April 15, 2021
Understanding and assessing the quality of machine learning systems

In the first post of this series, we discussed the underspecification challenge of state-of-the-art AI models and how they often learn to "cheat" by exploiting the particular dataset and metrics they were trained to optimize. In particular, we have seen how the standard approach of training using a fixed dataset can lead to many models […]

Read More
March 24, 2021
Model assessment beyond samples in your dataset

Despite impressive progress over the last decade, current AI models can perform poorly and unpredictably when deployed in the real world. In this blog series, we present the latest advances in assessing models, identifying their failure modes, and gaining insights into building quality datasets and models. This first post motivates the need for assessing models […]

Read More
March 3, 2021
SBB adopts AI to improve railway maintenance

LatticeFlow collaborates with SBB, Siemens, and ETH to enable safe, efficient, and cost-effective AI-based railway maintenance Swiss Federal Railways (SBB) has one of the world’s most dense railway networks spanning over 7,500 km of tracks with 300 tunnels and servicing over 1.25 million passengers every day. The intensive usage requires high investments into operational maintenance […]

Read More