Doesn’t the (relatively short) task my manager does, of breaking projects into component tasks for me to do entail knowledge of the specific subcomponents? Is there a particular reason to believe that this task won’t be solved by an AI that otherwise knows to accomplish tasks of similar length?
And automated adaptation (continual learning, test time training) should enable a lot of serial time that would overcome even issues with splitting a problem into subproblems (it’s not necessarily possible to solve a 10-year problem in 2 years with any number of competent researchers and managers). So to the extent in-context learning implements continual learning, presence of any visible bounds on time horizons in capabilities indicates and quantifies limitations of how well it actually does implement continual learning. A genuine advancement in continual learning might well immediately do away with any time horizons entirely.
Doesn’t the (relatively short) task my manager does, of breaking projects into component tasks for me to do entail knowledge of the specific subcomponents? Is there a particular reason to believe that this task won’t be solved by an AI that otherwise knows to accomplish tasks of similar length?
And automated adaptation (continual learning, test time training) should enable a lot of serial time that would overcome even issues with splitting a problem into subproblems (it’s not necessarily possible to solve a 10-year problem in 2 years with any number of competent researchers and managers). So to the extent in-context learning implements continual learning, presence of any visible bounds on time horizons in capabilities indicates and quantifies limitations of how well it actually does implement continual learning. A genuine advancement in continual learning might well immediately do away with any time horizons entirely.