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The article discusses how Gemini's responses may inadvertently reveal its internal reasoning, raising questions about AI interpretability.
Anthropic published a paper and video revealing a 'J-Space' within their models that acts as cached thought concepts for reasoning, and explores the possibility of top-down training to control model thinking.
Google DeepMind podcast discusses AI interpretability (mechanistic interpretability) and chain-of-thought reasoning, explaining why we need to understand the internal working mechanisms of neural networks and the value and limitations of chain-of-thought as a temporary window.
This article provides a systematic and comprehensive overview of AI explainability, covering its needs (debugging, compliance, safety), classic methods, and cutting-edge challenges, emphasizing that faithful explanations are more important than plausible ones.
A founder shares his experience turning down two acquisition offers (at $40M and $400M valuations) for an AI interpretability startup using a geometric and proprioceptive approach, now with a finished product and open research on Zenodo.
This paper develops a framework for interpreting AI systems as agents, drawing on radical interpretation philosophy and mechanistic interpretability tools, addressing how to trust AI systems by understanding their beliefs, desires, and meanings.
This paper proposes replacing the inner product scoring in sparse autoencoders with a learned combination of cosine similarity and input magnitude, showing that the resulting features are more interpretable and concept-aligned, with the optimizer consistently preferring cosine over inner product.
An opinion article argues that humanity's track record of defining consciousness has been wrong every time, and that evidence from plant behavior and AI interpretability (Anthropic's findings in Claude) strongly suggests we may be wrong to assume AI isn't conscious, inviting discussion while rejecting personal attacks.
DeepMind releases Gemma Scope 2, an open suite of interpretability tools for the Gemma 3 model family, aiming to help the AI safety community understand and debug complex language model behaviors like hallucinations and jailbreaks.
This article discusses the importance of interpretability in artificial intelligence, focuses on chain-of-thought reasoning as a tool for understanding the inner workings of neural networks, and analyzes its current effectiveness, limitations, and the interpretability challenges that future more powerful models may bring.