it can do everything humans can do within its trained modality of text, badly
I don’t know what you mean by “AGI” here. The sorts of things I expect baby AGIs to be able to do are e.g. “open-heart surgery in realistic hospital environments”. I don’t think that AGI is solving all of the kinds of cognitive problems you need in order to model a physical universe and strategically steer that universe toward outcomes (though it’s solving some!).
What’s an example of a specific capability you could have discovered ChatGPT lacks, that would have convinced you it’s “not an AGI” rather than “an incredibly stupid AGI”?
Transformers will be able to do open-heart surgery in realistic environments (but, unless trained as carefully as a human, badly) if trained in those environments, but I doubt anyone would certify them to do so safely and reliably for a while after they can do it. ChatGPT didn’t see anything but text, but displays human level generality while being less than human level IQ at the moment.
What’s an example of a specific capability you could have discovered ChatGPT lacks, that would have convinced you it’s “not an AGI” rather than “an incredibly stupid AGI”?
If it had had very significant trouble with a specific type of reasoning over its input, despite that domain’s presence in its training data. For examples of this, compare to the previous long-running SOTA, LSTMs; LSTMs have very serious trouble with recursive languages even when scaled significantly. They are also “sorta agi”, but are critically lacking capabilities that transformers do not. LSTMs cannot handle complex recursive languages; While transformers still have issues with them and demonstrate IQ limitations as a result, they’re able to handle more or less the full set of languages that humans can. Including, if run as a normalizing flow, motor control—however motor control is quite hard, and is an area where current transformers are still being scaled carefully rather than through full breadth pretraining.
Of course, ChatGPT isn’t very good at math. But that isn’t transformers’ fault overall—Math requires very high quality training data and a lot of practice problems. ChatGPT has only just started to be trained on math practice problems, and it’s unclear exactly how well it’s going to generalize. The new version with more math training does seem more coherent in math domains but only by a small margin. It really needs to be an early part of the training process for the model to learn high quality math skill.
Though, it’s pretty unreasonable to only call it agi if a baby version of it can do open heart surgery. No baby version of anything ever will be able to do open heart surgery, no matter how strong the algorithm is. That just requires too much knowledge. It’d have to cheat and simply download non-baby models.
I don’t know what you mean by “AGI” here. The sorts of things I expect baby AGIs to be able to do are e.g. “open-heart surgery in realistic hospital environments”. I don’t think that AGI is solving all of the kinds of cognitive problems you need in order to model a physical universe and strategically steer that universe toward outcomes (though it’s solving some!).
What’s an example of a specific capability you could have discovered ChatGPT lacks, that would have convinced you it’s “not an AGI” rather than “an incredibly stupid AGI”?
Transformers will be able to do open-heart surgery in realistic environments (but, unless trained as carefully as a human, badly) if trained in those environments, but I doubt anyone would certify them to do so safely and reliably for a while after they can do it. ChatGPT didn’t see anything but text, but displays human level generality while being less than human level IQ at the moment.
If it had had very significant trouble with a specific type of reasoning over its input, despite that domain’s presence in its training data. For examples of this, compare to the previous long-running SOTA, LSTMs; LSTMs have very serious trouble with recursive languages even when scaled significantly. They are also “sorta agi”, but are critically lacking capabilities that transformers do not. LSTMs cannot handle complex recursive languages; While transformers still have issues with them and demonstrate IQ limitations as a result, they’re able to handle more or less the full set of languages that humans can. Including, if run as a normalizing flow, motor control—however motor control is quite hard, and is an area where current transformers are still being scaled carefully rather than through full breadth pretraining.
[edit to clarify: specifically, it would have needed to appear to not be a Universal Learning Machine]
Of course, ChatGPT isn’t very good at math. But that isn’t transformers’ fault overall—Math requires very high quality training data and a lot of practice problems. ChatGPT has only just started to be trained on math practice problems, and it’s unclear exactly how well it’s going to generalize. The new version with more math training does seem more coherent in math domains but only by a small margin. It really needs to be an early part of the training process for the model to learn high quality math skill.
https://www.lesswrong.com/posts/ozojWweCsa3o32RLZ/list-of-links-formal-methods-embedded-agency-3d-world-models—again, if your metric is “can it robotics well enough to do open heart surgery”, you should be anticipating sudden dramatic progress once the existing scaling results get combined with work on making 3d models and causally valid models.
Though, it’s pretty unreasonable to only call it agi if a baby version of it can do open heart surgery. No baby version of anything ever will be able to do open heart surgery, no matter how strong the algorithm is. That just requires too much knowledge. It’d have to cheat and simply download non-baby models.