Nonsense. These types of problems have been “illuminating” researchers for the last 5 decades. These are real shortcomings and will not be solved for another 5 decades or more. It’s unbeliebable how much in denial the AI folks are in.
Overhyped marketing (Face ID, Las Vegas self-driving bus, HSBC voice id, "AI imagines a Bank Butt sunset")
Questionable design choices (Amazon Echo, Alexa)
Adversarial attacks ("Google AI looks at rifles", street sign hack)
Bias (Allo turban emoji)
wtf is this (Facebook chatbot)
ML does have real shortcomings, but the problems in this list does not exemplify them well.
> Some of them are illuminating parts of ML that are problems (copying society's bias, adversarial attacks), but most of the rest are simply things being overhyped/regular design errors (anything to do with Alexa/google home).
It's a personal opinion that adversarial examples will always exist (they exist for pretty much all kinds of machine learning models), and that they might exist for human vision too.
Aka : If a gun could be mistaken for an helicopter just by altering a few invisible pixels, then the behavior of that system is by definition not similar at all to « vision » in its common sense. And so if it’s not, then what is it ?
Humans do have the advantage that our vision system can draw on a huge repository of real-world knowledge and has been fed with decades of high resolution training data.
My impression was that ML image recognition systems have exactly the same to draw on, non?
Then they are not really AI failures “of 2017”.
More like “AI problems that are still unsolved”, at best.