Examining the methods, challenges, and open questions in ensuring artificial intelligence systems remain beneficial and controllable.
Interpretability research has crossed a threshold. Mechanistic methods can now trace specific behaviors to individual circuits in models up to 7B parameters, enabling unprecedented insight into how neural networks think.
RLHF produces alignment that is more surface than substance. Models learn to generate outputs that appear aligned without necessarily encoding the desired values in their internal representations.
Evaluation is the critical bottleneck. Existing safety benchmarks poorly capture the emergent behaviors that matter most in production, particularly under adversarial pressure or distributional shift.
Soft contamination in training data creates invisible risks. Subtle biases woven into corpora propagate through fine-tuning and resist standard debiasing techniques, creating a long tail of safety concerns.
Reverse-engineering neural network internals to understand the computational structures that produce specific model behaviors.
Using written principles to guide model behavior during training, reducing dependence on costly human feedback loops.
Systematic red-teaming and stress-testing to discover dangerous failure modes before models reach production environments.
Developing supervision methods that remain effective as AI systems grow more capable than their human operators.
Ensuring models perform reliably when real-world conditions diverge from training assumptions and distributions.
Building the institutional structures and international agreements needed to ensure responsible AI development and deployment.