Almost every machine learning algorithm comes with a large number of settings that we, the machine learning researchers and practitioners, need to specify…
A visual overview of neural attention, and the powerful extensions of neural networks being built on top of it.
Deep learning is an empirical science, and the quality of a group’s infrastructure is a multiplier on progress. Fortunately, today’s open-source ecosystem makes it possible for anyone to build great deep learning infrastructure.
The latest information about the Unconference is now available at the Unconference wiki, which will be periodically updated with more information for attendees.
In this second part of this series, we will look at some advanced techniques for model evaluation and techniques to estimate the uncertainty of our…
Impactful scientific work requires working on the right problems—problems which are not just interesting, but whose solutions matter.
We (along with researchers from Berkeley and Stanford) are co-authors on today’s paper led by Google Brain researchers, Concrete Problems in AI Safety. The paper explores many research problems around ensuring that modern machine learning systems opera…
OpenAI’s mission is to build safe AI, and ensure AI’s benefits are as widely and evenly distributed as possible.
This post describes four projects that share a common theme of enhancing or using generative models, a branch of unsupervised learning techniques in machine learning. In addition to describing our work, this post will tell you a bit more about generati…