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Comment by chillee | Hacker News Reader

Some of these are pretty eh.

"Facebook chatbot shut down" doesn't really show anything. It was a everyday failure of training that got overblown in the media.

"Google AI looks at rifles and sees helicopters": This wasn't new at all. Black box attacks have been around for a while. The paper purported to show a much more efficient attack, but it's not really a huge advancement.

In general, most of these aren't really what I would call "failures" of AI. 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).

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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.

Once you become aware of how many people on HN are part of that LessWrong AI immortality cult it becomes pretty obvious why they are so defensive.

Assuming you're talking about me, what in my comment came off as defensive? I definitely do not consider myself part of the whole "futurology"/Elon Musk/AGI in 15 years group, but I don't think this list exemplifies machine learning's weaknesses at all.

Could you elaborate on what you consider these "problems" are? The way I would break down these "problems" are:

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.

Maybe it's not a great list, but as far as I know, adversarial attacks are still a major unsolved research problem. Why pretend otherwise? They belong on the list, don't you think?

I'm not trying to pretend otherwise. Of the problems on this list, I think adversarial examples and bias are real problems.

> 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.

Adversarial attacks are a bit more than just that : they reveal how much we don’t have any intuitive knowledge on what the algorithm understands. In the sense that we cannot build any intuition on what the 1% mistakes cognitive ML-based algorithms will be triggered by.

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 ?

They changed a lot of pixels in a way that led a human to classify the image as not notably changed but led one AI to classify it as a different type of object. Of course the same thing is possible with human and AI reversed, there are images that are obvious to some AI but that would be misclassified by humans.

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.

> 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?

Humans also have the advantage to understand what world they’re living in, and be capable of reasoning. The way you describe human cognition using ML terms makes me think you’re confusing the map with the territory.

“last 5 decades”

Then they are not really AI failures “of 2017”.

More like “AI problems that are still unsolved”, at best.

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