Understanding Neural Networks
The conversation dives into the cutting-edge concepts of inner alignment and mechanistic interpretability, exploring how to uncover the latent knowledge within neural networks. Jeremie emphasizes the importance of understanding what models truly "believe" beyond their outputs. The complexity of human brains, with their vast neural connections and support cells, presents a challenge that contrasts sharply with simpler biological systems, highlighting the intricacies involved in AI development.In this clip
From this podcast
Super Data Science: ML & AI Podcast with Jon Krohn
668: GPT-4: Apocalyptic stepping stone? — with Jeremie Harris
Related Questions
I have a question about the episode AI Consciousness? Exploring the Possibility with Prof. Eric Schwitzgebel and the clip Mechanistic Interpretability. Will large language models scale all the way to artificial general intelligence (AGI) as discussed in the episode MEGATHREAT: The Dangers Of AI Are WEIRDER Than You Think! | Yoshua Bengio and the clip Neural Networks Explained?
How do we explain neural networks as discussed in the episode Mindscape 230 | Raphaël Millière on How Artificial Intelligence Thinks and the clip Neural Network Humility?
How complex is the human brain as discussed in the episode Andrej Karpathy: Tesla AI, Self-Driving, Optimus, Aliens, and AGI | Lex Fridman Podcast #333 and the clip Neural Networks vs. Biology?