For people who actually work with machine learning approaches (not the salesmen, but the people who use them), the post title isn’t news, but this story about Tesla makes an important point that also applies to ‘AI’ (boldface mine):

Tesla’s self-driving cars seem like a marvel of machine learning.

But in reality, the company relies on a small army of human “data annotators” who continuously improve how the cars drive by reviewing camera footage from thousands of Tesla drivers and teaching the vehicle how to behave like a human driver, like deciding when it’s appropriate to use a blinker or identifying a construction cone.

Business Insider has learned that those annotators focus their efforts on two high-profile categories of drivers: Tesla CEO Elon Musk and a select set of “VIP” drivers.

BI spoke with over a dozen current and former Tesla employees, all but one who spoke on condition of anonymity, who said images and video clips from Musk’s Teslas received meticulous scrutiny, while data from high-profile drivers like YouTubers received “VIP” treatment in identifying and addressing issues with the Full Self-Driving software. The result is that Tesla’s Autopilot and FSD software may better navigate routes taken by Musk and other high-profile drivers, making their rides smoother and more straightforward.

That means, experts say, Tesla’s resources are being unevenly distributed and could serve as a distraction toward the company’s larger mission of truly autonomous driving.

Ok, the part about placating Musk is both funny and pathetic, but the key point is that none of this works without “data annotators” who inform the machine learning algorithms what good and bad outputs are. Whether its initial training sets or ongoing maintenance, these systems are going to need people curating the input data and judging the output data. That’s going to be the expensive part and the thing that limits the profitability of this technology.