You can use this Notebook to see how things are progressing in specific subfields or AI/ML as a whole, as a place to report new results you’ve obtained, as a place to look for problems that might benefit from having new datasets/metrics designed for them, or as a source to build on for data science projects.
At EFF, we’re ultimately most interested in how this data can influence our understanding of the likely implications of AI. To begin with, we’re focused on gathering it.
Inspired by and merging data from:
- Rodrigo Benenson’s “Who is the Best at X / Are we there yet?” collating machine vision datasets & progress
- Jack Clark and Miles Brundage’s collection of AI progress measurements
- Sarah Constantin’s Performance Trends in AI
- Katja Grace’s Algorithmic Progress in Six Domains
- The Swedish Computer Chess Association’s History of Computer Chess performance
- Qi Wu et al.‘s Visual Question Answering: A survey of Methods and Datasets
- Eric Yuan’s Comparison of Machine Reading Comprehension Datasets
