12 ms·

I don't think the "hype" is built on test scores. It is built on the observation how fast AI is getting better. If the speed of improvement stays anywhere near

by mg 3y ago

I don't think the "hype" is built on test scores. It is built on the observation how fast AI is getting better. If the speed of improvement stays anywhere near the level it was the last two years, then over the next two decades, it will lead to massive changes in how we work and which skills are valuable. Just two years ago, I was mesmerized by GPT-3's ability to understand concepts: https://twitter.com/marekgibney/status/1403414210642649092 https://twitter.com/marekgibney/status/1403414210642649092 Nowadays, using it daily in a productive fashion feels completely normal. Yesterday, I was annoyed with how cumbersome it is to play long mp3s on my iPad. I asked GPT-4 something like "Write an html page which lets me select an mp3, play it via play/pause buttons and offers me a field to enter a time to jump to". And the result was usable out of the box and is my default mp3 player now. Two years ago it didn't even dawn on me that this would be my way of writing software in the near future. I have been coding for over 20 years. But for little tools like this, it is faster to ask ChatGPT now. It's hard to imagine where we will be in 20 years.

kristopolous 3y ago
Almost nothing happened in AI for about 50 years. That's the normal in the field.
ulnarkressty 3y ago
Most of the improvements apparently come from training larger models with more data. Which is part of the problem mentioned in the article - the probability that the model just memorizes the answers to the tests is greatly increased. AI is getting subjectively better, and we need better tests to figure out if this improvement is objectively significant or not.
- Nevermark 3y ago
  That’s backwards. Training a model on more data improves generalization not memorization. To store more information in the same number of parameters requires the commonality between examples to be encoded. In contrast, the less data trained on, especially if repeated, lets the network learn to provide good answers for that limited set without generalizing. I.e. memorizing. —— It’s the same as with people. The more variations people see of something, the more likely they intuit the underlying pattern. The fewer examples, the more likely they just pattern match.
  - Jensson 3y ago
    > It’s the same as with people. The more variations people see of something, the more likely they intuit the underlying pattern. > The fewer examples, the more likely they just pattern match. A kid who uses a calculator and just fills in the answer to every question will see a lot more examples than a kid that learned by starting from simple concepts and understanding each step. But the kid who focused on learning concepts and saw way fewer problems will obviously have a better understanding here. So no, you are clearly wrong here, humans doesn't learn that way at all. These models learn that way, you are right on that, but humans don't.
    - Nevermark 3y ago
      I have no idea where your calculator came from. In neither case did I introduce one. And since the calculator itself has already a general understanding, it would seem completely counter productive to start training a computer or child by first giving them a machine that has already solved the problem. Also, for what it’s worth, I am speaking from many years experience not just training models but creating the algorithms that train them.
      - Jensson 3y ago
        Replace "uses calculator" to "looks through solved problems", same thing. Not sure what you don't understand. Humans don't build understanding by seeing a lot of solved examples. To make a human understand we need to explain how things work to them. You don't just show examples. A human who is just shown a lot of examples wont understand much at all, even if he tries to replicate them. > Also, for what it’s worth, I am speaking from many years experience not just training models but creating the algorithms that train them. What does this has to do with how humans learn?
        Nevermark 3y ago
        Humans learn vast amounts of information from examples. They learn their first words, how to walk, what a cat looks like from many perspectives, how to parse a visual scene, how to parse the spoken word, interpret facial expressions and body language, how different objects move, how different creatures behave, different materials feel, what things cause pain, what things taste like and how they make them feel, how to get what they want, how to climb, how not to fall, all by trial & example. On and on. And yes, as we get older we get better and better at learning 2nd hand from others verbally, and when people have the time to show us something, or with tools other people already invented. Like how a post-trained model picks up on something when we explain it via a prompt. But that is not the kind of training being done by models at this stage. And yet they are learning concepts (pre-prompt) that, as you point out, you & I had to have explained to us.
- danielmarkbruce 3y ago
  Do we? I use gpt-4 daily and it matters not to me what the source of the "intelligence" is. It's subjective what "intelligence" even means. It's subjective how the brain works. Almost by definition AI is "things that can't be objectively measured".
- qup 3y ago
  I pretty much want the LLM to be great at memorizing things. That's what I'm not great at. If it had perfect recall I would be so thrilled. And just because it's memorized the data--as all intelligences would need to do to spit data out--doesn't mean it can't still do useful operations on the data, or explain it in different words, or whatever a human might do with it.
- tuatoru 3y ago
  > Most of the improvements apparently come from training larger models with more data. OpenAI is reportedly losing 4 cents per query. With a thousandfold increase in model size, and assuming linear scale in cost, that's a problem. Training time is going to go up too. Moore's law isn't going to help any more. Algorithmic improvements may help...if any significant ones can be found.
COAGULOPATH 3y ago
What's the benefit of doing this vs copying one of the many (far superior) Javascript mp3 players on the internet, such as here? https://freefrontend.com/javascript-music-players/ https://freefrontend.com/javascript-music-players/
- roenxi 3y ago
  It'd be a bit faster to get up and running with ChatGPT. In the AI, you'd have to phrase the instruction & copy the output into a file. For search, you have to do both those things and learn a UI that wasn't built to taste.
mensetmanusman 3y ago
I used gpt4 to write a script that I can ssh from my iPhone to a m1 that downloads the mp3 from a yt url on my iPhone clipboard. The only thing I am missing is automating the sync button when the iPhone is on the same home wifi to add the mp3 to the music app.
gmerc 3y ago
This. We are in a Cambrian Explosion on the software side and hardware hasn’t yet reacted to it. There’s a few years of mad discovery in front of us. People have different impressions as to the shape of the curve that’s going up and right, but only a fool would not stop and carefully take what is happening.
- kossTKR 3y ago
  Exactly and things are actually getting crazy now. Pardon the tangent but for some reason this hasn't reached the frontpage on HN yet: https://github.com/OpenBMB/ChatDev https://github.com/OpenBMB/ChatDev Making your own "internal family system" of AI's is a making this exponential (and frightening), like an ensemble on top of the ensemble, with specific "mindsets", that with shared memory can build and do stuff continuously. Found this from a comp sci professor on Tiktok so be warned: https://www.tiktok.com/@lizthedeveloper/video/7283577382026464554 https://www.tiktok.com/@lizthedeveloper/video/72835773820264... I remember a couple of comments here on HN when the hype began about how some dude thought he had figured out how to actually make an AGI - can't find it now, but it was something about having multiple ai's with different personalities discoursing with a shared memory - and now it seems to be happening. This coupled with access to linux containers that can be spawned on demand, we are in for a wild ride!
  - ChatGTP 3y ago
    [flagged]
  - dartos 3y ago
    I saw chatdev on hn and have been pretty disappointed with it :( Haven’t had it make anything usable that’s more complicated than a mad lib yet
Jensson 3y ago
> Two years ago it didn't even dawn on me that this would be my way of writing software in the near future So you were ignorant two years ago, GitHub Copilot was already available to users back then. The only new big thing the past two years was GPT-4, and nothing suggest anything similar will come the next two years. There are no big new things on the horizon, we knew for quite a while that GPT-4 was coming, but there isn't anything like that this time.
- mg 3y ago
  Copilot was not around when I wrote the Tweet. But when Copilot came out, I was indeed ignorant! I remember when a friend showed it to me for the first time. I was like "Yeah, it outputs almost correct boilerplate code for you. But thankfully my coding is so that I don't have to write boilerplate". I didn't expect it to be able to write fully functional tools and understand them well enough to actually write pretty nice code! Regarding "there isn't anything like that this time." : Quite the opposite! We have not figured out where using larger models and throwing more data at them will level off! This could go on for quite a while. With FSD 12, Tesla is already testing self driving with a single large neural net, without any glue code. I am super curious how that will turn out. The whole thing is just starting.
  - Jensson 3y ago
    Well, my point is that you perceive progress to be fast since you went from not understanding what existed to later getting in on it. That doesn't mean progress was that fast, it means that you just discovered a new domain. Trying to extrapolate actual progress is bad in itself, but trying to extrapolate your perceived progress is even worse.
    - deleted 3y ago
      [deleted]
    - james-revisoai 3y ago
      Yeah you have hit the nail on the head here. A lot was predictable with seeing that GPT-2 could reasonably stay within language and generate early coherent structures, that coming at the same time as instructions with the T5 stuff and the widespread use of embeddings from BERT told us this direction was likely, it's just for many people this came to awareness in 2021/22 rather than the 2018-2020 ramp up the field/hobbyists experienced.
rob74 3y ago
The article doesn't say that LLMs aren't useful - the "hype" they mean is overestimating their capabilities. An LLM may be able to pass a "theory of mind" test, or it may fail spectacularly, depending on how you prompt it. And that's because, despite all of its training data, it's not capable of actually reasoning. That may change in the future, but we're not there yet, and (AFAIK) nobody can tell how long it will take to get there.
- dinosaurdynasty 3y ago
  LLMs are trained to predict text, and one of the results of this is the LLM has as many "faces" as exist in the training data, so it's going to be _very_ different depending on the prompt. It's not a consistent entity like a human. RLHF is an attempt to mediate this, but it doesn't work perfectly.
- CryptoBojack 3y ago
  Isn't that the same as for humans? If you are speaking with me (prompting), my answers will be differents, based on how you prompted me for an answer.
- jug 3y ago
  I’m often confused over claims on the reasoning capabilities. It is often mentioned in debates as a clear and undeniable issue with current LLM’s. So since this claim can be made, where are said tests about reasoning skills that GPT-4 fails? If it’s a debate on the illusion of reasoning, I’d be careful how I step here, because it’s been found these things probably work so well because the human brain is also a biological real-time prediction machine and “just” guessing too: https://www.scientificamerican.com/article/the-brain-guesses-what-word-comes-ne/ https://www.scientificamerican.com/article/the-brain-guesses...
- kromem 3y ago
  That's not the case. It's very much in the realm of "we don't know what's going on in the network." Rather than a binary it's much more likely that it's a mix of factors going into results that includes basic reasoning capabilities developed from the training data (much like board representations and state tracking abilities developed feeding board game moves into a toy model in Othello-GPT) as well as statistic driven autocomplete. In fact often when I've seen GPT-4 get hung up with logic puzzle variations such as transparency, it tends to seem more like the latter is overriding the former, and changing up tokens to emoji representations or having it always repeat adjectives attached to nouns so it preserves variation context gets it over the hump to reproducible solutions (as would be expected from a network capable of reasoning) but by default it falls into the pattern of the normative cases. For something as complex as SotA neural networks, binary sweeping statements seem rather unlikely to actually be representative...
deleted 3y ago
[deleted]
james-revisoai 3y ago
A lot of the progress in the last 3-4 years was predictable from GPT-2 and especially GPT-3 onwards - combining instruction following and reinforcement learning with scaling GPT. With research being more closed, this isn't so true anymore. The mp3 case was predictable in 2020 - some early twitter GIFs showed vaguely similar stuff. Can you predict what will happen in 2026/7 though, with multimodal tech? I simply don't see it a being the same today. The obvious element of scaling or techniques that imply a useful overlap isn't there. Whereas before researchers brought together excellent and groundbreaking performance on different benchmarks and areas together as they worked on GPT-3, since 2020, except instruction following, less has been predictable. Multi modal could change everything (things like the ScienceQA paper suggest so), but also, it might not shift benchmarks. It's just not so clear that the future is as predictable or will be faster than the last few years. I do have my own beliefs similar to Yann Lecun about what architecture or rather infrastructure makes most sense intuitively going forward, and there's not really the openness we used to have from top labs to know if they are going these ways, or not. So you are absolutely right that it's hard to imagine where we will be in 20 years, but in a strange way, because it is much less clear than in 2020 where we will be in 3 years time onwards, I would say it is much less guaranteed progress than it is felt by many...
- huijzer 3y ago
  I was also thinking about how quickly AI may progress and am curious for your or other people's thoughts. When estimating AI progress, estimating orders of magnitude sounds like the most plausible way to do it, just like Moore's law has guessed the magnitude correctly for years. For AI, it is known that performance increases linearly when the model size increases exponentially. Funding currently increases exponentially meaning that performance will increase linearly. So, AI will increase linearly as long as the funding does too. On top of this, algorithms may be made more efficient, which may occasionally make an order of magnitude improvement. Does this reasoning make sense? I think it does but I could be completely wrong.
denton-scratch 3y ago
> I was mesmerized by GPT-3's ability to understand concepts This language embodies the anthropomorphic assumptions that the author is attacking.
- dboreham 3y ago
  Or the corollary: that there's really no such thing as anthropomorphic. There's inputs and outputs, and an observer's opinion on how well the outputs relate to the inputs. Thing producing the outputs, and the observer, can be human or not human. Same difference.
  - freejazz 3y ago
    Can AI people stop with the defense of "what if thing really is not a thing?" "what if thing is really what humans do?" These aren't answers to questions. Its deflecting nonsense posed as philosophical thought.
  - smcin 3y ago
    It absolutely is anthropomorphizing to claim "GPT-3's ability to understand concepts" rather than simply calling it "reproduce, mix and match text from an enormous corpus". And we can totally legitimately compare to a jury of human observers' opinions on how well(/badly) the output generated relates to the inputs. For the specific example the OP cited "War: like being eaten by a dragon and then having it spit you out" then unless its answer to "Where were you in between being eaten by a dragon and before it spat you out?" is "in the dragon's digestive system" that isn't understanding. And I'm curious to see it answer "Dragons only exist in mythology; does your analogy mean war doesn't exist either? Why not compare to an animal that exists?"
    - YetAnotherNick 3y ago
      Anyone who have used GPT-4 for any amount of time knows it is definitely not "reproduce, mix and match text from an enormous corpus". Also what's the point of the question. GPT has been specifically trained to not answer this. But in any case here is the answer with instruct model which generally is more open ended: Sure, I can compare it to a real animal such as bears. Just like dragons are depicted as fearsome and destructive creatures in mythology, bears are also portrayed as powerful and dangerous animals in many cultures. However, just as the existence of dragons is a matter of fiction, the concept of a "war on bears" would also be nonsensical. In reality, the issue of war is complex and multifaceted, involving political, economic, and societal factors. It cannot be reduced to a simplistic battle between humans and a particular animal species. Furthermore, the use of animals in analogies can be problematic as it may trivialize the gravity and complexity of real-world issues. Wars and conflicts have serious consequences for human lives, whereas animals do not possess the same agency or ability to wage war. In conclusion, while dragons may serve as a metaphor for war in mythology, comparing war to a fictional creature diminishes the seriousness of the topic and fails to capture the complexities of real conflicts.
happycube 3y ago
I got curious and did this myself. Needed a bit of nudging to get where I wanted, but I even had it make an Electron wrapper: https://chat.openai.com/share/29d695e6-7f23-4f03-b2be-29b7c947bc92 https://chat.openai.com/share/29d695e6-7f23-4f03-b2be-29b7c9...
- dchuk 3y ago
  This is awesome, thanks for sharing. Do you (or anyone) know of any products that allow for iterating on the generated output through further chatting with the ai? What I mean, is that each subsequent prompt here either generated a new whole output, or new chunks to add to the output. Ideally, whether generating code or prose, I’d want to keep prompting about the generated output and the AI further modifies the existing output until it’s refined to the degree I want it. Or is that effectively what Copilot/cursor do and I’m just a bad operator?
  - happycube 3y ago
    No problem, it was a fun morning exercise for me :) Copilot, at least from what little I did in vscode, isn't as powerful as this. I think there's a GPT4 mode for it that I haven't played with that'd be a lot closer to this.
  - robertlagrant 3y ago
    > Do you (or anyone) know of any products that allow for iterating on the generated output through further chatting with the ai? What I mean, is that each subsequent prompt here either generated a new whole output, or new chunks to add to the output. Ideally, whether generating code or prose, I’d want to keep prompting about the generated output and the AI further modifies the existing output until it’s refined to the degree I want it. ChatGPT does this.
adrians1 3y ago
> If the speed of improvement stays anywhere near the level it was the last two years, then over the next two decades, it will lead to massive changes in how we work and which skills are valuable. That's a big assumption to make. You can't assume that the rate of improvement will stay the same, especially over a period of 2 decades, which is a very long time. Every advance in technology hits diminishing returns at some point.
- mg 3y ago
  Why do you think so? Technological progress seems rather accelerated than diminishing to me. Computers are a great example: They have been getting more capable exponentially over the last decades. In terms of performance (memory, speed, bandwidth) and in terms of impact. First we had calculators, then we had desktop applications, then the Internet and now we have AI. And AI will help us get to the next stage even faster.
  - hashtag-til 3y ago
    I’m not putting my coins on this advances. More likely this will become the new “search” technology and get polluted with ads. People will lose trust and it will decay.
    - tuatoru 3y ago
      That is certainly where the economic incentives appear to be.
RC_ITR 3y ago
You can check my post history to see how unpopular this point of view is, but the big "reveal" that will come up is as follows: The way that LLMs and humans "think" is inherently different. Giving an LLM a test designed for humans is akin to giving a camera a 'drawing test.' A camera can make a better narrow final output than a human, but it cannot do the subordinate tasks that a human illustrator could, like changing shadings, line width, etc. An LLM can answer really well on tests, but it often fails at subordinate tasks like 'applying symbolic reasoning to unfamiliar situations.' Eventually the thinking styles may converge in a way that makes the LLMs practically more capable than humans on those subordinate tasks, but we are not there yet.

by mg 3y ago

kristopolous 3y ago

Almost nothing happened in AI for about 50 years. That's the normal in the field.

ulnarkressty 3y ago

Most of the improvements apparently come from training larger models with more data. Which is part of the problem mentioned in the article - the probability that the model just memorizes the answers to the tests is greatly increased. AI is getting subjectively better, and we need better tests to figure out if this improvement is objectively significant or not.

Nevermark 3y ago
That’s backwards. Training a model on more data improves generalization not memorization. To store more information in the same number of parameters requires the commonality between examples to be encoded. In contrast, the less data trained on, especially if repeated, lets the network learn to provide good answers for that limited set without generalizing. I.e. memorizing. —— It’s the same as with people. The more variations people see of something, the more likely they intuit the underlying pattern. The fewer examples, the more likely they just pattern match.
- Jensson 3y ago
  > It’s the same as with people. The more variations people see of something, the more likely they intuit the underlying pattern. > The fewer examples, the more likely they just pattern match. A kid who uses a calculator and just fills in the answer to every question will see a lot more examples than a kid that learned by starting from simple concepts and understanding each step. But the kid who focused on learning concepts and saw way fewer problems will obviously have a better understanding here. So no, you are clearly wrong here, humans doesn't learn that way at all. These models learn that way, you are right on that, but humans don't.
  - Nevermark 3y ago
    I have no idea where your calculator came from. In neither case did I introduce one. And since the calculator itself has already a general understanding, it would seem completely counter productive to start training a computer or child by first giving them a machine that has already solved the problem. Also, for what it’s worth, I am speaking from many years experience not just training models but creating the algorithms that train them.
    - Jensson 3y ago
      Replace "uses calculator" to "looks through solved problems", same thing. Not sure what you don't understand. Humans don't build understanding by seeing a lot of solved examples. To make a human understand we need to explain how things work to them. You don't just show examples. A human who is just shown a lot of examples wont understand much at all, even if he tries to replicate them. > Also, for what it’s worth, I am speaking from many years experience not just training models but creating the algorithms that train them. What does this has to do with how humans learn?
      - Nevermark 3y ago
        Humans learn vast amounts of information from examples. They learn their first words, how to walk, what a cat looks like from many perspectives, how to parse a visual scene, how to parse the spoken word, interpret facial expressions and body language, how different objects move, how different creatures behave, different materials feel, what things cause pain, what things taste like and how they make them feel, how to get what they want, how to climb, how not to fall, all by trial & example. On and on. And yes, as we get older we get better and better at learning 2nd hand from others verbally, and when people have the time to show us something, or with tools other people already invented. Like how a post-trained model picks up on something when we explain it via a prompt. But that is not the kind of training being done by models at this stage. And yet they are learning concepts (pre-prompt) that, as you point out, you & I had to have explained to us.
danielmarkbruce 3y ago
Do we? I use gpt-4 daily and it matters not to me what the source of the "intelligence" is. It's subjective what "intelligence" even means. It's subjective how the brain works. Almost by definition AI is "things that can't be objectively measured".
qup 3y ago
I pretty much want the LLM to be great at memorizing things. That's what I'm not great at. If it had perfect recall I would be so thrilled. And just because it's memorized the data--as all intelligences would need to do to spit data out--doesn't mean it can't still do useful operations on the data, or explain it in different words, or whatever a human might do with it.
tuatoru 3y ago
> Most of the improvements apparently come from training larger models with more data. OpenAI is reportedly losing 4 cents per query. With a thousandfold increase in model size, and assuming linear scale in cost, that's a problem. Training time is going to go up too. Moore's law isn't going to help any more. Algorithmic improvements may help...if any significant ones can be found.

COAGULOPATH 3y ago

What's the benefit of doing this vs copying one of the many (far superior) Javascript mp3 players on the internet, such as here? https://freefrontend.com/javascript-music-players/ https://freefrontend.com/javascript-music-players/

roenxi 3y ago
It'd be a bit faster to get up and running with ChatGPT. In the AI, you'd have to phrase the instruction & copy the output into a file. For search, you have to do both those things and learn a UI that wasn't built to taste.

mensetmanusman 3y ago

I used gpt4 to write a script that I can ssh from my iPhone to a m1 that downloads the mp3 from a yt url on my iPhone clipboard. The only thing I am missing is automating the sync button when the iPhone is on the same home wifi to add the mp3 to the music app.

gmerc 3y ago

This. We are in a Cambrian Explosion on the software side and hardware hasn’t yet reacted to it. There’s a few years of mad discovery in front of us. People have different impressions as to the shape of the curve that’s going up and right, but only a fool would not stop and carefully take what is happening.

kossTKR 3y ago
Exactly and things are actually getting crazy now. Pardon the tangent but for some reason this hasn't reached the frontpage on HN yet: https://github.com/OpenBMB/ChatDev https://github.com/OpenBMB/ChatDev Making your own "internal family system" of AI's is a making this exponential (and frightening), like an ensemble on top of the ensemble, with specific "mindsets", that with shared memory can build and do stuff continuously. Found this from a comp sci professor on Tiktok so be warned: https://www.tiktok.com/@lizthedeveloper/video/7283577382026464554 https://www.tiktok.com/@lizthedeveloper/video/72835773820264... I remember a couple of comments here on HN when the hype began about how some dude thought he had figured out how to actually make an AGI - can't find it now, but it was something about having multiple ai's with different personalities discoursing with a shared memory - and now it seems to be happening. This coupled with access to linux containers that can be spawned on demand, we are in for a wild ride!
- ChatGTP 3y ago
  [flagged]
- dartos 3y ago
  I saw chatdev on hn and have been pretty disappointed with it :( Haven’t had it make anything usable that’s more complicated than a mad lib yet

Jensson 3y ago

> Two years ago it didn't even dawn on me that this would be my way of writing software in the near future So you were ignorant two years ago, GitHub Copilot was already available to users back then. The only new big thing the past two years was GPT-4, and nothing suggest anything similar will come the next two years. There are no big new things on the horizon, we knew for quite a while that GPT-4 was coming, but there isn't anything like that this time.

mg 3y ago
Copilot was not around when I wrote the Tweet. But when Copilot came out, I was indeed ignorant! I remember when a friend showed it to me for the first time. I was like "Yeah, it outputs almost correct boilerplate code for you. But thankfully my coding is so that I don't have to write boilerplate". I didn't expect it to be able to write fully functional tools and understand them well enough to actually write pretty nice code! Regarding "there isn't anything like that this time." : Quite the opposite! We have not figured out where using larger models and throwing more data at them will level off! This could go on for quite a while. With FSD 12, Tesla is already testing self driving with a single large neural net, without any glue code. I am super curious how that will turn out. The whole thing is just starting.
- Jensson 3y ago
  Well, my point is that you perceive progress to be fast since you went from not understanding what existed to later getting in on it. That doesn't mean progress was that fast, it means that you just discovered a new domain. Trying to extrapolate actual progress is bad in itself, but trying to extrapolate your perceived progress is even worse.
  - deleted 3y ago
    [deleted]
  - james-revisoai 3y ago
    Yeah you have hit the nail on the head here. A lot was predictable with seeing that GPT-2 could reasonably stay within language and generate early coherent structures, that coming at the same time as instructions with the T5 stuff and the widespread use of embeddings from BERT told us this direction was likely, it's just for many people this came to awareness in 2021/22 rather than the 2018-2020 ramp up the field/hobbyists experienced.

rob74 3y ago

The article doesn't say that LLMs aren't useful - the "hype" they mean is overestimating their capabilities. An LLM may be able to pass a "theory of mind" test, or it may fail spectacularly, depending on how you prompt it. And that's because, despite all of its training data, it's not capable of actually reasoning. That may change in the future, but we're not there yet, and (AFAIK) nobody can tell how long it will take to get there.

dinosaurdynasty 3y ago
LLMs are trained to predict text, and one of the results of this is the LLM has as many "faces" as exist in the training data, so it's going to be _very_ different depending on the prompt. It's not a consistent entity like a human. RLHF is an attempt to mediate this, but it doesn't work perfectly.
CryptoBojack 3y ago
Isn't that the same as for humans? If you are speaking with me (prompting), my answers will be differents, based on how you prompted me for an answer.
jug 3y ago
I’m often confused over claims on the reasoning capabilities. It is often mentioned in debates as a clear and undeniable issue with current LLM’s. So since this claim can be made, where are said tests about reasoning skills that GPT-4 fails? If it’s a debate on the illusion of reasoning, I’d be careful how I step here, because it’s been found these things probably work so well because the human brain is also a biological real-time prediction machine and “just” guessing too: https://www.scientificamerican.com/article/the-brain-guesses-what-word-comes-ne/ https://www.scientificamerican.com/article/the-brain-guesses...
kromem 3y ago
That's not the case. It's very much in the realm of "we don't know what's going on in the network." Rather than a binary it's much more likely that it's a mix of factors going into results that includes basic reasoning capabilities developed from the training data (much like board representations and state tracking abilities developed feeding board game moves into a toy model in Othello-GPT) as well as statistic driven autocomplete. In fact often when I've seen GPT-4 get hung up with logic puzzle variations such as transparency, it tends to seem more like the latter is overriding the former, and changing up tokens to emoji representations or having it always repeat adjectives attached to nouns so it preserves variation context gets it over the hump to reproducible solutions (as would be expected from a network capable of reasoning) but by default it falls into the pattern of the normative cases. For something as complex as SotA neural networks, binary sweeping statements seem rather unlikely to actually be representative...

deleted 3y ago

[deleted]

james-revisoai 3y ago

A lot of the progress in the last 3-4 years was predictable from GPT-2 and especially GPT-3 onwards - combining instruction following and reinforcement learning with scaling GPT. With research being more closed, this isn't so true anymore. The mp3 case was predictable in 2020 - some early twitter GIFs showed vaguely similar stuff. Can you predict what will happen in 2026/7 though, with multimodal tech? I simply don't see it a being the same today. The obvious element of scaling or techniques that imply a useful overlap isn't there. Whereas before researchers brought together excellent and groundbreaking performance on different benchmarks and areas together as they worked on GPT-3, since 2020, except instruction following, less has been predictable. Multi modal could change everything (things like the ScienceQA paper suggest so), but also, it might not shift benchmarks. It's just not so clear that the future is as predictable or will be faster than the last few years. I do have my own beliefs similar to Yann Lecun about what architecture or rather infrastructure makes most sense intuitively going forward, and there's not really the openness we used to have from top labs to know if they are going these ways, or not. So you are absolutely right that it's hard to imagine where we will be in 20 years, but in a strange way, because it is much less clear than in 2020 where we will be in 3 years time onwards, I would say it is much less guaranteed progress than it is felt by many...

huijzer 3y ago
I was also thinking about how quickly AI may progress and am curious for your or other people's thoughts. When estimating AI progress, estimating orders of magnitude sounds like the most plausible way to do it, just like Moore's law has guessed the magnitude correctly for years. For AI, it is known that performance increases linearly when the model size increases exponentially. Funding currently increases exponentially meaning that performance will increase linearly. So, AI will increase linearly as long as the funding does too. On top of this, algorithms may be made more efficient, which may occasionally make an order of magnitude improvement. Does this reasoning make sense? I think it does but I could be completely wrong.

denton-scratch 3y ago

> I was mesmerized by GPT-3's ability to understand concepts This language embodies the anthropomorphic assumptions that the author is attacking.

dboreham 3y ago
Or the corollary: that there's really no such thing as anthropomorphic. There's inputs and outputs, and an observer's opinion on how well the outputs relate to the inputs. Thing producing the outputs, and the observer, can be human or not human. Same difference.
- freejazz 3y ago
  Can AI people stop with the defense of "what if thing really is not a thing?" "what if thing is really what humans do?" These aren't answers to questions. Its deflecting nonsense posed as philosophical thought.
- smcin 3y ago
  It absolutely is anthropomorphizing to claim "GPT-3's ability to understand concepts" rather than simply calling it "reproduce, mix and match text from an enormous corpus". And we can totally legitimately compare to a jury of human observers' opinions on how well(/badly) the output generated relates to the inputs. For the specific example the OP cited "War: like being eaten by a dragon and then having it spit you out" then unless its answer to "Where were you in between being eaten by a dragon and before it spat you out?" is "in the dragon's digestive system" that isn't understanding. And I'm curious to see it answer "Dragons only exist in mythology; does your analogy mean war doesn't exist either? Why not compare to an animal that exists?"
  - YetAnotherNick 3y ago
    Anyone who have used GPT-4 for any amount of time knows it is definitely not "reproduce, mix and match text from an enormous corpus". Also what's the point of the question. GPT has been specifically trained to not answer this. But in any case here is the answer with instruct model which generally is more open ended: Sure, I can compare it to a real animal such as bears. Just like dragons are depicted as fearsome and destructive creatures in mythology, bears are also portrayed as powerful and dangerous animals in many cultures. However, just as the existence of dragons is a matter of fiction, the concept of a "war on bears" would also be nonsensical. In reality, the issue of war is complex and multifaceted, involving political, economic, and societal factors. It cannot be reduced to a simplistic battle between humans and a particular animal species. Furthermore, the use of animals in analogies can be problematic as it may trivialize the gravity and complexity of real-world issues. Wars and conflicts have serious consequences for human lives, whereas animals do not possess the same agency or ability to wage war. In conclusion, while dragons may serve as a metaphor for war in mythology, comparing war to a fictional creature diminishes the seriousness of the topic and fails to capture the complexities of real conflicts.

happycube 3y ago

I got curious and did this myself. Needed a bit of nudging to get where I wanted, but I even had it make an Electron wrapper: https://chat.openai.com/share/29d695e6-7f23-4f03-b2be-29b7c947bc92 https://chat.openai.com/share/29d695e6-7f23-4f03-b2be-29b7c9...

dchuk 3y ago
This is awesome, thanks for sharing. Do you (or anyone) know of any products that allow for iterating on the generated output through further chatting with the ai? What I mean, is that each subsequent prompt here either generated a new whole output, or new chunks to add to the output. Ideally, whether generating code or prose, I’d want to keep prompting about the generated output and the AI further modifies the existing output until it’s refined to the degree I want it. Or is that effectively what Copilot/cursor do and I’m just a bad operator?
- happycube 3y ago
  No problem, it was a fun morning exercise for me :) Copilot, at least from what little I did in vscode, isn't as powerful as this. I think there's a GPT4 mode for it that I haven't played with that'd be a lot closer to this.
- robertlagrant 3y ago
  > Do you (or anyone) know of any products that allow for iterating on the generated output through further chatting with the ai? What I mean, is that each subsequent prompt here either generated a new whole output, or new chunks to add to the output. Ideally, whether generating code or prose, I’d want to keep prompting about the generated output and the AI further modifies the existing output until it’s refined to the degree I want it. ChatGPT does this.

adrians1 3y ago

> If the speed of improvement stays anywhere near the level it was the last two years, then over the next two decades, it will lead to massive changes in how we work and which skills are valuable. That's a big assumption to make. You can't assume that the rate of improvement will stay the same, especially over a period of 2 decades, which is a very long time. Every advance in technology hits diminishing returns at some point.

mg 3y ago
Why do you think so? Technological progress seems rather accelerated than diminishing to me. Computers are a great example: They have been getting more capable exponentially over the last decades. In terms of performance (memory, speed, bandwidth) and in terms of impact. First we had calculators, then we had desktop applications, then the Internet and now we have AI. And AI will help us get to the next stage even faster.
- hashtag-til 3y ago
  I’m not putting my coins on this advances. More likely this will become the new “search” technology and get polluted with ads. People will lose trust and it will decay.
  - tuatoru 3y ago
    That is certainly where the economic incentives appear to be.

RC_ITR 3y ago

You can check my post history to see how unpopular this point of view is, but the big "reveal" that will come up is as follows: The way that LLMs and humans "think" is inherently different. Giving an LLM a test designed for humans is akin to giving a camera a 'drawing test.' A camera can make a better narrow final output than a human, but it cannot do the subordinate tasks that a human illustrator could, like changing shadings, line width, etc. An LLM can answer really well on tests, but it often fails at subordinate tasks like 'applying symbolic reasoning to unfamiliar situations.' Eventually the thinking styles may converge in a way that makes the LLMs practically more capable than humans on those subordinate tasks, but we are not there yet.