We investigate the rate at which algorithms for pre-training language models have improved since the advent of deep learning. Using 200+ evaluations (2012–2023) on Wikitext and Penn Treebank, we find the compute required to reach a fixed performance halves roughly every 8 months (95% CI ≈ 5–14 months), faster than Moore's Law. Augmented scaling laws quantify algorithmic progress versus scaling, showing that increased compute still contributed even more to improvements over this period, despite rapid algorithmic advances.

Quantum computing promises transformational gains for solving some problems, but little to none for others. We introduce a framework—cast as a race between quantum and classical computers—to determine which problems benefit. While classical machines operate faster, quantum algorithms can sometimes be more efficient; which advantage dominates determines where quantum wins. Our analysis suggests many small-to-moderate problems common in business will not benefit from quantum computing, whereas larger problems or those with exceptionally large algorithmic gains may see advantages.

The risks posed by Artificial Intelligence (AI) are of considerable concern to academics, auditors, policymakers, AI companies, and the public. However, a lack of shared understanding of AI risks can impede our ability to comprehensively discuss, research, and react to them. This paper addresses this gap by creating an AI Risk Repository to serve as a common frame of reference. This comprises a living database of 777 risks extracted from 43 taxonomies, which can be filtered based on two overarching taxonomies and easily accessed, modified, and updated via our website and online spreadsheets. We construct our Repository with a systematic review of taxonomies and other structured classifications of AI risk followed by an expert consultation. We develop our taxonomies of AI risk using a best-fit framework synthesis. Our high-level Causal Taxonomy of AI Risks classifies each risk by its causal factors (1) Entity: Human, AI; (2) Intentionality: Intentional, Unintentional; and (3) Timing: Pre-deployment; Post-deployment. Our mid-level Domain Taxonomy of AI Risks classifies risks into seven AI risk domains: (1) Discrimination & toxicity, (2) Privacy & security, (3) Misinformation, (4) Malicious actors & misuse, (5) Human-computer interaction, (6) Socioeconomic & environmental, and (7) AI system safety, failures, & limitations. These are further divided into 23 subdomains. The AI Risk Repository is, to our knowledge, the first attempt to rigorously curate, analyze, and extract AI risk frameworks into a publicly accessible, comprehensive, extensible, and categorized risk database. This creates a foundation for a more coordinated, coherent, and complete approach to defining, auditing, and managing the risks posed by AI systems.