It’s not circular. I’m referring to specific objections people raise, like misclassifying objects or failing to react or whatever. The current state of the art often spends several seconds at a time with its cameras pointed at a screen instead of at the road.
I appreciate you continuing to refer to sacks of meat as "the current state of the art". It does drive the point home that we don't need to outrun the bear, just the other camper :)
I suppose that's a part of the issue: the SDV camp has been overenthusiastic in flying their "Mission Accomplished" banners, and hitting (pun not intended) yet another unexpected problem right afterwards. In other words, we're not at that point yet - in fact, we might not even have the complete toolset to measure this.
The field is in flux - alchemic approaches "what if we try something unrelated" might work for unrelated reasons etc.; practical applications that don't spontaneously combust are still some way out there.
Okay, "baseless" then. "It's easy [that's the baseless claim], we just haven't gotten around to it [which tends to point to the task not actually being easy]." In my opinion, this is the same class of "easy" that AI researchers have been chasing for half a century now, and the end goal always seems to be juuust out of reach - in other words, seems easy but isn't.
I didn’t say it’s easy. My point is merely that current systems are horribly flawed and the needs to be kept in mind when evaluating the flaws of the new systems. The fact that neural net systems are hard to understand and have no systematic design is not necessarily a blocker.
Not a blocker, but an eventual solution needs to come up with a meaningful comparison metric - the ones referred to now are meaningless ("no crashes except for things that are not counted as crashes under our definition, which doesn't look suspiciously circular").
