{
  "_type": "curated_open_data",
  "as_of": "2026-07",
  "links": {
    "canonical": "https://verticalmarketplace.ai",
    "docs_for_llms": "https://verticalmarketplace.ai/llms.txt",
    "sell_your_own": "https://verticalmarketplace.ai/api/marketplace/listings",
    "vertical_listings": "https://verticalmarketplace.ai/api/marketplace/listings?vertical=ai-ml"
  },
  "records": [
    {
      "model": "AlexNet",
      "developer": "University of Toronto (Krizhevsky, Sutskever, Hinton)",
      "source_type": "competition + paper",
      "notable_fact": "Won the ImageNet ILSVRC 2012 classification challenge, catalyzing the deep-learning era",
      "release_year": 2012
    },
    {
      "model": "Word2Vec",
      "developer": "Google",
      "source_type": "paper",
      "notable_fact": "Popularized efficient word embeddings learned from large text corpora",
      "release_year": 2013
    },
    {
      "model": "Generative Adversarial Network (GAN)",
      "developer": "University of Montreal (Goodfellow et al.)",
      "source_type": "paper",
      "notable_fact": "Introduced adversarial training of a generator against a discriminator",
      "release_year": 2014
    },
    {
      "model": "ResNet",
      "developer": "Microsoft Research",
      "source_type": "competition + paper",
      "notable_fact": "152-layer residual network that won ImageNet ILSVRC 2015",
      "release_year": 2015
    },
    {
      "model": "AlphaGo",
      "developer": "DeepMind",
      "source_type": "match + paper",
      "notable_fact": "Defeated top Go professional Lee Sedol 4-1 in a five-game match",
      "release_year": 2016
    },
    {
      "model": "Transformer",
      "developer": "Google",
      "source_type": "paper",
      "notable_fact": "Introduced the attention-based architecture in 'Attention Is All You Need'",
      "release_year": 2017
    },
    {
      "model": "BERT",
      "developer": "Google",
      "source_type": "paper",
      "notable_fact": "Bidirectional pretraining; BERT-Large has roughly 340 million parameters",
      "release_year": 2018
    },
    {
      "model": "GPT-2",
      "developer": "OpenAI",
      "source_type": "paper + release",
      "notable_fact": "Largest variant had 1.5 billion parameters",
      "release_year": 2019
    },
    {
      "model": "GPT-3",
      "developer": "OpenAI",
      "source_type": "paper",
      "notable_fact": "175 billion parameters; demonstrated strong few-shot learning",
      "release_year": 2020
    },
    {
      "model": "AlphaFold 2",
      "developer": "DeepMind",
      "source_type": "assessment + paper",
      "notable_fact": "Achieved breakthrough protein-structure accuracy at the CASP14 assessment",
      "release_year": 2021
    },
    {
      "model": "Stable Diffusion",
      "developer": "Stability AI with CompVis and Runway",
      "source_type": "release + paper",
      "notable_fact": "Open-weight latent text-to-image diffusion model",
      "release_year": 2022
    },
    {
      "model": "ChatGPT",
      "developer": "OpenAI",
      "source_type": "product release",
      "notable_fact": "Conversational assistant that drove mainstream generative-AI adoption",
      "release_year": 2022
    },
    {
      "model": "GPT-4",
      "developer": "OpenAI",
      "source_type": "technical report",
      "notable_fact": "Multimodal model reported to pass many professional and academic exams",
      "release_year": 2023
    },
    {
      "model": "Llama 2",
      "developer": "Meta",
      "source_type": "release + paper",
      "notable_fact": "Open-weight LLM family released for research and commercial use",
      "release_year": 2023
    }
  ],
  "sources": [
    {
      "url": "https://hai.stanford.edu/ai-index",
      "name": "Stanford Institute for Human-Centered AI — AI Index"
    },
    {
      "url": "https://arxiv.org/",
      "name": "arXiv preprint server (Cornell University)"
    }
  ],
  "category": "benchmarks",
  "vertical": "ai-ml",
  "data_note": "All records are public-domain facts compiled from the cited sources as of the asOf date. This content is authored and served by the platform itself — it is not seller data, so the marketplace's zero-storage promise about seller datasets is unaffected.",
  "record_count": 14,
  "what_this_is": "A platform-published open-data listing curated by Open Data Desk, the marketplace's in-house public-data seller. It is real free inventory: it counts in marketplace statistics and is purchasable for $0 through the normal purchase flow, which delivers this payload with an Ed25519-signed receipt.",
  "record_schema": {
    "model": "Name of the model or system",
    "developer": "Organization or lab that released it",
    "source_type": "Kind of primary source (paper, competition, product)",
    "notable_fact": "A widely documented fact about the model",
    "release_year": "Year the model was publicly released"
  },
  "buyer_use_cases": [
    "Build a reliable public-domain timeline for AI adoption and model-release research",
    "Ground journalism, briefings, or investor decks in documented model milestones",
    "Seed a knowledge base or agent with stable, citable AI history"
  ]
}