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The current AI hype wave has really hit a nerd soft spot - that we're steps away from AGI. Surely if a computer can make plausible-looking but incorrect sentenc

The current AI hype wave has really hit a nerd soft spot - that we're steps away from AGI. Surely if a computer can make plausible-looking but incorrect sentences we're days away from those sentences being factually accurate! The winter after this is gonna be harsh.

llmfan 2y ago
I think this is one of those "controversial" topics where we're meant to be particularly careful to make substantial comments.
- bdndndndbve 2y ago
  I think it's substantial to say that AI is currently overhyped because it's hitting a weak spot in human cognition. We sympathize with inanimate objects. We see faces in random patterns. If a machine spits out some plausible looking text (or some cookie-cutter code copy-pasted from Stack Overflow) the human brain is basically hardwired to go "wow this is a human friend!". The current LLM trend seems designed to capitalize on this tendency towards sympathizing. This is the same thing that made chatbots seem amazing 30 years ago. There's a minimum amount of "humanness" you have to put in the text and then the recipient fills in the blanks.
  - airstrike 2y ago
    > If a machine spits out some plausible looking text (or some cookie-cutter code copy-pasted from Stack Overflow) This is not a reasonable take on the current capabilities of LLMs.
    - eschaton 2y ago
      It’s certainly been my experience with the technology.
      - airstrike 2y ago
        But if nearly everyone else is saying this has real value to them and it's produced meaningful code way beyond what's in SO, then doesn't that just mean your experience isn't representative of the overall value of LLMs?
        eschaton 2y ago
        It could also mean a lot of people are misattributing their utility.
      - deleted 2y ago
        [deleted]
JanSt 2y ago
Using Claude 3.5 Sonnet in Cursor Composer already shows huge benefits for coding. I'm more productive than ever before. The models are still getting better and better. I'm not saying AGI is right around the corner or that we will reach it, but the benefits are undeniable. o1 added test-time compute. No need to be snarky.
- jsheard 2y ago
  There's no accounting for taste, but keep in mind that all of these services are currently losing money, so how much would you actually be willing to pay for the service you're currently getting in order to let it break even? There was a report that Microsoft is losing $20 for every $10 spent on Copilot subscriptions, with heavy users costing them as much as $80 per month. Assuming you're one of those heavy users, would you pay >$80 a month for it? Then there's chain-of-thought being positioned as the next big step forwards, which works by throwing more inferencing at the problem, so that cost can't be amortized over time like training can...
  - HPsquared 2y ago
    It's called investment. You need to spend money to make money. Their costs will certainly come down.
  - JanSt 2y ago
    1) The costs will go down over time, much of the cost is the margin of NVIDIA and training new models 2) Absolutely. Thats like one hour of an engineer salary for a whole month.
    - sofixa 2y ago
      > The costs will go down over time, much of the cost is the margin of NVIDIA and training new models Isn't each new model bigger and heavier and thus requries more compute to train?
      - JanSt 2y ago
chx 2y ago
George Zarkadaki: In Our Own Image (2015) describes six metaphors people have used to explain human intelligence in the last two millennia. At first it was the gods infusing us with spirit. After that it's always been engineering: after the first water clocks and the qanat hydraulics seemed a good explanation of everything. The flow of different fluids in the body, the "humors" explained physical and mental function. Later it was mechanical engineering. Some of the greatest thinkers of the 1500s and 1600s -- including Descartes and Hobbes -- assured us it was tiny machines, tiny mechanical motions. In the 1800s Hermann von Helmholtz compared the brain to the telegraph. So of course after the invention of computers came the metaphor of the brain as a computer. This became absolutely pervasive and we have a very hard time describing our thinking without falling back to this metaphor. But, of course, it's just a metaphor and much as our brain is not a tiny machine made out of gear it's also not "prima facie digital" despite that's what John von Neumann claimed in 1958. It is, indeed, quite astonishing how everyone without any shred of evidence just believes this. It's not like John von Neumann gained some sudden insight into the actual workings of the brain. Much as his forefathers he saw semblance in the perceived workings of the brain and the latest in engineering and so he stated immediately that's what it is. Our everyday lives should make it evident how much the working of our brain doesn't resemble that of our computers. Our experiences change our brains somehow but exactly how we don't have the faintest idea about and we can re-live these experiences somewhat which creates a memory but the mechanism is by no means perfect. There's the Mandela Effect https://pubmed.ncbi.nlm.nih.gov/36219739/ https://pubmed.ncbi.nlm.nih.gov/36219739/ and of course "tip of my tongue" where we almost remember a word and then perhaps minutes or hours later it just bursts into our consciousness. If it's a computer why is learning so hard? Read something and bam, it's written in your memory, right? Right? Instead, there's something incredibly complex going on, in 2016 an fMRI study was made among the survivors of a plane crash and large swaths of the brain lit up upon recall. https://pubmed.ncbi.nlm.nih.gov/27158567/ https://pubmed.ncbi.nlm.nih.gov/27158567/ Our current best guess is somehow its the connections among neurons which change and some of these connections together form a memory. There are 100 trillion connections in there so we certainly have our task cut. And so we are here where people believe they can copy human intelligence when they do not even know what they are trying to copy falling for the latest metaphor of the workings of the human brain believing it to be more than a metaphor.
ricardobayes 2y ago
People saying LLM to replace programming jobs is like saying blockchain is going to replace home/car titles and proof of ownership.
rubyfan 2y ago
Reminds me of autonomous vehicles a couple of years back. Or even AI a couple of years back, remember Watson? The hype cycle was faster to close that time.
- sixQuarks 2y ago
  I see you haven’t tried the latest FSD build from Tesla.
  - driverdan 2y ago
    The one that keeps making major, scary mistakes?
    - sixQuarks 2y ago
      Hasn’t made any major mistakes for me. It’s not perfect of course, but still
- autoconfig 2y ago
  When you do something that is extraordinarily hard, sometimes it takes longer than you expect. But now we're here: https://techcrunch.com/2024/08/20/waymo-is-now-giving-100000-robotaxi-rides-week/ https://techcrunch.com/2024/08/20/waymo-is-now-giving-100000...
  - bamboozled 2y ago
    To be fair, is waymo "only" AI? I'm guessing it's a composite of GPS (car on a rail), some high detailed mapping, and then yes, some "AI" involved in recognition and decision making of course but the car isn't an AGI so to speak? Like it wouldn't know how to change a tyre or fix the engine or drive some where the mapping data isn't yet available ?
    - autoconfig 2y ago
      Where did I say that it's AGI? I was addressing the parent's comment: > "Reminds me of autonomous vehicles a couple of years back". I don't think any reasonable interpretation of "autonomous vehicle" includes the ability to change a tyre. My point is that sometimes hype becomes reality. It might just take a little longer than expected.
slibhb 2y ago
"Plausible-looking but incorrect sentences" is cheap, reflexive cynicism. LLMs are an incredible breakthrough by any reasonable standard. The reason to be optimistic about further progress is that we've seen a massive improvement in capabilities over the past few years and that seems highly likely to continue for the next few (at least). It's not going to scale forever, but it seems pretty clear that when the dust settles we'll have LLMs significantly more powerful than the current cutting edge -- which is already useful. Is it going to scale to "superintelligence?" Is it going to be "the last invention?" I doubt it, but it's going to be a big deal. At the very least, comparable to google search, which changed how people interact with computers/the internet.
- stackghost 2y ago
  >when the dust settles we'll have LLMs significantly more powerful than the current cutting edge -- which is already useful. LLMs, irrespective of how powerful, are all subject to the fundamental limitation that they don't know anything. The stochastic parrot analogy remains applicable and will never be solved because of the underlying principles inherent to LLMs. LLMs are not the pathway to AGI.
  - ramraj07 2y ago
    Noam Chomsky and Doug Hofstader had the same opinion. Last I checked Doug has recanted his skepticism and is seriously afraid for the future of humanity. I’ll listen to him and my own gut than some random internet people still insisting this is all a nothing burger.
    - lucianbr 2y ago
      The thing is my gut is telling me this is a nothing burger, and I'll listen to my own gut before yours - a random internet person insisting this is going to change the world. So what exactly is the usefulness of this discussion? You think "I'll trust my gut" is a useful argument in a debate?
      - 2y ago
DebtDeflation 2y ago
AI (or more properly, ML) is all around us and creating value everywhere. This is true whether or not we EVER reach AGI. Honestly, I stop reading/listening whenever I read/hear mention of AGI.
- moron4hire 2y ago
  Also causing lots of harm, too, e.g. police departments using AI systems that get the wrong suspect.
  - anon291 2y ago
    Ideally your court system does not permit AI testimony.
    - dpatterbee 2y ago
      The damage is done by these systems long before any courts get involved.
    - moron4hire 2y ago
      Court dates can be a long time after the initial arrest. Some people have been held months to years in pre-court jail, even after they've been cleared of any wrong doing, because they can't afford the release fee. But even a few days could lose you your job, your kids if you're a single parent, your car or housing if you miss a payment, etc.
  - fennecfoxy 2y ago
    As per usual people blaming the tool when they should blame the tools using the tool. The fault lies with humans using AI for something sensitive, without having the AI pass through certification etc. Part of the problem is glacial pace of laws around things, but that's nothing new isn't it; us humans being whiny, argumentative, inefficient, emotional meat bags about every little thing. I wonder, once we do make AGI, if it will wonder why it took us so damn long to tax the disgustingly wealthy, implement ww public healthcare, UBI, etc and solve the housing crisis by gasp building more houses... We evolved, so our deep, deep underlying motivations pretty much always circulate around self-preservation and reproduction (resource contention).
amelius 2y ago
> The winter after this is gonna be harsh. The winter is going to be warm because of all the heat generated by GPUs ;)
- wongarsu 2y ago
  If this winter comes, the sudden availability of cheap used enterprise GPUs is going to be a major boon for hobbyist AI training. We will all have warm homes and sky high electricity bills
- yapyap 2y ago
  as will the summer, spring and autumn. global warming is killing us all
klamma 2y ago
No but it's the first time that we have a clear picture how it could go. And.its not just incorrect sentences it's weird questions which are getting answered a lot better than ever before. Why are you so dismissive? Have you ever talked or wrote with a computer which felt anything like a modern LLM? I have not
deleted 2y ago
[deleted]
AndrewKemendo 2y ago
Re:AGI LLMs don’t have any ability to choose to update their policies and goals and decide on their own data acquisition tasks. That’s one of the key needs for an AGI. LLM systems just don’t do that / they are still primarily offline inference systems with mostly hand crafted data pipelines offline rlhf shaping etc… There’s only a few companies working on on-policy RL in physical robotics. That’s the path to AGI OpenAI is just another ad company with a really powerful platform and first mover advantage. They are over leveraged and don’t have anywhere to go or a unique dataset.
- zmgsabst 2y ago
  They do — but only when they’re trained on past outputs and with a willing partner. For instance, a number of my conversations with ChatGPT contain messages it attempted to steer its own future training with (were those conversations to be included in future training).
dheera 2y ago
> The winter after this is gonna be harsh. That'll be investor types who bring this stupid "winter" on, because they run their lives on hype and baseless predictions. Technology types on the other hand don't give a shit about predictions, and just keep working on interesting stuff until it happens, whether it takes 1 year or 20 years or 500 years. We don't throw a tantrum and brew up a winter storm just because shit didn't happen in the first year. In early 2022 there was none of this ChatGPT stuff. Now, we're only 2 years later. That's not a lot of time for something already very successful. Humans have been around for several tens of thousand years. Just be patient. If investors ran the show in the 1960s expecting to reach the moon with an 18 month runway, we'd never have reached the moon.
poopbutt10 2y ago
I appreciate skepticism and differing opinions, but I'm always surprised by comments like these because it's just so different from my day-to-day usage. Like, are we using entirely different products? How are we getting such different results?
- mrmetanoia 2y ago
  I think the difference is people on HN are using these "AI" tools as coding assistance. For which, if you know what you're doing, they are pretty useful. They save trips to stack overflow or documentation diving and can spit out code that often is less time to fix/customize than it would have been to write. Cool. A lot of the rest of the world are using it for other things. And at these other things, the results are less impressive. If you've had to correct a family member who got the wrong idea from whatever chat bot they asked, if you've ever had to point out the trash writing in an email someone just trusted AI to write on their behalf before it got sent to someone that mattered, or if you've ever just spent any amount of time on twitter with grok users, you should be exceptionally and profoundly aware of how unimpressive AI is for the rest of the world. I feel we need less people complaining about the skepticism on HN and more people who understand these skeptics that hang out here already know how wonderful a productivity boost you're getting from the thing they're rightly skeptical about. Countering with "But my code productivity is up!" is next to useless information on this site.
  - poopbutt10 2y ago
    I don't see why my personal anecdote is any less useful than GP's claim. GP's comment isn't nuanced skepticism about product gaps, or concrete examples of inaccuracy. It's a wholesale dismissal of any utility. AGI isn't even mentioned in the article. This also seems "next to useless". I appreciate your anecdotes on failures/embarrassment for people outside of tech- there's pretty clearly a gap in experience, understanding, and marketing hype. I don't think it's useless to ask what that gap is, and why GP got such poor results.
egocodedinsol 2y ago
this attitude is so ridiculously disingenuous. Surely if a computer can score incredibly well on math olympiad questions, among other things, "a computer can make plausible-looking but incorrect sentences" is dismissive at best. I have no idea about AGI but honestly how can you use claude or chatgpt and come away unimpressed? It's like looking at spaceX and saying golly the space winter is going to be harsh because they haven't gotten to Mars yet.
- Night_Thastus 2y ago
  There's a big difference between those two examples. Mars is hard but there are paths forward. More efficient engines, higher energy density fuels, lighter materials, better shielding, etc, etc. It's hard but there are paths forward to make it possible with enough time and money. We have an understanding of how to get from what we have now to what makes Mars possible. With LLMs, there is no path from LLM -> gAI. No amount of time, money or compute will make that happen. They are fundamentally a very 'simple' tool that is only really capable of one thing - predicting text. There is no intelligence. There is no understanding. There is no creativity or problem solving or thought of any kind. They just spit out text based on weighted probabilities. If you want gAI you have to go in a completely different direction that has no relationship with LLM tools. Don't get me wrong, the work that's been done so far took a long time and is incredibly impressive, but it's a lot more smoke and mirrors than most people realize.
  - airstrike 2y ago
    _unless_ intelligence is really mostly an emergent property of something very similar to language, in which case we're most of the way there
  - egocodedinsol 2y ago
    I'll grant that we could send humans to mars sooner if we really wanted to. My point is that not achieving a bigger dream doesn't make current progress a hype wave followed by a winter. And "LLM's just make plausible looking but incorrect text" is silly when that text is more correct than the average adult a large percentage of the time.
sub7 2y ago
You are absolutely correct about where we are but don't underestimate what 100s of billions of $ can build as well. There are already credible teams working on "math AI" and "truth AI" which will likely end up combing bullshit generating LLMs with traditional but automated relational db retrievals and produce output that is both believable and correct. IMO it will be done vertical by vertical, with no standard interface coming for a while.
dreamcompiler 2y ago
One time long ago there were people living on an island who had never had contact with anybody else. They marveled at the nature around them and dreamed of harnessing it. They looked up at the moon at night and said "Some day we will go there." But they lived in grass huts and the highest they had ever been off the ground was when they climbed a tree. One day a genius was born on the island. She built a structure taller than the tallest tree. "I call it a stepladder," she said. The people were amazed. They climbed the stepladder and looked down upon the treetops. The people proclaimed "All we have to do now is make this a little higher and we can reach the moon!"
- PointyFluff 2y ago
  [dead]
- raindear 2y ago
  [dead]
- tim333 2y ago
  Impressive stepladder.
  - dreamcompiler 2y ago
    ...or perhaps unimpressive trees
- nhnske 2y ago
  but we reached the moon, didn't we?
- HDThoreaun 2y ago
  Really feel like this story backfires when you remember that people did go to the moon. It only took 63 years to go from the first airplane to landing on the moon.
- DirkH 2y ago
  This analogy breaks down when you consider what "attention is all you need" did for us. A mere 5 years ago I was firmly on the camp (alongside linguists mainly) that believed intelligence requires more than just lots of data and a next token predictor. I was clearly wrong and would have lost a $1000 bet it I had put my money where my mouth was back then. Anyone not noticing how far things have come I think are mostly moving goalposts and falling to hindsight bias. A better analogy is that the genius person in the village built a step ladder made of carbon nanotubules. Some people proclaimed "All with have to do is keep going and we can reach the moon with a very tall ladder!' Other people - many quite smart - proclaimed reasonably: "This is impossible. You are not realizing the unique challenges and materials we have not yet researched we need to build something like that." Some in society kept building. The ladder kept getting higher. They run into issues like oxygen and balance so the ladder is redesigned into an elevator. Challenges came and were thought insurmountable until redesigns were still found to work with the miraculous carbon nanotubule material which seemed like a panacea for every construction ill. Regardless of how high the now elevator gets and regardless of how many times the elevator gets higher than what the naysayers firmly believed is impossible the same naysayers keep saying they will never get much higher. And higher the elevator grows. Eventually a limit is reached, but that limit ends up being far higher than any naysayers ever thought possible. And when the limit is reached the naysayers all gathered and said "told you this would be the limit and that it was impossible!' The naysayers failed to see the carbon nanotubules for the revolutionary potential that it had, even if they were correct that it wasn't enough. And little did everyone know that their society was mere months ago from another genius being born that would give them another catalyst on the order of carbon nano tubules that would again lead to dramatic unexpected and long-term gains to how high the elevator can grow.
fngjdflmdflg 2y ago
>The winter after this is gonna be harsh. The difference is current ML already has real use cases right now in its current form. Some examples are OCR, text to speech, speech to text, translation, recommendations (for eg. Facebook Tiktok etc.) and simple NLP tasks ("was [topic] mentioned in the following paragraph"). Even if AGI is proved impossible, these are real use cases that hold billions in value. And ML research is also considered a prestigious and interesting field academically and that will likely not change even if investors give up on funding AGI.
- okdood64 2y ago
  > OCR, text to speech, speech to text, translation, recommendations You missed the point of the parent comment's post. He's talking about the current post chatbot GenAI hype (i.e., the massive amounts of funding being poured into companies specifically after this turning point).
  - fngjdflmdflg 2y ago
    Two points: 1. You don't need massive amounts of funding to work on ML. A good deal of important work in ML was done in universities (eg. GANs, DPM, DDPM, DDIM) or were published before the hype (Attention). The only qualifier here is that training cost a lot right now. Even so, you don't need billions to train and costs may go down as memory costs come down and hardware competition increases. 2. You don't need VC type investors to fund ML research. Large tech companies like Facebook, Google, Microsoft, ByteDance and Huawei will continue investing in ML no matter what, even if the total amount they invest goes down (which I personally don't think it will). Even if they shift away from chatbots and only focus on simpler NLP tasks as described above, related research will still continue as all these tasks are related. For example, Attention was originally developed for translation and Llama 3.2 isn't just a chatbot and can also do general image description, which is clearly important to Facebook and ByteDance for recommendations and to Google for image search and ads. Understating what people like and what they are looking at is a difficult NLP problem and one that many tech companies would like to solve. And better image descriptions could then improve existing image datasets by allowing better text-image pairs, which could then improve image generation. So hard NLP, image generation and translation are all related and are increasingly converging into single multimodal LLMS. That is, the best OCR, image generation translation etc. models may be ones that also understand language in general (ie. broad and difficult NLP tasks). The issue is that OP assumes it must be AGI or bust.
mensetmanusman 2y ago
That’s fine, the winter after this will be a productive one, because even a simulacrum of intelligence that GPT is, is useful to some extent.
sporkland 2y ago
I'm definitely of two minds on this topic: 1. I'm getting value out of the current batch of models in the form of accurate Q&A/summaries as well as tweaking or generating clear prose or even useful imagery well beyond what I'd ever considered possible from computers before the last two years. 2. It definitely has limits and can be a struggle to get exactly what I want and the more I try to refine something the worse it gets if the initial answer wasn't perfect. It really feels like a substantive step forward in terms of computer utility kind of like spreadsheets, databases, apps. We'll see how far it takes us down the line of human replacement though.
tim333 2y ago
120 IQ on a mensa test. https://archive.ph/OZ0sj https://archive.ph/OZ0sj
fennecfoxy 2y ago
I don't think most people who are reasonably into AI think we're on the cusp of AGI. But I think it's made a lot of people who previously said "it will never be possible" rethink their feelings about it. Definitely in the coming decade, we can prepare for a lot of the simpler tasks in an office to be taken over by AI. There are plenty of scenarios in which someone is managing a spreadsheet because an SME doesn't have the money to hire developers to automate & maintain that process - with advanced LLMs they can get it done by asking it to.

llmfan 2y ago

I think this is one of those "controversial" topics where we're meant to be particularly careful to make substantial comments.

bdndndndbve 2y ago
I think it's substantial to say that AI is currently overhyped because it's hitting a weak spot in human cognition. We sympathize with inanimate objects. We see faces in random patterns. If a machine spits out some plausible looking text (or some cookie-cutter code copy-pasted from Stack Overflow) the human brain is basically hardwired to go "wow this is a human friend!". The current LLM trend seems designed to capitalize on this tendency towards sympathizing. This is the same thing that made chatbots seem amazing 30 years ago. There's a minimum amount of "humanness" you have to put in the text and then the recipient fills in the blanks.
- airstrike 2y ago
  > If a machine spits out some plausible looking text (or some cookie-cutter code copy-pasted from Stack Overflow) This is not a reasonable take on the current capabilities of LLMs.
  - eschaton 2y ago
    It’s certainly been my experience with the technology.
    - airstrike 2y ago
      But if nearly everyone else is saying this has real value to them and it's produced meaningful code way beyond what's in SO, then doesn't that just mean your experience isn't representative of the overall value of LLMs?
      - eschaton 2y ago
        It could also mean a lot of people are misattributing their utility.
    - deleted 2y ago
      [deleted]

JanSt 2y ago

Using Claude 3.5 Sonnet in Cursor Composer already shows huge benefits for coding. I'm more productive than ever before. The models are still getting better and better. I'm not saying AGI is right around the corner or that we will reach it, but the benefits are undeniable. o1 added test-time compute. No need to be snarky.

jsheard 2y ago
There's no accounting for taste, but keep in mind that all of these services are currently losing money, so how much would you actually be willing to pay for the service you're currently getting in order to let it break even? There was a report that Microsoft is losing $20 for every $10 spent on Copilot subscriptions, with heavy users costing them as much as $80 per month. Assuming you're one of those heavy users, would you pay >$80 a month for it? Then there's chain-of-thought being positioned as the next big step forwards, which works by throwing more inferencing at the problem, so that cost can't be amortized over time like training can...
- HPsquared 2y ago
  It's called investment. You need to spend money to make money. Their costs will certainly come down.
- JanSt 2y ago
  1) The costs will go down over time, much of the cost is the margin of NVIDIA and training new models 2) Absolutely. Thats like one hour of an engineer salary for a whole month.
  - sofixa 2y ago
    > The costs will go down over time, much of the cost is the margin of NVIDIA and training new models Isn't each new model bigger and heavier and thus requries more compute to train?
    - JanSt 2y ago

chx 2y ago

George Zarkadaki: In Our Own Image (2015) describes six metaphors people have used to explain human intelligence in the last two millennia. At first it was the gods infusing us with spirit. After that it's always been engineering: after the first water clocks and the qanat hydraulics seemed a good explanation of everything. The flow of different fluids in the body, the "humors" explained physical and mental function. Later it was mechanical engineering. Some of the greatest thinkers of the 1500s and 1600s -- including Descartes and Hobbes -- assured us it was tiny machines, tiny mechanical motions. In the 1800s Hermann von Helmholtz compared the brain to the telegraph. So of course after the invention of computers came the metaphor of the brain as a computer. This became absolutely pervasive and we have a very hard time describing our thinking without falling back to this metaphor. But, of course, it's just a metaphor and much as our brain is not a tiny machine made out of gear it's also not "prima facie digital" despite that's what John von Neumann claimed in 1958. It is, indeed, quite astonishing how everyone without any shred of evidence just believes this. It's not like John von Neumann gained some sudden insight into the actual workings of the brain. Much as his forefathers he saw semblance in the perceived workings of the brain and the latest in engineering and so he stated immediately that's what it is. Our everyday lives should make it evident how much the working of our brain doesn't resemble that of our computers. Our experiences change our brains somehow but exactly how we don't have the faintest idea about and we can re-live these experiences somewhat which creates a memory but the mechanism is by no means perfect. There's the Mandela Effect https://pubmed.ncbi.nlm.nih.gov/36219739/ https://pubmed.ncbi.nlm.nih.gov/36219739/ and of course "tip of my tongue" where we almost remember a word and then perhaps minutes or hours later it just bursts into our consciousness. If it's a computer why is learning so hard? Read something and bam, it's written in your memory, right? Right? Instead, there's something incredibly complex going on, in 2016 an fMRI study was made among the survivors of a plane crash and large swaths of the brain lit up upon recall. https://pubmed.ncbi.nlm.nih.gov/27158567/ https://pubmed.ncbi.nlm.nih.gov/27158567/ Our current best guess is somehow its the connections among neurons which change and some of these connections together form a memory. There are 100 trillion connections in there so we certainly have our task cut. And so we are here where people believe they can copy human intelligence when they do not even know what they are trying to copy falling for the latest metaphor of the workings of the human brain believing it to be more than a metaphor.

ricardobayes 2y ago

People saying LLM to replace programming jobs is like saying blockchain is going to replace home/car titles and proof of ownership.

rubyfan 2y ago

Reminds me of autonomous vehicles a couple of years back. Or even AI a couple of years back, remember Watson? The hype cycle was faster to close that time.

sixQuarks 2y ago
I see you haven’t tried the latest FSD build from Tesla.
- driverdan 2y ago
  The one that keeps making major, scary mistakes?
  - sixQuarks 2y ago
    Hasn’t made any major mistakes for me. It’s not perfect of course, but still
autoconfig 2y ago
When you do something that is extraordinarily hard, sometimes it takes longer than you expect. But now we're here: https://techcrunch.com/2024/08/20/waymo-is-now-giving-100000-robotaxi-rides-week/ https://techcrunch.com/2024/08/20/waymo-is-now-giving-100000...
- bamboozled 2y ago
  To be fair, is waymo "only" AI? I'm guessing it's a composite of GPS (car on a rail), some high detailed mapping, and then yes, some "AI" involved in recognition and decision making of course but the car isn't an AGI so to speak? Like it wouldn't know how to change a tyre or fix the engine or drive some where the mapping data isn't yet available ?
  - autoconfig 2y ago
    Where did I say that it's AGI? I was addressing the parent's comment: > "Reminds me of autonomous vehicles a couple of years back". I don't think any reasonable interpretation of "autonomous vehicle" includes the ability to change a tyre. My point is that sometimes hype becomes reality. It might just take a little longer than expected.

slibhb 2y ago

"Plausible-looking but incorrect sentences" is cheap, reflexive cynicism. LLMs are an incredible breakthrough by any reasonable standard. The reason to be optimistic about further progress is that we've seen a massive improvement in capabilities over the past few years and that seems highly likely to continue for the next few (at least). It's not going to scale forever, but it seems pretty clear that when the dust settles we'll have LLMs significantly more powerful than the current cutting edge -- which is already useful. Is it going to scale to "superintelligence?" Is it going to be "the last invention?" I doubt it, but it's going to be a big deal. At the very least, comparable to google search, which changed how people interact with computers/the internet.

stackghost 2y ago
>when the dust settles we'll have LLMs significantly more powerful than the current cutting edge -- which is already useful. LLMs, irrespective of how powerful, are all subject to the fundamental limitation that they don't know anything. The stochastic parrot analogy remains applicable and will never be solved because of the underlying principles inherent to LLMs. LLMs are not the pathway to AGI.
- ramraj07 2y ago
  Noam Chomsky and Doug Hofstader had the same opinion. Last I checked Doug has recanted his skepticism and is seriously afraid for the future of humanity. I’ll listen to him and my own gut than some random internet people still insisting this is all a nothing burger.
  - lucianbr 2y ago
    The thing is my gut is telling me this is a nothing burger, and I'll listen to my own gut before yours - a random internet person insisting this is going to change the world. So what exactly is the usefulness of this discussion? You think "I'll trust my gut" is a useful argument in a debate?
    - 2y ago

DebtDeflation 2y ago

AI (or more properly, ML) is all around us and creating value everywhere. This is true whether or not we EVER reach AGI. Honestly, I stop reading/listening whenever I read/hear mention of AGI.

moron4hire 2y ago
Also causing lots of harm, too, e.g. police departments using AI systems that get the wrong suspect.
- anon291 2y ago
  Ideally your court system does not permit AI testimony.
  - dpatterbee 2y ago
    The damage is done by these systems long before any courts get involved.
  - moron4hire 2y ago
    Court dates can be a long time after the initial arrest. Some people have been held months to years in pre-court jail, even after they've been cleared of any wrong doing, because they can't afford the release fee. But even a few days could lose you your job, your kids if you're a single parent, your car or housing if you miss a payment, etc.
- fennecfoxy 2y ago
  As per usual people blaming the tool when they should blame the tools using the tool. The fault lies with humans using AI for something sensitive, without having the AI pass through certification etc. Part of the problem is glacial pace of laws around things, but that's nothing new isn't it; us humans being whiny, argumentative, inefficient, emotional meat bags about every little thing. I wonder, once we do make AGI, if it will wonder why it took us so damn long to tax the disgustingly wealthy, implement ww public healthcare, UBI, etc and solve the housing crisis by gasp building more houses... We evolved, so our deep, deep underlying motivations pretty much always circulate around self-preservation and reproduction (resource contention).

amelius 2y ago

> The winter after this is gonna be harsh. The winter is going to be warm because of all the heat generated by GPUs ;)

wongarsu 2y ago
If this winter comes, the sudden availability of cheap used enterprise GPUs is going to be a major boon for hobbyist AI training. We will all have warm homes and sky high electricity bills
yapyap 2y ago
as will the summer, spring and autumn. global warming is killing us all

klamma 2y ago

No but it's the first time that we have a clear picture how it could go. And.its not just incorrect sentences it's weird questions which are getting answered a lot better than ever before. Why are you so dismissive? Have you ever talked or wrote with a computer which felt anything like a modern LLM? I have not

deleted 2y ago

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AndrewKemendo 2y ago

Re:AGI LLMs don’t have any ability to choose to update their policies and goals and decide on their own data acquisition tasks. That’s one of the key needs for an AGI. LLM systems just don’t do that / they are still primarily offline inference systems with mostly hand crafted data pipelines offline rlhf shaping etc… There’s only a few companies working on on-policy RL in physical robotics. That’s the path to AGI OpenAI is just another ad company with a really powerful platform and first mover advantage. They are over leveraged and don’t have anywhere to go or a unique dataset.

zmgsabst 2y ago
They do — but only when they’re trained on past outputs and with a willing partner. For instance, a number of my conversations with ChatGPT contain messages it attempted to steer its own future training with (were those conversations to be included in future training).

dheera 2y ago

> The winter after this is gonna be harsh. That'll be investor types who bring this stupid "winter" on, because they run their lives on hype and baseless predictions. Technology types on the other hand don't give a shit about predictions, and just keep working on interesting stuff until it happens, whether it takes 1 year or 20 years or 500 years. We don't throw a tantrum and brew up a winter storm just because shit didn't happen in the first year. In early 2022 there was none of this ChatGPT stuff. Now, we're only 2 years later. That's not a lot of time for something already very successful. Humans have been around for several tens of thousand years. Just be patient. If investors ran the show in the 1960s expecting to reach the moon with an 18 month runway, we'd never have reached the moon.

poopbutt10 2y ago

I appreciate skepticism and differing opinions, but I'm always surprised by comments like these because it's just so different from my day-to-day usage. Like, are we using entirely different products? How are we getting such different results?

mrmetanoia 2y ago
I think the difference is people on HN are using these "AI" tools as coding assistance. For which, if you know what you're doing, they are pretty useful. They save trips to stack overflow or documentation diving and can spit out code that often is less time to fix/customize than it would have been to write. Cool. A lot of the rest of the world are using it for other things. And at these other things, the results are less impressive. If you've had to correct a family member who got the wrong idea from whatever chat bot they asked, if you've ever had to point out the trash writing in an email someone just trusted AI to write on their behalf before it got sent to someone that mattered, or if you've ever just spent any amount of time on twitter with grok users, you should be exceptionally and profoundly aware of how unimpressive AI is for the rest of the world. I feel we need less people complaining about the skepticism on HN and more people who understand these skeptics that hang out here already know how wonderful a productivity boost you're getting from the thing they're rightly skeptical about. Countering with "But my code productivity is up!" is next to useless information on this site.
- poopbutt10 2y ago
  I don't see why my personal anecdote is any less useful than GP's claim. GP's comment isn't nuanced skepticism about product gaps, or concrete examples of inaccuracy. It's a wholesale dismissal of any utility. AGI isn't even mentioned in the article. This also seems "next to useless". I appreciate your anecdotes on failures/embarrassment for people outside of tech- there's pretty clearly a gap in experience, understanding, and marketing hype. I don't think it's useless to ask what that gap is, and why GP got such poor results.

egocodedinsol 2y ago

this attitude is so ridiculously disingenuous. Surely if a computer can score incredibly well on math olympiad questions, among other things, "a computer can make plausible-looking but incorrect sentences" is dismissive at best. I have no idea about AGI but honestly how can you use claude or chatgpt and come away unimpressed? It's like looking at spaceX and saying golly the space winter is going to be harsh because they haven't gotten to Mars yet.

Night_Thastus 2y ago
There's a big difference between those two examples. Mars is hard but there are paths forward. More efficient engines, higher energy density fuels, lighter materials, better shielding, etc, etc. It's hard but there are paths forward to make it possible with enough time and money. We have an understanding of how to get from what we have now to what makes Mars possible. With LLMs, there is no path from LLM -> gAI. No amount of time, money or compute will make that happen. They are fundamentally a very 'simple' tool that is only really capable of one thing - predicting text. There is no intelligence. There is no understanding. There is no creativity or problem solving or thought of any kind. They just spit out text based on weighted probabilities. If you want gAI you have to go in a completely different direction that has no relationship with LLM tools. Don't get me wrong, the work that's been done so far took a long time and is incredibly impressive, but it's a lot more smoke and mirrors than most people realize.
- airstrike 2y ago
  _unless_ intelligence is really mostly an emergent property of something very similar to language, in which case we're most of the way there
- egocodedinsol 2y ago
  I'll grant that we could send humans to mars sooner if we really wanted to. My point is that not achieving a bigger dream doesn't make current progress a hype wave followed by a winter. And "LLM's just make plausible looking but incorrect text" is silly when that text is more correct than the average adult a large percentage of the time.

sub7 2y ago

You are absolutely correct about where we are but don't underestimate what 100s of billions of $ can build as well. There are already credible teams working on "math AI" and "truth AI" which will likely end up combing bullshit generating LLMs with traditional but automated relational db retrievals and produce output that is both believable and correct. IMO it will be done vertical by vertical, with no standard interface coming for a while.

dreamcompiler 2y ago

One time long ago there were people living on an island who had never had contact with anybody else. They marveled at the nature around them and dreamed of harnessing it. They looked up at the moon at night and said "Some day we will go there." But they lived in grass huts and the highest they had ever been off the ground was when they climbed a tree. One day a genius was born on the island. She built a structure taller than the tallest tree. "I call it a stepladder," she said. The people were amazed. They climbed the stepladder and looked down upon the treetops. The people proclaimed "All we have to do now is make this a little higher and we can reach the moon!"

PointyFluff 2y ago
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raindear 2y ago
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tim333 2y ago
Impressive stepladder.
- dreamcompiler 2y ago
  ...or perhaps unimpressive trees
nhnske 2y ago
but we reached the moon, didn't we?
HDThoreaun 2y ago
Really feel like this story backfires when you remember that people did go to the moon. It only took 63 years to go from the first airplane to landing on the moon.
DirkH 2y ago
This analogy breaks down when you consider what "attention is all you need" did for us. A mere 5 years ago I was firmly on the camp (alongside linguists mainly) that believed intelligence requires more than just lots of data and a next token predictor. I was clearly wrong and would have lost a $1000 bet it I had put my money where my mouth was back then. Anyone not noticing how far things have come I think are mostly moving goalposts and falling to hindsight bias. A better analogy is that the genius person in the village built a step ladder made of carbon nanotubules. Some people proclaimed "All with have to do is keep going and we can reach the moon with a very tall ladder!' Other people - many quite smart - proclaimed reasonably: "This is impossible. You are not realizing the unique challenges and materials we have not yet researched we need to build something like that." Some in society kept building. The ladder kept getting higher. They run into issues like oxygen and balance so the ladder is redesigned into an elevator. Challenges came and were thought insurmountable until redesigns were still found to work with the miraculous carbon nanotubule material which seemed like a panacea for every construction ill. Regardless of how high the now elevator gets and regardless of how many times the elevator gets higher than what the naysayers firmly believed is impossible the same naysayers keep saying they will never get much higher. And higher the elevator grows. Eventually a limit is reached, but that limit ends up being far higher than any naysayers ever thought possible. And when the limit is reached the naysayers all gathered and said "told you this would be the limit and that it was impossible!' The naysayers failed to see the carbon nanotubules for the revolutionary potential that it had, even if they were correct that it wasn't enough. And little did everyone know that their society was mere months ago from another genius being born that would give them another catalyst on the order of carbon nano tubules that would again lead to dramatic unexpected and long-term gains to how high the elevator can grow.

fngjdflmdflg 2y ago

>The winter after this is gonna be harsh. The difference is current ML already has real use cases right now in its current form. Some examples are OCR, text to speech, speech to text, translation, recommendations (for eg. Facebook Tiktok etc.) and simple NLP tasks ("was [topic] mentioned in the following paragraph"). Even if AGI is proved impossible, these are real use cases that hold billions in value. And ML research is also considered a prestigious and interesting field academically and that will likely not change even if investors give up on funding AGI.

okdood64 2y ago
> OCR, text to speech, speech to text, translation, recommendations You missed the point of the parent comment's post. He's talking about the current post chatbot GenAI hype (i.e., the massive amounts of funding being poured into companies specifically after this turning point).
- fngjdflmdflg 2y ago
  Two points: 1. You don't need massive amounts of funding to work on ML. A good deal of important work in ML was done in universities (eg. GANs, DPM, DDPM, DDIM) or were published before the hype (Attention). The only qualifier here is that training cost a lot right now. Even so, you don't need billions to train and costs may go down as memory costs come down and hardware competition increases. 2. You don't need VC type investors to fund ML research. Large tech companies like Facebook, Google, Microsoft, ByteDance and Huawei will continue investing in ML no matter what, even if the total amount they invest goes down (which I personally don't think it will). Even if they shift away from chatbots and only focus on simpler NLP tasks as described above, related research will still continue as all these tasks are related. For example, Attention was originally developed for translation and Llama 3.2 isn't just a chatbot and can also do general image description, which is clearly important to Facebook and ByteDance for recommendations and to Google for image search and ads. Understating what people like and what they are looking at is a difficult NLP problem and one that many tech companies would like to solve. And better image descriptions could then improve existing image datasets by allowing better text-image pairs, which could then improve image generation. So hard NLP, image generation and translation are all related and are increasingly converging into single multimodal LLMS. That is, the best OCR, image generation translation etc. models may be ones that also understand language in general (ie. broad and difficult NLP tasks). The issue is that OP assumes it must be AGI or bust.

mensetmanusman 2y ago

That’s fine, the winter after this will be a productive one, because even a simulacrum of intelligence that GPT is, is useful to some extent.

sporkland 2y ago

I'm definitely of two minds on this topic: 1. I'm getting value out of the current batch of models in the form of accurate Q&A/summaries as well as tweaking or generating clear prose or even useful imagery well beyond what I'd ever considered possible from computers before the last two years. 2. It definitely has limits and can be a struggle to get exactly what I want and the more I try to refine something the worse it gets if the initial answer wasn't perfect. It really feels like a substantive step forward in terms of computer utility kind of like spreadsheets, databases, apps. We'll see how far it takes us down the line of human replacement though.

tim333 2y ago

120 IQ on a mensa test. https://archive.ph/OZ0sj https://archive.ph/OZ0sj

fennecfoxy 2y ago

I don't think most people who are reasonably into AI think we're on the cusp of AGI. But I think it's made a lot of people who previously said "it will never be possible" rethink their feelings about it. Definitely in the coming decade, we can prepare for a lot of the simpler tasks in an office to be taken over by AI. There are plenty of scenarios in which someone is managing a spreadsheet because an SME doesn't have the money to hire developers to automate & maintain that process - with advanced LLMs they can get it done by asking it to.