Announcing Epoch: A research initiative investigating the road to transformative AI
- We are a new research initiative working on investigating trends in Machine Learning and forecasting the development of transformative Artificial Intelligence
- This work is done in close collaboration with other organizations, like Rethink Priorities and Open Philanthropy
- We will be hiring for 2-4 full-time roles this summer – more information here
What is Epoch?
Epoch is a new research organization that works to support AI strategy and improve forecasts around the development of transformative Artificial Intelligence – 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.
Tamay is a researcher focusing on the Economics of Computing and big-picture trends in Machine Learning. In addition to his role at Epoch, Tamay is a researcher at the Future Tech Lab at MIT. Previously, he led strategy for Metaculus, consulted for the UK Government, and worked at the Future of Humanity Institute.
Pablo has a background in Mathematics and Computer Science. After spending some time as a software engineer, he decided to pivot towards AI. His interests include the economic consequences of advanced AI systems and the role of algorithmic improvements in AI progress.
David studied at Deep Springs college, and graduated from the University of Colorado, Boulder, with a degree in Computer Science and Mathematics. Before coming to Epoch, he did some NLP research and then worked as a software engineer. He’s interested in model interpretability and forecasting.
Ben’s research interests include the diffusion of AI capabilities among actors, and measuring the effects of different inputs to AI progress. Previously, he was a Research Fellow at Rethink Priorities, and spent time as a software engineer. Ben has a background in Machine Learning.
Lennart is a researcher on AI and compute. His research interests include the role of compute in the AI production function, the compute landscape/supply chain, security of AI systems, and forecasting emerging technologies. He is a research affiliate with the Centre for the Governance of AI in Oxford and has a background in Computer Engineering.
Jenny is a Ph.D. candidate at Columbia University researching the AI industry in the U.S. and China. She has conducted research on the generative AI landscape in both countries, China’s AI hardware startup ecosystem, and the policy impacts of the release of AI models. She is an affiliate at the AI consulting firm Concordia Consulting.
Our work involves close collaboration with other organizations, Open Philanthropy, and Rethink Priorities’ AI Governance and Strategy team. We are advised by Tom Davidson from Open Philanthropy and Neil Thompson. Rethink Priorities is also our fiscal sponsor.
Epoch seeks to clarify when and how transformative AI capabilities will be developed.
We see these two problems as core questions for informing AI strategy decisions by grantmakers, policy-makers, and technical researchers.1
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 research agenda
Our work at Epoch encompasses two interconnected lines of research:
The analysis of trends in Machine Learning. We aim to gather data on what has been happening in the field during the last two decades, explain it, and extrapolate the results to inform our views on the future of AI.
The development of quantitative forecasting models related to advanced AI capabilities. We seek to use techniques from economics and statistics to predict when and how AI will be developed.
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.
Our work so far
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 transformative AI.
You can see more of our work on our blog. Here is a selection of further work by Epoch members:
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.