Grantmaking
Funding groundbreaking research in AI safety
FAR.AI Grant Program
FAR.AI supports academics and independent researchers in developing innovative solutions to critical AI risks through our targeted grantmaking program. Currently, due to limited evaluation capacity, we are only able to consider researchers nominated by experts with a strong track record. We plan to launch public requests for proposals (RFPs) soon, focused on high-impact research areas. Our grantmaking is funded by a $12 million grant generously provided by Open Philanthropy.
Grants:
Explaining Superhuman AI Decisions
Nicholas Tomlin, UC Berkeley
Using weak-to-strong generalization to explain superhuman AI systems’ decisions, focusing on domains like chess/Go where superhuman AI already exists.
Comprehensive Red-Teaming Framework
Wenbo Guo, UC Santa Barbara
Building automated testing systems for LLM alignment against both training-phase threats and testing-phase threats, with a focus on developing agent-based systems that can generate adversarial prompts.
Securing Alignment
Ashwinee Panda, University of Maryland College Park
Developing methods to make alignment more secure against jailbreaks, prefilling attacks, and finetuning attacks, with approaches spanning the entire model lifecycle.
Failure Modes in Superhuman Systems
Florian Tramer, ETH Zurich
Broad project examining robustness across four vectors: data poisoning, consistency checks, model stealing, and prompt injection.
