scottviteri

conversationism, the mathematics behind communication and understanding, used to raise the artificial intelligence as a child with love, as a painting, by midjourney Intro There seems to be a stark contrast between my alignment research on ontology maps and on cyborgism via Neuralink. I claim that the path has consisted of a series of forced moves, and that my approach to the second follows from my conclusions about the first. This post is an attempt to document those forced moves, in the hope that others do not have to duplicate my work. Ontology maps are about finding shape semantic-preserving functions between the internal states of agents. Each commutative diagram denotes a particular way of training neural networks to act as conceptual bridges between a human and AI. I originally started playing with these diagrams as a way of addressing the Eliciting Latent Knowledge problem. Erik Jenner has been thinking about similar objects here and here. Cyborgism via Neuralink is about using a brain-computer interface to read neural activation and write those predictions back to the brain, in the hope that neuroplasticity will allow you to harness the extra compute. The goal is to train an artificial neural network with a particularly human inductive bias. Given severe bandwidth constraints (currently three thousand read/write electrodes), the main reason to think this might work is that the brain and the transistor-based brain predictor are being jointly trained -- the brain can learn to send inputs to the predictor such that the predictions are useful for some downstream task. In other words, both the brain and the neural prosthesis are trained online. Think of predicting the activations of a fixed neural network given time series data (hard) versus the same task, but you pass gradients with respect to the base neural network's final loss through the bandwidth bottleneck into the predictor (less hard). In the section "An Alternative Form of Feedback", I introduce