 4y ago
It should be evident at this point that there are a lot of people who feel threatened by AI, and will click on anything that can give hope.
JohnFen 4y ago
> while some people just can't to seem to let it sink in. Just because people may have opinions different from yours doesn't mean they're denying reality. They just have a different opinion. The hard, cold truth is that nobody knows the future. Everybody is just guessing.
- B1FF_PSUVM 4y ago
  > The hard, cold truth is that nobody knows the future. Probably, yes, especially after what happened to Cassandra. Still a fun counterfactual to develop. Lessee, you do know the future, but do not run your mouth off. Matter of fact, you pooh-pooh any such notion ...
- nuancebydefault 4y ago
  Yes that is the truth,nobody knows the future. But we you see something coming right at us, why still so much doubt?
  - johnfn 4y ago
    A lot of people saw crypto coming right at us as well.

pixl97 4y ago

> Eden writes, “[Which] areas of the economy can deal with 99% correct solutions? My answer is: ones that don’t create/capture most of the value.” And >Take for example the sorting of randomly generated single-digit integer lists. These seem like very confused statements to me. For example, lets take banking. It's actually two (well far more) different parts. You have calculating things like interest rates and issues like 'sorting integers' like above. This is very well solved in simple software at extremely low energy costs. If you're having your AI model spend $20 trying to figure out if 45827 is prime, you're doing it wrong. The other half of banking is figuring out where to invest your money for returns. If you're having your AI read all the information you can feed it for consumer sentiment and passing that to other models, you're probably much closer to doing it right. And guess what, ask SVB about 99% correct correct solutions that do/don't capture value. Solutions that have correct answers are quickly commoditized and have little value in themselves. Really the most important statement is the last one, mostly the article is telling is the reasons why AI could fail, not that those reasons are very likely. >I still think an AI winter looks really unlikely. At this point I would put only 5% on an AI winter happening by 2030, where AI winter is operationalised as a drawdown in annual global AI investment of ≥50%. This is unfortunate if you think, as I do, that we as a species are completely unprepared for TAI.

mirekrusin 4y ago
Can you ever be prepared for TAI? What does it even mean?

blintz 4y ago

I'm not an expert, but I see the main threat to continued improvement as running out of high-quality data. LLM's are a cool thing you can produce only because there is a bunch of freely available high-quality text representing (essentially) the sum of human knowledge. What if GPT-4 (or 5, or 6) is the product of that, and then further improvements are difficult? This seems like the most likely way for improvement to slow or halt; the article cites synthetic data as a fix, but I'm suspicious that that could really work.

brucethemoose2 4y ago
Even just finetuning these models, they will pick up on the weirdest things a human wouldn't see, and artificial datasets will contain all kinds of invisible artifacts that further "inbreeding" is going to massively amplify. I am not speaking speculatively either. I have seen it happen finetuning ESRGAN on previous upscales that I even personally vetted as "good," and these generative models are way more sensitive than the old GANs.
marcyb5st 4y ago
Googler, but opinion are my own. More than that, I believe we will hit a ceiling when the impossibility of these models to incorporate causality becomes evident. Right now, LLMs are trained by predicting the next word given a context (the prompt). This approach, IMHO, gives a resemblance of cause/effect because the training data is made by humans and obviously we are able to express ourselves and reason in those terms. So we have a poor proxy for that which, also IMHO, partially explains why LLMs performance degrades when asked to solve novel problems (there was an entry few days ago about this).
- akiselev 4y ago
  After seeing LangChain and the reasoning paper I think it's fairly obvious that we're just starting to scratch the surface of AI architectures, dictated largely by scalability of GPU resources. The LLMs we're playing with are at best the proof of concept of what will eventually be the message passing for the next generation of models.

anonzzzies 4y ago

AI winter will arrive if we don’t get the models to depend on ‘just’ more training data to get better. There is no more training data. We need models the same or better than gpt-4 but trained on roughly what an 18 year old >100 iq human would digest to get to that point. Which is vastly less than what gpt 4 gets fed. If advancing means ever larger and more expensive systems and ever more data, we will enter a cold winter soon.

pixl97 4y ago
There is nearly an unlimited amount of training data. As of so far models have been eating up text. We still have sound, image, video, temperature, gravimetrics, and other sources that we can feed multimodal models. And that's not even including training models from learning from the world itself.
- anonzzzies 4y ago
  Sound, image and video we’ll ravish through (already happening); is temp and gravimetrics valuable? (I don’t know; it’s a question) Games might be a source too. But yes, the world itself is a good source. So maybe that won’t freeze it over; computing power/energy?
  - pixl97 4y ago
    >computing power/energy? Energy is "probably" not a big deal. If you're looking at long term oversupply from green energy sources, it's probably not hard to train with bursty, but very cheap power like this. Compute is currently the biggest limitation, and will remain so far a long time, as long as scaling continues.
- akiselev 4y ago
  > gravimetrics Calling it now: humanity will seal its fate the day we hook up LIGO to ChatGPT and it gets corrupted by gravitational waves from the beginning of time when the Great Old Ones freely roamed the universe.
stevenhuang 4y ago

mpsprd 4y ago

>“[Which] areas of the economy can deal with 99% correct solutions? My answer is: ones that don’t create/capture most of the value.” The entertainment industry disagrees with this. These systems are transformative for any creative works and in first world countries, this is no small part of the economy.

ahofmann 4y ago

I think the assumption that companies are willing to spend 10 billion dollars on AI training is unrealistic. Even the biggest companies would find such an investment to be a financial burden.

pixl97 4y ago
You're "probably" right, but it's not something I'm going to place bets on with the level of uncertainty. When you have some companies still sitting on war chests of tens of billions of dollars, and almost nothing to spend it on, not investing in AI is a risk in itself. If your competitor succeeds they may rapidly take parts of your market while you now attempt to reproduce their work. Also if these 10 billion dollar models are 'AGI' level, then your model pays for itself if you can find enough interesting work to throw at it.
PeterisP 4y ago
If they really believe that it will bring appropriate returns, a $10B investment is manageable. It is a significant burden, but even much larger investments have been made - e.g. Starlink satellite fleet on the private side, or the Large Hadron Collider from government funds.

chess_buster 4y ago

Write a counterpoint to the article posted. Your goal is to refute all claims by giving correct facts with references. Cite your sources. Make it 3 paragraphes. As a poem. In Klingon.

nico 4y ago

At the same time this AI revolution is happening, there is also a psychedelic revolution happening. When this happened in the 60s-70s, the psychedelic revolution was crushed by the government. And we entered an AI winter. I’m not implying causation. Just pointing out a curious correlation between the two things. I wonder what will happen now.

DennisAleynikov 4y ago
I really hope the government does not intervene in this process but I know for a fact they will want to.
- nico 4y ago
  You are correct. Today on HNs front page there was an article talking about how the EU has already said they are going to regulate AI.
  - layer8 4y ago
    That was targeting pre-GPT AI. They’re now trying to figure out how GPT fits into their considerations.
    - nico 4y ago
      Which shows that governments are trying to control this thing already.
      - layer8 4y ago
        It means they don't want to institute regulations that don't make sense for GPT-type AI. See https://digital-strategy.ec.europa.eu/en/policies/regulatory-framework-ai https://digital-strategy.ec.europa.eu/en/policies/regulatory... for the rationale and general approach.

superb-owl 4y ago

We're only just seeing expectations for the tech inflate now. VCs will probably pump money into LLM-related companies for at least a couple years, and it'll be a couple years after that before things really start to decline. It's late spring right now, a strange time to start forecasting winter.

Hizonner 4y ago

Well, I'm HOPING for that, but not RELYING on it...

DennisAleynikov 4y ago
this person has the right idea :) pray for a winter but prepare for a societal upheaval

endisneigh 4y ago

I think the AI winter will come, but not for why the author asserts (quality, reliability, etc.). I think the current crop of AI is good enough. It will happen because people will actually grow resentful of things that AI can do. I anticipate a small, yet growing segment of populations worldwide to start minimizing internet usage. This, will result in fewer opportunities for AI to be used and thus the lack of investment and subsequent winter.

numinary1 4y ago

Being old as dirt, my observation is that potential tech revolutions take ten years after the initial exuberance to be realized broadly, or three to five years to fizzle. Of those that fizzle, some were bad ideas and some were good ideas replaced by better ideas. FWIW

lostmsu 4y ago
This one started with GPT-2, IMHO. So 4 years into making.
mirekrusin 4y ago
And how long did it take for those tech revolutions to gain first 100M users?
- KaiserPro 4y ago
  openai having 100million users seems a bit suspect to me. given that they need a _huge_ amount of GPU power to server that kind of traffic, I suspect that 100million seems out by an order of magnitude.
- PaulDavisThe1st 4y ago
  Well, during the initial "tech" (broadly interpreted to mean computational processes that somehow involve the internet) revolutions, there were not 100M, nor even 1M users to be gained. And what is a "user" when it comes to ChatGPT and it's ilk? How does that compare with the definition of a user of, say, the web? Or of SMS ?
  - pixl97 4y ago
    With most other technologies before this there was the issue with rollout of physical infrastructure. And that is still true somewhat. All the big AI players want more AI processors and would take possession of as many as they could get. Buy the other side of physical infrastructure is already here. The internet, cellphones, and computers already exist in mass and can be utilized by AI now. We didn't need to build new highways for this. No new towers. No wires put in the ground. Those fields already exist and are ready for planting.

Havoc 4y ago

> reliability Humans are unreliable AF and we employ them just fine. Better reliability would certainly be nice but I don’t think it is strictly speaking necessary

gumby 4y ago

The "winter" analogy (I remember the AAAI when marvin made that comment) was to the so-called "nuclear winter" that was widely discussed at the time: a devestating pullback. It did indeed come to pass. I don't see that any time soon. I think the rather breathless posts (which I also remember from the 80s and apparently used to be common in the 60s when computers just appeared) will die down as the limits of the LLMs become more widely understood, and they become ubiquitous where they make sense.

unsupp0rted 4y ago
Unless of course the limits of LLMs are just outside the bounds of LLMs being able to write their own successors. In which case progress becomes unlimited and there’s never an AI winter again.

muyuu 4y ago

idk if there will be that much of a winter, but i would welcome it in the late 90s and early 2000s, neural network had a significant stigma for being dead ends and were unpromising grads were sent - people didn't want to go there because it was a self-fulfilled prophecy that if you went to research ANNs then you were a loser, and you were seen as such, and in academia that is all you need to be one but, in real life, they worked sure, not for everything because of hardware limitations among other things, but these things worked and they were a useful arrow in your quiver as everybody else just did whatever was fashionable at the time (simulated annealing, SVMs, conditional random fields, you name it) hype or no hype, if you know what you are doing and the stuff you do works, you will be okay

karmasimida 4y ago

There will never be another winter moving forward. ChatGPT as is, is already transformative. It CAN do human level reasoning really well. The only winter I can see, is the AI gets so good, there is little incentive to improve upon it.

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throwbadubadu 4y ago
The dissonance what people see in it is really gross.. maybe I'm too stupid, but session from simple to more complicated problems have all shown me: It is definitely not reasoning at all, it doesn't understand anything.. it is a different version of stackoverflow that sometimes gets you quicker to target, but for me even more often not. shrug
- ux-app 4y ago
  have you read this[1]? >It is definitely not reasoning at all read the paper. GPT4 is the real deal. [1]https://arxiv.org/pdf/2303.12712.pdf https://arxiv.org/pdf/2303.12712.pdf
  - throwbadubadu 4y ago
    Sure I did. Did you try it yourself?
    - deleted 4y ago
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codelord 4y ago

IMHO the prospect of an AI winter is 0%. As someone who has done research in ML I think ML technology is moving forward much faster than we anticipated. ChatGPT shouldn’t work based on what we knew. It’s incredible that it works. Makes you think what other things that shouldn’t work we should scale up and see if they would work. And then there are things that we think they should work. Each new paper or result opens the door for many more ideas. And there are massive opportunities in applying what we already have to all industries. You can absolutely build high precision ML models. Using a transformer LM to sum numbers is dumb because the model makes little assumptions about the data by design, you can modify the architecture to optimize for this type of number manipulation or you can change the problem to generating code for summing values. In fact Google is using RL to optimize matmul implementations. That’s the right way of doing it.

dragonwriter 4y ago
> As someone who has done research in ML I think ML technology is moving forward much faster than we anticipated. The past AI winters have been preceded by periods of AI moving forward faster than anticipated. Its when the limits of easy advancement with the current approaches are reached without a new approach that allows continued rapid progress you get an AI winter.
- deleted 4y ago
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RivieraKid 4y ago
> ChatGPT shouldn’t work based on what we knew Why?
- layer8 4y ago
  I guess because we still don’t understand why it works. We still have no basis on which to predict that it works as well as it does, except for the manifest fact that it does.

WheelsAtLarge 4y ago

I give it a 95% chance that an AI winter is coming. Winter in a sense that there won't be any new ways to move forward towards AGI. The current crop of AIs will be very useful but it won't lead to the scary AGI people predict. Reasons: 1) We are currently mining just about all the internet data that's available. We are heading towards a limit and the AIs aren't getting much better. 2) There's a limit to the processing power that can be used to assemble the LLM's and the more that's used the more it will cost. 3) People will guard their data more and will be less willing to share it. 4) The basic theory that got us to the current AI crop was defined decades ago and no new workable theories have been put forth that will move us closer to an AGI. It won't be a huge deal since we probably have decades of work to sort out what we have now. We need to figure out its impact on society. Things like how to best use it and how to limit its harm. Like they say,"interesting times are ahead."

Uupis 4y ago
> 3) People will guard their data more and will be less willing to share it. I recently came across this myself when writing a reply on another forum. A feeling of reluctance to attempt to contribute something maybe-useful in a public, 'minable' space. Almost feels like this has tarnished the 'magic' of Internet, of sharing information and knowledge. I'm not saying this is the appropriate reaction, but it is what it is.
- HarHarVeryFunny 4y ago
  > I recently came across this myself when writing a reply on another forum. A feeling of reluctance to attempt to contribute something maybe-useful in a public, 'minable' space I used to be like this, but the reality is that ideas are a dime a dozen. Any idea that you or I (or anyone else) have had, have likely also been thought by thousands (or maybe add a few more 0's) of people. What makes things happen is people not ideas. Talk to any VC or people involved in startups - it's all about the team, not the ideas they are working on.
chongli 4y ago
5) AI-generated content will become more and more common over time. This will inevitably end up in the training sets of future AI. By recycling its own training material, AI will get less meaningful results over time.

javaunsafe2019 4y ago

I don’t even understand why we call models that predict text output to a question AI. For sure we will get a lot stuff automated with it in the near future but this is far away from anything real intelligent. It just doesn’t really understand and or feel things. It’s dead cause it just outputs data based on it’s model. Intelligence contains a will and chaos.

stephc_int13 4y ago

The current expectations around AI are extremely high and frankly quite a few of them are bordering into speculative territory. That said, I don't think we're going to see a new AI winter anytime soon, what we're seeing is already useful and potentially transformative with a few iterative improvements and infrastructure.

collaborative 4y ago

> cheap text data has been abundant The winter before the AI winter will consist in all the cheap data disappearing. What fun will it be to write a blog post so that it can be scraped by a bot and regurgitated without attribution? Dito for code Or, how will info sites survive without ad revenue? Last I checked bots don't consume ads When the internet winter comes, all remaining sites will be behind login screens and a strict ToS popup

zitterbewegung 4y ago

I think many technologies go from spring , summer and eventually winter. The last one focused on good old fashioned AI . The next one was big data with ML and this one is large language models.

HarHarVeryFunny 4y ago

Let's not forget that GPT-4 was finished over 6 months ago, with OpenAI now presumably well into 4.5 or 5, and Altman appearing confident on what's to come ... In the meantime we've got LangChain showing what's possible when you give systems like this a chance to think more than one step ahead ... I don't see an AI winter coming anytime soon... this seems more like an industry changing iPhone or AlexNet moment, or maybe something more. ChatGPT may be the ENIAC of the AI age we are entering.

karmasimida 4y ago

Previous AI winters are due to overpromise and no delivery: Big promise of what AI can do, but never able to actually even deliver a prototype of that in reality. ChatGPT, at least for GPT-4, can already be considered as someone coined, baby AGI. It is already practical and useful, so it HAS to be REAL. If it is already REAL, there is no need for another winter to ever come to reap the heads of liars. Instead AI will become applied technology, like cars, like chips. It will evolve continuously, and never go away.

roflyear 4y ago
It's not a babi agi. You can't teach it things. Often you ask it to not do something and it does anyway. Say don't use that function and it uses it in the example. It's crazy impressive but that's not 0.00001% of an AGI.
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PaulDavisThe1st 4y ago
How old were you when expert systems arrived on the scene?

blurbleblurble 4y ago

People love talking about AI winter.

christkv 4y ago

We are all ready using GPT 4 for a ton of BS documents we have to write for our planning permission and other semi legal paper work. My lawyer has been doing pretty much every public filing for civil cases and licenses assisted by GPT. So much bureaucracy could probably be removed by just having GPT validated permissions and manage the correctness of the submissions leaving a human to rubber stamp the final result if at all.

skybrian 4y ago

Sometimes unreliability can be worked around with human supervision. You wouldn't normally want to write a script that just chooses the first Google result, but that doesn't mean search engines aren't useful. The goal when improving a search engine is to put some good choices on the first page, not perfection, which isn't even well-defined. The AI generators work similarly. They're like slot machines, literally built on random number generators. If you don't like the result, try again. When you get something you like, you keep it. There are diminishing returns to re-running the same query once you got a good result, because most results are likely to be worse than what you have. Randomness can make games more fun at first. But I wonder how much of a grind it will be once the novelty wears off?

Karrot_Kream 4y ago
I think that will be the hard part of productionizing these models. Some domains absolutely need high reliability, like AVs, and applying these ML models is fraught because of how imprecise their outputs are. Problem domains where unreliable results are useful either through human mitigation (e.g. expert search engines, customer service chatbots) or because they're just not that reliable anyway (like image generation) will be the domains where these models will have sticking power.
christkv 4y ago
The fact that they produce valuable content is more a reflection of the mediocre output of many people. I hold it up against the response I would expect from a human and in a lot of cases I’m not sure the human wins.
- pixl97 4y ago
  Yea, I see a lot of "AI models need to be perfect", but man, that is really neglecting that humans on average suck at a great many things.
- skybrian 4y ago
  Often when you have a question, you don't have access to a qualified expert you could ask for help. So there's less competition than you might think.

beepbooptheory 4y ago

All this stuff can't transform anything if you can't afford to keep the computer on. Which is really, to me, the bigger/most convincing point in the thread this article links at the top. If there isn't a winter, will ChatGPT et al be able solve the energy crises they might be implicated in? Is there something in its magic text completion that can stop global warming? Coming famines? Is perhaps the fixation on these LLMs right now, however smart and full of Reason they are, not paying the fullest attention to the existential threats of our meat world, and how they might interfere with what ever speculative dystopia/utopia we can imagine at the moment?

ChatGTP 4y ago

AI Winter will likely come because we've not addressed climate change...instead of blowing billions / trillions on our survival, we're yet again blowing it on moonshots. We have the brains collectively, already to solve the problems should we want to, we don't because that's not where "the money" is. Silicon Valley Tech is already promising that AI will be the likely solution to climate change..., if there is any more disruption to the economy it's just going to yet again slow down mitigation steps for climate change, thus having negative affects on the amount of capital available for these projects. Printing money works, until it doesn't.

CatWChainsaw 4y ago
Humanity: Oh great AI, what is the solution to climate change? AI: Stop building me, I take up far too many resources and generate way too much heat.

boringuser1 4y ago

The reason why these types of claims are baseless is because of the key fact that if AI tech stopped progressing right now, it's already a game-changer once companies adopt.

stuckinhell 4y ago

I strongly disagree. ChatGPT is bleeding into everything. Midjourney is too damn good see the example below. The avengers if they had 90's actors is going viral. https://cosmicbook.news/avengers-90s-actors-ai-art https://cosmicbook.news/avengers-90s-actors-ai-art Also the avengers as a dark sci fi https://www.tiktok.com/@aimational/video/7186426442413215022 https://www.tiktok.com/@aimational/video/7186426442413215022 AI art and generative text is just astounding, and it's only getting better.

kromem 4y ago

I increasingly think we're underestimating what's ahead. Two years ago was an opinion piece from NIST on the impact optoelectronics would bring specifically to neural networks and AGI, and watching as nearly every major research institution has collectively raised probably half a billion for AI photonics plays through their VC partnerships or internal resource allocations on the promise of order of magnitude improvements much closer than something like quantum computing, I think we really haven't seen anything yet. We're probably just at the very beginning of this curve, not approaching its diminishing returns. And that's both very exciting and terrifying. After decades in tech (including having published a prediction over a decade ago that mid 2020s would see roles shift away from programming towards emergence of specialized roles for knowing how to ask AI to perform work in natural language) I think this is the sort of change so large and breaking from precedent we really don't know how to forecast it.

thelazydogsback 4y ago

> I put 5% on an AI winter happening by 2030 lol. 5%? - that's really laying it on the line

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atleastoptimal 4y ago

More so that there's a 5% chance there won't be a human winter in the next 5 years

mikewarot 4y ago

It is entirely possible that Moore's Law gets assassinated by supply chain destruction as deglobalization continues. There are too many single source suppliers in the chain up to EUV lithography. We may in fact be at peak IC.

HarHarVeryFunny 4y ago
People have been predicting the end of Moore's law for decades ... eventually they will be right, but it seems there are at least a few generations of process reduction and transistor design left before we start to hit any hard physical limits. Of course there hasn't been much increase in single core clock speeds or cost reduction for a while now (certainly nothing from Intel), but TSMC and Samsung do continue to make progress, with multiple future process generations under development. As far as AI, or more specifically training (and running) massive neural nets, it's not single core performance that matters at all - it's all about specialized matrix multiplication hardware whether NVidia's Tensor Cores or Google's TPUs, and more importantly the ability to bring many of these specialized units together to work on a single problem. Distributed NN training has been a solved problem for a long time, with things like GPT-4 being trained across 10,000 machines. This type of massive parallelism can continue to grow regardless of what is happening to Moore's law and single core performance.
- mikewarot 4y ago
  I'm not suggesting a slowdown of innovation, but rather supply chain collapse which is a completely different kettle of fish. Our ability to make high end chips may plato due to a complete inability of AMSL to source all the parts for EUV steppers. Many of those components themselves have multi-national supply chains.
  - HarHarVeryFunny 4y ago
    Right, but what I was trying to say is that we're not really that dependent on cutting edge chips, at least not for AI development. Even if all we had was older pre-EUV fabs we could still train in parallel on as many less powerful chips as needed. I'm surprised that the US didn't consider tech self-reliance as a national security issue a long time ago.

happycube 4y ago

There'll be an AI Winter from a VC standpoint... but even in the 90's there was some (GOF)AI stuff still going on after that. There are too many actually useful things coming out of this for a true winter. And for there not to be a bubble.

nojvek 4y ago

There's a few things at play here. LLMs - OpenAI, Google Brain, Meta FAIR, HuggingFace and others are now routinely training models with the entire corpus of the internet in a few months. The models are getting larger and more efficient. Diffusion models - MidJourney, StableDiffusion, Dall-E and it's control net cousins - Trained on terabytes of images, almost entire corpus of internet. Same with voice and other multimodal models. The transformer algorithm is magical but we're just getting started. There are now multiple competing players who can drop millions of dollars on compute and have access to internet sized datasets. The compute, the datasets, the algorithms, the human reinforcement loops, all are getting better week over week. Millions of users are interacting with these systems daily. A large subset even paying for it. There is the gold rush.

_nalply 4y ago

I think, this time it is different. Of course, there's a bubble, but after that bubble pops, people will realize that current models are useful enough, even with their quirks. People all have quirks and mostly they get along, so they will accept quirks from machines. Anthropomorphizing machines will help accepting models. I know, this is dangerous, but I have this mental image: a doll in form of a seal baby with soft white fur with a Whisper model helping lonely handicapped people (note that I myself am a person with a disability, so don't cancel me, please). Or someone who technically is not very adept phoning for support and a Whisper model helping along and having a lot of time to chit-chat. And technically I think, something will happen in about five years. A new floating number format, the posit (https://spectrum.ieee.org/floating-point-numbers-posits-processor https://spectrum.ieee.org/floating-point-numbers-posits-proc...), is too useful to be ignored. It will take years because we need new hardware. Why do I think that posits are very useful? Posits could encode weights using very little storage (down to 6 bit per weight). Models perhaps need to be retrained using these weights because 6 bits are not precise. After all, you have only 64 different values. And I think with the new hardware supporting posits they will also have more memory for the weights. Cell phones will be able to run large and complex models efficiently. In other words, Moore's law is not dead yet. It just shifted to a different, more efficient computation implementation. When this happens, immediate feedback could become feasible. With immediate feedback we do another step to achieve AGI. I could imagine that people get delivered a partially trained model and then they have a personal companion helping them through life.

wnkrshm 4y ago
> then they have a personal companion helping them through life or a personal cop/political commissar for every citizen
- _nalply 4y ago
  In this case people won't trust AI.