Happy May the 4th from Convergence Analysis! Cross-posted on the EA Forum. As part of Convergence Analysis’s scenario research, we’ve been looking into how AI organisations, experts, and forecasters make predictions about the future of AI. In February 2023, the AI research institute Epoch published a report in which its authors use neural scaling laws to make quantitative predictions about when AI will reach human-level performance and become transformative. The report has a corresponding blog post, an interactive model, and a Python notebook. We found this approach really interesting, but also hard to understand intuitively. While trying to follow how the authors derive a forecast from their assumptions, we wrote a breakdown that may be useful to others thinking about AI timelines and forecasting. In what follows, we set out our interpretation of Epoch’s ‘Direct Approach’ to forecasting the arrival of transformative AI (TAI). We’re eager to see how closely our understanding of this matches others’. We’ve also fiddled with Epoch’s interactive model and include some findings on its sensitivity to plausible changes in parameters. The Epoch team recently attempted to replicate DeepMind’s influential Chinchilla scaling law, an important quantitative input to Epoch’s forecasting model, but found inconsistencies in DeepMind’s presented data. We’ll summarise these findings and explore how an improved model might affect Epoch’s forecasting results. We’ve accidentally filled this post with Star Wars references. Disclaimer: we do not actually mean to suggest that the Direct Approach is quick or easy (though it is definitely seductive). This is where the fun begins (the assumptions) The goal of Epoch’s Direct Approach is to quantitatively predict the progress of AI capabilities. The approach is ‘direct’ in the sense that it uses observed scaling laws and empirical measurements to directly predict performance improvements as computing power increases. This stands in c