In the last few decades the field of Machine Learning has seen rapid advances. More is to come in the next few decades. Understanding better what has happened and might happen will lead us to better AI governance and prioritization of AI risk.

This sequence is a compilation of publication from Epoch. We are researching trends in the inputs and performance of Machine Learning systems.

Our core research program centers around trends in parameters, compute and data. This is motivated from previous work on ML scaling, showing regular improvements in capabilities associated with these three factors.

We are mantaining a public dataset of parameters, compute and data of milestone ML models. An interactive visualization is also available. We encourage other researchers to build on top of our work.