Database

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Founder @ Nisit Sam Yan | Empowering Youth Through Publishing | Trying to be a Good Ancestor

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4-month stipend to study AI Alignment,apply for ML Safety Courses and implemen it on RL models

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Astera's Neuro & AGI program is an in-house research effort that draws on neuroscience to develop safe and aligned artificial general intelligence, operating under the Astera Institute founded by Jed McCaleb.

Research into the nature of optimization, knowledge, and agency, with relevance to AI alignment

AI safety researcher who runs AI Lab Watch, where he collects safety recommendations for frontier AI companies, tracks what they are doing, evaluates them on safety, maintains related information collections, and blogs about how companies can prevent extreme risks such as AI takeover and human extinction.

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Calling on policymakers to implement a global moratorium on large AI training runs until alignment is solved.

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An AI security accelerator and research lab based in San Francisco that invests in and supports early-stage startups building infrastructure for safe AGI deployment.

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A curated online hub for researchers to discuss technical AI alignment research, operated by Lightcone Infrastructure. It serves as the primary venue for sharing and coordinating cutting-edge alignment ideas across organizations including MIRI, OpenAI, DeepMind, CHAI, and others.

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Co-founder and President of Safe Superintelligence Inc., also serving as Co-founder and Principal Scientist, and previously a member of technical staff at OpenAI.

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Georgetown University think tank providing decision-makers with data-driven analysis on the security implications of emerging technologies.

Member of Technical Staff at AI Digest, working on the organization’s interactive AI explainers and demos including projects like the AI Village.

Financial support for career exploration and related project in AI alignment upon completion of Masters in Computer Science

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Richard Higgins is a researcher at Softmax with a PhD from the University of Michigan whose work spans compositional image editing with latent diffusion (ICCV 2025), hand–object segmentation in video (CVPR 2023, NeurIPS 2021), and solar magnetic field estimation; he has previously been a visiting academic at NYU Courant and a computer vision research intern at Meta.

A constraint-first approach to ensuring non-authoritative, fail-closed behavior in large language models under ambiguity and real-world pressure

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Senior researcher whose work at ACS focuses on understanding the civilisational ramifications of powerful AI systems and developing better accounts of how agency works in AI systems, alongside a research affiliation with the University of Toronto computer science department and contributions to the 2026 International AI Safety Report.

Help Keep AI Safety ANZ Alive

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Curriculum Developer & Instructor at the Center for Applied Rationality. Davis has been involved with CFAR since early 2014 in operations, finance, curriculum development, and teaching, and also has experience as a competitive tabletop gamer, commentator, and writer, with a bachelor’s degree in psychology from Carleton College.

Purdue University is a major public research university in West Lafayette, Indiana, whose computer science department has received AI safety funding for research on language model robustness and adversarial deception detection.

A fast, comprehensive directory of the people and orgs in AI safety: search, filter, and match.

Arkose is a field-building nonprofit that supports mid-career ML professionals to enter the field of AI safety.

Thomas Kwa is a researcher on the technical staff at METR (Model Evaluation & Threat Research), where he focuses on measuring AI capabilities and autonomous task completion. He holds a Computer Science degree from Caltech and previously worked at MIRI and conducted interpretability research through the MATS/SERI MATS program with Adrià Garriga-Alonso and Jason Gross. He is the lead author of the influential METR paper introducing the '50% time horizon' metric—the length of tasks AI models can complete autonomously with 50% probability—which found this metric has been doubling roughly every seven months. He also co-authored 'Catastrophic Goodhart,' a 2024 paper demonstrating that KL divergence regularization in RLHF fails to prevent reward hacking under heavy-tailed reward misspecification, presented at ICML and NeurIPS 2024. Thomas is an active contributor to LessWrong and the AI Alignment Forum, where he publishes research on interpretability, Goodhart's Law, and AI safety methodology.

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Matt Clifford CBE is cofounder and Chair of Entrepreneurs First, a global talent investor, and serves as Chair of ARIA, the UK’s Advanced Research and Invention Agency. He also plays a leading role in UK AI policy, including work on the AI Safety Summit and the UK AI Safety Institute.

Clark Wisenbaker is Operations Manager at Macroscopic Ventures, managing the organization’s operational systems after previously holding various leadership roles at other nonprofits and for-profit startups and working as a practicing attorney.

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A research project that investigates decision-relevant questions about the future of artificial intelligence, including AI timelines, expert forecasts, and the potential societal impacts of advanced AI systems.

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Incubate AI safety research and develop the next generation of global AI safety talent via research sprints and research fellowships

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Applying experience from mechanistic interpretability of genetic networks to neural networks

A 4-5 day workshop for agent foundations researchers at Carnegie Mellon University in March, 2025

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Patrick Udeh is a programs and partnerships professional based in Lagos, Nigeria, with experience in startup and technology ecosystems. He has worked as Programs and Partnerships Manager at Kuvora and previously held roles at organizations including the International Association for Safe & Ethical AI, Ctrl+C Academy and Meta, alongside hands-on work on AI data annotation projects that support machine learning initiatives and product development.

Rajashree Agrawal is a machine learning research engineer and co‑founder of Theorem, where she leads work on training AI models that power the company’s formal verification pipeline; her background spans machine learning, programming languages, and AI safety research, including participation in programs like MATS.

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