Thanks. I think this argument assumes that the main bottleneck to AI progress is something like research engineering speed, such that accelerating research engineering speed would drastically increase AI progress?
I think that that makes sense as long as we are talking about domains like games / math / programming where you can automatically verify the results, but that something like speed of real-world interaction becomes the bottleneck once shifting to more open domains.
Consider an AI being trained on a task such as “acting as the CEO for a startup”. There may not be a way to do this training other than to have it actually run a real startup, and then wait for several years to see how the results turn out. Even after several years, it will be hard to say exactly which parts of the decision process contributed, and how much of the startup’s success or failure was due to random factors. Furthermore, during this process the AI will need to be closely monitored in order to make sure that it does not do anything illegal or grossly immoral, slowing down its decision process and thus whole the training. And I haven’t even mentioned the expense of a training run where running just a single trial requires a startup-level investment (assuming that the startup won’t pay back its investment, of course).
Of course, humans do not learn to be CEOs by running a million companies and then getting a reward signal at the end. Human CEOs come in with a number of skills that they have already learned from somewhere else that they then apply to the context of running a company, shifting between their existing skills and applying them as needed. However, the question of what kind of approach and skill to apply in what situation, and how to prioritize between different approaches, is by itself a skillset that needs to be learned… quite possibly through a lot of real-world feedback.
Thanks. I think this argument assumes that the main bottleneck to AI progress is something like research engineering speed, such that accelerating research engineering speed would drastically increase AI progress?
I think that that makes sense as long as we are talking about domains like games / math / programming where you can automatically verify the results, but that something like speed of real-world interaction becomes the bottleneck once shifting to more open domains.
Consider an AI being trained on a task such as “acting as the CEO for a startup”. There may not be a way to do this training other than to have it actually run a real startup, and then wait for several years to see how the results turn out. Even after several years, it will be hard to say exactly which parts of the decision process contributed, and how much of the startup’s success or failure was due to random factors. Furthermore, during this process the AI will need to be closely monitored in order to make sure that it does not do anything illegal or grossly immoral, slowing down its decision process and thus whole the training. And I haven’t even mentioned the expense of a training run where running just a single trial requires a startup-level investment (assuming that the startup won’t pay back its investment, of course).
Of course, humans do not learn to be CEOs by running a million companies and then getting a reward signal at the end. Human CEOs come in with a number of skills that they have already learned from somewhere else that they then apply to the context of running a company, shifting between their existing skills and applying them as needed. However, the question of what kind of approach and skill to apply in what situation, and how to prioritize between different approaches, is by itself a skillset that needs to be learned… quite possibly through a lot of real-world feedback.