Epoch is a new research organization that works to support AI strategy and improve forecasts around the development of Transformative Artificial Intelligence (TAI) – AI systems that have the potential to have an effect on society as large as that of the industrial revolution.
Our founding team consists of seven members – Jaime Sevilla, Tamay Besiroglu, Lennart Heim, Pablo Villalobos, Eduardo Infante-Roldán, Marius Hobbhahn, and Anson Ho. Collectively, we have backgrounds in Machine Learning, Statistics, Economics, Forecasting, Physics, Computer Engineering, and Software Engineering.
Our work involves close collaboration with other organizations, such as MIT CSAIL, Open Philanthropy, and Rethink Priorities’ AI Governance and Strategy team. We are advised by Tom Davidson from Open Philanthropy and Neil Thompson from MIT CSAIL. Rethink Priorities is also our fiscal sponsor.
Epoch seeks to clarify when and how TAI capabilities will be developed.
We see these two problems as core questions for informing AI strategy decisions by grantmakers, policy-makers, and technical researchers.
We believe that to make good progress on these questions we need to advance towards a field of AI forecasting. We are committed to developing tools, gathering data and creating a scientific ecosystem to make collective progress towards this goal.
Our work at Epoch encompasses two interconnected lines of research:
These two research strands feed into each other: the analysis of trends informs the choice of parameters in quantitative models, and the development of these models brings clarity on the most important trends to analyze.
Besides this, we also plan to opportunistically research topics important for AI governance where we are well positioned to do so. These investigations might relate to compute governance, near-term advances in AI and other topics.
Earlier this year we published Compute Trends Across Three Eras of Machine Learning. We collected and analyzed data about the training compute budget of >100 Machine Learning models across history. Consistent with our commitment to field building, we have released the associated dataset and an interactive visualization tool to help other researchers understand these trends better. This work has been featured in Our World in Data, in The Economist and at the OECD.
More recently we have published Grokking “Forecasting TAI with biological anchors” and Grokking “Semi-informative priors over AI timelines”. In these pieces, Anson Ho dissects two popular AI forecasting models. These are the two first installments of a series of articles covering work on quantitative forecasting of when we will develop TAI.
You can see more of our work on our blog. Here is a selection of further work by Epoch members:
Projecting compute trends in Machine Learning
Estimating training compute of Deep Learning models
Estimating the backward-forward FLOP ratio
Parameter counts in Machine Learning
We expect to be hiring for several full-time research and management roles this summer. Salaries range from $60,000 for entry roles to $80,000 for senior roles.
If you think you might be a good fit for us, please apply! If you’re unsure whether this is the right role for you, we strongly encourage you to apply anyway. Please register your interest for these roles through our webpage.
Nice. Congrats on the launch! This is an extremely necessary line of effort.
So happy to see this, and such an amazing team!