Parameters vs. training dataset size in notable AI systems, by researcher affiliation
Parameters are variables in an AI system whose values are adjusted during training to establish how input data gets transformed into the desired output.Training dataset size refers to the volume of text that is employed to train a model effectively.
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- Affiliation of research teams building notable AI systems, by year of publication
- Annual attendance at major artificial intelligence conferences
- Annual global corporate investment in artificial intelligence, by type
- Annual granted patents related to artificial intelligence, by industry
- Annual industrial robots installed in top 5 countries
- Annual patent applications related to AI per million people
- Annual patent applications related to AI, by status
- Annual patent applications related to artificial intelligence
- Annual private investment in artificial intelligenceCSET
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- Annual private investment in artificial intelligence, by focus areaNetBase Quid
- Annual professional service robots installed globally, by application area
- Annual scholarly publications on artificial intelligence
- Artificial intelligence: Performance on knowledge tests vs. dataset size
- Artificial intelligence: Performance on knowledge tests vs. number of parameters
- Artificial intelligence: Performance on knowledge tests vs. training computation
- Computation used to train notable AI systems, by affiliation of researchers
- Computation used to train notable artificial intelligence systems, by domain
- Countries with national artificial intelligence strategies
- Cumulative AI-related bills passed into law since 2016, as of 2023
- Cumulative number of large-scale AI models by domain since 2017
- Cumulative number of large-scale AI systems by country since 2017
- Cumulative number of notable AI systems by domain
- Datapoints used to train notable artificial intelligence systems
- Domain of notable artificial intelligence systems, by year of publication
- Employer of new AI PhDs in the United States and Canada
- Exponential growth of computation in the training of notable AI systems
- Exponential growth of datapoints used to train notable AI systems
- Exponential growth of parameters in notable AI systems
- GPU computational performance per dollar
- Global annual number of reported artificial intelligence incidents and controversies
- Global investment in generative AI
- Global views about AI's impact on society in the next 20 years, by demographic group
- Global views about the safety of riding in a self-driving car, by demographic group
- Hardware and energy cost to train notable AI systems
- Highest chess rating ever achieved by computers
- How worried are Americans about their work being automated?
- ImageNet: Top-performing AI systems in labeling images
- Industrial robots: Annual installations and total in operation
- Market share for logic chip production, by manufacturing stage
- Newly-funded artificial intelligence companies
- Number of large-scale AI systems released per year
- Parameters in notable artificial intelligence systems
- Protein folding prediction accuracy
- Scholarly publications on artificial intelligence per million people
- Share of artificial intelligence jobs among all job postings
- Share of companies using artificial intelligence technology
- Share of women among new computer science PhDs, United States and Canada
- Test scores of AI systems on various capabilities relative to human performance
- Top performing AI systems in coding, math, and language-based knowledge tests
- Total number of AI PhD graduates in US and Canada
- Training computation vs. dataset size in notable AI systems, by researcher affiliation
- Training computation vs. parameters in notable AI systems, by domain
- Training computation vs. parameters in notable AI systems, by researcher affiliation
- Views about AI's impact on society in the next 20 years
- Views about the safety of riding in a self-driving car
- Views of Americans about robot vs. human intelligence