At the 1 hr 38 min mark, when they're talking about the brain consuming a lot of energy I've been building an app using TensorFlow, used autoencoders, and wondered if human brains also implemented autoencoders -- where the general idea is to throw away information but still retain enough information to allow the prediction to be made. e.g. to train an autoencoder neural net for recognising a picture of a cat, your inputs and outputs are cat image samples, but the neural net is configured to have a pinch point, e.g. 100 neurons -&gt; 20 neurons -&gt; 100 neurons. And then, once trained, you have a machine that can take a full-resolution image of a cat (represented by 100 neurons) and crunch it down in a lossy way to 20 neurons. Switching over to the brain analogy, it seems cheaper to only have to maintain 20 neurons to be able to identify cats rather than 100. Within the same discussion area, I wondered if idiot savants could not potentially build autoencoders in their brains. So that's why they can, for example, photo-realistically recreate photos, etc.