The Coming Technological Singularity
Vinge coined the Singularity as a near-term horizon beyond which superhuman intelligence makes prediction impossible, framing the urgency that drives alignment timelines today.
AI forecasting & timelines
Scaling laws, takeoff dynamics, emergent abilities, and timeline forecasting for transformative AI.
Browse the full interactive library →
Language Models are Few-Shot Learners (GPT-3)
GPT-3 demonstrated in-context learning at scale, forcing the field to rethink assumptions about what pretrained models can do and compressing alignment timelines.
Scaling Laws for Neural Language Models
Kaplan et al. quantified predictable performance scaling with compute, data, and parameters, enabling labs to forecast capability jumps and estimate safety lead time.
Training Compute-Optimal Large Language Models (Chinchilla)
Chinchilla reframed scaling laws by showing optimal performance requires balancing model size and training tokens, redirecting how labs plan capability and safety investment.
Emergent Abilities of LLMs
Wei et al. documented capability discontinuities appearing at key scale thresholds, raising concern that dangerous abilities could emerge unpredictably in larger models.
Sparks of Artificial General Intelligence
Bubeck et al. documented broad GPT-4 capability jumps across domains, compressing alignment timelines and stress-testing whether current safety evaluations are sufficient.
Are Emergent Abilities a Mirage?
Schaeffer et al. argued apparent emergence can be a measurement artifact rather than a true phase change, complicating how we forecast dangerous capability thresholds.
The Singularity is Near
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.
The Age of Spiritual Machines
Kurzweil's early timeline forecasts shaped modern discourse on AI trajectories and remain a key reference point for evaluating long-horizon predictions.
Superforecasting
Tetlock teaches the cognitive tools needed to predict technological risks with better-than-random accuracy, directly useful for AI timeline and governance forecasting.
Profiles of the Future
Clarke's forecasting framework, including his famous three laws, remains a classic guide to thinking clearly about radical technological change.
R.U.R.
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.
Accelerando
Stross depicts rapid recursive technological acceleration outpacing institutional response, a narrative model of hard-to-govern AI takeoff dynamics across three generations.
Clearer Thinking: AI Risk
Episodes on AI risk, timelines, and decision-making under deep uncertainty, with a rationalist focus on calibrating beliefs about transformative AI.
Machine Learning Street Talk
Technical ML interviews with regular deep dives into interpretability, scaling laws, emergent capabilities, and the safety implications of frontier model development.
Jacob Steinhardt's blog
Research and commentary on ML safety, forecasting, and robustness from a Berkeley professor working on practical safety problems.
Gwern Branwen's blog
Deeply researched essays on ML, scaling, AI art, and technology forecasting, known for rigorous analysis and independent thinking.
AI Impacts
Empirical research on AI timelines, historical technology analogies, and quantitative estimates of AI progress and impact.