Gordon Wilson is the CEO of neuromorphics.com/">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 Related Podcasts * 002 – Jeff Hawkins * 020 – Mary Beth Henderson * 024 – Brian Pepin Disclaimer