{
  "kind": "framework",
  "value": "transformer-architecture",
  "collectionKey": "slides:framework:transformer-architecture:all-document-kinds:all-producers:all-orientations",
  "filters": {
    "documentKinds": [],
    "sourceTypes": [],
    "orientations": []
  },
  "total": 3,
  "page": 1,
  "pageSize": 24,
  "pageCount": 1,
  "rows": [
    {
      "docId": "019dd923-5e88-73ef-bd5e-164a6fb27ba9",
      "docSlug": "1420f7eff28e00b5",
      "documentTitle": "2025 Andrej Karpathy YC School presentation",
      "authorId": "misc",
      "authorName": "Andrej Karpathy",
      "documentKindSlug": "consulting-deck",
      "documentKindLabel": "Consulting deck",
      "sourceTypeSlug": "independent_analyst",
      "sourceTypeLabel": "Independent analyst",
      "presentationDate": null,
      "orientation": "landscape",
      "aspectRatio": 1.777,
      "pageNumber": 41,
      "slideType": "thesis_headline",
      "function": "establish_context",
      "notes": "The slide uses the 'Attention Is All You Need' Transformer architecture diagram to ground the philosophical claim.",
      "imagePath": null,
      "matchCount": 1,
      "evidence": "Visual representation of the standard Transformer decoder block",
      "slideHref": "/slides/019dd923-5e88-73ef-bd5e-164a6fb27ba9/41",
      "deckHref": "/decks/019dd923-5e88-73ef-bd5e-164a6fb27ba9",
      "deckAnchorHref": "/decks/019dd923-5e88-73ef-bd5e-164a6fb27ba9#slide-41",
      "loopMatches": [],
      "arcBeatMatches": [],
      "imagePathAlt": null,
      "thumbSrc": null,
      "thumbSrcAlt": null,
      "locked": true
    },
    {
      "docId": "019dd923-5de0-76bd-a168-6159b949e2c2",
      "docSlug": "ff5d549e5027972a",
      "documentTitle": "GPT-3 and the actuarial landscape",
      "authorId": "OliverWyman",
      "authorName": "Oliver Wyman",
      "documentKindSlug": "consulting-deck",
      "documentKindLabel": "Consulting deck",
      "sourceTypeSlug": "strategy_consulting",
      "sourceTypeLabel": "Strategy consulting",
      "presentationDate": null,
      "orientation": "landscape",
      "aspectRatio": 1.778,
      "pageNumber": 30,
      "slideType": "other",
      "function": "present_framework",
      "notes": "The diagram maps the standard Transformer architecture (Attention is All You Need) to a specific example involving actuarial text processing.",
      "imagePath": null,
      "matchCount": 1,
      "evidence": "Visual representation of the Transformer model components (Attention, Feed Forward, Linear, Softmax)",
      "slideHref": "/slides/019dd923-5de0-76bd-a168-6159b949e2c2/30",
      "deckHref": "/decks/019dd923-5de0-76bd-a168-6159b949e2c2",
      "deckAnchorHref": "/decks/019dd923-5de0-76bd-a168-6159b949e2c2#slide-30",
      "loopMatches": [],
      "arcBeatMatches": [
        {
          "to": 31,
          "from": 17,
          "beatId": "5259c9cc-c0ba-4428-ac14-7a6ba8d02be1",
          "arcName": "The Sparkline",
          "arcSlug": "sparkline",
          "beatName": "What Could Be",
          "beatSlug": "sparkline-what-could-be-2",
          "evidence": "The deck explores the potential applications of GPT-3 in the actuarial field.",
          "position": 2,
          "confidence": 0.8,
          "parentBeatName": "Turn",
          "parentBeatSlug": "turn"
        }
      ],
      "imagePathAlt": null,
      "thumbSrc": null,
      "thumbSrcAlt": null,
      "locked": true
    },
    {
      "docId": "019de06f-b8a3-7331-8935-2a15d6d59d93",
      "docSlug": "041e12e95b005675c8303cd029ca09ef",
      "documentTitle": "OpenAI | Product Presentation Deck | 64 slides",
      "authorId": "openai",
      "authorName": "OpenAI",
      "documentKindSlug": "conference-presentation",
      "documentKindLabel": "Conference presentation",
      "sourceTypeSlug": "investor_relations",
      "sourceTypeLabel": "Investor relations",
      "presentationDate": "2018-09-01 00:00:00",
      "orientation": "landscape",
      "aspectRatio": 1.7777778,
      "pageNumber": 39,
      "slideType": "other",
      "function": "present_framework",
      "notes": "The slide shows a 12x repeated transformer block on the left and specific input/output processing pipelines for four different NLP tasks on the right.",
      "imagePath": null,
      "matchCount": 1,
      "evidence": "Visual representation of transformer blocks and input processing",
      "slideHref": "/slides/019de06f-b8a3-7331-8935-2a15d6d59d93/39",
      "deckHref": "/decks/019de06f-b8a3-7331-8935-2a15d6d59d93",
      "deckAnchorHref": "/decks/019de06f-b8a3-7331-8935-2a15d6d59d93#slide-39",
      "loopMatches": [],
      "arcBeatMatches": [],
      "imagePathAlt": null,
      "thumbSrc": null,
      "thumbSrcAlt": null,
      "locked": true
    }
  ]
}