Safety in AI Models
Nathan discusses the fragility of safety mechanisms in AI models, particularly how fine-tuning can inadvertently strip away ingrained safety behaviors. He emphasizes that safety is a holistic system rather than just a feature of the model, suggesting that the implications of fine-tuning on safety are more of a business concern than a technical crisis. The conversation highlights the ongoing evolution of research in this area and the complexities involved in maintaining safety standards across different AI applications.In this clip
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Related Questions
How are Large Language Models (LLMs) fine-tuned post-training as discussed in the episode #174 - Odyssey Text-to-Video, Groq LLM Engine, OpenAI Security Issues, and the clip Covert Model Manipulation?
How are Large Language Models (LLMs) fine-tuned post-training as discussed in the episode #174 - Odyssey Text-to-Video, Groq LLM Engine, OpenAI Security Issues and the clip Covert Model Manipulation?
How are Large Language Models (LLMs) fine-tuned post-training as discussed in the episode #174 - Odyssey Text-to-Video, Groq LLM Engine, OpenAI Security Issues and the clip Covert Model Manipulation?