Hello everyone,

we are researchers from TU-Berlin and UL FRI, and we are doing a research on how people interact with certain explainable AI techniques. We are currently in the process of gathering data and we need people to take part in our survey. If you have 15-20 minutes to spare to participate that would be extremely helpfull. All the details about the survey are explained in the survey itself.

The survey is reachable at this link: https://dss.vicos.si/. It is meant to be solved on a computer. It was tested and is working on Google Chrome and Safari.

After we analyze the data we will obviously share the paper 🙂 Thank you in advance!

On a new image dataset, unedited, without adversarial noise injection, ResNeXt-50 and DenseNet-121 see their accuracies drop to under 3%. Other former SOTA approaches plummet likewise by unacceptable margins:

– Natural Adversarial Examples – original paper, July 2019

– These Images Fool Neural Networks – TwoMinutePapers clip, 5 mins

So who says it’s a scandal? Well, I do – and I’ve yet to hear an uproar over it. A simple yet disturbing interpretation of these results is – there are millions of images out there that we humans can identify with obviousness and ease, yet our best AI completely flunk.

Thoughts on this? I summarize some of mine below, along a few of authors’ findings.

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Where’d they get the images? The idea’s pretty simple: select a subset classified incorrectly by several top classifiers, and find alike images.

Why do the NN’s fail? Misclassified images tend to have a set of features in common, that can be systematically exploited –> adversarial attacks. Instead of artificially injecting such features, authors find images already containing them: “Networks may rely too heavily on texture and color cues, for instance misclassifying a dragonfly as a banana presumably due to a nearby yellow shovel” (pg. 4).

Implications for research: self-attention mechanisms, e.g. Squeeze-and-Excite, improve accuracy on ImageNet by ~1% – but on this new dataset, by 10%. Likewise, related methods for increased robustness may improve performance on benchmark datasets by a little, but by a lot on adversarial ones.

• Thus, instead of pooling all efforts into maximizing F1-score on dataset A, testing against engineered robustness metrics that’ll promise improvement on an unsampled dataset B may be more worthwhile (e.g. “mean corruption error” pg. 8).

Implications for business: you don’t want your bear-catching drone to tranquilize a kid with a teddy.

with all the advances in image recognition technology, are image based “type your distorted text” captchas relevant now ? what will be the future of captcha ?

I’d imagine this dataset would consist of nodes being each Wikipedia page and connections being web links between pages. The nodes could also have its text as a feature matrix?

Does this exist already?

I’m trying out this BERT model from TFHub.

If my input tokens are [CLS] Hello world ! [SEP] [PAD] [PAD] [PAD] [PAD]

Do I need to make a mask like [1 1 1 1 1 0 0 0 0] or should I do [1 1 1 1 1 1 1 1 1] and let the pad token take care of it?

How can we deliver a video of Obama’s speech to a person in India who knows only Hindi?

In our paper published at ACM Multimedia 2019, we proposed a system to automatically translate a person speaking in one language into another with accurate lip synchronization. Please check out the demo video: https://www.youtube.com/watch?v=aHG6Oei8jF0

Project Page: https://cvit.iiit.ac.in/research/projects/cvit-projects/facetoface-translation

Code & Models: https://github.com/Rudrabha/LipGAN

I have developed a Python library for a very specific subfield of machine learning, and I have written a technical paper illustrating its philosophy and features.

I was planning to submit it to an ICLR workshop that unfortunately was not approved this year. I am left with this 4-page paper that I would like to see published at a top venue, and ICML is almost here.

Is it ok to submit it to ICML or should I look elsewhere?

I have a question regarding the use of C-VAE.

Say you had an image you want to do pose estimation on, and you have 1 or 2 landmarks but not the rest. How can you leverage this information as a prior as well as encode some kind of shape constraint implicitly? Could you use C-VAE’s somehow to represent possible human poses in the latent space, and then condition it using the ground truth landmarks along with the predicted heatmap? If not, how else could you model this?

Any advice, reading material or pointers would be hugely appreciated. Thanks!

https://slatestarcodex.com/2020/01/06/a-very-unlikely-chess-game/

Turns out, you can actually train GPT-2 to play chess by just having it predict the next move, represented by a string such as “e2e4”. I don’t believe it’s even given the board state, simply the list of previous moves. By just training on this, it’s able to successfully perform opening moves/strategies and into the midgame, though longer games tend to eventually fail due to the model outputting moves that simply aren’t valid.

The author emphasizes that this was a small project done in only a few days of work, but the initial results are pretty exciting.

The linked tweets have more detail: https://twitter.com/theshawwn/status/1212272510470959105