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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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O wow thanks for this man, it's clear you do this for the love of science
Thanks this is amazing.
A neved hallattáig azt hittem, indiai akcentust hallok, bocs.
Great video! I enjoyed this much more than all the new graphics rendering engines.
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.
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.
Could you perhaps talk about the StarCraft 2 AI API that DeepMind has been working on? Great video as always!
I love this channel so much. Thank you for providing such quality content
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?
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
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.
super cool
Even my earliest ANNs, more then 20 years ago were trained with added random noise. Would that not harden them against adversarial inputs?
Could an AI be trained to translate any given neuron network to a fixed mathematical function?
This is amazing! Just yesterday, I was discussing the problem of unpredictable failure modes in deep learning aircraft navigation systems in another comment thread on Youtube: https://www.youtube.com/watch?v=gB_-LabED68&lc=z12giviohl3hvlusb22agplbaxivip1ax04.1502588991481293
Another fantastic video! Thank you so much!
Edit: I love the internet!
Please make another video about other YouTube channel and website suggestions by you
love your videos!
Awesome to see that Neural Network will become more stable !
Thanks for this one
watch?v=5WVu798pmRc
This is the kind of papers that I like!
Would selective Gaussian blur, that you can find in GIMP for example, help against that noise attack?
Amazing as always!
Microsoft's AI apparently spoke in public like donald trump does in private.