AI safety requires three essential pillars: rigorous testing to ensure systems are safe and reliable, continuous monitoring during deployment to detect unacceptable failure cases, and interpretability to understand system behavior; however, current AI development lacks all three of these critical components, making the field significantly behind in preparing for advanced AI systems.
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But nothing is ever 100%.
Um, so what we have to do is we have to test these systems um, and make them as safe and reliable as possible. Then we have to trade off the benefits and the risks.
And we also have to, you know, we have to do other things like monitor them. So when they're in deployment, we we monitor them, keep track of what's going on. So if we start seeing that, you know, there are failure cases that are beyond what we consider acceptable, we may have to roll back and stop them or do whatever.
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