A recording of this talk is available online.

The importance of understanding the principles of brain computation and incorporating them into artificial systems is often considered necessary to advance AI technologies. However, the recent advent of large, "Foundational" vision and language models casts doubt on this assumption, as recent AI architectures differ considerably from the brain. Yet, the human brain consumes far less energy to solve tasks similar to large AI models while demonstrating greater resilience to ambiguous cues, physical damage, and superior reasoning capabilities. This raises important questions: Can one emulate the brain's efficiency and robustness? Will such brain-inspired solutions enhance state-of-the-art AI algorithms or will they lead to fundamentally different solutions? This lecture aims to shed light on these questions from the perspective of brain-inspired "neuromorphic computing", explaining how current AI was shaped by neuroscience, what stands in the way of emulating the brain, and the potential benefits of taking a deeper dive into how life shapes computation.

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