Pablo Villalobos

Announcing Epoch: A research organization investigating the road to Transformative AI

Summary * We are a new research organization 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, Open Philanthropy, and MIT CSAIL * We will be hiring for 2-4 full-time roles this summer – more information here * You can find up-to-date information about Epoch on our website 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 (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. Our mission 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. Epoch´s website 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

97Jun 27, 2022

Pablo Villalobos

Message

Previously Staff Researcher at Epoch, these days I'm mostly thinking about better frameworks for understanding superintelligence.

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

Summary: 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 by estimating the total stock of available unlabeled data over the next decades. Read the full paper in arXiv....

Nov 14, 202275

Announcing Epoch: A research organization investigating the road to Transformative AI

Jun 27, 202297

Compute Trends Across Three eras of Machine Learning

https://arxiv.org/abs/2202.05924 What do you need to develop advanced Machine Learning systems? Leading companies don’t know. But they are very interested in figuring it out. They dream of replacing all these pesky workers with reliable machines who take no leave and have no morale issues. So when they heard that throwing...

Feb 16, 202294

Parameter counts in Machine Learning

In short: we have compiled information about the date of development and trainable parameter counts of n=139 machine learning systems between 1952 and 2021. This is, as far as we know, the biggest public dataset of its kind. You can access our dataset here, and the code to produce an...

Jun 19, 202147

AI ALIGNMENT FORUM
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AI ALIGNMENT FORUM
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Pablo Villalobos

Pablo Villalobos

Pablo Villalobos

Announcing Epoch: A research organization investigating the road to Transformative AI

Compute Trends Across Three eras of Machine Learning

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

Parameter counts in Machine Learning

Pablo Villalobos

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

Announcing Epoch: A research organization investigating the road to Transformative AI

Compute Trends Across Three eras of Machine Learning

Parameter counts in Machine Learning

Announcing Epoch: A research organization investigating the road to Transformative AI

Compute Trends Across Three eras of Machine Learning

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

Parameter counts in Machine Learning

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

Announcing Epoch: A research organization investigating the road to Transformative AI

Compute Trends Across Three eras of Machine Learning

Parameter counts in Machine Learning