Being able to perfectly imitate a Chimpanzee would probably also require superhuman intelligence. But such a system would still only be able to imitate chimpanzees. Effectively, it would be much less intelligent than a human. Same for imitating human text. It’s very hard, but the result wouldn’t yield large capabilities.
Do please read the post. Being able to predict human text requires vastly superhuman capabilities, because predicting human text requires predicting the processes that generated said text. And large tracts of text are just reporting on empirical features of the world.
I did read your post. The fact that something like predicting text requires superhuman capabilities of some sort does not mean that the task itself will result in superhuman capabilities. That’s the crucial point.
It is much harder to imitate human text than to write while being a human, but that doesn’t mean the imitated human itself is any more capable than the original.
An analogy. The fact that building fusion power plants is much harder than building fission power plants doesn’t at all mean that the former are better. They could even be worse. There is a fundamental disconnect between the difficulty of a task and the usefulness of that task.
Being able to perfectly imitate a Chimpanzee would probably also require superhuman intelligence. But such a system would still only be able to imitate chimpanzees. Effectively, it would be much less intelligent than a human. Same for imitating human text. It’s very hard, but the result wouldn’t yield large capabilities.
It depends on your ability to extract the information from the model. RLHF and instruction tuning are one such algorithm that allow certain capabaliities besides next-token prediction to be extracted from the model. I suspect many other search and extraction techniques will be found, which can leverage latent capabalities and understandings in the model that aren’t modelled in its’ text outputs.
I aware of just three methods to modify GPTs: In-context learning (prompting), supervised fine-tuning, reinforcement fine-tuning. The achievable effects seem rather similar.
There’s many other ways to search the network in the literature, such as Activation Vectors. And I suspect we’re just getting started on these sorts of search methods.
“The upper bound of what can be learned from a dataset is not the most capable trajectory, but the conditional structure of the universe implicated by their sum”.
Being able to perfectly imitate a Chimpanzee would probably also require superhuman intelligence. But such a system would still only be able to imitate chimpanzees. Effectively, it would be much less intelligent than a human. Same for imitating human text. It’s very hard, but the result wouldn’t yield large capabilities.
Do please read the post. Being able to predict human text requires vastly superhuman capabilities, because predicting human text requires predicting the processes that generated said text. And large tracts of text are just reporting on empirical features of the world.
Alternatively, just read the post I linked.
I did read your post. The fact that something like predicting text requires superhuman capabilities of some sort does not mean that the task itself will result in superhuman capabilities. That’s the crucial point.
It is much harder to imitate human text than to write while being a human, but that doesn’t mean the imitated human itself is any more capable than the original.
An analogy. The fact that building fusion power plants is much harder than building fission power plants doesn’t at all mean that the former are better. They could even be worse. There is a fundamental disconnect between the difficulty of a task and the usefulness of that task.
It depends on your ability to extract the information from the model. RLHF and instruction tuning are one such algorithm that allow certain capabaliities besides next-token prediction to be extracted from the model. I suspect many other search and extraction techniques will be found, which can leverage latent capabalities and understandings in the model that aren’t modelled in its’ text outputs.
This approach doesn’t seem to work with in-context learning. Then it is unclear whether fine-tuning could be more successful.
I think there are probably many approaches that don’t work.
I aware of just three methods to modify GPTs: In-context learning (prompting), supervised fine-tuning, reinforcement fine-tuning. The achievable effects seem rather similar.
There’s many other ways to search the network in the literature, such as Activation Vectors. And I suspect we’re just getting started on these sorts of search methods.