Project
6-month salary to research AI alignment, specifically the interaction between goal-inference and choice-maximisation
Led bySamuel Brown
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6-month salary to research AI alignment, specifically the interaction between goal-inference and choice-maximisation
12-month salary for independent research, upskilling, and finding a stable position in AI-Safety
No summary available yet.
A research funding program run by Schmidt Sciences that supports foundational technical research on understanding, predicting, and controlling risks from frontier AI systems. The program funds academic and nonprofit researchers working on AI safety science, evaluation methodology, and oversight of advanced AI.
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Philipp Bongartz is a German machine learning engineer and independent AI alignment researcher who received a Long-Term Future Fund grant of approximately $25,000 for prosaic alignment research using chess as a model domain. He has developed a research agenda centered on building a multi-modal chess-language model with an encoder-decoder architecture, using chess as a testbed for investigating symbol grounding and truthfulness in AI models. His GitHub projects include Chess2Vec, ChessTransformer, and other chess-related ML tools, as well as bioinformatics work suggesting a computational background spanning machine learning and life sciences. He holds a PhD (defended in early 2020) from the Heidelberg Institute for Theoretical Studies (HITS). He has been an active contributor to the LessWrong and Alignment Forum communities under the handle p.b. since December 2020, with over 26 posts and 295 comments on topics ranging from chess AI to model scaling and alignment methodology. He has worked as a Senior Consultant and Data Scientist at Exxeta AG in Germany and is a rated chess player with a FIDE standard rating of approximately 2155.
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Cole Wyeth is a PhD student in computer science at the University of Waterloo, supervised by Professor Ming Li and advised by Professor Marcus Hutter. He holds an M.S. in mathematics from the University of Minnesota, Twin Cities, and previously worked as a machine learning intern at Dexai Robotics with a publication at ICRA. His research focuses on algorithmic information theory, sequential decision theory, and the AIXI reinforcement learning framework, with a particular interest in theoretical AI safety and agent foundations. He has published work on reflective AIXI, embeddedness failures in universal AI, and the grain of truth problem for multi-agent reasoning. He organizes the AIXI research community at uaiasi.com and serves as an advisor to the AI Safety Research Fund. He received a grant from the Long-Term Future Fund to study extensions of the AIXI model to reflective agents in order to understand the behavior of self-modifying AGI.
Physicist, sustainable energy engineer, entrepreneur, and founder-director of the Existential Risk Observatory, focusing on reducing existential risks, especially from advanced AI, by informing public debate.
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ICLR is one of the world's premier annual academic conferences dedicated to deep learning and representation learning research. It was founded in 2013 by Yann LeCun and Yoshua Bengio.
Co‑Director of Kairos, an AI safety organization focused on supporting early‑career people entering the field, and a generalist software engineer with interests in computer science, cybersecurity, and using technology for social impact.
Joseph Bloom is a mechanistic interpretability researcher currently leading the White Box Control Team at the UK AI Security Institute (AISI), where his team focuses on estimating and addressing risks associated with deceptive alignment in frontier AI systems. He studied computational biology and statistics at the University of Melbourne, Australia, and previously worked as a data scientist at Mass Dynamics, a proteomics startup. He became a MATS 5.0 scholar under Neel Nanda and completed the ARENA 1.0 program, during which he developed expertise in sparse autoencoders (SAEs) and mechanistic interpretability of transformer models. He created SAELens, an open-source library for training and analyzing sparse autoencoders on language models (over 1,300 GitHub stars), and served as a maintainer of the TransformerLens library (over 3,200 stars). He also co-founded Decode Research, an AI safety research infrastructure nonprofit, and helped build Neuronpedia, an open platform for hosting and analyzing sparse autoencoder features. Earlier independent work on decision transformer interpretability was funded by the Long-Term Future Fund, Manifund, and Lightspeed Grants.
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Rob Sherman is Vice President and Deputy Chief Privacy Officer for Policy at Meta, where he leads work on privacy, security, and online trust across the company’s products and technologies. Before joining Meta (then Facebook) in 2012, he was an attorney at Covington & Burling advising technology and digital media companies on regulatory and public policy issues.
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Co-founder and Chief Scientist at Goodfire; previously co-founded the interpretability team at DeepMind before moving from London to South Park Commons in San Francisco to build the company’s interpretability research program.
A Canadian registered charity that increases public and scientific awareness of AI's catastrophic risks through education and research.
1-year salary and costs to connect, expand and enable the AGI governance and safety community in Canada
Dr. Jess Whittlestone is Head of AI Policy at the Centre for Long-Term Resilience (CLTR), a UK-based think tank working to reduce extreme risks. In this role she leads CLTR's work developing and advocating for policy recommendations to reduce extreme risks from AI, including frontier AI regulation and model evaluation for catastrophic risks. She was previously a Senior Research Fellow and Deputy Director of the AI: Futures and Responsibility Programme at the Leverhulme Centre for the Future of Intelligence and the Centre for the Study of Existential Risk (CSER) at the University of Cambridge. She holds a PhD in Behavioural Science from the University of Warwick and a first-class degree combining Mathematics and Philosophy from the University of Oxford. She is also an affiliate of the Centre for the Governance of AI (GovAI) and has received funding from the Long-Term Future Fund to research the links between short- and long-term AI policy while building technical ML knowledge. She was named one of TIME's 100 Most Influential People in AI in 2023.
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8-month salary to work on technical AI safety research, working closely with a DPhil candidate at Oxford/FHI
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Imran Khan is a strategist and writer working at the intersection of science, technology, and society, with a focus on AI safety, psychedelics, and how emerging technologies reshape human experience. He publishes essays and resources on these topics at his personal site and collaborates with the Center for Humane Technology on AI‑related explainers and analysis.
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PhD student @ the Bau Lab
Independent researcher building PetriLabs, an experimental framework for studying autonomous AI agent behavior under real economic mortality. Previously built Revelation Cloud (blockchain federated learning). IBA, VU Amsterdam.
Gregory Kollmer is the Chief Technology Officer and co‑founder at Lucid Computing and holds a Bachelor of Science degree from Georgetown University.
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AI safety engineer at Gray Swan AI and Member of Technical Staff at Cohere, focusing on high-quality, diverse post-training data for large language models and on keeping AI systems safe and secure.
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Elav Horwitz is Chief Innovation Officer at WPP, where she leads applied innovation and helps global brands harness emerging technologies such as generative AI, XR, and Web3 to drive growth. She has over two decades of experience in technology-driven marketing and previously led innovation and partnerships for McCann Worldgroup.
Conrad Helminger is a Senior Associate at the Effective Institutions Project, supporting research, monitoring, and prioritization across institutions and issue areas. He holds an MA in International Political Economy and is a PhD candidate at the University of Amsterdam, where he studies global shifts in power, norms, and institutions.
I self-funded research into a new threat model. It is demonstrating impact (accepted at multiple venues, added to BlueDot's curriculum).
Sara Price is a Member of Technical Staff at Anthropic working on AI safety, based in the Bay Area. She has worked in machine learning since 2016 and transitioned into AI alignment research in early 2024 through the MATS 5.0 (Spring 2024) program, after which she received a 6-month stipend to continue independent research on situational awareness and deception in AI systems. Her research focuses on adversarial robustness of multimodal LLMs, scheming and deception, control, and model organisms of misalignment. She was a co-author on Petri, an open-source AI safety auditing tool developed at Anthropic that automates evaluation of concerning model behaviors such as deception, sycophancy, and self-preservation. She now serves as a mentor for the MATS Summer 2026 program in the Anthropic and OpenAI Megastream.
Hardware engineer
Ihor Kendiukhov is a bioinformatician and machine learning and quant finance professional whose research spans AI for biology, AI safety, econophysics, and longevity; he has authored work on single-cell foundation model interpretability and other ML applications in biology, and holds investment and deep-tech roles such as Deputy General Partner at Deep Knowledge Group.
Replacing reduction in income due to moving from full- to part-time work in order to pursue an AI safety-related PhD
Zaha Hassan is a human rights lawyer and senior fellow in the Middle East Program at the Carnegie Endowment for International Peace; her work focuses on Palestinian‑Israeli peace efforts, the use of international legal mechanisms by political movements, and U.S. foreign policy in the region.
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10-month salary for research on AI safety/alignment, scaling laws, and potentially interpretability
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Econ + AI
