Unveiling the Challenges of Computational Irreducibility in Neural Networks

The chapter explores the capabilities and limitations of neural networks in understanding complex systems like cellular automata and predicting natural processes. It discusses the struggle of neural nets with computational irreducibility in complex systems and the distinction between AI tasks that mimic human intelligence and those that directly understand non-human processes. The conversation also delves into the difficulties in determining protein shape and functional properties and how neural networks can provide subjective success in scientific problems like protein folding.