as a side note: not all the video was using autopilot mode, the icon changes to show where there was manual intervention

You make a succinct point. Can you elaborate on the difference between Station Keeping and Lane Keeping, what is generally available now as LKAS?

Compared to the current generation of “self-driving cars” I’d say humans excel at driving.

Adversarial attacks with low friction for the attacker e.g malicious software updates.

I think the difference is that software tends to be much more fragile - and more predictable - than humans. Paint fake lane markers, and an autopilot might drive full speed into a wall because it trusts them; the next 5 cars with autopilots will all do the same thing. An attacker can verify that an autopilot will do this ahead of time. A human on the other hand will be more likely to notice that things are amiss - they can pick up on contextual clues, like the fresh paint, and the fact they've driven that road hundreds of times before and instantly notice the change, and the pile of burning self-driving cars.

> Adversarial Attacks? Like

Like literally the article we’re commenting on. Image recognition systems in general are much more susceptible to errors in cases where humans wouldn’t even think twice.

And yes, “hey, a stop sign was here just yesterday” is also a situation for which humans are uniquely equipped, and computers aren’t.

I'm not sure why hackers prefer to hack large scale computer systems rather than individual humans, but they do. So we have to protect neural networks against adversarial examples for the same reason we have to protect databases against sql injections.

> so what's the critical difference here?

If neural networks are deployed at scale in self driving cars, a single bug could trigger millions of accidents.

Printing an adversarial example on billboards would lead to crashes all around the country. Are we going to assume no one is going to try? (btw: real world adversarial examples are easy to craft [1]).

[1] https://arxiv.org/pdf/1707.07397.pdf

Siri, at least, is total garbage. Half the time I try to dictate a simple reminder, Siri botches it. (The other day, I tried to text my wife that both Maddie and Tae sing. Siri kept transcribing “sing” as “sick.”) Siri at least is no better than Dragon Naturally Speaking was in the 1990s. The Windows 10 speech recognizer is somewhat better, but it’s still not usable (what was the last time you saw anybody use it?).

I assume you're basing your claim on WER on data sets like Switchboard, which is a garbage metric: https://medium.com/descript/challenges-in-measuring-automati....

Humans may not be particularly great at speech transcription, but they're phenomenal at speech recognition, because they can fill in any gaps in transcription from context and memory. At 95% accuracy, you're talking about a dozen errors per printed page. Any secretary that made that many errors in dictation, or a court reporter that made that many errors in transcribing a trial, would quickly be fired. In reality, you'd be hard pressed to find one obvious error in dozens of pages in a transcript prepared by an experienced court reporter. It is not uncommon in the legal field to receive a "proof" of a deposition transcript, comprising hundreds of pages, and have only a handful of substantive errors that need to be corrected. That is to say, whether or not the result is exactly what was said, it's semantically indistinguishable from what was actually said. (And that is why WER is a garbage metric--what matters is semantically meaningful errors.)

The proof of the pudding is in the eating. If automatic speech recognition worked, people would use it. (After all, executives used to dictate letters to secretaries back in the day.) The rare occasions you see people dictate something into Siri and Android, more often then not what you see is hilarious results.