I like the use of L-knowledge to split the questions we insist on getting answered from those we don’t. That indeed seems to divide the space nicely!
What this means is that picking out the direct translator from all models consistent with the data must depend on the predictor. Otherwise, if the same training process is used for all predictors, it could give the human simulator on some even while giving the direct translator for others.
I don’t follow this point. If I take a reporter trained to be a direct translator on one predictor and hook it up to a different predictor I expect I’ll get some incoherent output rather than a human simulator. Why should I get a human simulator in this instance?
I don’t necessarily think we’d get an incoherent output, since it needs to be able to generalize to new questions, I expect a direct translator to answer questions by using computations to understanding a predictor (plus a model of natural language), rather than a function that maps the state of a particular predictor to answers for each question.
One reporter might only be able to understand the predictor up to a human level. If it gets a predictor with a human level understanding of the world, it can act as a direct translator, but if it gets a more complex predictor it would act as a human translator.
Ah! So you’re imagining the reporter not as a module that knows how to interpret the signals in a given predictor, but instead as a meta-learning system that figures out whatever predictor happens to be in front of it. Is that right? That seems like a much harder sort of model to build...
I think some generality is necessary, otherwise we’d have to retrain the reporter every time the predictor is updated. That would rule out a lot of desirable uses for a reporter, like using its output in the training process.
I like the use of L-knowledge to split the questions we insist on getting answered from those we don’t. That indeed seems to divide the space nicely!
I don’t follow this point. If I take a reporter trained to be a direct translator on one predictor and hook it up to a different predictor I expect I’ll get some incoherent output rather than a human simulator. Why should I get a human simulator in this instance?
I don’t necessarily think we’d get an incoherent output, since it needs to be able to generalize to new questions, I expect a direct translator to answer questions by using computations to understanding a predictor (plus a model of natural language), rather than a function that maps the state of a particular predictor to answers for each question.
One reporter might only be able to understand the predictor up to a human level. If it gets a predictor with a human level understanding of the world, it can act as a direct translator, but if it gets a more complex predictor it would act as a human translator.
Ah! So you’re imagining the reporter not as a module that knows how to interpret the signals in a given predictor, but instead as a meta-learning system that figures out whatever predictor happens to be in front of it. Is that right? That seems like a much harder sort of model to build...
I think some generality is necessary, otherwise we’d have to retrain the reporter every time the predictor is updated. That would rule out a lot of desirable uses for a reporter, like using its output in the training process.