Facebook AI researchers have done a comprehensive study of cultural bias in object-recognition systems. As computer vision systems today are often trained on labeled datasets containing just one culture’s version of an object or event (like toothpaste or a wedding), they fail to recognize other cultures’ versions of those things.
Facebook AI researchers have published the first systematic study that measures the accuracy of object-recognition systems for different communities across the world. … Using a publicly available third-party data set of photos of household items in 50 countries, we found accuracy for all these systems was indeed significantly lower for images from certain regions and from households with lower income levels.
Google Brain has released a new football (soccer) reinforcement learning (RL) environment.
Currently, there are popular frameworks by OpenAI and DeepMind that RL researchers use to teach computers to play Go
, Dota 2
, Starcraft 2
, and Atari console games
. The Google Research Football Environment joins this lineup as a new challenge for RL agents to tackle, with a highly optimized C++ physics-based 3D football engine that serves as environment. It contains three Football Benchmarks to pit an agent against hand-engineered easy, medium and hard opponent teams, as well as a Football Academy meant for curriculum learning
research. This looks like it’ll be much nicer to use than the deprecated football environment I had to use in my RL course this year (HFO
Thanks for the link, Art
Chris Olah wrote a post about collaboration in ML research. The TL;DR biggest actionable items are:
- Be generous.
- Use author contribution statements.
- Put “author order not finalized” if it hasn’t been.
Beyond that, it’s also a very good read that touches on the links between credit issues and privilege/power, and that goes deeper into Olah’s own core collaboration principles. This one seems especially useful:
In drafts, keep a running list of people to acknowledge. This reduces the risk of you forgetting to acknowledge someone. It also signals to them that you’re taking this stuff seriously.
I’ve added this to my thesis draft, and I encourage everyone reading this to do the same for their current projects! Read Olah’s full post here for more tips: Collaboration & Credit Principles
The Matrix Calculus You Need For Deep Learning is a 33-page reference PDF that does exactly what it says on the can. Link: arXiv abstract
- Texar is a TensorFlow toolkit designed for fast prototyping of a broad set of machine learning text generation tasks like translation, dialog, summarization, and language modelling. Link: asyml/texar
- PyTorch Hub is an API and workflow that can be used to improve machine learning research reproducibility, with support for Colab and Papers With Code. Link: PyTorch docs