Why I’m Still All-In on TSLA: Part 3 — Tesla AI/Robotics

Welcome to Part 3 of the series. Part 1 covers Tesla’s manufacturing advantage. Part 2 covers Tesla’s energy business. In this article, I will give my assessment of Tesla’s Artificial Intelligence efforts and my take on its bottom line.

Disclaimer: I’m not a financial advisor. These are just my opinions. Do your own homework, blah blah blah.

What is AI? ML?

I’m not a trained AI engineer, but I’ve worked on simple Machine Learning prototypes while at Google. Machine Learning is a subset of Artificial Intelligence where machines learn by itself from data. I’d like to think of ML like programming turned inside out.

Traditionally, a software engineer is given a set of inputs and outputs as “specifications”, writes the rules, and checks that the program generates the expected outputs given the expected inputs. We call this Test Driven Development. This is easy for things like well understood computations and if-then-else type logic, but much more difficult for problems like distinguishing a cat from a dog where the rules aren’t as obvious.

ML flips this around. Given lots of inputs, a software team creates a neural network, which is usually a complex set of matrix multiplications, so that each input generates the expected output, like identifying a cat vs. a dog. The set of multiplications have adjustable coefficients, possibly millions of them, and “training” tries random combinations to find the best…