In my OP, my claim was basically “you probably can get human-level output out of something GPT-like by giving it longer term rewards/punishments, and having it continuously learn” (i.e. give it an actual incentive to figure out how to fight fires in novel situations, which current GPT doesn’t have).
I realize that leaves a lot of fuzziness in “well, is it really GPT if has a different architecture that continuously learns and has longterm rewards?”. My guess was that it’d be fairly different from GPT architecturally, but that it wouldn’t depend on architectural insights we haven’t already made, it’d just be work to integrate existing insights.
Is your claim “this is insufficient – you still need working memory and the ability to model scenarios, and currently we don’t know how to do that, and there are good reasons to think that throwing lots of data and better reward structures at our existing algorithms won’t be enough to cause this to develop automatically via Neural Net Magic?”
Is your claim “this is insufficient – you still need working memory and the ability to model scenarios, and currently we don’t know how to do that, and there are good reasons to think that throwing lots of data and better reward structures at our existing algorithms won’t be enough to cause this to develop automatically via Neural Net Magic?”
So at this point I’m pretty uncertain of what neural nets can or can not learn to do. But at least I am confident in saying that GPT isn’t going to learn the kinds of abilities that would be required for actually fighting fires, as it is trained and tested on a fundamentally static task, as opposed to one that requires adapting your behavior to a situation as it develops. For evaluating at progress on those, projects like AlphaStar look like more relevant candidates.
I don’t feel confident in saying whether some combination of existing algorithms and training methods could produce a system that approached the human level on dynamic tasks. Most people seem to agree that we haven’t gotten neural nets to learn to do good causal reasoning yet, so my understanding of the expert consensus is that current techniques seem inadequate… but then the previous expert consensus would probably also have judged neural nets to be inadequate for doing many of the tasks that they’ve now mastered.
So, doublechecking my comprehension:
In my OP, my claim was basically “you probably can get human-level output out of something GPT-like by giving it longer term rewards/punishments, and having it continuously learn” (i.e. give it an actual incentive to figure out how to fight fires in novel situations, which current GPT doesn’t have).
I realize that leaves a lot of fuzziness in “well, is it really GPT if has a different architecture that continuously learns and has longterm rewards?”. My guess was that it’d be fairly different from GPT architecturally, but that it wouldn’t depend on architectural insights we haven’t already made, it’d just be work to integrate existing insights.
Is your claim “this is insufficient – you still need working memory and the ability to model scenarios, and currently we don’t know how to do that, and there are good reasons to think that throwing lots of data and better reward structures at our existing algorithms won’t be enough to cause this to develop automatically via Neural Net Magic?”
So at this point I’m pretty uncertain of what neural nets can or can not learn to do. But at least I am confident in saying that GPT isn’t going to learn the kinds of abilities that would be required for actually fighting fires, as it is trained and tested on a fundamentally static task, as opposed to one that requires adapting your behavior to a situation as it develops. For evaluating at progress on those, projects like AlphaStar look like more relevant candidates.
I don’t feel confident in saying whether some combination of existing algorithms and training methods could produce a system that approached the human level on dynamic tasks. Most people seem to agree that we haven’t gotten neural nets to learn to do good causal reasoning yet, so my understanding of the expert consensus is that current techniques seem inadequate… but then the previous expert consensus would probably also have judged neural nets to be inadequate for doing many of the tasks that they’ve now mastered.