- See Also
-
Links
- “Pet Cloning Is Booming in China”, Bateman 2022
- “Progress in Mathematical Programming Solvers 2001–2020”, Koch et al 2022
- “From Stroke to Stoke: The Multiple Sporting Legacies of the Southern California Home Swimming Pool”, Ryan & Tolga 2021
- “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”, Anonymous 2020
- “Measuring Hardware Overhang”, hippke 2020
- “A Time Leap Challenge for SAT Solving”, Fichte et al 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 & Brown 2020
- “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 & Brown 2020
- “2019 Recent Trends in GPU Price per FLOPS”, Bergal 2020
- “Accelerating Self-Play Learning in Go”, Wu 2019
- “Training Imagenet in 3 Hours for $25; and CIFAR-10 for $0.26”, Howard 2018
- “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
- “Technology Forecasting: The Garden of Forking Paths”, Gwern 2014
- “What You Should Know About Megaprojects and Why: An Overview”, Flyvbjerg 2014
- “Algorithmic Progress in Six Domains”, Grace 2013
- “Intelligence Explosion Microeconomics”, Yudkowsky 2013
- “The Wait Calculation: The Broader Consequences of the Minimum Time from Now to Interstellar Destinations and Its Statistical-Significance to the Space Economy”, Kennedy 2013
- “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”, Nemet 2009
- “Policy and Innovation in Low-Carbon Energy Technologies”, Nemet 2007
- “Two Centuries of Productivity Growth in Computing”, Nordhaus 2007
- “Interstellar Travel: The Wait Calculation and the Incentive Trap of Progress”, Kennedy 2006
- “Moore's Law and the Technology S-Curve”, Bowden 2004
- “Solving Real-World Linear Programs: A Decade and More of Progress”, Bixby 2002
- “On Proebsting's Law”, Scott 2001
- “The Learning Curve and the Yield Factor: the Case of Korea’s Semiconductor Industry”, Chung 2001
- “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”, Proebsting 1998
- “Competitive Cost Dynamics: The Experience Curve”, Hax & Majluf 1982
- “Some Thoughts on Education and Political Priorities, Cummings 2013”
- “Using Learning Curve Theory to Redefine Moore's Law”
- “Now Anyone Can Train Imagenet in 18 Minutes”
- “Analysis of World Records in Speedrunning [LINKPOST]”
- “Now You Can Sequence Your Whole Genome for Just $200”
- Sort By Magic
- Wikipedia
- Miscellaneous
- Link Bibliography
See Also
Links
“Pet Cloning Is Booming in China”, Bateman 2022
“Progress in Mathematical Programming Solvers 2001–2020”, Koch et al 2022
“From Stroke to Stoke: The Multiple Sporting Legacies of the Southern California Home Swimming Pool”, Ryan & Tolga 2021
“From Stroke to Stoke: The Multiple Sporting Legacies of the Southern California Home Swimming Pool”
“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”, Anonymous 2020
“Measuring Progress in Deep Reinforcement Learning Sample Efficiency”
“Measuring Hardware Overhang”, hippke 2020
“A Time Leap Challenge for SAT Solving”, Fichte et al 2020
“Danny Hernandez on Forecasting and the Drivers of AI Progress”, Koehler et al 2020
“Danny Hernandez on forecasting and the drivers of AI progress”
“Task Selection and Workload: A Focus on Completing Easy Tasks Hurts Performance”, KC et al 2020
“Task Selection and Workload: A Focus on Completing Easy Tasks Hurts Performance”
“Measuring the Algorithmic Efficiency of Neural Networks”, Hernandez & Brown 2020
“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 & Brown 2020
“2019 Recent Trends in GPU Price per FLOPS”, Bergal 2020
“Accelerating Self-Play Learning in Go”, Wu 2019
“Training Imagenet in 3 Hours for $25; and CIFAR-10 for $0.26”, Howard 2018
“Training Imagenet in 3 hours for $25; and CIFAR-10 for $0.26”
“Innovation and Cumulative Culture through Tweaks and Leaps in Online Programming Contests”, Miu et al 2018
“Innovation and cumulative culture through tweaks and leaps in online programming contests”
“How Well Do Experience Curves Predict Technological Progress? A Method for Making Distributional Forecasts”, Lafond et al 2017
“Technology Forecasting: The Garden of Forking Paths”, Gwern 2014
“What You Should Know About Megaprojects and Why: An Overview”, Flyvbjerg 2014
“What You Should Know About Megaprojects and Why: An Overview”
“Algorithmic Progress in Six Domains”, Grace 2013
“Intelligence Explosion Microeconomics”, Yudkowsky 2013
“The Wait Calculation: The Broader Consequences of the Minimum Time from Now to Interstellar Destinations and Its Statistical-Significance to the Space Economy”, Kennedy 2013
“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
“Implications of Historical Trends in the Electrical Efficiency of Computing”
“Interim Monitoring of Cost Dynamics for Publicly Supported Energy Technologies”, Nemet 2009
“Interim monitoring of cost dynamics for publicly supported energy technologies”
“Policy and Innovation in Low-Carbon Energy Technologies”, Nemet 2007
“Two Centuries of Productivity Growth in Computing”, Nordhaus 2007
“Interstellar Travel: The Wait Calculation and the Incentive Trap of Progress”, Kennedy 2006
“Interstellar Travel: The Wait Calculation and the Incentive Trap of Progress”
“Moore's Law and the Technology S-Curve”, Bowden 2004
“Solving Real-World Linear Programs: A Decade and More of Progress”, Bixby 2002
“Solving Real-World Linear Programs: A Decade and More of Progress”
“On Proebsting's Law”, Scott 2001
“The Learning Curve and the Yield Factor: the Case of Korea’s Semiconductor Industry”, Chung 2001
“The learning curve and the yield factor: the case of Korea’s semiconductor industry”
“The Effects of Moore’s Law and Slacking on Large Computations”, Gottbrath et al 1999
“The Effects of Moore’s Law and Slacking on Large Computations”
“Proebsting’s Law: Compiler Advances Double Computing Power Every 18 Years”, Proebsting 1998
“Proebsting’s Law: Compiler Advances Double Computing Power Every 18 Years”
“Competitive Cost Dynamics: The Experience Curve”, Hax & Majluf 1982
“Some Thoughts on Education and Political Priorities, Cummings 2013”
“Some thoughts on education and political priorities, Cummings 2013”
“Using Learning Curve Theory to Redefine Moore's Law”
“Now Anyone Can Train Imagenet in 18 Minutes”
“Analysis of World Records in Speedrunning [LINKPOST]”
“Now You Can Sequence Your Whole Genome for Just $200”
Sort By Magic
Annotations sorted by machine learning into inferred 'tags'. This provides an alternative way to browse: instead of by date order, one can browse in topic order. The 'sorted' list has been automatically clustered into multiple sections & auto-labeled for easier browsing.
Beginning with the newest annotation, it uses the embedding of each annotation to attempt to create a list of nearest-neighbor annotations, creating a progression of topics. For more details, see the link.
progress
technology-forecasting
performance-economics
Wikipedia
Miscellaneous
-
https://aiimpacts.org/time-for-ai-to-cross-the-human-performance-range-in-chess/ -
https://constructionphysics.substack.com/p/why-did-we-wait-so-long-for-wind-498 -
https://en.chessbase.com/post/komodo-8-the-smartphone-vs-desktop-challenge -
https://thehighergeometer.wordpress.com/2023/08/09/no-order-10-projective-planes-via-sat/ -
https://www.amazon.com/Turings-Cathedral-Origins-Digital-Universe/dp/1400075998/ -
https://www.businesswire.com/news/home/20181129005208/en/Ancestry-Breaks-November-Sales-Record -
https://www.construction-physics.com/p/construction-ford-and-a-lever-to -
https://www.construction-physics.com/p/how-did-solar-power-get-cheap-part -
https://www.construction-physics.com/p/the-birth-of-the-grid -
https://www.construction-physics.com/p/the-rise-of-steel-part-ii -
https://www.construction-physics.com/p/the-science-of-production -
https://www.construction-physics.com/p/the-story-of-titanium -
https://www.construction-physics.com/p/where-do-economies-of-scale-come -
https://www.construction-physics.com/p/why-are-nuclear-power-construction-c3c -
https://www.genome.gov/about-genomics/fact-sheets/DNA-Sequencing-Costs-Data -
https://www.genome.gov/about-genomics/fact-sheets/Sequencing-Human-Genome-cost -
https://www.lesswrong.com/posts/4MLBK7iCW3vYd93Mn/a-closer-look-at-chess-scalings-into-the-past -
https://www.lesswrong.com/posts/J6gktpSgYoyq5q3Au/benchmarking-an-old-chess-engine-on-new-hardware -
https://www.lesswrong.com/posts/Q3XaZTExzDpCLr4wu/efficiency-and-resource-use-scaling-parity -
https://www.metaculus.com/questions/9784/min-cost-of-dog-cloning-in-2030/ -
https://www.usenix.org/publications/loginonline/bcrypt-25-retrospective-password-security
Link Bibliography
-
2021-murtha.pdf: “From Stroke to Stoke: The Multiple Sporting Legacies of the Southern California Home Swimming Pool”, Murtha Ryan, Ozyurtcu Tolga -
2021-martinezplumed.pdf: “Research Community Dynamics behind Popular AI Benchmarks”, Fernando Martínez-Plumed, Pablo Barredo, Seán Ó hÉigeartaigh, José Hernández-Orallo -
2020-kc.pdf: “Task Selection and Workload: A Focus on Completing Easy Tasks Hurts Performance”, Diwas S. KC, Bradley R. Staats, Maryam Kouchaki, Francesca Gino -
https://www.fast.ai/2018/04/30/dawnbench-fastai/: “Training Imagenet in 3 Hours for $25; and CIFAR-10 for $0.26”, Jeremy Howard -
forking-path: “Technology Forecasting: The Garden of Forking Paths”, Gwern -
2013-kennedy.pdf: “The Wait Calculation: The Broader Consequences of the Minimum Time from Now to Interstellar Destinations and Its Statistical-Significance to the Space Economy”, Andrew Kennedy -
2009-nemet.pdf: “Interim Monitoring of Cost Dynamics for Publicly Supported Energy Technologies”, Gregory F. Nemet -
2007-nemet.pdf: “Policy and Innovation in Low-Carbon Energy Technologies”, Gregory Frank Nemet -
2006-kennedy.pdf: “Interstellar Travel: The Wait Calculation and the Incentive Trap of Progress”, Andrew Kennedy