“‘Experience Curves’ Tag”,2019-09-07 (AI economics, tech economics; backlinks)⁠:

Bibliography for tag economics/experience-curve, most recent first: 1 related tag, 62 annotations, & 34 links (parent).

• See Also
• Gwern
  • “Technology Forecasting: The Garden of Forking Paths”, Gwern2014
• Links
  • “New AI Battle Adopts Old Price War Strategy As Chinese Tech Giants Keep Start-Ups at Bay behind the Great Firewall”, Jiang & Le2024
  • “Pet Cloning Is Booming in China”, Bateman2022
  • “Progress in Mathematical Programming Solvers 2001^–₁₉2020”, Koch et al 2022
  • “From Stroke to Stoke: The Multiple Sporting Legacies of the Southern California Home Swimming Pool”, Ryan & Tolga2021
  • “Research Community Dynamics behind Popular AI Benchmarks”, Martínez-Plumed et al 2021
  • “How to Train BERT With an Academic Budget”, Izsak et al 2021
  • “Measuring Progress in Deep Reinforcement Learning Sample Efficiency”, Anonymous2020
  • “A Time Leap Challenge for SAT Solving”, Fichte et al 2020
  • “Measuring Hardware Overhang”, hippke 2020
  • “Danny Hernandez on Forecasting and the Drivers of AI Progress”, Koehler et al 2020
  • “Task Selection and Workload: A Focus on Completing Easy Tasks Hurts Performance”, KC et al 2020
  • “Measuring the Algorithmic Efficiency of Neural Networks”, Hernandez & Brown2020
  • “AI and Efficiency: We’re Releasing an Analysis Showing That Since 2012 the Amount of Compute Needed to Train a Neural Net to the Same Performance on ImageNet Classification Has Been Decreasing by a Factor of 2 Every 16 Months”, Hernandez & Brown2020
  • “2019 Recent Trends in GPU Price per FLOPS”, Bergal2020
  • “Accelerating Self-Play Learning in Go”, Wu2019
  • “Training Imagenet in 3 Hours for $25; and CIFAR-10 for $0.26”, Howard2018
  • “Innovation and Cumulative Culture through Tweaks and Leaps in Online Programming Contests”, Miu et al 2018
  • “How Well Do Experience Curves Predict Technological Progress? A Method for Making Distributional Forecasts”, Lafond et al 2017
  • “What You Should Know About Megaprojects and Why: An Overview”, Flyvbjerg2014
  • “Algorithmic Progress in Six Domains”, Grace2013
  • “Intelligence Explosion Microeconomics”, Yudkowsky2013
  • “The Wait Calculation: The Broader Consequences of the Minimum Time from Now to Interstellar Destinations and Its Statistical-Significance to the Space Economy”, Kennedy2013
  • “Statistical Basis for Predicting Technological Progress”, Nagy et al 2012
  • “The International SAT Solver Competitions”, Järvisalo et al 2012
  • “Implications of Historical Trends in the Electrical Efficiency of Computing”, Koomey et al 2011
  • “Interim Monitoring of Cost Dynamics for Publicly Supported Energy Technologies”, Nemet2009
  • “Policy and Innovation in Low-Carbon Energy Technologies”, Nemet2007
  • “Two Centuries of Productivity Growth in Computing”, Nordhaus2007
  • “Interstellar Travel: The Wait Calculation and the Incentive Trap of Progress”, Kennedy2006
  • “Moore’s Law and the Technology S-Curve”, Bowden2004
  • “Solving Real-World Linear Programs: A Decade and More of Progress”, Bixby2002
  • “On Proebsting’s Law”, Scott2001
  • “The Learning Curve and the Yield Factor: the Case of Korea’s Semiconductor Industry”, Chung2001
  • “The Effects of Moore’s Law and Slacking on Large Computations”, Gottbrath et al 1999
  • “Proebsting’s Law: Compiler Advances Double Computing Power Every 18 Years”, Proebsting1998
  • “How Learning by Doing Is Done: Problem Identification in Novel Process Equipment”, Hippel & Tyre1995
  • “Competitive Cost Dynamics: The Experience Curve”, Hax & Majluf1982
  • “Startup Management”, Baloff1970
  • “Allocative Efficiency vs. ‘X-Efficiency’”, Leibenstein1966
  • “Profit from the Learning Curve”, Hirschmann1964
  • “Time for AI to Cross the Human Performance Range in Chess”
  • “Energy Prices Are Naturally Turbulent”
  • “Komodo 8: the Smartphone vs Desktop Challenge”
  • “Let’s Reproduce GPT-2 (1.6B): One 8×H100 Node, 24 Hours, $672”
  • “Make Megaprojects More Modular”
  • “The Price of Batteries Has Declined by 97% in the Last Three Decades”
  • “Why Did Renewables Become so Cheap so Fast?”
  • “Performance Curve Database”
  • “Some Thoughts on Education and Political Priorities, Cummings2013”
  • “Using Learning Curve Theory to Redefine Moore’s Law”
  • “Getting Materials out of the Lab”
  • “Construction, Ford, and a Lever to Move the World”
  • “How to Build a $20 Billion Semiconductor Fab”
  • “The Science of Production”, Potter2024
  • “Now Anyone Can Train Imagenet in 18 Minutes”
  • “DNA Sequencing Costs: Data”
  • “The Cost of Sequencing a Human Genome”
  • “A Closer Look at Chess Scalings (into the Past)”
  • “Benchmarking an Old Chess Engine on New Hardware”
  • “Analysis of World Records in Speedrunning [LINKPOST]”
  • “$400 Million Investment Programme Positions Ireland for Global Leadership in Genomic Research and Advanced Life Sciences”
  • “Now You Can Sequence Your Whole Genome for Just $200”
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    • learning-curve
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