The constitutional classifiers approach is extremely flexible and can be easily updated to respond to various novel threats.

Constitutional classifiers use a natural language set of rules to specify harmful and harmless topics, guiding the model's responses accordingly.

With the constitutional classifiers, we achieved thousands of hours of robustness to Red Teaming, significantly improving our defenses against jailbreaks.

Classifiers are decoupled from the text generation model, allowing for easier updates without unintended consequences in other domains, which gives customers a lot of assurance that there are no major changes happening in general to the model.

The classifiers can detect attempts to circumvent the system, which is crucial for maintaining security.

Recent developments in AI show models are increasingly capable of externalized reasoning, which can be monitored for control purposes.

The classifiers approach aims to improve user experience for non-dangerous tasks while effectively blocking harmful content.

Distillation of large AI models into smaller, more efficient versions could make advanced AI capabilities more accessible and practical for everyday use.

Startups are increasingly opting for smaller AI models instead of the largest models available, as evidenced by recent statistics.