Back in 2015, everyone thought their kids wouldn’t need to learn how to drive. Supervised machine learning (under the auspices of being “AI”) was advancing so quickly — in just a few years it had gone from mostly recognizing cats to more-or-less driving. It seemed that AI was following a Moore’s Law Curve:
...
Projecting that progress forward, all of humanity would certainly be economically uncompetitive in the near future. We would need basic income to cope, to connect with machines to stand a chance, etc.
Five years later and AV professionals are no longer promising Artificial General Intelligence after the next code commit. Instead, the consensus has become that we’re at least 10 years away from self-driving cars.
It’s widely understood that the hardest part of building AI is how it deals with situations that happen uncommonly, i.e. edge cases. In fact, the better your model, the harder it is to find robust data sets of novel edge cases. Additionally, the better your model, the more accurate the data you need to improve it. Rather than seeing exponential improvements in the quality of AI performance (a la Moore’s Law),
we’re instead seeing exponential increases in the cost to improve AI systems — supervised ML seems to follow an S-Curve.
The S-Curve here is why Comma.ai, with 5–15 engineers, sees performance not wholly different than Tesla’s 100+ person autonomy team. Or why at Starsky we were able to become one of three companies to do on-public road unmanned tests (with only 30 engineers).