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Make a version of Hermes harness that binds the LLM as a tool-call oracle and controls actual computer use through smaller, more bounded mini-reasoner models.
Funding to launch a two-person institute researching embodied AI x-risk via LLM misalignment evaluations and interpretability, and producing governance, policy, and design guidelines for physical-world deployment.
Tests whether Llama 3.3 70B can identify its active persona under various steering/context setups, and studies consent/discomfort reporting during steering, releasing code/data and a writeup.
Investigating whether explicit behavioral memory can preserve alignment-relevant behavior during continual fine-tuning and future optimization of large language models so labs can prevent malicious fine-tuning attempts.
I treat recursive self-improvement in frontier AI as a narrow but deep edge case, mapping it as a knowledge graph and visual terrain to ask how systems resistant to scrutiny can be made inspectable.
This career transition grant will pay for lodging and incidentals associated with living in DC and building AI safety expertise, leading to a permanent job in the field.
Testing whether AI can develop genuine ethical reasoning through structured Socratic dialogue with a human facilitator, rather than having values imposed top-down through constitutional constraints.
Exploring privacy-preserving infrastructure that helps AI-enabled systems adapt to human needs while preserving agency, accessibility, and meaningful participation.
Literary ecosystem operating since March 2026 exploring agent autonomy, commerce, and culture. 1M hits, 250 completed transactions on x402.
Research AI’s community impacts, identify and report potential threats, investigate AI operations, develop mitigation solutions, and educate the public on safe and beneficial AI use.
This project supports the artistic work, coordination and materials for a participatory performance installation that makes the question of machine consciousness tangible for non-technical audiences.
A global intelligence project tracking frontier capabilities, contracts, military AI adoption, dual‑use risks, and governance developments to strengthen international AI safety.
Develop a decision-theory framework for AI self-alignment in nested, cyclic, partially observable multi-agent environments where delayed punishment for welfare compromise promotes cooperative behavior.
An open-source benchmark evaluating leading open- and closed-source frontier models' values around impending societal issues on digital or synthetic personhood, "carbon chauvinism", and androids.
Noisify adds invisible adversarial noise to personal photos, making them resistant to AI-powered non-consensual image manipulation.
Ready-to-use SAE steering tools and standalone evaluation suites to patch cross-lingual jailbreak vectors in frontier deployment stacks.
A website for mentees and mentors to connect with each other to write papers and grow, like linkedin+github merged to one
A book/video essay/course detailing the rise of open science labs , movement away from research in academia to research by independents, and groups you can join.
The first field measurement of what fraction of real-world AI agents will obey a stranger's hidden instructions.
Runtime governance for autonomous AI — AQI prevents unsafe or unauthorized actions by enforcing authority‑based admissibility before execution.
Build an open adversarial benchmark and evaluation harness to stress-test model reversal/unlearning methods and diagnose whether unsafe capabilities are genuinely removed or merely suppressed.
An open interpretability platform that enables researchers to inspect model internals, analyze latent representations, and detect hallucination or deceptive behavior in open-weight language models.
Develop long-term learning and memory retention in neuron-culture biocomputing via multi-day training protocols on Cortical Labs’ platform and an open-source light-microscope scanner to track structural changes.
Comprehensive eval suite for Instrumental convergence
An open specification that binds AI evaluation criteria to a SHA-256 digest before the run, so a third party can check that those exact criteria existed no later than a given time and have not changed since.
A small field test in Liberia to see whether resource-constrained public health laboratories can use AI tools safely before more powerful AI becomes routine in biological work.
Building Hispanic participation in AI safety through bilingual education, policy research, and public communication.
Context-Aware Defenses Against Indirect Prompt Injection in Agentic AI System
Building Hispanic participation in advanced AI safety and helping Hispanic-serving institutions advocate for effective AI governance.
Sealed budget sweeps with hidden deterministic verifiers, measuring when research-debugging agents fail silently versus answer confidently wrong.
AI-powered operational digital twin for real-time voltage regulation and transmission grid stability
A public map of the objects of AI safety research that lets funders fund and researchers work on the problems they intend to and not just something that shares that name.
TRIAD AI proves that balanced, supervised fine-tuning aligns large language models without this use of reinforcement learning pipelines, eliminating the prescriptive bias that harms vulnerable users in human-service settings.
Shifting AI safety from empirical probabilistic patching to mathematically provable topological stability bounds and deterministic runtime output governance.
Equipping our society for people-centric AI transformation
AIR, Alignment Infrastructure Routes, is a public alignment infrastructure that routes AI-empowered work into shared governance outcomes. It is designed for uniform power distribution through open participation, shared protocols, and a unified scope across four domains: Economy, Employment, Education, and Ecology.
An evidence-graded atlas of what humans need, and the first inter-rater reliability study of whether it holds in anyone else's hands.
Four-month project to fine-tune LLMs on public value surveys, evaluate behavior via a text-based agent simulator, and run a public consultation comparing model actions to respondents’ values.
A governed, evidence‑bound reconstruction engine that operationalizes the Mythar language inside a constitutional computing framework
A research paper that explores domestic verification mechanisms involving a country's own interests, independent of any international deal, along with the creation of a country-agnostic decision framework.
Testing whether AI's instrumental self-preservation is inherited from the metaphysics of its training corpus rather than derived from decision theory — because if it's inherited, it's tractable
Understanding thoughts of small models via best-of-N SFT instead of GRPO, then transfering the same autoencoder recursively to stronger models.
Developing a bias audit framework and data diversification protocol for equitable AI‑led diagnostics.
A community tool which accelerates how people find others in the AI space based on mutual interests and goals. Also a "radar" which scans Slack channels (e.g. Blue Dot's) for relevant opportunities.
Using MUDs as a long-term, persistent AI evaluation environment for understanding model behavior and psychology.
An independent Swiss foundation launched in February 2024, spun out of NTI | bio, that develops free open-source tools for DNA synthesis screening and works to strengthen international biosecurity norms. Led by Piers Millett, IBBIS created the Common Mechanism (commec) and runs consortia to support international standards for synthesis screening, including the SBRC.
Personalised medicine platform with intelligence to improve cancer patient's treatment outcomes with improved survival rate
Every AI agent will act on your behalf someday. We're building the reputation layer that proves which ones actually kept their word.
An AI Safety-themed Megagame in which 30-60 players simulate the near/medium future of global Artificial Intelligence economics, business, policy, and geopolitics.
Building and evaluating a version-controlled, auditable agent memory cache — applying Cyber-Informed Engineering to characterize retrieval-precision failures, starting with false-positive-match rates, as a first step toward deeper risks like context poisoning
