Without disclosing the details for the sake of anonymity: I’ve found a severe mistake in a competing paper, making it harder to publish my own. The performance metrics of the competing paper are computed incorrectly, meaning that their actual error rates are much higher than what the authors claim to have.

The competing paper has already been published in the conference proceedings of AAAI. My question is how I should deal with this situation. – Should I report on that mistake in my own work? – Should I inform the AAAI organization about this? I’ve tried this using their contact form, but got no response whatsoever. – Anything else I can do with regard to the competing work to increase the odds of getting my paper published?

I’m 99 percent certain that it really is a mistake as I managed to run and debug the author’s code to confirm my suspicion.

Hi, we are launching the third challenge in a row, to advance understanding of human motor control, using reinforcement learning: https://www.aicrowd.com/challenges/neurips-2019-learn-to-move-walk-around

I love working the machine learning field. It is so exciting to be working on what literally feels like the future of humanity and technology every day.

The flip side to that, though, is this constant feeling of pressure from knowing that I’m not quite fully in the know of the latest developments or trends at any given time. I’ve been feeling this more and more lately. As the field is expanding, there seems to be a total deluge of new ideas and findings and developments at any given time. A rational part of me says that this is just part of working in a dynamic field. Probably best to just focus on a few areas of expertise and accept that while ideas develop quickly, the everyday work is fairly predictable and controllable anyway. And I might not even feel this way if I could just stay off twitter and hackernews! But a deeper part of me can’t help but feel unsatisfied by the fact that I have to make compromises when it comes to how much research I can do relative to how much actual work and experimentation I can do. And granted, I work in industry. I feel like it would be even harder if I were in an academic setting. I wonder if any of you feel the same.

Part of the reason I’m asking this question is because I’ve begun fantasizing about building and providing some sort of service to help reduce this drowning feeling in regards to the deluge combination of arxiv, twitter, this subreddit, hackernews, etc. etc. Of course that would be premised on the idea that I’m not the only one who feels this way, which is part of what I’m curious about in posting this here. The main problem is that as I’ve thought about it more, I realize that this would have to be a paid service. Arxiv-sanity, semantic scholar etc. get us somewhat in the direction of reducing the burden, but I think they show the natural limits of what an automatic, freely-provided feed can offer us to this end at the moment. I think so much more can be done, by way of summarizing, connecting, and organizing papers as they come out, but that such features would require full time human curatorial and engineering work to be feasible. (Well, I don’t know if it being paid would actually an issue or not, but I could see certain members of the community scoffing at the idea. We’re all happily accustomed to freely available content, so it would have to involve some sort of compromise in which the content was free, but some other feature was paid.)

So I’m looking for a sanity check: would something addressing this actually be helpful? Or am I unique in experiencing the field in this way? I would appreciate any inputs or thoughts on the topic.

Here are the links

https://www.eventbrite.com/e/angelhack-2019-silicon-valley-tickets-58773239341

https://angelhack-2019-silicon-valley.devpost.com/

Details on the ML competition

All attendees will receive $100 in AWS credits during the Hackathon

Challenge: Use any AWS Artificial Intelligence or Machine Learning service to integrate intelligence, learning, analytics or security into your project.

Prize:

The team that integrates the best AI/ML solution will win one (1) AWS Deeplens per team member

The runner up team will win one (1) Fire TV Cube per team member

The 2nd runner up team will win one (1) Adabox per team member

I emailed the organizer and ask if my remote teammates can participate virtually if I’m there in person, and they said yes.

There’s a page for people looking for teammates, you can connect with them there

https://angelhack-2019-silicon-valley.devpost.com/participants?search%5Bonly_looking_for_teammates%5D=1

My team also has some slots open. Feel free to PM to share backgrounds.

As an undergrad student who graduates next year, I was looking for a masters to apply to, but I realized that there are no master programs specialized in deep learning, which is the topic I love and have studied more from machine learning.

Also, if you look through the internships and job opportunities for ML in the big tech companies, almost all of them ask for a PhD as a minimum requisite.

So, in your experience, how necessary is a PhD if you want to work in the Deep Learning field? What are the pros and cons?

Hi /ml, I presented a paper at CVPR last week that seemed to go over well, so I thought I’d promote it beyond the vision community.

Video (much more approachable than the paper, and identical to the talk): https://www.youtube.com/watch?v=BmNKbnF69eY

Abstract: We present a generalization of the Cauchy/Lorentzian, Geman-McClure, Welsch/Leclerc, generalized Charbonnier, Charbonnier/pseudo-Huber/L1-L2, and L2 loss functions. By introducing robustness as a continuous parameter, our loss function allows algorithms built around robust loss minimization to be generalized, which improves performance on basic vision tasks such as registration and clustering. Interpreting our loss as the negative log of a univariate density yields a general probability distribution that includes normal and Cauchy distributions as special cases. This probabilistic interpretation enables the training of neural networks in which the robustness of the loss automatically adapts itself during training, which improves performance on learning-based tasks such as generative image synthesis and unsupervised monocular depth estimation, without requiring any manual parameter tuning.

Arxiv: https://arxiv.org/abs/1701.03077

TensorFlow Code: https://github.com/google-research/google-research/tree/master/robust_loss

PyTorch Code: https://github.com/jonbarron/robust_loss_pytorch

We’re globally in a perilous situation with a severe risk of runaway climate change due to major Arctic gas leaks even if we stop all human emissions today. Unless you’re a ML researcher, I call upon everyone to focus their voluntary experimental efforts and solutions mainly on climate and/or healthcare problems only. This means forgetting about other toy problems like the Titanic which was a polluting behemoth anyway. Under no circumstances would you want to work for a firm that seriously and actively harms the climate. If you read and understand the linked article, you will acknowledge that our global civilization has never been at a bigger risk of collapse. I believe that a single person can make a big difference if they try.

As for the motivation for healthcare, it should be more obvious to you, especially if you’re 30 or older and are no longer in perfect health. If you’re younger, you will get there and will then understand.

Alphastar is a neural network that plays a real time game.

Biological brains are made out of trillions of neurons connected to each other. Aren’t animals just neural networks that are playing a game? The game is a 3d survival game that the network controls through a body. In order to win it has to learn to: Know to avoid predators, hunt food, reproduce ect.

In order to train Alphastar they let it play against slightly different variations of itself billions of times, each time the winning network move forward it’s information. That’s pretty similar to natural selection and survival of the fittest, where only networks with superior survival skills pass on their genes.