016 Differential Outcomes with James Fodor

James Fodor is a science podcaster, an essayist, and currently, a PhD candidate in computational neuroscience and computational linguistics at the University of Melbourne (Australia). His intellectual and research interests cover such diverse areas as cognitive science, computer science, philosophy, theology, and economics. In this conversation, KMO and James discuss: 02:07 – A brief history of "The Science of Everything" 10:08 – How neural networks learn vs how humans learn 12:52 – The uncomfortable question of back-propagation 16:00 – Acquiring language and concepts 19:52 – Why and when machines' way of learning is important 23:22 – Illogical language models and the Internet lurking behind 25:52 – Conversation starters and GPT's talking to one another 27:45 – Modeling the mind vs the "just a neural network" cop-out 33:08 – A space of possible minds and complementing human intelligence 36:50 – Idiot chat bots and fearing AI 38:15 – The near-term future of AI 41:38 – Predicting the prosperity of nations James Fodor: The Science of Everything Podcast : fods12.podbean.com The Godless Theist Blog: thegodlesstheist.com James on YouTube: youtube.com/c/JamesFodor/ KMO: Twitter: @Kayemmo en.padverb.com/kmo Padverb: The Padverb Telegram Channel: t.me/padverbpodcast