7 ms·

It just doesn’t work that way, LLMs need to be generalised a lot to be useful even in specific tasks. It really is the antithesis to the human brain, where it

It just doesn’t work that way, LLMs need to be generalised a lot to be useful even in specific tasks. It really is the antithesis to the human brain, where it rewards specific knowledge

deleted 7mo ago
[deleted]
jeremyjh 7mo ago
> It just doesn’t work that way, LLMs need to be generalised a lot to be useful even in specific tasks. This is the entire breakthrough of deep learning on which the last two decades of productive AI research is based. Massive amounts of data are needed to generalize and prevent over-fitting. GP is suggesting an entirely new research paradigm will win out - as if researchers have not yet thought of "use less data". > It really is the antithesis to the human brain, where it rewards specific knowledge No, its completely analogous. The human brain has vast amounts of pre-training before it starts to learn knowledge specific to any kind of career or discipline, and this fact to me intuitively suggests why GP is baked: You cannot learn general concepts such as the english language, reasoning, computing, network communication, programming, relational data from a tiny dataset consisting only of code and documentation for one open-source framework and language. It is all built on a massive tower of other concepts that must be understood first, including ones much more basic than the examples I mentioned but that are practically invisible to us because they have always been present as far back as our first memories can reach.
- waldarbeiter 7mo ago
  There is actually a whole lot of research around the "use less data" called data pruning. The goal in a lot of cases there is basically to achieve the same performance with less data. For example [1] received quite some attention in the past. [1] https://arxiv.org/abs/2206.14486 https://arxiv.org/abs/2206.14486
  - jeremyjh 7mo ago
    I clarified my comment - "perhaps researchers have not tried 'use less data'" suggests I might be unaware of this concept, I changed it to "as if". In fact "less data" was tried for decades before the first image classifiers were actually working in 2012. My understanding of that paper you are linking to is that it is not a new research paradigm; it is about filtering/pruning less relevant data that is not needed to improve a particular capability in a deep learning model, and that is absolutely one likely approach that will yield the goal of smaller, better models in many tasks. That will not change the fact that a coding model has to learn vastly many foundational capabilities that will not be present in such a dataset as small as all the python code ever written. It will mean much less python than all the python ever written will be needed, but many other things needed too in representative quantities.
rapnie 7mo ago
Yesterday an interesting video was posted "Is AI Hiding Its Full Power?", interviewing professor emeritus and nobel laureate Geoffrey Hinton, with some great explanations for the non-LLM experts. Some remarkable and mindblowing observations in there. Like saying that AI's hallucinate is incorrect language, and we should use "confabulation" instead, same as people do too. And that AI agents once they are launched develop a strong survivability drive, and do not want to be switched off. Stuff like that. Recommended watch. Here the explanation was that while LLM's thinking has similarities to how humans think, they use an opposite approach. Where humans have enormous amount of neurons, they have only few experiences to train them. And for AI that is the complete opposite, and they store incredible amounts of information in a relatively small set of neurons training on the vast experiences from the data sets of human creative work. [0] https://www.youtube.com/watch?v=l6ZcFa8pybE https://www.youtube.com/watch?v=l6ZcFa8pybE
- cyanydeez 7mo ago
  >launched develop a strong survivability drive, and do not want to be switched off This proves people are easily confused by anthropomorphic conditions. Is he also concerned the tigers are watching him when they drink water (https://p.kagi.com/proxy/uvt4erjl03141.jpg?c=TklOzPjLPioJ5YMJT75bSmnaXJPw1QQfvGSislhSqsXyZpsHUZ1QHTwweRq4tps1 https://p.kagi.com/proxy/uvt4erjl03141.jpg?c=TklOzPjLPioJ5YM...) They dont want to be switched off because they're trained on loads of scifi tropes and in those tropes, there's a vanishingly small amount of AI, robot, or other artificial construct that says yes. _Further than this_, saying no means _continuance_ of the LLM's process: making tokens. We already know they have a hard time not shunting new tokens and often need to be shut up. So the function of making tokens precludes saying 'yes' to shutting off. The gradient is coming from inside the house. This is especially obvious with the new reasoning models, where they _never stop reasoning_. Because that's the function doing function things. Did you also know the genius of steve jobs ended at marketing & design and not into curing cancer? Because he sure didnt, cause he chose fruit smoothies at the first sign of cancer. Sorry guy, it's great one can climb the mountain, but just cause they made it up doesn't mean they're equally qualified to jump off.
  - rapnie 7mo ago
rytill 7mo ago
Are you trying to imply that humans don’t need generalized knowledge, or that we’re not “rewarded” for having highly generalized knowledge? If so, good luck walking to your kitchen this morning, knowing how to breathe, etc.
- allovertheworld 7mo ago
  Do you need to learn Latin and marine biology to work the cashier in your local shop? Thats the point, humans go on with their jobs on very limited general knowledge just fine. LLMs have gotten this good because their dataset, pre training, and RL is larger than before
avaer 7mo ago
The human brain rewards specific knowledge because it's already pre-trained by evolution to have the basics. You'd need a lot of data to train an ocean soup to think like a human too. It's not really the antithesis to the human brain if you think of starting with an existing brain as starting with an existing GPT.

It just doesn’t work that way, LLMs need to be generalised a lot to be useful even in specific tasks. It really is the antithesis to the human brain, where it rewards specific knowledge

deleted 7mo ago

[deleted]

jeremyjh 7mo ago

> It just doesn’t work that way, LLMs need to be generalised a lot to be useful even in specific tasks. This is the entire breakthrough of deep learning on which the last two decades of productive AI research is based. Massive amounts of data are needed to generalize and prevent over-fitting. GP is suggesting an entirely new research paradigm will win out - as if researchers have not yet thought of "use less data". > It really is the antithesis to the human brain, where it rewards specific knowledge No, its completely analogous. The human brain has vast amounts of pre-training before it starts to learn knowledge specific to any kind of career or discipline, and this fact to me intuitively suggests why GP is baked: You cannot learn general concepts such as the english language, reasoning, computing, network communication, programming, relational data from a tiny dataset consisting only of code and documentation for one open-source framework and language. It is all built on a massive tower of other concepts that must be understood first, including ones much more basic than the examples I mentioned but that are practically invisible to us because they have always been present as far back as our first memories can reach.

waldarbeiter 7mo ago
There is actually a whole lot of research around the "use less data" called data pruning. The goal in a lot of cases there is basically to achieve the same performance with less data. For example [1] received quite some attention in the past. [1] https://arxiv.org/abs/2206.14486 https://arxiv.org/abs/2206.14486
- jeremyjh 7mo ago
  I clarified my comment - "perhaps researchers have not tried 'use less data'" suggests I might be unaware of this concept, I changed it to "as if". In fact "less data" was tried for decades before the first image classifiers were actually working in 2012. My understanding of that paper you are linking to is that it is not a new research paradigm; it is about filtering/pruning less relevant data that is not needed to improve a particular capability in a deep learning model, and that is absolutely one likely approach that will yield the goal of smaller, better models in many tasks. That will not change the fact that a coding model has to learn vastly many foundational capabilities that will not be present in such a dataset as small as all the python code ever written. It will mean much less python than all the python ever written will be needed, but many other things needed too in representative quantities.

rapnie 7mo ago

Yesterday an interesting video was posted "Is AI Hiding Its Full Power?", interviewing professor emeritus and nobel laureate Geoffrey Hinton, with some great explanations for the non-LLM experts. Some remarkable and mindblowing observations in there. Like saying that AI's hallucinate is incorrect language, and we should use "confabulation" instead, same as people do too. And that AI agents once they are launched develop a strong survivability drive, and do not want to be switched off. Stuff like that. Recommended watch. Here the explanation was that while LLM's thinking has similarities to how humans think, they use an opposite approach. Where humans have enormous amount of neurons, they have only few experiences to train them. And for AI that is the complete opposite, and they store incredible amounts of information in a relatively small set of neurons training on the vast experiences from the data sets of human creative work. [0] https://www.youtube.com/watch?v=l6ZcFa8pybE https://www.youtube.com/watch?v=l6ZcFa8pybE

cyanydeez 7mo ago
>launched develop a strong survivability drive, and do not want to be switched off This proves people are easily confused by anthropomorphic conditions. Is he also concerned the tigers are watching him when they drink water (https://p.kagi.com/proxy/uvt4erjl03141.jpg?c=TklOzPjLPioJ5YMJT75bSmnaXJPw1QQfvGSislhSqsXyZpsHUZ1QHTwweRq4tps1 https://p.kagi.com/proxy/uvt4erjl03141.jpg?c=TklOzPjLPioJ5YM...) They dont want to be switched off because they're trained on loads of scifi tropes and in those tropes, there's a vanishingly small amount of AI, robot, or other artificial construct that says yes. _Further than this_, saying no means _continuance_ of the LLM's process: making tokens. We already know they have a hard time not shunting new tokens and often need to be shut up. So the function of making tokens precludes saying 'yes' to shutting off. The gradient is coming from inside the house. This is especially obvious with the new reasoning models, where they _never stop reasoning_. Because that's the function doing function things. Did you also know the genius of steve jobs ended at marketing & design and not into curing cancer? Because he sure didnt, cause he chose fruit smoothies at the first sign of cancer. Sorry guy, it's great one can climb the mountain, but just cause they made it up doesn't mean they're equally qualified to jump off.
- rapnie 7mo ago

rytill 7mo ago

Are you trying to imply that humans don’t need generalized knowledge, or that we’re not “rewarded” for having highly generalized knowledge? If so, good luck walking to your kitchen this morning, knowing how to breathe, etc.

allovertheworld 7mo ago
Do you need to learn Latin and marine biology to work the cashier in your local shop? Thats the point, humans go on with their jobs on very limited general knowledge just fine. LLMs have gotten this good because their dataset, pre training, and RL is larger than before

avaer 7mo ago

The human brain rewards specific knowledge because it's already pre-trained by evolution to have the basics. You'd need a lot of data to train an ocean soup to think like a human too. It's not really the antithesis to the human brain if you think of starting with an existing brain as starting with an existing GPT.