The Parallel Processing of Transformers

In this episode, we sit down with Konrad Kording, a neuroscientist and professor who delves into the intersection of brains, AI, and causality. We explore the evolution of machine learning and delve into a deep learning-based view of the brain. Konrad shares his experiences coding neural networks 20 years ago and how he eventually arrived at the idea of gradient descent. We also discuss deconstraints in both biology and machine learning and how these systems allow for faster progress in AI. Finally, we reflect on the current state of LLMs and explore the potential evolution of physical embodiment in the next few years. Tune in for an insightful conversation with one of the leading voices in neuroscience and AI. Don't forget to support the podcast on Patreon: https://www.patreon.com/rhyslindmark Full show notes and resources at: https://www.roote.co/episodes