I’m aware of what the pace is. You might want to check up on the current controversy surrounding OpenAI’s math “achievements”.
By my reckoning, the difference between Mythos and Opus is smaller than the difference between Opus and Sonnet. Same with the difference between GPT 5.5 to 5.6 is smaller than the difference between GPT 4 to GPT 5.
The size of improvements over time is diminishing. We’re not in “big bang” territory anymore and we’re about two years into the “incremental refinement” period. We’re about to enter the next AI Winter unless somebody comes up with a new architectural component as revolutionary as transformers have been for ML models.
The core problem is that LLMs do not create. Full stop. All creativity is borne by the human inputs. Until that changes - until the model gains the capability to truly create new information, we’ve hit the limits in raw capability.
You might want to check up on the current controversy surrounding OpenAI’s math “achievements”.
I’m aware of the controversies, this benchmark isn’t about making new proofs on previously unsolved problems, it’s whether it can answer complex math problems, which it’s getting better at.
By my reckoning, the difference between Mythos and Opus is smaller than the difference between Opus and Sonnet. Same with the difference between GPT 5.5 to 5.6 is smaller than the difference between GPT 4 to GPT 5.
Do you have any benchmarks or data to back this “reckoning”
Do you have any benchmarks or data to back this “reckoning”
I work with LLMs daily. I read papers as they hit arxiv. Also daily. You clearly don’t.
I’m not interested in convincing anyone, which is why I’m speaking non-technically.
The benchmarks being cited aren’t as interesting as you appear to believe they are. You’ve not fully grasped the fact that solving pre-made problems where the solutions are known or knowable isn’t anywhere close to the same thing as asking truly novel research questions independent of a human prompt. For OpenAI to also be embroiled in allegations of plagiarism only serves to underscore the gap between the two concepts.
The size of improvements over time is diminishing. We’re not in “big bang” territory anymore and we’re about two years into the “incremental refinement” period. We’re about to enter the next AI Winter unless somebody comes up with a new architectural component as revolutionary as transformers have been for ML models.
The models are also getting extremely big as well, since the big improvement currently seems to largely be stuffing the model with more parameters, and making that work.
I can only imagine that the training cost has also been skyrocketing.
The core problem is that LLMs do not create. Full stop. All creativity is borne by the human inputs. Until that changes - until the model gains the capability to truly create new information, we’ve hit the limits in raw capability.
The models suppress outliers by design. Statistically speaking, the most novel thing is a garbled mess of random words, but random noise is useless, so it ends up being suppressed. You can see by fiddling the samplers, or increasing the temperature.
The Library of Babel is the most creative thing in the world, containing every possible combination of English words and letters. You can basically act like an LLM by trying to find a new coherent sentence in it, but also one that hasn’t been said before. It’s basically impossible.
But that is what an improvement is supposed to be. Compare that to finding a sentence that has been said, or something close to it.
I’m aware of what the pace is. You might want to check up on the current controversy surrounding OpenAI’s math “achievements”.
By my reckoning, the difference between Mythos and Opus is smaller than the difference between Opus and Sonnet. Same with the difference between GPT 5.5 to 5.6 is smaller than the difference between GPT 4 to GPT 5.
The size of improvements over time is diminishing. We’re not in “big bang” territory anymore and we’re about two years into the “incremental refinement” period. We’re about to enter the next AI Winter unless somebody comes up with a new architectural component as revolutionary as transformers have been for ML models.
The core problem is that LLMs do not create. Full stop. All creativity is borne by the human inputs. Until that changes - until the model gains the capability to truly create new information, we’ve hit the limits in raw capability.
I’m aware of the controversies, this benchmark isn’t about making new proofs on previously unsolved problems, it’s whether it can answer complex math problems, which it’s getting better at.
Do you have any benchmarks or data to back this “reckoning”
I work with LLMs daily. I read papers as they hit arxiv. Also daily. You clearly don’t.
I’m not interested in convincing anyone, which is why I’m speaking non-technically.
The benchmarks being cited aren’t as interesting as you appear to believe they are. You’ve not fully grasped the fact that solving pre-made problems where the solutions are known or knowable isn’t anywhere close to the same thing as asking truly novel research questions independent of a human prompt. For OpenAI to also be embroiled in allegations of plagiarism only serves to underscore the gap between the two concepts.
Isn’t it having the ability to create that would make it extremely potentially dangerous?
The models are also getting extremely big as well, since the big improvement currently seems to largely be stuffing the model with more parameters, and making that work.
I can only imagine that the training cost has also been skyrocketing.
The models suppress outliers by design. Statistically speaking, the most novel thing is a garbled mess of random words, but random noise is useless, so it ends up being suppressed. You can see by fiddling the samplers, or increasing the temperature.
The Library of Babel is the most creative thing in the world, containing every possible combination of English words and letters. You can basically act like an LLM by trying to find a new coherent sentence in it, but also one that hasn’t been said before. It’s basically impossible.
But that is what an improvement is supposed to be. Compare that to finding a sentence that has been said, or something close to it.