About this episode
The episode discusses the implications of 'distillation attacks' on AI capabilities and the importance of careful terminology in the discourse surrounding AI.
‘Distillation attacks’ is a horrible term for what is happening right now. Yes, some Chinese labs are hacking or jailbreaking APIs to attempt to extract more signal from model APIs — stopping this is important to maintain the U.S.’s lead in AI capabilities. Referring to this as distillation attack is going to irrevocably associate all distillation with this behavior, and distillation generally is a core technique needed to diffuse AI capabilities broadly through academic and economic activities. We went through this sort of language transition with the open source vs open weight debate. All the terms just reduced to open models – very few people in the large AI community know exactly how open-source differs from open-weights. And terminology matters, as the less informed people who still care about — and influence — the technology are bound by different terms they use. If we’re not careful with the discourse around distillation, many people could associate this broad technique used for research and development of new models as an act at the boundary of corporate manipulation and crime. I’ve recently written a more technical piece on estimating how impactful state-of-the-art…
People in this episode
Host: Nathan Lambert
Topics covered
- distillation attacks
- AI capabilities
- terminology in AI
- open source vs open weights
- policy implications
Keywords
- distillation attacks
- AI
- Chinese labs
- open source
- policy
- terminology
- machine learning
Mentioned in this episode
Organizations: Anthropic, Chinese labs
Places: China, U.S.
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