Great to see these views are becoming more mainstream. The talk does not mention it, but it's likely convergent with neuroscience ideas of: - Bayesian Brain -

Great to see these views are becoming more mainstream. The talk does not mention it, but it's likely convergent with neuroscience ideas of: - Bayesian Brain - Theory of constructed emotions (https://www.youtube.com/watch?v=0gks6ceq4eQ https://www.youtube.com/watch?v=0gks6ceq4eQ) - Free energy principle and Active Inference (https://www.youtube.com/watch?v=Y1egnoCWgUg https://www.youtube.com/watch?v=Y1egnoCWgUg) A good overview is also here: https://towardsdatascience.com/why-intelligence-might-be-simpler-than-we-think-1d3d7feb5d34 https://towardsdatascience.com/why-intelligence-might-be-sim... The issue to be reconciled is though that some of those ideas are talking about "keeping uncertainty in the sweet spot, not too high or low" while others about "minimising uncertainty/prediction error". I think the difference will turn out to be only in relation to how far of a future/prediction we are talking about. So optimising for long-term vs the short-term.