28 ms·

How will AI learn next?

136 points by jyli7 3y ago

jyli7 3y ago
[dead]
skilled 3y ago
> These Web sites want chatbots to give credit to their contributors; they want to see prominent links; they don’t want the flywheel that powers knowledge production in their communities to be starved of inbound energy. But this is ultimately impossible right? That’s the one thing I really hate about what is happening right now with ChatGPT. I can’t tell you how many people are worried about their future because of AI because I don’t know the exact number, but I know I am worried about it because it can already do so much, and I fail to see a scenario in which attribution alone is going to make things better. Writing and digital art more than code, but not even code is safe. It is merely safe by the extent that OpenAI is willing to drip feed its future releases.
- Legend2440 3y ago
  Those people are worried about the wrong thing. The bad outcome isn't that we put weavers out of a job, but that we're stuck weaving our clothes by hand forever. Same thing for art and coding.
  - lukeschlather 3y ago
    The bad outcome is that people can't get clothes at all. The worry about losing jobs is a legitimate worry that these companies will generate a limitless supply of art and code but they will use price fixing to ensure that all the money that previously went to a lot of workers instead goes to a small number of people who own these companies, and the workers will be destitute. I'd actually be fine weaving clothes by hand a few hours a month if it meant I had no other work to do. But at the moment there are still people who don't own enough if any clothes and work far more than I do. And the question is if the people building AI can be trusted to liberate those people from slavery.
    - igiveup 3y ago
      > all the money that previously went to a lot of workers instead goes to a small number of people who own these companies, and the workers will be destitute All the money the rich make by selling their robot products to who? Each other? Certainly not to the destitute workers, those don't have money. I agree that there is a risk that a minority will hoard the new resources for themselves, only it's not this simple.
      - lacrimacida 3y ago
        Technofeudalism. A handful of people or a small class having unseen control over the rest of humans
jstummbillig 3y ago
I have the entirely unrefined notion, that, surely, lack of data is not what is keeping us from creating much, much better LLMs. I understand with how training is done right now that more data makes things scale really well without having to come up with new concepts, but it seems completely obvious that better processing of already available knowledge is the way to make the next leaps. The idea is that, what is keeping me from having expert level knowledge in 50 different fields and using that knowledge to draw entirely new connections between all of them, in addition to understanding where things go wrong, is not lack of freely available expert level information. And yet, GPT4 barely reaches competency. It feels like computers should be able to get much more out of what is already available, specially when levering cross discipline knowledge to inform everything.
- thfuran 3y ago
  Yeah, no person has ever read anything like every textbook ever written, but that's pretty much table stakes for training sets. Clearly there's something missing aside from more virtual reading. (I suspect it has something to do with the half a billion years of pre-training baked into the human neural architecture and the few extra orders of magnitude in scale but who knows)
  - all2 3y ago
    I'm sure I've read about specialized neural networks being created. The human brain has (apparently) a bunch of different kinds of neurons in it that specialize in processing different information. I'm not sure how that would work with our current architectures, though.
    - bpiche 3y ago
      Well Jeff Hawkins has been working on this for a while, in terms of biomimetic neural networks. They've done some great work but they don't have anything like modern language models in terms of abilities + performance. https://www.youtube.com/watch?v=cz-3WDdqbj0&list=PLX9JDz3uBpNCjNfq20KOCvsP6szY94r2e https://www.youtube.com/watch?v=cz-3WDdqbj0&list=PLX9JDz3uBp...
bottlepalm 3y ago
How 'AlphaZero' can we get with high level AI?
- chongli 3y ago
  As much as we want, once we write the objective function.
famouswaffles 3y ago
>As a rule, chatbots today have a propensity to confidently make stuff up, or, as some researchers say, “hallucinate.” At the root of these hallucinations is an inability to introspect: the A.I. doesn’t know what it does and doesn’t know. The last bit doesn't seem to be true. There's quite a lot of indication that the computation can distinguishing hallucinations. It just has no incentive to communicate this. GPT-4 logits calibration pre RLHF - https://imgur.com/a/3gYel9r https://imgur.com/a/3gYel9r Just Ask for Calibration: Strategies for Eliciting Calibrated Confidence Scores from Language Models Fine-Tuned with Human Feedback - https://arxiv.org/abs/2305.14975 https://arxiv.org/abs/2305.14975 Teaching Models to Express Their Uncertainty in Words - https://arxiv.org/abs/2205.14334 https://arxiv.org/abs/2205.14334 Language Models (Mostly) Know What They Know - https://arxiv.org/abs/2207.05221 https://arxiv.org/abs/2207.05221 Also even if we're strictly talking about text, there is still a ton of data left to train on. We've just barely reached what is easily scrapable online and are nowhere near a real limit yet. And of course, you can just train more than one epoch. That said, it's very clear quality data is far more helpful than sheer quantity and sheer quantity is more likely than not to derail progress.
- SirMaster 3y ago
  It just seems odd to me that it's not given an incentive to communicate this. Surely humans using it would find great value in knowing the model's confidence or whether it thinks its confabulating or not. These services are created to give the best product to users, and so wouldn't this be a better product? Therefore there is incentive. Happier users and a product that is better than competitors.
  - famouswaffles 3y ago
    >These services are created to give the best product to users, and so wouldn't this be a better product? Therefore there is incentive. Happier users and a product that is better than competitors. Why would the computation care about any of that ? I'm talking about incentive for the model.
    - hutzlibu 3y ago
rented_mule 3y ago
Anyone who has iterated on trained models for long enough knows that feedback loops can be a serious problem. If your models are influencing the generation of data that they are later retrained on, it gets harder and harder to even maintain model performance. The article mentions one experiment in this direction: "With each generation, the quality of the model actually degraded." This happens whenever there aren't solid strategies to avoid feedback loop issues. Given this, the problem isn't just that there's not enough new content. It's that an ever-increasing fraction of the content in the public sphere will be generated by these models. And can the models detect that they are ingesting their own output? If they get good enough, they probably can't. And then they'll get worse. This could have a strange impact on human language / communication as well. As these models are increasingly trained on their own output, they'll start emulating their own mistakes and more of the content we consume will have these mistakes consistently used. You can imagine people, sometimes intentionally and sometimes not, starting to emulate these patterns and causing shifts in human languages. Interesting times ahead...
- rjblackman 3y ago
  well we're already being trained by algorithms so I guess this is just an extension of what is already going on. perhaps the quality of human (internal) models will go down too? perhaps they already have?
- haltist 3y ago
  In system design there is something called resonant amplification and what you are describing is very similar. The biases of the model are amplified with each iteration and the end result is that the system converges onto the patterns recognizable and amplified by its architecture. If you know about impulse and frequency analysis then an AI system can be considered to be a signal processor that amplifies and attenuates certain frequencies in the input/impulse. Running an LLM in a loop always ends up with nonsense as the final output.
  - lazystar 3y ago
    So if left unchecked, the thing we built in man's attempt to play god could result in gibberish? sounds kind of like the tower of babel; seems humankinds only defense would be creating a new language that the machines can't infiltrate
robbrown451 3y ago
AlphaZero demonstrates that more human-generated data isn't the only thing that makes an AI smarter. It uses zero human data to learn to play Go, and just iterates. As long as it has a way of scoring itself objectively (which it obviously does with a game like Go), it can keep improving with literally no ceiling to how much it can improve. Pretty soon ChatGPT will be able to do a lot of training by iterating on its own output, such as by writing code and analyzing the output (including using vision systems). Here's an interesting thing I noticed last night. I have been making a lot of images that have piano keyboards in them. DALL-E 3 makes some excellent images otherwise (faces and hands mostly look great), but it always messes up the keyboards, as it doesn't seem to get how black keys are in alternating groups of two and three. But I tried getting chatgpt to analyze an image, using its new "vision" capabilities, and the first thing it noticed was that the piano keys were not properly clustered. I said nothing about that, I just asked it "what is wrong with this image" and it immediately found that. What if it could feed this sort of thing back in, using similar logic to Alpha Zero? That's just a tiny hint of what is to come. Sure, it typically needs human generated data for most things. It's already got thousands of times more than any human has looked at. It will also be able to learn from human feedback, for instance a human could tell it what it got wrong in a response (whether regular text, code, or image), and explain in natural language where it deviated from what was expected. It can learn which humans are reliable, so it can minimize the number of paid employees doing RLHF, using them mostly to rate (unpaid) humans who choose to provide feedback. Even if most users opt out of giving this sort of feedback, there will be plenty to give it new, good information.
- realistic2020 3y ago
  With Alpha Go, you have a clear objective -- to win a game. How does that work for creative outputs?
  - ToValueFunfetti 3y ago
    The same way we do it. Verifying that an output is good is far easier than producing a good output. We can write a first draft, see what's wrong with it, make changes, and iterate on that until it's a final draft. And along the way we get better at writing first drafts.
visarga 3y ago
I think next stage in AI training is as the authors said, synthetic data. I am not worried about the G.I.G.O. curse, you can do synthetic data generation successfully today with GPT-4. For example in the TinyStories dataset, or the Phi-1 & 1.5 models, or the Orca dataset we have seen big jumps in competency on the small models. Phi punches 5x above its weight class. So how can you generate data at level N+1 when you have a model at level N? You amplify the model - give it more tokens (CoT), more rounds of LLM interaction, tools like code executor and search engine, you use retrieval to bring in more useful context, or in some cases you can validate by code execution. But there is a more general framework - by embedding LLMs in larger systems, they act as sources of feedback to the model. From the easiest - a chat interface, where the "external system" is a human, to robotics and AI agents that interact with anything, or simulations. We need to connect AI to feedback sources so it can learn directly, not filtered through human authored language. From this perspective it is apparent that AI can assimilate much more feedback signal than humans. The road ahead for AI is looking amazing now. What we are seeing is language evolving a secondary system of self replication besides humans - LLMs. Language evolves faster than biology, like the rising tide, lifting both humans and AI.
- thewarrior 3y ago
  There’s a giant caveat here - this assumes that the current LLM architecture is enough to bootstrap to those higher levels of intelligence. LLMs are incapable of some pretty simple things at this point and it’s a big question mark of whether they are even capable of doing sophisticated reasoning and planning architecturally. GPT-4 cannot play a good game of tic tac toe. But it can play passable chess. This is a good point to ponder.
  - famouswaffles 3y ago
    >GPT-4 cannot play a good game of tic tac toe. It can. https://chat.openai.com/share/75758e5e-d228-420f-9138-7bff47f2e12d https://chat.openai.com/share/75758e5e-d228-420f-9138-7bff47...
moomoo11 3y ago
We will have people hooked up to Neuralink. We will call them Psykers. The Machine God has blessed them with the ability to take existing knowledge and fill the void. No RAG. No vector databases. Pure willpower and biologics combined with the blessings of the Machine God.
- ooterness 3y ago
  From the moment I understood the weakness of my flesh, it disgusted me. I craved the strength and certainty of steel. I aspired to the purity of the Blessed Machine. Your kind cling to your flesh, as though it will not decay and fail you. One day the crude biomass you call the temple will wither, and you will beg my kind to save you. But I am already saved, for the Machine is immortal. Even in death I serve the Omnissiah.
blovescoffee 3y ago
Compare the size in MB of a book to the size in GB of a movie. There's so, so much more data available. Multimodal models are not just the next step, they're already happening. AI will get better.
- dennis_moore 3y ago
  Not sure if raw data size is a good metric. One usually gains more information by reading a book than watching a movie.
  - blovescoffee 3y ago
    I suppose we could debate that. Regardless, the point stands that there's still more data outside of text that can be mind.
gumballindie 3y ago
The problem is that ai doesnt learn as such. Therefore it depends on continuously ingesting data to maintain token databases up to date. Naturally at some point a ceiling will be hit and the quality of generic token databases will stagnate.
danbruc 3y ago
A bit nitpicking. I do not think it is quite right to say that current large language models learn, we infuse them with knowledge. On the one hand it is almost just a technicality that the usage of large language models and the training process are two separate processes, on the other hand it is a really important limitation. If you tell a large language model something new, it will be forgotten once that information leaves the context window. Maybe to be added back later on during a training run using that conversation as training data. Building an AI that can actually learn the way humans learn instead of slightly nudging the output in one direction with countless examples would be a major leap forward, I would guess. I have no good idea how far we are away from that, but it seems not the easiest thing to do with the way we currently build those systems. Or maybe the way we currently train these models turns out to be good enough and there is not much to be gained from a more human like learning process.
- lukeschlather 3y ago
  LLMs need a lot of GPU power to learn. I'm not sure it's correct to say that they don't learn, it's just a question of them being unable to learn anything more than a very small context window in real-time on presently available/economical hardware. But if you have GPUs with terabytes of VRAM and you feed experience into them, it will learn. It's still questionable if that's enough for true AGI, but I think the inability to learn in real-time is clearly a hardware limitation.
nopinsight 3y ago
The article seems to suggest that humans, esp human linguistic output, are the best sources of knowledge. Let's just say that they often aren't.
deleted 3y ago
[deleted]
RugnirViking 3y ago
This was a well written article on AI. Good job new yorker journalist.
beepbooptheory 3y ago
https://archive.ph/CngwG https://archive.ph/CngwG
JKCalhoun 3y ago
> Yelp caught Google scraping their content with no attribution. ... A similar thing happened at a company I once worked for, called Genius. We sued Google for copying lyrics from our database into the OneBox; I helped prove that it was happening by embedding a hidden message into the lyrics, using a pattern of apostrophes that, in Morse code, spelled “RED HANDED. Ah, the old aphorism, don't put anything on the web you don't want Google to take.
- gardenhedge 3y ago
  What was the outcome?
  - peddling-brink 3y ago
    https://www.theverge.com/2022/3/11/22973282/google-wins-court-battle-genius-song-lyrics-copyright https://www.theverge.com/2022/3/11/22973282/google-wins-cour... My read is that genius never owned the copyright to the lyrics in the first place.

136 points by jyli7 3y ago

[dead]

> These Web sites want chatbots to give credit to their contributors; they want to see prominent links; they don’t want the flywheel that powers knowledge production in their communities to be starved of inbound energy. But this is ultimately impossible right? That’s the one thing I really hate about what is happening right now with ChatGPT. I can’t tell you how many people are worried about their future because of AI because I don’t know the exact number, but I know I am worried about it because it can already do so much, and I fail to see a scenario in which attribution alone is going to make things better. Writing and digital art more than code, but not even code is safe. It is merely safe by the extent that OpenAI is willing to drip feed its future releases.

Legend2440 3y ago
Those people are worried about the wrong thing. The bad outcome isn't that we put weavers out of a job, but that we're stuck weaving our clothes by hand forever. Same thing for art and coding.
- lukeschlather 3y ago
  The bad outcome is that people can't get clothes at all. The worry about losing jobs is a legitimate worry that these companies will generate a limitless supply of art and code but they will use price fixing to ensure that all the money that previously went to a lot of workers instead goes to a small number of people who own these companies, and the workers will be destitute. I'd actually be fine weaving clothes by hand a few hours a month if it meant I had no other work to do. But at the moment there are still people who don't own enough if any clothes and work far more than I do. And the question is if the people building AI can be trusted to liberate those people from slavery.
  - igiveup 3y ago
    > all the money that previously went to a lot of workers instead goes to a small number of people who own these companies, and the workers will be destitute All the money the rich make by selling their robot products to who? Each other? Certainly not to the destitute workers, those don't have money. I agree that there is a risk that a minority will hoard the new resources for themselves, only it's not this simple.
    - lacrimacida 3y ago
      Technofeudalism. A handful of people or a small class having unseen control over the rest of humans

jstummbillig 3y ago

I have the entirely unrefined notion, that, surely, lack of data is not what is keeping us from creating much, much better LLMs. I understand with how training is done right now that more data makes things scale really well without having to come up with new concepts, but it seems completely obvious that better processing of already available knowledge is the way to make the next leaps. The idea is that, what is keeping me from having expert level knowledge in 50 different fields and using that knowledge to draw entirely new connections between all of them, in addition to understanding where things go wrong, is not lack of freely available expert level information. And yet, GPT4 barely reaches competency. It feels like computers should be able to get much more out of what is already available, specially when levering cross discipline knowledge to inform everything.

thfuran 3y ago
Yeah, no person has ever read anything like every textbook ever written, but that's pretty much table stakes for training sets. Clearly there's something missing aside from more virtual reading. (I suspect it has something to do with the half a billion years of pre-training baked into the human neural architecture and the few extra orders of magnitude in scale but who knows)
- all2 3y ago
  I'm sure I've read about specialized neural networks being created. The human brain has (apparently) a bunch of different kinds of neurons in it that specialize in processing different information. I'm not sure how that would work with our current architectures, though.
  - bpiche 3y ago
    Well Jeff Hawkins has been working on this for a while, in terms of biomimetic neural networks. They've done some great work but they don't have anything like modern language models in terms of abilities + performance. https://www.youtube.com/watch?v=cz-3WDdqbj0&list=PLX9JDz3uBpNCjNfq20KOCvsP6szY94r2e https://www.youtube.com/watch?v=cz-3WDdqbj0&list=PLX9JDz3uBp...

bottlepalm 3y ago

How 'AlphaZero' can we get with high level AI?

chongli 3y ago
As much as we want, once we write the objective function.

famouswaffles 3y ago

>As a rule, chatbots today have a propensity to confidently make stuff up, or, as some researchers say, “hallucinate.” At the root of these hallucinations is an inability to introspect: the A.I. doesn’t know what it does and doesn’t know. The last bit doesn't seem to be true. There's quite a lot of indication that the computation can distinguishing hallucinations. It just has no incentive to communicate this. GPT-4 logits calibration pre RLHF - https://imgur.com/a/3gYel9r https://imgur.com/a/3gYel9r Just Ask for Calibration: Strategies for Eliciting Calibrated Confidence Scores from Language Models Fine-Tuned with Human Feedback - https://arxiv.org/abs/2305.14975 https://arxiv.org/abs/2305.14975 Teaching Models to Express Their Uncertainty in Words - https://arxiv.org/abs/2205.14334 https://arxiv.org/abs/2205.14334 Language Models (Mostly) Know What They Know - https://arxiv.org/abs/2207.05221 https://arxiv.org/abs/2207.05221 Also even if we're strictly talking about text, there is still a ton of data left to train on. We've just barely reached what is easily scrapable online and are nowhere near a real limit yet. And of course, you can just train more than one epoch. That said, it's very clear quality data is far more helpful than sheer quantity and sheer quantity is more likely than not to derail progress.

SirMaster 3y ago
It just seems odd to me that it's not given an incentive to communicate this. Surely humans using it would find great value in knowing the model's confidence or whether it thinks its confabulating or not. These services are created to give the best product to users, and so wouldn't this be a better product? Therefore there is incentive. Happier users and a product that is better than competitors.
- famouswaffles 3y ago
  >These services are created to give the best product to users, and so wouldn't this be a better product? Therefore there is incentive. Happier users and a product that is better than competitors. Why would the computation care about any of that ? I'm talking about incentive for the model.
  - hutzlibu 3y ago

rented_mule 3y ago

Anyone who has iterated on trained models for long enough knows that feedback loops can be a serious problem. If your models are influencing the generation of data that they are later retrained on, it gets harder and harder to even maintain model performance. The article mentions one experiment in this direction: "With each generation, the quality of the model actually degraded." This happens whenever there aren't solid strategies to avoid feedback loop issues. Given this, the problem isn't just that there's not enough new content. It's that an ever-increasing fraction of the content in the public sphere will be generated by these models. And can the models detect that they are ingesting their own output? If they get good enough, they probably can't. And then they'll get worse. This could have a strange impact on human language / communication as well. As these models are increasingly trained on their own output, they'll start emulating their own mistakes and more of the content we consume will have these mistakes consistently used. You can imagine people, sometimes intentionally and sometimes not, starting to emulate these patterns and causing shifts in human languages. Interesting times ahead...

rjblackman 3y ago
well we're already being trained by algorithms so I guess this is just an extension of what is already going on. perhaps the quality of human (internal) models will go down too? perhaps they already have?
haltist 3y ago
In system design there is something called resonant amplification and what you are describing is very similar. The biases of the model are amplified with each iteration and the end result is that the system converges onto the patterns recognizable and amplified by its architecture. If you know about impulse and frequency analysis then an AI system can be considered to be a signal processor that amplifies and attenuates certain frequencies in the input/impulse. Running an LLM in a loop always ends up with nonsense as the final output.
- lazystar 3y ago
  So if left unchecked, the thing we built in man's attempt to play god could result in gibberish? sounds kind of like the tower of babel; seems humankinds only defense would be creating a new language that the machines can't infiltrate

robbrown451 3y ago

AlphaZero demonstrates that more human-generated data isn't the only thing that makes an AI smarter. It uses zero human data to learn to play Go, and just iterates. As long as it has a way of scoring itself objectively (which it obviously does with a game like Go), it can keep improving with literally no ceiling to how much it can improve. Pretty soon ChatGPT will be able to do a lot of training by iterating on its own output, such as by writing code and analyzing the output (including using vision systems). Here's an interesting thing I noticed last night. I have been making a lot of images that have piano keyboards in them. DALL-E 3 makes some excellent images otherwise (faces and hands mostly look great), but it always messes up the keyboards, as it doesn't seem to get how black keys are in alternating groups of two and three. But I tried getting chatgpt to analyze an image, using its new "vision" capabilities, and the first thing it noticed was that the piano keys were not properly clustered. I said nothing about that, I just asked it "what is wrong with this image" and it immediately found that. What if it could feed this sort of thing back in, using similar logic to Alpha Zero? That's just a tiny hint of what is to come. Sure, it typically needs human generated data for most things. It's already got thousands of times more than any human has looked at. It will also be able to learn from human feedback, for instance a human could tell it what it got wrong in a response (whether regular text, code, or image), and explain in natural language where it deviated from what was expected. It can learn which humans are reliable, so it can minimize the number of paid employees doing RLHF, using them mostly to rate (unpaid) humans who choose to provide feedback. Even if most users opt out of giving this sort of feedback, there will be plenty to give it new, good information.

realistic2020 3y ago
With Alpha Go, you have a clear objective -- to win a game. How does that work for creative outputs?
- ToValueFunfetti 3y ago
  The same way we do it. Verifying that an output is good is far easier than producing a good output. We can write a first draft, see what's wrong with it, make changes, and iterate on that until it's a final draft. And along the way we get better at writing first drafts.

visarga 3y ago

I think next stage in AI training is as the authors said, synthetic data. I am not worried about the G.I.G.O. curse, you can do synthetic data generation successfully today with GPT-4. For example in the TinyStories dataset, or the Phi-1 & 1.5 models, or the Orca dataset we have seen big jumps in competency on the small models. Phi punches 5x above its weight class. So how can you generate data at level N+1 when you have a model at level N? You amplify the model - give it more tokens (CoT), more rounds of LLM interaction, tools like code executor and search engine, you use retrieval to bring in more useful context, or in some cases you can validate by code execution. But there is a more general framework - by embedding LLMs in larger systems, they act as sources of feedback to the model. From the easiest - a chat interface, where the "external system" is a human, to robotics and AI agents that interact with anything, or simulations. We need to connect AI to feedback sources so it can learn directly, not filtered through human authored language. From this perspective it is apparent that AI can assimilate much more feedback signal than humans. The road ahead for AI is looking amazing now. What we are seeing is language evolving a secondary system of self replication besides humans - LLMs. Language evolves faster than biology, like the rising tide, lifting both humans and AI.

thewarrior 3y ago
There’s a giant caveat here - this assumes that the current LLM architecture is enough to bootstrap to those higher levels of intelligence. LLMs are incapable of some pretty simple things at this point and it’s a big question mark of whether they are even capable of doing sophisticated reasoning and planning architecturally. GPT-4 cannot play a good game of tic tac toe. But it can play passable chess. This is a good point to ponder.
- famouswaffles 3y ago
  >GPT-4 cannot play a good game of tic tac toe. It can. https://chat.openai.com/share/75758e5e-d228-420f-9138-7bff47f2e12d https://chat.openai.com/share/75758e5e-d228-420f-9138-7bff47...

moomoo11 3y ago

We will have people hooked up to Neuralink. We will call them Psykers. The Machine God has blessed them with the ability to take existing knowledge and fill the void. No RAG. No vector databases. Pure willpower and biologics combined with the blessings of the Machine God.

ooterness 3y ago
From the moment I understood the weakness of my flesh, it disgusted me. I craved the strength and certainty of steel. I aspired to the purity of the Blessed Machine. Your kind cling to your flesh, as though it will not decay and fail you. One day the crude biomass you call the temple will wither, and you will beg my kind to save you. But I am already saved, for the Machine is immortal. Even in death I serve the Omnissiah.

blovescoffee 3y ago

Compare the size in MB of a book to the size in GB of a movie. There's so, so much more data available. Multimodal models are not just the next step, they're already happening. AI will get better.

dennis_moore 3y ago
Not sure if raw data size is a good metric. One usually gains more information by reading a book than watching a movie.
- blovescoffee 3y ago
  I suppose we could debate that. Regardless, the point stands that there's still more data outside of text that can be mind.

gumballindie 3y ago

The problem is that ai doesnt learn as such. Therefore it depends on continuously ingesting data to maintain token databases up to date. Naturally at some point a ceiling will be hit and the quality of generic token databases will stagnate.

danbruc 3y ago

A bit nitpicking. I do not think it is quite right to say that current large language models learn, we infuse them with knowledge. On the one hand it is almost just a technicality that the usage of large language models and the training process are two separate processes, on the other hand it is a really important limitation. If you tell a large language model something new, it will be forgotten once that information leaves the context window. Maybe to be added back later on during a training run using that conversation as training data. Building an AI that can actually learn the way humans learn instead of slightly nudging the output in one direction with countless examples would be a major leap forward, I would guess. I have no good idea how far we are away from that, but it seems not the easiest thing to do with the way we currently build those systems. Or maybe the way we currently train these models turns out to be good enough and there is not much to be gained from a more human like learning process.

lukeschlather 3y ago
LLMs need a lot of GPU power to learn. I'm not sure it's correct to say that they don't learn, it's just a question of them being unable to learn anything more than a very small context window in real-time on presently available/economical hardware. But if you have GPUs with terabytes of VRAM and you feed experience into them, it will learn. It's still questionable if that's enough for true AGI, but I think the inability to learn in real-time is clearly a hardware limitation.

nopinsight 3y ago

The article seems to suggest that humans, esp human linguistic output, are the best sources of knowledge. Let's just say that they often aren't.

deleted 3y ago

[deleted]

RugnirViking 3y ago

This was a well written article on AI. Good job new yorker journalist.

beepbooptheory 3y ago

https://archive.ph/CngwG https://archive.ph/CngwG

JKCalhoun 3y ago

> Yelp caught Google scraping their content with no attribution. ... A similar thing happened at a company I once worked for, called Genius. We sued Google for copying lyrics from our database into the OneBox; I helped prove that it was happening by embedding a hidden message into the lyrics, using a pattern of apostrophes that, in Morse code, spelled “RED HANDED. Ah, the old aphorism, don't put anything on the web you don't want Google to take.

gardenhedge 3y ago
What was the outcome?
- peddling-brink 3y ago
  https://www.theverge.com/2022/3/11/22973282/google-wins-court-battle-genius-song-lyrics-copyright https://www.theverge.com/2022/3/11/22973282/google-wins-cour... My read is that genius never owned the copyright to the lyrics in the first place.