My understanding of what everyone is producing (Carlsmith, Beckstead etc) is their point estimate / most likely probability for some proposition being true. Shifting this point estimate to below 10% would be near enough a prize, but plenty of real-world applications have highish point estimates with a lower bound uncertainty that is very low.
The application where I am most familiar with this effect is clinical trials for oncology drugs; it isn’t uncommon for the point estimate for a drug’s effectiveness to be (say) 50% better than all other drugs on the market, but with a 95% confidence interval that covers no better at all, or even sometimes substantially worse. It seems to me to be quite a radical claim that we have better knowledge of AI Risk across nearly all parameters than we have of an oncology drug across a single parameter following a clinical trial.
My understanding of what everyone is producing (Carlsmith, Beckstead etc) is their point estimate / most likely probability for some proposition being true. Shifting this point estimate to below 10% would be near enough a prize, but plenty of real-world applications have highish point estimates with a lower bound uncertainty that is very low.
The application where I am most familiar with this effect is clinical trials for oncology drugs; it isn’t uncommon for the point estimate for a drug’s effectiveness to be (say) 50% better than all other drugs on the market, but with a 95% confidence interval that covers no better at all, or even sometimes substantially worse. It seems to me to be quite a radical claim that we have better knowledge of AI Risk across nearly all parameters than we have of an oncology drug across a single parameter following a clinical trial.