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Dr. Jeff Beck, a computational neuroscientist, discusses Bayesian modeling in empirical inquiry, active inference, and the integration of internalist and externalist cognition. They also explore the challenges of using a Bayesian approach in multi-agent systems, the relationship between data cardinality and representation, and the idea of creating specialized agents. Additionally, they touch on representation learning in neural networks and the development of fully Bayesian and generative transformers.
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Quick takeaways
- Bayesian analysis is a normative and logical approach to empirical investigation.
- Active inference involves building dynamic models in real-time for optimal experimental design and dynamic updating.
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Modeling Humans and Animal Behaviors as Bayesian Inference
The podcast episode explores the work of computational neuroscientist Jeff Beck, who studies how human and animal behaviors can be modeled as Bayesian inference. They are interested in how the brain encodes probability distributions and performs probabilistic reasoning.
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Listen to the best highlights from the podcasts you love and dive into the full episode