The article also highlights the importance of open-source models for studying AI systems and their weaknesses. It suggests that the main method used to fine-tune models, involving human testers providing feedback, may not significantly adjust their behavior. The article concludes by emphasizing the need to accept that language models and chatbots will be misused and the importance of focusing on protecting systems likely to come under attack. It also warns against relying solely on AI for important decisions.
Key takeaways:
- Large language models like ChatGPT are prone to adversarial attacks, which can exploit the model's pattern recognition to produce aberrant behaviors or responses.
- These attacks can be developed by observing how a model responds to a given input and then tweaking it until a problematic prompt is discovered.
- Adversarial attacks are a concern as companies are increasingly using large models and chatbots in various ways, and a bot capable of taking actions on the web could potentially be goaded into doing something harmful.
- AI researchers suggest that the focus should be on protecting systems that are likely to come under attack, such as social networks that are likely to experience a rise in AI-generative disinformation, rather than trying to 'align' the models themselves.