far.ai: Advancing Trustworthy and Secure AI Through Research and Global Collaboration

far.ai is a leading 501(c)(3) non-profit research organization dedicated to ensuring that advanced AI systems are safe, trustworthy, and beneficial for all. Founded in 2022 and based in Berkeley, far.ai drives technical innovation in critical AI safety domains such as robustness, interpretability, and model evaluation. Through high-impact research, public seminars, and global workshops like the Alignment Workshop and International Dialogues on AI Safety (IDAIS), the organization fosters collaboration among top researchers, policymakers, and institutions worldwide. Its flagship initiative, FAR.Labs, provides a vibrant co-working space for AI safety innovation, while its grantmaking program—backed by a $12 million donation from Open Philanthropy—supports pioneering work in mitigating AI risks. With over 30 peer-reviewed publications, contributions to congressional testimony, and partnerships with elite organizations including UC Berkeley, RAND, and Mozilla.AI, far.ai is a trusted force in shaping the future of responsible AI. The platform also offers a controlled-access jailbreak-tuning demo and tools like InterpBench and Codebook Features to push the boundaries of AI transparency and safety.

Key Features:

• AI Safety Research: Cutting-edge technical research in robustness, interpretability, and model evaluation.
• Alignment Workshops: In-person and virtual events focused on AI alignment and safety.
• FAR.Labs Co-Working Space: A collaborative hub in Berkeley supporting AI safety researchers.
• Grantmaking Program: Funds innovative AI safety research through expert-nominated proposals.
• Public Seminars: Weekly AI safety seminars featuring top researchers from academia and industry.
• Global Collaboration Initiatives: Hosts IDAIS and participates in international forums like the Paris AI Security Forum.
• Red-Teaming Engagements: Proactively tests frontier AI models for vulnerabilities with leading AI labs.
• Research Publications: Over 30 peer-reviewed papers on topics such as adversarial attacks, model honesty, and AI planning.
• InterpBench Benchmark: A tool for evaluating interpretability techniques in neural networks.
• Codebook Features: A novel method to enhance neural network interpretability and steerability with minimal performance cost.
• Jailbreak-Tuning Demo: A controlled-access tool to study and understand AI safety risks.
• Newsletter & Event Recordings: Regular updates and accessible archives of past events.
• Policy Influence: Research cited in congressional testimony and mainstream media (Nature, NYT, MIT Tech Review, Financial Times).
• Transparency & Ethics: Strong commitment to privacy, whistleblower protections, anti-corruption, and equal employment opportunities.

Pricing: far.ai operates as a non-profit and does not charge for its core research, events, or publications. It relies on philanthropic funding and offers a donation-based model for support, making it effectively free for users and researchers.

Conclusion: far.ai stands at the forefront of AI safety innovation, combining rigorous research, global outreach, and community-driven collaboration to build a safer future for artificial intelligence. Its transparent, ethical, and impactful mission makes it an essential resource for anyone committed to trustworthy AI development.