With the benefit of hindsight, we have a better sense of our takeaways from our first adversarial training project (paper). Our original aim was to use adversarial training to make a system that (as far as we could tell) never produced injurious completions. If we had accomplished that, we think it would have been the first demonstration of a deep learning system avoiding a difficult-to-formalize catastrophe with an ultra-high level of reliability. Presumably, we would have needed to invent novel robustness techniques that could have informed techniques useful for aligning TAI. With a successful system, we also could have performed ablations to get a clear sense of which building blocks were most important. Alas, we fell well short of that target. We still saw failures when just randomly sampling prompts and completions. Our adversarial training didn’t reduce the random failure rate, nor did it eliminate highly egregious failures (example below). We also don’t think we've successfully demonstrated a negative result, given that our results could be explained by suboptimal choices in our training process. Overall, we’d say this project had value as a learning experience but produced much less alignment progress than we hoped.Source:https://www.alignmentforum.org/posts/n3LAgnHg6ashQK3fF/takeaways-from-our-robust-injury-classifier-project-redwoodNarrated for AI Safety Fundamentals by TYPE III AUDIO.---A podcast by BlueDot Impact.Learn more on the AI Safety Fundamentals website.

Ver todos los episodios