I want to point out the two main pillars I think your model has to assume for it to be the best model for prediction. (I think they’re good assumptions)
1. There is a steep difficulty increase in training NNs that act over larger time spans. 2. This is the best metric to use as it outcompetes all other metrics when it comes to making useful predictions about the efficacy of NNs.
I like the model and I think it’s better than not having one. I do think it misses out on some of the things Steven Byrnes responds with. There’s a danger of it being too much of a Procrustes bed or overfitted as specific subtasks and cognition that humans have evolved might be harder to replicate than others. The main bottlenecks might then not lay in the temporal planning distance but in something else.
My prior on the t-AGI not being overfitting is probably something like 60-80% due to the bitter lesson, which to some extent tells us that cognition can be replicated quite well with Deep Learning. So I tend to agree but I would have liked to see a bit more epistemic humility to this point I guess.
I want to point out the two main pillars I think your model has to assume for it to be the best model for prediction. (I think they’re good assumptions)
1. There is a steep difficulty increase in training NNs that act over larger time spans.
2. This is the best metric to use as it outcompetes all other metrics when it comes to making useful predictions about the efficacy of NNs.
I like the model and I think it’s better than not having one. I do think it misses out on some of the things Steven Byrnes responds with. There’s a danger of it being too much of a Procrustes bed or overfitted as specific subtasks and cognition that humans have evolved might be harder to replicate than others. The main bottlenecks might then not lay in the temporal planning distance but in something else.
My prior on the t-AGI not being overfitting is probably something like 60-80% due to the bitter lesson, which to some extent tells us that cognition can be replicated quite well with Deep Learning. So I tend to agree but I would have liked to see a bit more epistemic humility to this point I guess.