ai safety podcast

Benjamin @bschne · 1h Listened to an interview with Nick Patterson, a mathematician now with the Broad Institute who formerly worked at RenTech, and this bit really stuck with me. The smartest practitioners often use surprisingly simple tools, they're just better at applying the right ones right. [Embedded quote block] It's funny that I think the most important thing to do on data analysis is to do the simple things right. So here's a kind of non-secret about what we did at Renaissance. In my opinion, our most important statistical tool was simple regression with one target and one independent variable. It's the simplest statistical model you can imagine, any reasonably smart high school student can do it. Now we have some of the smartest people around working in our Hedge Fund. We have string theorists we recruited from Harvard. And they're doing simple regression. Is this stupid or pointless? Should we be hiring stupid people and paying them less? And the answer is no. And the reason is nobody tells you what the variables you should be regressing. What's the target? Should you do a non-linear transform before you regress? What's the source? Should you clean your data? Do you notice when your results are obviously rubbish? And so on. And the smarter you are, the less likely you are to make a stupid mistake. And that's why I think you often need smart people who appear to be doing something technically very easy, but actually, usually it's not so easy. We're able to do it carefully and precisely. Nick Patterson on "Talking Machines" "AI Safety and The Legacy of Bletchley Park" (S02E04)