Lukas_Gloor

Predicting the future is hard, so it’s no surprise that we occasionally miss important developments. However, several times recently, in the contexts of Covid forecasting and AI progress, I noticed that I missed some crucial feature of a development I was interested in getting right, and it felt to me like I could’ve seen it coming if only I had tried a little harder. (Some others probably did better, but I could imagine that I wasn't the only one who got things wrong.) Maybe this is hindsight bias, but if there’s something to it, I want to distill the nature of the mistake. First, here are the examples that prompted me to take notice: Predicting the course of the Covid pandemic: * I didn’t foresee the contribution from sociological factors (e.g., “people not wanting to get hospitalized” – Zvi called it “the control system”). * As a result, I overpredicted the difference between countries with a lockdown policy vs ones without. (Note that this isn’t necessarily an update against the cost-effectiveness of lockdowns because the update goes both ways: lockdowns saved fewer lives than I would’ve predicted naively, but costs to the economy were also lower compared to the counterfactual where people already social-distanced more than expected of their own accord since they were reading the news about crowded hospitals and knew close contacts who were sick with the virus.) Predicting AI progress: * Not foreseeing that we’d get an Overton window shift in AI risk awareness. * Many EAs were arguably un(der)prepared for the possibility of a “chat-gpt moment,” where people who weren’t paying attention to AI progress previously got to experience a visceral sense of where AI capabilities progress is rapidly heading. As a result, it is now significantly easier to make significant policy asks to combat AI risks. * Not foreseeing wide deployment of early-stage “general” AI and the possible irrelevance of AI boxing. * Early discussions of AI risk used to involve th