WBE and DRL: a Middle Way of imitation learning from the human brain : r/reinforcementlearning

Reinforcement learning is a subfield of AI/statistics focused on exploring/understanding complicated environments and learning how to optimally acquire rewards. Examples are AlphaGo, clinical trials & A/B tests, and Atari game playing. Most deep learning methods attempt to learn artificial neural networks from scratch, using architectures or neurons or approaches often only very loosely inspired by biological brains; on the other hand, most discussions of 'whole brain emulation' assume that one will have to learn every or almost every neuron in large regions of or the entire brain from a specific person, and the debate is mostly about how realistic (and computationally demanding) those neurons must be before it yields a useful AGI or an 'upload' of that person. This is a false dichotomy: there's a lot of approaches in between. Highlighted by u/starspawn0 a year ago ("A possible unexpected path to strong A.I. (AGI)"), there's an interesting vein of research which takes the middle way of

saved by

• Paul Scotti