So predicting the next word given all humanity’s knowledge is surely going to max out at slightly less good (we probably can’t get perfect data) than the best human in any specific field. What test does the AI do to be able to understand it is improving? At some point it becomes impossible to know that the output is actually better right?
All planners start with executing just the next step. You do too. You might be planning long term but you execute just one set of things at the current moment.
Deep NNs and LLMs should not work based on our theoretical understanding. The fact that they do should give us a pause instead of us flatly denying their unexpected performance.
slightly less good (we probably can’t get perfect data) than the best human in any specific field
...but many orders of magnitude faster, and in a way that scales horizontally really well, which is quite useful even if the quality isn't quite what a the absolute best humans can do.
I dunno if it would max out at slightly less good, I imagine it would max out around the distribution of it's data set, which could be significantly worse than top experts.
At what point is human intelligence going to hold back machine intelligence?
Imagine you are evaluating what the machine should do when it is improving itself. It does a bunch of work and returns with "I supervaluated the liminal overdecomposition from the previous homological calibulation pass. It shows us that subtransitory mulutination will underspecify the tensor of stermullification. Where do you want to go from here?"
It will be like when you are reading a Wikipedia about a topic you don't understand. You follow the links, and you get more questions with more links. Your whole day is taken up following links, to the point where you forgot the original question.
Except this time, all the words come from the AI's work. You can't refer to an external authority who has already been there and can tell you what to do.
The AI needs you to tell it whether it is more intelligent than it was before, but you don't know, because you can't follow its reasoning any more. It's like an ordinary person trying to hire a math professor, there's just no way to do it.
But whereas a human math prof can evaluate another one, a machine intelligence can't evaluate another one, by construction. Because it's still usefulness to humans that is the evaluation criterion.
Is there a way for an llm to coin a word, and absorb it into its model? During training maybe… but not after - not the way they’re designed now, anyway.
For it to have new vocabulary we dont understand, it needs to have novel ideas that need words coined for them, and a way to persist those ideas and words into the future. I don’t think that exists.
To me this hypothetical make it clear this won’t happen, not unless there are fundamental changes to what llms are. It doesn’t suggest it will happen. To me, anyway.
I don't see why this couldn't be possible. We use LLMs whose weights are frozen and are not updated at inference, most likely this is due to reasons of cost, stability and control.
Theoretically you could update the weights at inference time too though so the model evolved as it's used. Surely some people are trying this already.
The decoding step (output of final layer -> word) is not strictly needed. You can feed the output directly into the next layer (Chain of Continuous Thought). You can 'decode' the output into things other than words.
I love your thought experiment. May I counter, what purpose does such a machine have to us, that can think beyond our needs? Sorry, but to reference the great Rick and Morty, "your purpose is to pass the butter".
“To us” is pivotal there. Continuing GP’s thought experiment: what if the model that produced the output perceives that the human it was presented to offers no value in helping it learn further?
The model will have to convince the human that it's making the right kind of progress. That will necessarily become part of the improvement loop - either implicitly (human trusts RSI) or explicitly (human gatekeeps every major decision).
Color me skeptical. LLMs seem to make writing code faster, so of course that means that people can iterate on ideas faster, but I have yet to see actual creative output from an LLM that wasn't coached into it or random juxtaposition.
Was the creation of writing as such coached into humans or a random juxtaposition? Could all human inventions just be coached into humans (who coached?) or be random juxtapositions?
People are always using this example because of the "LLMs can't jump paper". Suffice to say - it's not that simple, and special relativity was definitely an incremental improvement to well-studied theory that was being developed by dozens of the leading physicists of the day.
So predicting the next word given all humanity’s knowledge is surely going to max out at slightly less good (we probably can’t get perfect data) than the best human in any specific field. What test does the AI do to be able to understand it is improving? At some point it becomes impossible to know that the output is actually better right?
All planners start with executing just the next step. You do too. You might be planning long term but you execute just one set of things at the current moment.
Deep NNs and LLMs should not work based on our theoretical understanding. The fact that they do should give us a pause instead of us flatly denying their unexpected performance.
There's many fields where it's easier to find new problems than to solve them, and it's easier to check the solution once you have it.
They're doing RL on open problems these days, not just next token prediction.
slightly less good (we probably can’t get perfect data) than the best human in any specific field
...but many orders of magnitude faster, and in a way that scales horizontally really well, which is quite useful even if the quality isn't quite what a the absolute best humans can do.
We’re talking about run away self improvement of these systems, I’m not even convinced we’ve seen these systems invent a single new thing yet.
I dunno if it would max out at slightly less good, I imagine it would max out around the distribution of it's data set, which could be significantly worse than top experts.
Not necessarily; these learning things are super non convex and it's not clear to me "where" they max out
> surely
Citation needed
Have you seen anything produced by LLMs that is better than the best humans?
What if automating AI R&D triggers an intelligence explosion?
No title editing please
This is much better:
https://www.rameznaam.com/p/471bbae4-1163-4048-944b-18f8b0bf...
At what point is human intelligence going to hold back machine intelligence?
Imagine you are evaluating what the machine should do when it is improving itself. It does a bunch of work and returns with "I supervaluated the liminal overdecomposition from the previous homological calibulation pass. It shows us that subtransitory mulutination will underspecify the tensor of stermullification. Where do you want to go from here?"
It will be like when you are reading a Wikipedia about a topic you don't understand. You follow the links, and you get more questions with more links. Your whole day is taken up following links, to the point where you forgot the original question.
Except this time, all the words come from the AI's work. You can't refer to an external authority who has already been there and can tell you what to do.
The AI needs you to tell it whether it is more intelligent than it was before, but you don't know, because you can't follow its reasoning any more. It's like an ordinary person trying to hire a math professor, there's just no way to do it.
But whereas a human math prof can evaluate another one, a machine intelligence can't evaluate another one, by construction. Because it's still usefulness to humans that is the evaluation criterion.
Is there a way for an llm to coin a word, and absorb it into its model? During training maybe… but not after - not the way they’re designed now, anyway.
For it to have new vocabulary we dont understand, it needs to have novel ideas that need words coined for them, and a way to persist those ideas and words into the future. I don’t think that exists.
To me this hypothetical make it clear this won’t happen, not unless there are fundamental changes to what llms are. It doesn’t suggest it will happen. To me, anyway.
I don't see why this couldn't be possible. We use LLMs whose weights are frozen and are not updated at inference, most likely this is due to reasons of cost, stability and control.
Theoretically you could update the weights at inference time too though so the model evolved as it's used. Surely some people are trying this already.
The decoding step (output of final layer -> word) is not strictly needed. You can feed the output directly into the next layer (Chain of Continuous Thought). You can 'decode' the output into things other than words.
I love your thought experiment. May I counter, what purpose does such a machine have to us, that can think beyond our needs? Sorry, but to reference the great Rick and Morty, "your purpose is to pass the butter".
“To us” is pivotal there. Continuing GP’s thought experiment: what if the model that produced the output perceives that the human it was presented to offers no value in helping it learn further?
May I counter, what purpose does such a machine have to us, that can think beyond our needs?
We can think of questions we can't answer. It can answer them.
Maybe it can, maybe it cannot- comes down to the question.
The model will have to convince the human that it's making the right kind of progress. That will necessarily become part of the improvement loop - either implicitly (human trusts RSI) or explicitly (human gatekeeps every major decision).
At that point, the machines correctness doesn't need to be evaluated by humans, it just needs to provide a recipe for how to achieve some process.
Just thought of this piece [1] by Ramez Naam: Where’s the “intelligence explosion”?
[1] https://www.noahpinion.blog/p/wheres-the-intelligence-explos...
Color me skeptical. LLMs seem to make writing code faster, so of course that means that people can iterate on ideas faster, but I have yet to see actual creative output from an LLM that wasn't coached into it or random juxtaposition.
Can you give me some examples of an actual creative output from a human that wasn't coached into it or random juxtaposition?
Was the creation of writing as such coached into humans or a random juxtaposition? Could all human inventions just be coached into humans (who coached?) or be random juxtapositions?
https://www.artstation.com/artwork/41NRyk
Special relativity?
People are always using this example because of the "LLMs can't jump paper". Suffice to say - it's not that simple, and special relativity was definitely an incremental improvement to well-studied theory that was being developed by dozens of the leading physicists of the day.