Discovery of a Universal Function Approximator and Predictability in Various Problems

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The discovery of a universal function approximator has led to the possibility of creating a universal computer that can be taught. This discovery became evident while working on computer vision and trying to improve CUDA as a computer vision system. The effectiveness of Alex net in computer vision made researchers question why it was so successful. This led to the realization that in many problems, causality is not important, but predictability is. Whether it is predicting consumer preferences, movie choices, music preferences, or even weather, the focus is on the ability to predict outcomes rather than understanding the underlying causality.

We finally sit down with the man himself: Nvidia Cofounder & CEO Jensen Huang. After three parts and seven+ hours of covering the company, we thought we knew everything but — unsurprisingly — Jensen knows more. A couple teasers: we learned that the company’s initial motivation to enter the datacenter business came from perhaps not where you’d think, and the roots of Nvidia’s platform strategy stretch back beyond CUDA all the way to the origin of the company.

We also got a peek into Jensen’s mindset and calculus behind “betting the company” multiple times, and his surprising feelings about whether he’d go on the founder journey again if he could rewind time. We can’t think of any better way to tie a bow on our Nvidia series (for now). Tune in!

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