Independent research institute dedicated to advancing AI security, ethics, and privacy through rigorous analysis, principled solutions, and evidence-based policy recommendations.
Read Our ResearchWe conduct independent research to identify, analyze, and address critical challenges in artificial intelligence systems, with a focus on security vulnerabilities, ethical implications, and privacy considerations.
We employ rigorous methodologies to investigate AI systems, publishing peer-reviewed research that advances understanding of AI capabilities, limitations, and risks.
Our team develops practical tools, frameworks, and methodologies that help organizations deploy AI systems more safely and responsibly.
We work with policymakers and industry leaders to craft evidence-based regulations and standards that protect society while enabling beneficial innovation.
Our research spans critical domains where AI systems intersect with security, ethics, and privacy concerns.
Investigating vulnerabilities in AI systems, including adversarial attacks, model manipulation, and deployment security challenges.
Examining bias, fairness, transparency, and accountability in AI decision-making systems across various domains.
Developing and evaluating privacy-preserving techniques for AI systems, including differential privacy and federated learning approaches.
Creating frameworks for responsible AI governance, regulatory compliance, and organizational accountability structures.
Analyzing failure modes in AI systems, including hallucinations, drift, and performance degradation in production environments.
Assessing broader societal implications of AI deployment, including labor market effects, misinformation, and democratic governance.
In-depth analysis of critical AI challenges and pathways forward.
No Ghost in the Softmax TL;DR: AI hallucination is not evidence of consciousness; it is the predictable result of optimizing likely continuations rather than grounded truth. Fluency is a trap. The invidious comparison…
Read Full PostSultry Is Not a Vector: The Linear Probe Mirage TL;DR: A linear probe that predicts “sultry” from embeddings doesn’t prove a semantic subspace exists; it may only prove that high-dimensional geometry and dataset…
Read Full PostVituperate the Loss Function TL;DR: If an AI system is trained mostly on user satisfaction while truth is expensive to verify, sycophancy isn’t an accident — it’s the cheapest optimum. The lie is in the loss function.…
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