Gordon Wilson is the CEO of Rain Neuromorphics, a company developing neuromorphic computer chips to enable brain-like artificial intelligence. Gordon holds a B.S. in Statistics and Mathematics from the University of Florida.
Top 3 Takeaways

* Training deep learning algorithms is expensive.
* To understand the brain, you need to build one.
* Modern computing hardware doesn’t have the parallelism and energy efficiency of the brain.

Show Notes

* [1:12] Building a processor for brain math.
* [2:40] The cost of artificial neural networks.
* [3:36] What is “brain-inspired hardware”?
* [4:50] Nanowires and memristors.
* [6:25] Cross-disciplinary chip design.
* [7:30] Size of the brain vs. size of artificial neural networks.
* [9:05] Research vs. development.
* [12:00] Bridging brain science and AI.
* [13:54] Neuromorphics vs. GPUs.
* [18:00] Chips on the market.
* [20:40] Go-to-market: matrix multiplication.
* [22:22] Cost and energy of Rain’s hardware.
* [23:33] Does chip design impact software development?
* [24:08] Fusing training and inference.
* [26:26] Wide learning vs. deep learning.
* [29:30] Sparse learning.
* [32:10] Gordon’s book recommendations.

Selected Links

* A talk by Gordon
* An article about Rain’s technology
* OpenAI’s blog
* On Intelligence, by Jeff Hawkins
* WaitButWhy, a blog by Tim Urban

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* 020 – Mary Beth Henderson
* 024 – Brian Pepin

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