Home » Verifying Mission-Critical AI Programs | Two Minute Papers #179 financial deepmind

Verifying Mission-Critical AI Programs | Two Minute Papers #179 financial deepmind



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Verifying Mission-Critical AI Programs | Two Minute Papers #179

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Verifying Mission-Critical AI Programs | Two Minute Papers #179
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24 thoughts on “Verifying Mission-Critical AI Programs | Two Minute Papers #179 financial deepmind”

  1. The problem with Neural Networks in critical systems is that humans can't comprehend how they make their decisions. If a network makes a mistake, it is very hard to determine what exactly caused the problem because there is no fixed set of rules by which decisions are made. This is the real issue with NNs, and IMO this is why NNs are not the ultimate solution to all optimization problems, which seems to be the general opinion in recent years.

  2. I found the non relevant video very distracting. I get that youtbe rewards great thumbnails, but for the content just a static screenshot of the paper would be better.

  3. Why are these noise attacks even possible? I mean, humans can see false patterns in the noise too, but these tiny features don't have a higher priority over bigger ones. What are we doing wrong?

  4. This is a great one, I know that alot of autonomous vehicle companies cannot really benifit from ML because of unpredictability, this can be a game changer in the autonomous vehicle industry in the future

  5. So far all of the classifiers operate on tiny chunks of the images and their relative differences. We can easily see a school bus, with or without the adversarial input (which to us, humans looks like noise by the way). I don't know if it's even possible, based on 2D images alone, to create a system that would actually "understand" the basic structure and model of objects (just like we have a 'model' of a bus in our minds) and would be able to recognize them on this macro-level. I can recognize a cat because I know it has four legs, a tail and a head with pointy ears, I don't stop at the level of characteristic gradients and edges.

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