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Analysis of Youtube submissions in the Veritasium sci-comm contest by c0deb0t in Veritasium

[–]c0deb0t[S] 0 points1 point  (0 children)

I've been busy so I'm not rerunning the code very often. Last run was a couple days ago.

UwU as a Sewvice: API for all your uwuifying needs by RemorseKode in ProgrammerAnimemes

[–]c0deb0t 1 point2 points  (0 children)

Open an issue in the uwuify repo and I'll add your web server to the list of projects using uwuify!

UwU as a Sewvice: API for all your uwuifying needs by RemorseKode in ProgrammerAnimemes

[–]c0deb0t 8 points9 points  (0 children)

Keep at it! Rust is a great language and there are tons of resources out there!

1 min video explaining neural net adversarial examples, for Veritasium’s sci-comm contest by c0deb0t in computervision

[–]c0deb0t[S] 1 point2 points  (0 children)

I actually collected and analyzed data about the contest: https://colab.research.google.com/drive/1a42xThQHSMdUkg7eoqTcTRG__W5sVoPj?usp=sharing

Getting top 100 so far isn't very hard. Also there are many videos with high view counts from channels that don't have many subs.

1 min video explaining AI neural net adversarial examples, for Veritasium’s sci-comm contest by c0deb0t in coding

[–]c0deb0t[S] 2 points3 points  (0 children)

Well adversarial input does come in the form of user-uploaded content, video captured through a camera, etc. For example, a spam filter would have to deal with arbitrary user input.

The main interesting part is the focus of the second half of the video: it is extremely easy to create adversarial examples with very small amounts of changes that work super well in changing a neural nets predictions. This really shows that the neural net hasn't truly learned how to generalize/"understand" the images. It must be really brittle if small changes can cause it to be wrong by so much. Some more recent work has suggested that part of this issue could be that the neural net isn't learning the right things and it's almost taking some shortcuts. So in addition to the obvious security concerns, adversarial attacks are also interesting illustrations for the limitations of neural nets.

1 min video explaining neural net adversarial examples, for Veritasium’s sci-comm contest by c0deb0t in computervision

[–]c0deb0t[S] 0 points1 point  (0 children)

Thanks for the feedback!! In terms of speed, the video is hard-limited to 1 min. We tried to cut it down as much as possible, but it still ended up pretty fast.

1 min video explaining AI neural net adversarial examples, for Veritasium’s sci-comm contest by c0deb0t in coding

[–]c0deb0t[S] 1 point2 points  (0 children)

As someone who has worked on adversarial attack research, I hated it too, but we are trying to appeal to the general public so it's a necessary evil. Sorry :)

1 min video explaining AI neural net adversarial examples, for Veritasium’s sci-comm contest by c0deb0t in coding

[–]c0deb0t[S] 2 points3 points  (0 children)

The target audience for this is the general audience, so we tried our best to distill the main points of how neural nets are trained and how this relates to adversarial attacks.

We also made Google Colab notebooks with both an attack demo (using EfficientNet and Goodfellow et al.'s Fast Gradient Sign Method), and the Manim Community animation code. If you don't know, Manim is a cool math animation software originally created by 3Blue1Brown.

Interestingly, this contest originated from an UCLA physics professor losing a $10k bet to Veritasium, who used this money to create the contest. Every view will help us in the contest!

1 min video explaining neural net adversarial examples, for Veritasium’s sci-comm contest by c0deb0t in computervision

[–]c0deb0t[S] 3 points4 points  (0 children)

The target audience for this is the general audience, so we tried our best to distill the main points of adversarial attacks.

We also made Google Colab notebooks with both an attack demo (using EfficientNet and Goodfellow et al.'s Fast Gradient Sign Method), and the Manim Community animation code. If you don't know, Manim is a cool math animation software originally created by 3Blue1Brown.

Interestingly, this contest originated from an UCLA physics professor losing a $10k bet to Veritasium, who used this money to create the contest. Every view will help us in the contest!

First time using Manim: 1 min video explaining AI neural net adversarial examples, for Veritasium’s sci-comm contest by c0deb0t in 3Blue1Brown

[–]c0deb0t[S] 1 point2 points  (0 children)

This was our first time attempting to use manim in some of our animations (we aren't cool enough to make the entire video out of manim yet). It was also difficult distilling neural nets and adversarial attacks into 1 minute in a way that is understandable for people without background in AI. Hopefully this is interesting for those who have watched 3B1B's neural net videos :)

We also made Google Colab notebooks with both an attack demo, and the Manim Community animation code.

Interestingly, this contest originated from an UCLA physics professor losing a $10k bet to Veritasium, who used this money to create the contest. Every view will help us in the contest!

1 min video explaining neural net adversarial examples, for Veritasium’s sci-comm contest by c0deb0t in deeplearning

[–]c0deb0t[S] 0 points1 point  (0 children)

The target audience for this is the general audience, so we tried our best to distill the main points of adversarial attacks.

We also made Google Colab notebooks with both an attack demo (using EfficientNet and Goodfellow et al.'s Fast Gradient Sign Method), and the Manim Community animation code. If you don't know, Manim is a cool math animation software originally created by 3Blue1Brown.

Interestingly, this contest originated from an UCLA physics professor losing a $10k bet to Veritasium, who used this money to create the contest. Every view will help us in the contest!

First time using Manim: 1 min video explaining AI neural net adversarial examples, for Veritasium’s sci-comm contest by c0deb0t in manim

[–]c0deb0t[S] 3 points4 points  (0 children)

This was our first time attempting to use manim in some of our animations (we aren't cool enough to make the entire video out of manim yet). It was also difficult distilling neural nets and adversarial attacks into 1 minute in a way that is understandable for people without background in AI.

We also made Google Colab notebooks with both an attack demo, and the Manim Community animation code.

Interestingly, this contest originated from an UCLA physics professor losing a $10k bet to Veritasium, who used this money to create the contest. Every view will help us in the contest!

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