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 results. The more pictures of cats and dogs, and faster training iterations, the better the “computer program”, aka neural network. It’s a bit like Darwinism of randomly mutating software.
In other words, the company with more data, better data and faster training hardware, wins.
Tesla’s Data Advantage
Most folks would consider Google’s Waymo as the leader in autonomous vehicles. Despite boasting orders of 62,000 Chrysler vans and 20,000 electric Jaguars, Waymo’s official fleet size in 2021 is about 600 cars. (source) Each car must be disassembled by hand and reconstructed with Waymo’s own sensors and computers, making them super complex to build, scale, and debug.
In reality, skilled disassembly is required. Engineers must take apart the cars and put them back together by hand. One misplaced wire can leave engineers puzzling for days over where the problem is. (source)