{"id": 1015499, "name": "Annual number of large-scale AI models by domain", "unit": "AI systems", "createdAt": "2025-03-15T08:53:17.000Z", "updatedAt": "2026-03-08T06:32:08.000Z", "coverage": "", "timespan": "2019-2026", "datasetId": 6998, "shortUnit": "", "columnOrder": 0, "shortName": "yearly_count", "catalogPath": "grapher/artificial_intelligence/2025-03-12/epoch_compute_intensive_domain/epoch_compute_intensive_domain#yearly_count", "descriptionShort": "Describes the specific area, application, or field in which a large-scale AI model is designed to operate. The 2026 data is incomplete and was last updated 07 March 2026.", "descriptionFromProducer": "A foreign key field categorizing the system\u2019s domain of machine learning. This field links to the [ML Domains table](https://airtable.com/appDFXXgaG1xLtXGL/shrhzolGiQCVnwOY5/tbleYEsZORsiYRVTM), and domains are selected from the options in that table.", "descriptionProcessing": "The count of large-scale AI models AI systems per domain is derived by tallying the instances of machine learning models classified under each domain category. It's important to note that a single machine learning model can fall under multiple domains. The classification into domains is determined by the specific area, application, or field that the AI model is primarily designed to operate within.", "type": "int", "grapherConfigIdETL": "01959901-f5e8-73fb-88aa-a850a2911afd", "dataChecksum": "16510075815509913065", "metadataChecksum": "-7932074578706767779", "datasetName": "Large-scale AI systems by domain type", "updatePeriodDays": 31, "datasetVersion": "2025-03-12", "nonRedistributable": false, "display": {"unit": "AI systems"}, "schemaVersion": 2, "processingLevel": "major", "presentation": {"topicTagsLinks": ["Artificial Intelligence"]}, "descriptionKey": ["Game systems are specifically designed for games and excel in understanding and strategizing gameplay. For instance, AlphaGo, developed by DeepMind, defeated the world champion in the game of Go. Such systems use complex algorithms to compete effectively, even against skilled human players.", "Language systems are tailored to process language, focusing on understanding, translating, and interacting with human languages. Examples include chatbots, machine translation tools like Google Translate, and sentiment analysis algorithms that can detect emotions in text.", "Multimodal systems are artificial intelligence frameworks that integrate and interpret more than one type of data input, such as text, images, and audio. ChatGPT-4 is an example of a multimodal model, as it has the capability to process and generate responses based on both textual and visual inputs.", "Vision systems focus on processing visual information, playing a pivotal role in image recognition and related areas. For example, Facebook's photo tagging model uses vision AI to identify faces.", "Speech systems are dedicated to handling spoken language, serving as the backbone of voice assistants and similar applications. They recognize, interpret, and generate spoken language to interact with users.", "Biology systems analyze biological data and simulate biological processes, aiding in drug discovery and genetic research.", "Image generation systems create visual content from text descriptions or other inputs, used in graphic design and content creation."], "dimensions": {"years": {"values": [{"id": 2019}, {"id": 2020}, {"id": 2021}, {"id": 2022}, {"id": 2023}, {"id": 2024}, {"id": 2025}, {"id": 2026}]}, "entities": {"values": [{"id": 371230, "name": "All large-scale AI systems", "code": null}, {"id": 369579, "name": "Audio", "code": null}, {"id": 369045, "name": "Biology", "code": null}, {"id": 368122, "name": "Games", "code": null}, {"id": 306044, "name": "Image generation", "code": null}, {"id": 368116, "name": "Language", "code": null}, {"id": 369580, "name": "Mathematics", "code": null}, {"id": 368118, "name": "Multimodal", "code": null}, {"id": 35473, "name": "Other", "code": null}, {"id": 369044, "name": "Robotics", "code": null}, {"id": 368119, "name": "Speech", "code": null}, {"id": 369576, "name": "Video", "code": null}, {"id": 368120, "name": "Vision", "code": null}]}}, "origins": [{"id": 14138, "title": "Tracking Compute-Intensive AI Models", "description": "A dataset that tracks compute-intensive AI models, with training compute over 10\u00b2\u00b3\u00a0floating point operations (FLOP). This corresponds to training costs of hundreds of thousands of dollars or more.\n\nTo identify compute-intensive AI models, the team at Epoch AI used various resources, estimating compute when not directly reported. They included benchmarks and repositories, such as Papers With Code and Hugging Face, to find models exceeding 10\u00b2\u00b3 FLOP. They also explored non-English media and specific leaderboards, particularly focusing on Chinese sources.\n\nAdditionally, they examined blog posts, press releases from major labs, and scholarly literature to track new models. A separate table was created for models with unconfirmed but plausible compute levels. Despite thorough methods, proprietary and secretive models may have been missed.", "producer": "Epoch AI", "citationFull": "Robi Rahman, David Owen and Josh You (2024), \"Tracking Compute-Intensive AI Models\". Published online at epochai.org. Retrieved from: 'https://epoch.ai/blog/tracking-compute-intensive-ai-models' [online resource]", "urlMain": "https://epoch.ai/blog/tracking-compute-intensive-ai-models", "urlDownload": "https://epoch.ai/data/epochdb/large_scale_ai_models.csv", "dateAccessed": "2026-03-07", "datePublished": "2025", "license": {"url": "https://epoch.ai/blog/how-much-does-it-cost-to-train-frontier-ai-models", "name": "CC BY 4.0"}}]}