I think pure researchers will be gone except for google or facebook, since deep learning frameworks are designed and updated for easy use. I guess deep learning will be normal tech in the future.

https://www.youtube.com/watch?v=Y2uKVhATv68

100% of the vocals here were generated by my model, not spoken by Ben Shapiro himself, and do not reflect Shapiro’s views. Shapiro’s voice was created with a TTS model I trained using my implementation of the papers “Style Tokens: Unsupervised Style Modeling, Control and Transfer in End-to-End Speech Synthesis” (https://arxiv.org/abs/1803.09017) and “Towards End-to-End Prosody Transfer for Expressive Speech Synthesis with Tacotron” (https://arxiv.org/abs/1803.09047), using only just over 2 hours of Shapiro audio (though I suppose that’s more like 3-4 hours worth of speech for the average person). After learning Shapiro’s speech patterns it’s amusing that the speech generated by this model is even faster than the average speed Eminem raps this song (only the part at 3:12 is sped up 1.5x).

Can seemingly NSFW images can fool AIs as well as humans? To answer this, I gathered 50 popular images with NSFW optical illusions and fed them to 8 publicly available image detectors. Results? Yes, they are foolable. Read more in my Medium article and contribute with your input.

Hey reddit,

In my latest weekend-project I have been using a Variational Autoencoder to build a feature-based face editor. The model is explained in my youtube video:

https://youtu.be/uszj2MOLY08

You can inspect the code at Github:

https://github.com/SteffenCzolbe/FeatureTransferApp

The feature editing is based on modifying the latent distribution of the VAE. After training of the VAE is completed, the latent space is mapped by encoding the training data once more. Latent space vectors of each feature are determined based on the labels of the training data. Then to edit an image, we can add a combination of feature vectors to its latent distribution, and then reconstruct it. The reconstruction creates an altered version of the original image, based on the featrures we added to the latent representation.

The model used is heavily inspired by the Bate-VAE used in this paper by google deepmind (https://pdfs.semanticscholar.org/a902/26c41b79f8b06007609f39f82757073641e2.pdf). I made some adjustments to it to incorporate more recent advancements in neural network architecture, like using a Leaky ReLu activation function. The dataset used is celebA, which consist of 200.000 annotated images of celebrities. I aligned and cropped the images to a 64×64 resolution before training. The model is implememted in PyTorch, and PyGame has been used for the GUI. Training on my single consumer grade GPU took about 1:30h. The finished application, inducing the trained model, runs smoothly even without GPU support.

This project has been quite cool, playing with the result has been good fun. I got a lot of hands-on experience with VAEs. Creating a YouTube video explaining the project let me to learn much more about video editing and presentation techniques. I’m testing the waters with presenting this project in video form, lets see if it pays off!

I started working on the Toxicity Detection Kaggle challenge. I’d be curious to know how people familiar with NLP handle this kind of problems.

My problem is that I want to try things out, but running anything on the full model takes hours, and running on a subset of the data doesn’t always tell me how good the result is.

I have multiple computers and GPUs at home so I juggle between them but my workflow is generally a mess. It’s difficult to keep track of what I’m doing, what experiments I should prioritize, optimize hyperparameters etc.

Is there an all-in-one solution out there to manage my experiments? I’m starting to experiment with Kubernetes but it’s a lot of overhead to configure and run jobs.

I guess I could use a beefier tool like Kubeflow but I’m wondering if that wouldn’t be too big of a tool for the job.

How are professionals handling things?

I found a copy of the Society of Mind by Minsky here

AI has not gone this way since. Faster computation and better algorithms have each contributed about the same improvement. Better bigger datasets have also been a huge deal.

The big AI breakthroughs Deep Blue, Watson, AlphaGo, Alphafold*, self driving cars, image recognition have not come from either agents or encoding human expertise into algorithms. But from better data and faster processing.
The Bitter Lesson by Rich Sutton is good on how improving datasets, algorithms and hardware has improved AI http://www.incompleteideas.net/IncIdeas/BitterLesson.html

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In The book of Why Pearl talks about the scruffies versus the neats and why the scruffies that just get things to work are in the ascendant at the moment. I am probably being unfair to Minsky here as I read his book 20 years ago. But I read it as more about finding underlying principles of cognition that we would put into use. And I do not see many cases where we have.

But how much of Minsky’s vision has happened? And will more happen in future?

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*This is arguable as there was a good amount of NLP in the original Watson. Or that the Alphas are doing similar hierarchical reasoning to what Minsky talked about.

Live stream & paper:

https://aisc.a-i.science/events/2019-04-16/

This is a small work I did for my MSc final year project.
https://arxiv.org/abs/1904.00796

Would love to read your responses.

I am delighted to announce that I have submitted my first-ever preprint to arXiv. Any feedback would be highly appreciated. https://arxiv.org/abs/1904.07200

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Abstract: Can neural networks learn to solve partial differential equations (PDEs)? We investigate this question for two (systems of) PDEs, namely, the Poisson equation and the steady Navier–Stokes equations. The contributions of this paper are five-fold. (1) Numerical experiments show that small neural networks (< 500 learnable parameters) are able to accurately learn complex solutions for systems of partial differential equations. (2) It investigates the influence of random weight initialization on the quality of the neural network approximate solution and demonstrates how one can take advantage of this non-determinism using ensemble learning. (3) It investigates the suitability of the loss function used in this work. (4) It studies the benefits and drawbacks of solving (systems of) PDEs with neural networks compared to classical numerical methods. (5) It proposes an exhaustive list of possible directions of future work.