GaleHawkins said:I think Autonomous is going to become huge it the next 5 years.
Really? AVs are more difficult than some realized five years ago:
In 2015, I got obsessed with the idea of driverless trucks and started Starsky Robotics. In 2016, we became the first street-legal vehicle to be paid to do real work without a person behind the wheel. In 2018, we became the first street-legal truck to do a fully unmanned run, albeit on a closed road. In 2019, our truck became the first fully-unmanned truck to drive on a live highway. And in 2020, we’re shutting down.
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.
There are too many problems with the AV industry to detail here: the professorial pace at which most teams work, the lack of tangible deployment milestones, the open secret that there isn’t a robotaxi business model, etc. The biggest, however, is that supervised machine learning doesn’t live up to the hype. It isn’t actual artificial intelligence akin to C-3PO, it’s a sophisticated pattern-matching tool.
Our competitors, on the other hand, invested their engineering efforts in building additional AI features. Decision makers which could sometimes decide to change lanes, or could drive on surface streets (assuming they had sufficient map data). Really neat, cutting- edge stuff.
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).
https://medium.com/starsky-robotics-blog/the-end-of-starsky-robotics-acb8a6a8a5f5