AI Futures Project
We are interested in mentoring projects in AI forecasting and governance. This work would build on the AI 2027 report to either do more scenario forecasting or explore how to positively affect key decision points, informed by our scenario.
Stream overview
We are interested in mentoring projects in AI forecasting and governance. Potential projects include but aren't limited to:
- Scenario forecasting: Writing scenarios that branch off of our mainline AI 2027 scenario, exploring what might happen if important variables are changed. This could include detailed concrete threat modeling or detailed theories of victory.
- Improve and run tabletop exercises (TTXs): We've created a TTX that many have found valuable, for which we'd like to expand the range of audiences that enjoy it and scale it to more people.
- Policy strategy research e.g. menu for the intelligence explosion: Creating a menu of policy options for soon after AGI is developed.
- Targeted policy research: Do research to inform policymakers based on what seems important and in-demand.
Mentors
Daniel is working on forecasting detailed AI scenarios with Eli Lifland, Thomas Larsen, Jonas Vollmer, and Romeo Dean.
Eli is working on AI scenario forecasting with the AI Futures Project, where he co-authored AI 2027. He advises Sage, an organization he cofounded that works on AI Digest (interactive AI explainers) and forecasting tools. He previously worked on the AI-powered research assistant Elicit.
Romeo is working on forecasting detailed AI scenarios and developing policy recommendations with the AI Futures Project. He focuses primarily on compute and security forecasting. Previously he was an IAPS Policy Fellow and graduated with a concurrent masters in Computer Science at Harvard with a systems and hardware focus.
Thomas is a researcher at the AI Futures Project. He was a co-author on the widely read AI 2027 scenario forecast. He previously founded the Center for AI Policy, an AI safety advocacy organization, and worked on AI safety research at the Machine Intelligence Research Institute.
Mentorship style
We will have meetings each week to check in and discuss next steps. We will be consistently available on Slack in between meetings to discuss your research, project TODOs, etc.
Representative papers
Scholars we are looking for
The most important characteristics include:
- Strong reasoning and writing abilities
- Excitement about AI forecasting/governance research
- Autonomous research skills
- Ability to learn quickly
Also important, but not required characteristics include:
- Significant background knowledge in at least one area relevant to AI forecasting and governance (e.g. government experience, technical background, etc.)
- Significant background knowledge in AGI and existential risks
Can independently find collaboraters, but not required
Project selection
We will talk through project ideas with scholar
Community at MATS
MATS Research phase provides scholars with a community of peers.
.webp)
During the Research phase, scholars work out of a shared office, have shared housing, and are supported by a full-time Community Manager.
Working in a community of independent researchers gives scholars easy access to future collaborators, a deeper understanding of other alignment agendas, and a social network in the alignment community.
Previous MATS cohorts included regular lightning talks, scholar-led study groups on mechanistic interpretability and linear algebra, and hackathons. Other impromptu office events included group-jailbreaking Bing chat and exchanging hundreds of anonymous compliment notes. Scholars organized social activities outside of work, including road trips to Yosemite, visits to San Francisco, and joining ACX meetups.