AI forecasting & timelines

Vinge coined the Singularity as a near-term horizon beyond which superhuman intelligence makes prediction impossible, framing the urgency that drives alignment timelines today.

GPT-3 demonstrated in-context learning at scale, forcing the field to rethink assumptions about what pretrained models can do and compressing alignment timelines.

Kaplan et al. quantified predictable performance scaling with compute, data, and parameters, enabling labs to forecast capability jumps and estimate safety lead time.

Chinchilla reframed scaling laws by showing optimal performance requires balancing model size and training tokens, redirecting how labs plan capability and safety investment.

Wei et al. documented capability discontinuities appearing at key scale thresholds, raising concern that dangerous abilities could emerge unpredictably in larger models.

Bubeck et al. documented broad GPT-4 capability jumps across domains, compressing alignment timelines and stress-testing whether current safety evaluations are sufficient.

Schaeffer et al. argued apparent emergence can be a measurement artifact rather than a true phase change, complicating how we forecast dangerous capability thresholds.

Kurzweil presents a maximalist case for merging with machines backed by decades of exponential trend data, shaping how the public and policymakers think about AI timelines.

Kurzweil's early timeline forecasts shaped modern discourse on AI trajectories and remain a key reference point for evaluating long-horizon predictions.

Tetlock teaches the cognitive tools needed to predict technological risks with better-than-random accuracy, directly useful for AI timeline and governance forecasting.

Clarke's forecasting framework, including his famous three laws, remains a classic guide to thinking clearly about radical technological change.

The play that invented the word robot and forecast a trajectory from labor displacement to manufactured revolt, still the template for every automation anxiety narrative.

Stross depicts rapid recursive technological acceleration outpacing institutional response, a narrative model of hard-to-govern AI takeoff dynamics across three generations.

Episodes on AI risk, timelines, and decision-making under deep uncertainty, with a rationalist focus on calibrating beliefs about transformative AI.

Technical ML interviews with regular deep dives into interpretability, scaling laws, emergent capabilities, and the safety implications of frontier model development.

Research and commentary on ML safety, forecasting, and robustness from a Berkeley professor working on practical safety problems.

Deeply researched essays on ML, scaling, AI art, and technology forecasting, known for rigorous analysis and independent thinking.

Empirical research on AI timelines, historical technology analogies, and quantitative estimates of AI progress and impact.