Computational neuroscience

Computational models for brain science

Computational models for brain science

Dr. Laschowski discusses his lab's research in computational neuroscience, focusing on three core areas: reverse-engineering human motor control using reinforcement and optimal control models, developing high-accuracy neural decoding algorithms for brain-machine interfaces (BMIs), and creating brain-inspired deep learning models for computer vision. The talk highlights a long-term vision of discovering the fundamental principles of intelligence to build more efficient and robust AI.

907: Neuroscience, AI and the Limitations of LLMs — with Dr. Zohar Bronfman

907: Neuroscience, AI and the Limitations of LLMs — with Dr. Zohar Bronfman

Zohar Bronfman discusses why current LLMs are not on a path to AGI, contrasting their combinatorial creativity with the transformational, domain-general intelligence of humans. He argues that predictive models, not generative ones, deliver the most business value and explains how his platform, Pecan AI, automates the critical data preparation bottleneck to democratize predictive analytics for all businesses.