Hi all,

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Hope this is not considered spammy, genuinely think it’s of interest to the community of this subreddit. For some context, I am Andrey Kurenkov, a PhD at Stanford. For a while now I’ve been running this thing called Skynet Today, with the mission of “Putting AI News In Perspective” or in other words debunking inaccurate portrayals of AI research in media. As many people here are researchers and feel annoyed at hype/misconceptions about AI, I wonder if any of you might want to join our effort (we are basically a rag tag group of grad students pulling this together in spare time). If interested, please consider taking a look at our join or just fill out our contribution survey directly, or just message me. Thanks!

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TLDR: I run a site to debunk misperceptions of AI news, pls join if you wanna help

Let’s say I have a chatbot that can generate random sentences (and hence we have no ground truth to compare to) like:

1. “I like the weather today because it is very sunny and bright”.

2. “I like the cat today because it is rainy”.

3. “I like cat yesterday it is because”.

What are the best methods to evaluate if each sentence makes sense based on both its grammar and/or content? Example 1. above would make the most sense in terms of content and grammar, 2. makes grammatical sense but not in terms of content (makes no sense to like a cat because it is rainy), and 3. above has horrible grammar.

The issue here is these are randomly generated sentences with no ground truth to compare to (unlike, for example, machine translation where you have an expected translation and hence can use BLEU or perplexity).

I just wanted to have an overview of the new stuff since I’ve been out of the loop for a while. Are there any github projects that implement the GAN and upscaling etc yet? Which one produces the most pleasant, hidef images these days? Thanks!

I’m recently into studying about deep learning accelerators, and the one I’m most interested in is TPU v3. Given that faster multiplication of much larger matrices is crucial for further development of the cutting edge generative models (GPT-2, Sparse Transformer), MAC bandwidth is becoming a severe bottleneck to the speed. To resolve this issue, I’d like to contribute to the development of TPU at various scales. However, given the scarcity of the publicly available documents of TPU v3 and its ongoing research, I have no idea what their research/development group considers to be the current bottleneck to their project. What can I do? I’m a PhD student of ML.

Hi there!

Those of you interested in VAEs and/or their applications probably know that having an expressive family of variational approximations is important. I wrote a small post on using Neural Samplers as variational approximations in preparation for the upcoming release of my own work on the topic, hope that’d be useful!

Excited to share our new research on reinforcement learning and robotics. To the best of our knowledge this is the first time a neural network policy has produced stable 3D walking in a human scale biped robot.

Video link: https://www.youtube.com/watch?v=TgFrcrARao0

Arxiv link: https://arxiv.org/abs/1903.09537

Abstract: Deep reinforcement learning (DRL) is a promising approach for developing legged locomotion skills. However, the iterative design process that is inevitable in practice is poorly supported by the default methodology. It is difficult to predict the outcomes of changes made to the reward functions, policy architectures, and the set of tasks being trained on. In this paper, we propose a practical method that allows the reward function to be fully redefined on each successive design iteration while limiting the deviation from the previous iteration. We characterize policies via sets of Deterministic Action Stochastic State (DASS) tuples, which represent the deterministic policy state-action pairs as sampled from the states visited by the trained stochastic policy. New policies are trained using a policy gradient algorithm which then mixes RL-based policy gradients with gradient updates defined by the DASS tuples. The tuples also allow for robust policy distillation to new network architectures. We demonstrate the effectiveness of this iterative-design approach on the bipedal robot Cassie, achieving stable walking with different gait styles at various speeds. We demonstrate the successful transfer of policies learned in simulation to the physical robot without any dynamics randomization, and that variable-speed walking policies for the physical robot can be represented by a small dataset of 5-10k tuples.

I’ll try my best to answer any questions!

Hi everybody! I am writing this book with Apress. So I thought I should just post about it here on Reddit (I am not sure if r/swift would be a better place) so I came here.

I would like to know what do you all think about this relatively a new differentiable programming language (Swift for TensorFlow). It can use nearly any Python libraries right in Swift.

And also if you want me to cover some important topics about neural networks or Swift just comment below.