From Jumping Spiders to Silicon: Neuroscience and the Future of Computing - Intel on AI Season 3, Episode 1

Intel on AI

In this episode of Intel on AI host Amir Khosrowshahi and Bruno Olshausen talk about neuroscience and the future of computing. Bruno is a professor at Berkeley with appointments in the Helen Wills Neuroscience Institute and School of Optometry. He is also the director of the Redwood center for Theoretical Neuroscience, which brings the fields of physics, mathematics, engineering, and neuroscience together to study how networks of neurons in the brain process information.

In the episode, Bruno and Amir discuss research about recording large populations of neurons, hyperdimensional computing, and discovering new types of engineering principles. Bruno talks about how in order to understand intelligence and its underpinnings, we have to understand the origins of intelligence and perceptual psychology outside of mammalian brains. He points to the sophisticated visual system of jumping spiders as inspiration for developing systems that use low energy in a small form factor. By better understanding the origins of perception and other biophysical structures, Bruno theorizes the artificial intelligence field may evolve beyond image recognition tasks of current neural networks. Bruno and Amir close the episode by talking about the elementary units of computation, the idea of “listening to silicon” as proposed by Carver Mead, neuromorphic computing, and what the future of research might hold.

Academic research discussed in the podcast episode:

• Spatially Distributed Local Fields in the Hippocampus Encode Rat Position
• Beyond inspiration: Three lessons from biology on building intelligent machines
• The Chinese Room Argument
• Digital tissue and what it may reveal about the brain
• Principles of Neural Design (Bruno calls this a “must read”)
• Experiencing and Perceiving Visual Surfaces
• Analog VLSI Implementation of Neural Systems
• OIM: Oscillator-based Ising Machines for Solving Combinatorial Optimisation Problems