Graham discusses the evolution of architecture engineering in AI, highlighting the shift towards meta learning algorithms that can autonomously optimize processes like merging multimodal data. He references innovative work from Google Brain and Twitter, emphasizing the potential of algorithms to automate tasks traditionally handled by skilled researchers. Sam reflects on the journey to understand deep neural network architecture, noting the emergence of various automated methods, including Bayesian approaches.
