Several of these seem trivially solvable (in term of limitation, not necessarily in terms of power). If GPT-4 is given access to itself as a tool, it can continue to “reason” across calls. It can probably also be plugged into continuous learning trivially (just keep updating weights when you detect something worth learning).
Things not present in the training data are beyond the model’s learning capacity
We can’t see in infrared or UV, but it seems like we’re able to reason over them through the use of tools.
If the entire representation of a complex task or problem is collapsed into a text, reading that text and trying to push further is not really “reasoning across calls”. I expect that you can go further with that, but not much further. At least that’s what it looks like currently.
I don’t think you can learn to solve very specific complex problems with the kind of continuous learning that would be possibly to implement with current models. Some of the theorem-prover papers have continuous learning loops that basically try to do this but those still seem very inefficient and are applied to only highly formalised problems whose solutions can be automatically verified.
Several of these seem trivially solvable (in term of limitation, not necessarily in terms of power). If GPT-4 is given access to itself as a tool, it can continue to “reason” across calls. It can probably also be plugged into continuous learning trivially (just keep updating weights when you detect something worth learning).
We can’t see in infrared or UV, but it seems like we’re able to reason over them through the use of tools.
A lot of these don’t seem like hard limitations.
If the entire representation of a complex task or problem is collapsed into a text, reading that text and trying to push further is not really “reasoning across calls”. I expect that you can go further with that, but not much further. At least that’s what it looks like currently.
I don’t think you can learn to solve very specific complex problems with the kind of continuous learning that would be possibly to implement with current models. Some of the theorem-prover papers have continuous learning loops that basically try to do this but those still seem very inefficient and are applied to only highly formalised problems whose solutions can be automatically verified.
Yes, multi-modality is not a hard limitation.