Announcing Epoch’s Dashboard of Key Trends and Figures in Machine Learning
We are launching a dashboard that provides key data from our research on Machine Learning, aiming to serve as a valuable resource for understanding the present and future of the field.
Developments in Machine Learning have been happening extraordinarily fast, and as their impacts become increasingly visible, it becomes ever more important to develop a quantitative understanding of these changes. However, relevant data has thus far been scattered across multiple papers, has required expertise to gather accurately, or has been otherwise hard to obtain.
Given this, Epoch is thrilled to announce the launch of our new dashboard, which covers key numbers and figures from our research to help understand the present and future of Machine Learning. This includes:
- Training compute requirements
- Model size, measured by the number of trainable parameters
- The availability and use of data for training
- Trends in hardware efficiency
- Algorithmic improvements for achieving better performance with fewer resources
- The growth of investment in training runs over time
Our dashboard gathers all of this information in a single, accessible place. The numbers and figures are accompanied by further information such as confidence intervals, labels representing our degree of uncertainty in the results, and links to relevant research papers. These details are especially useful to illustrate which areas may require further investigation, and how much you should trust our findings.
Beyond accessibility, bringing these figures together allows us to easily compare and contrast trends and drivers of progress. For example, we can verify that growth in training compute is driven by improvements to hardware performance and rising investments:
We can also see that performance improvements have historically been driven by algorithmic progress and training compute growth by comparable amounts:
Overall, we hope that our dashboard will serve as a valuable resource for researchers, policymakers, and anyone interested in the future of Machine Learning.