Too much efficiency makes everything worse: overfitting and the strong version of Goodhart’s law

window.dataLayer = window.dataLayer || []; function gtag(){dataLayer.push(arguments);} gtag('js', new Date()); [^netdetails]: In more detail, the baseline neural network architecture, design, and training configuration is as follows: [^saturation]: The discerning reader may have noticed that training diverges when the output learning rate is made...

window.dataLayer = window.dataLayer || []; function gtag(){dataLayer.push(arguments);} gtag('js', new Date());

window.dataLayer = window.dataLayer || []; function gtag(){dataLayer.push(arguments);} gtag('js', new Date()); [^katjagrace]: See Katja Grace's excellent [*Counterarguments to the basic AI x-risk case*](https://aiimpacts.org/counterarguments-to-the-basic-ai-x-risk-case/), for more discussion of the assumption of goal-direction, or coherence, in...