3 ms·

FWIW, we now know Uber's technology didn't do this, but we don't necessarily know the nitty-gritty details of how other self-driving systems work. For me, the

FWIW, we now know Uber's technology didn't do this, but we don't necessarily know the nitty-gritty details of how other self-driving systems work. For me, the most important take-away isn't something about Uber being sloppy. It's that the distinction between "machine learning" and "artificial intelligence" really matters, and the efforts of marketing teams to over-hype ML by selling it as AI may be hindering our society's ability to make cogent public policy decisions around these technologies. (No, I'm not really interested in haggling over ideas like "hard AI" and "soft AI" here, either. Normal people don't split those hairs. Nerds generally don't, either, except when they're trying to score easy points in a debate.)

wongarsu 7y ago
The machine learning part could have gone better: it didn't expect a pedestrian and jumped between vehicle, other, bicylce and unknown. But that would have been good enough, the failure is in the code around that. When your classification is unstable or low confidence you can't trust it, which in a safety critical system would mean falling back to something else. That could be using a different classifier (e.g. linear interpolation of the object's position to find out if we come anywhere close to it) or just giving up and stopping the vehicle. Just continuing as if the fluctuating results were reliable wouldn't be acceptable in any industry.
- Faark 7y ago
  What kind of tech do you expect them to use? While I hope it wasn't, I'd not be surprised with them just running a classifier over the current scan, have it spit out "vehicle" at coordinate A, search the previous scan's analysis for a matching vehicle close by. How do you handle uncertain/flip classifications? Or objects appearing to suddenly "spawn"/"despawn"? A proper "what object was that in the past" would likely require its own AI. Reliable linear interpolation doesn't seem that simple to me.

wongarsu 7y ago

The machine learning part could have gone better: it didn't expect a pedestrian and jumped between vehicle, other, bicylce and unknown. But that would have been good enough, the failure is in the code around that. When your classification is unstable or low confidence you can't trust it, which in a safety critical system would mean falling back to something else. That could be using a different classifier (e.g. linear interpolation of the object's position to find out if we come anywhere close to it) or just giving up and stopping the vehicle. Just continuing as if the fluctuating results were reliable wouldn't be acceptable in any industry.

Faark 7y ago
What kind of tech do you expect them to use? While I hope it wasn't, I'd not be surprised with them just running a classifier over the current scan, have it spit out "vehicle" at coordinate A, search the previous scan's analysis for a matching vehicle close by. How do you handle uncertain/flip classifications? Or objects appearing to suddenly "spawn"/"despawn"? A proper "what object was that in the past" would likely require its own AI. Reliable linear interpolation doesn't seem that simple to me.