Sharing learnings from the first algorithmic bias bounty challenge

How Twitter moved from a home grown logging platform to the commercially available Splunk and the challenges we’ve encountered along the way.

Describe how the Observability team applied Flame graphs to a novel use case (internal usage of Twitter’s time series database) outside of their typical application

As part of this year’s DEFCON AI Village, we’re trying something radical by introducing the industry’s first algorithmic bias bounty competition.

In this post, we will discuss our recent work on neural graph diffusion networks.

Twitter describes how to design local and computationally efficient provably powerful graph neural networks.

Twitter shares a technical analysis of its assessment for potential bias in its image cropping algorithm as part of its efforts to be more transparent around how it uses machine learning to improve pe

Twitter engineers found and fixed a Linux kernel bug in memory shrinker which caused OOM for us.

This blog post is based on Michael Bronstein’s keynote talk at ICLR 2021 and the paper M. M. Bronstein et al., Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges (2021)

Twitter annotated its data to a defined taxonomy by leveraging machine learning in its data platform by fusing Elasticsearch with neural networks.