Research

Epoch is a research group trying to forecast the development of transformative Artificial Intelligence. We try to understand how progress in AI happens and what economic impacts we might see from advanced AI.

We want to enable better governance during this economic transition by informing about the timing of new developments and what levers can be used to influence AI progress.

Research agenda

Our research agenda includes both empirical and theoretical work.

Modelling the future of AI

We are developing statistical and economic models to answer crucial questions about the direction and impact of AI in the coming decades.

Studying trends in Machine Learning

Curating and analizing information about significant ML systems like compute budgets, training dataset sizes, cost and performance.


Publications

Will we run out of ML data? Evidence from projecting dataset size trends

Paper

Oct. 10, 2022

Pablo Villalobos, Jaime Sevilla, Lennart Heim, Tamay Besiroglu, Marius Hobbhahn and Anson Ho

Based on our previous analysis of trends in dataset size, we project the growth of dataset size in the language and vision domains. We explore the limits of this trend...

The longest training run

Report

Aug. 17, 2022

Jaime Sevilla, Tamay Besiroglu, Owen Dudney and Anson Ho

Training runs of large Machine Learning systems are likely to last less than 14-15 months. This is because longer runs will be outcompeted by runs that start later and therefore...

Machine Learning Model Sizes and the Parameter Gap

Paper

Jul. 05, 2022

Pablo Villalobos, Jaime Sevilla, Tamay Besiroglu, Lennart Heim, Anson Ho and Marius Hobbhahn

The model size of notable Machine Learning systems has grown ten times faster than before since 2018. After 2020 growth has not been entirely continuous: there was a jump of...

Projecting compute trends in Machine Learning

Report

Mar. 07, 2022

Tamay Besiroglu, Lennart Heim and Jaime Sevilla

Projecting forward 70 years worth of trends in the amount of compute used to train Machine Learning models.

Compute Trends Across Three Eras of Machine Learning

Paper

Feb. 11, 2022

Jaime Sevilla, Tamay Besiroglu, Anson Ho, Lennart Heim, Marius Hobbhahn, and Pablo Villalobos

Compute, data, and algorithmic advances are the three fundamental factors that guide the progress of modern Machine Learning (ML). In this paper we study trends in the most readily quantified...

Estimating Training Compute of Deep Learning Models

Report

Jan. 20, 2022

Jaime Sevilla, Lennart Heim, Marius Hobbhahn, Tamay Besiroglu, Anson Ho and Pablo Villalobos

We describe two approaches for estimating the training compute of Deep Learning systems, by counting operations and looking at GPU time.

Parameters, Compute and Data Trends in Machine Learning

Database

Jaime Sevilla et al.

Public dataset

Toward Transparent AI: A Survey on Interpreting the Inner Structures of Deep Neural Networks

Paper

Jul. 2022

Tilman Rauker, Anson Ho, Stephen Casper, and Dylan Hadfield-Menell

The last decade of machine learning has seen drastic increases in scale and capabilities, and deep neural networks (DNNs) are increasingly being deployed across a wide range of domains. However,...