How to train large deep learning models as a startup

Training large deep learning models is expensive and slow. Yet, startups are all about iterating fast. In this post, we share the lessons we've learned over the past few years.

Facebook is researching AI systems that see, hear, and remember everything you do

Facebook is pouring a lot of time and money into augmented reality, including building its own AR glasses with Ray-Ban. Right now, these gadgets can only record and share imagery, but what does the company think such devices will be used for in the future?

Our approach to building transparent and explainable AI systems

Delivering the best member and customer experiences with a focus on trust is core to everything that we do at LinkedIn. As we continue to build on our Responsible AI program that we recently outlined three months ago, a key part of our work is designing products that provide the right protections, mitigate unintended consequences, and ultimately better serve our members, customers, and society.

Approximate Differentiable One-Pixel Point Rendering

We present a novel point-based, differentiable neural rendering pipeline for scene refinement and novel view synthesis. The input are an initial estimate of the point cloud and the camera parameters. The output are synthesized images from arbitrary camera poses. The point cloud rendering is performed by a differentiable renderer using multi-resolution one-pixel point rasterization.

Advancing real-world few-shot learning using teachable object recognition

Object recognition systems have made spectacular advances in recent years, but they rely on training datasets with thousands of high-quality, labelled examples per object category. Learning new objects from only a few examples could open the door to many new applications. For example, robotics manufacturing requires a system to quickly learn new parts, while assistive technologies need to be adapted to the unique needs and abilities of every individual.