That&#x27;s true, there are certainly many optimization objectives computationally intractable, or perhaps too abstract to be useful for learning.<p>However, I would argue the prior of Bayesian modeling can be just as nebulous and computationally intractable as an optimization objective. Like supervised learning, Bayesian modeling is just a tool.<p>I&#x27;m skeptical that we will reach AI through a deep understanding or modeling of the brain. Technology and computer science advances more quickly than the biological sciences, at least in recent times. You might argue a success in robotics like [0] is a motor control system. But they built this extending mathematical frameworks not being biologically inspired, and the big wins there didn&#x27;t come from fixating on a learning framework or biological mimicry; just like humans learning to fly didn&#x27;t come about from flapping wings like a bird. At some point we hacked an engine (invented for other purposes) onto a wing and came up with powered flight.<p>As an aside, only seeing input a limited number of times would likely improve your ability to find models that generalize as your model must be able to take these one off learnings and unify them in some way to achieve high training performance. With respect to human learning, a specific individual only has one chance, but nature has had many. We are only a selection of those chances that seemed to work well enough. There are many commonalities to existence that allow for this to work well in practice.<p>[0] <a href="http:&#x2F;&#x2F;groups.csail.mit.edu&#x2F;rrg&#x2F;papers&#x2F;icra12_aggressive_flight.pdf" rel="nofollow">http:&#x2F;&#x2F;groups.csail.mit.edu&#x2F;rrg&#x2F;papers&#x2F;icra12_aggressive_fli...</a>