Artificial Intelligence and Brain Research

Patrick Krauss studied medicine, computer science and physics. After completing his doctorate in neuroscience, he habilitated in linguistics on the subject of language processing in neural networks and the brain. He researches and teaches at the University of Erlangen-Nuremberg and the University Hospital Erlangen on topics at the interface of neuroscience, artificial intelligence and language. His scientific work includes over 80 publications.

Part I: Brain research.- The most complex system in the universe.- Building blocks of the nervous system.- Organization of the nervous system.- Organization of the cortex.- Imaging techniques: Watching the brain think.- Memory.- Language.- Cognitive maps and navigation in mental spaces.- Consciousness.- Part II: Artificial intelligence.- What is artificial intelligence.- How does artificial intelligence learn.- Playful artificial intelligence.- Recurrence: learning is not a one-way street.- Creativity: generative artificial intelligence.- Language-talented AI: ChatGPT and co.- How AI learns to learn.- What are AI developers researching today?.- Part III: Challenges.- What is a toaster? Dangerous stickers and other attacks.- Images in rain and sun: It's all about the data.- Hallucinating machines: Fact checks and world models.- Alchemy, reproducibility and black boxes.- A critical appraisal: What AI can't (yet) do.- Challenges of brain research.- What does it mean to understand a system?.- Part IV: Integration.- AI as a tool in brain research.- AI as a model for the brain.- Neuroscience 20: Using brain research to understand AI.- The brain as a template for AI.- Conscious machines?.- Outlook: Holodecks, uploads, brains in tanks and the singularity.