{
  "kind": "framework",
  "value": "information-bottleneck-theory",
  "collectionKey": "slides:framework:information-bottleneck-theory:all-document-kinds:all-producers:all-orientations",
  "filters": {
    "documentKinds": [],
    "sourceTypes": [],
    "orientations": []
  },
  "total": 1,
  "page": 1,
  "pageSize": 24,
  "pageCount": 1,
  "rows": [
    {
      "docId": "019dd923-5e88-73ef-bd5c-f341d4394195",
      "docSlug": "46f66c49fd159048",
      "documentTitle": "2018 Air Street Capital The State of AI Report 2018",
      "authorId": "AirStreetCapital",
      "authorName": "Air Street Capital",
      "documentKindSlug": "consulting-deck",
      "documentKindLabel": "Consulting deck",
      "sourceTypeSlug": "vc_research",
      "sourceTypeLabel": "VC research",
      "presentationDate": null,
      "orientation": "landscape",
      "aspectRatio": 1.777,
      "pageNumber": 13,
      "slideType": "industry_trends",
      "function": "present_framework",
      "notes": "The slide illustrates the Information Bottleneck theory, showing the evolution of layers (L1-L5) through phases A-E.",
      "imagePath": null,
      "matchCount": 1,
      "evidence": "The slide describes the process of memorizing data and then compressing it to retain only relevant information for prediction.",
      "slideHref": "/slides/019dd923-5e88-73ef-bd5c-f341d4394195/13",
      "deckHref": "/decks/019dd923-5e88-73ef-bd5c-f341d4394195",
      "deckAnchorHref": "/decks/019dd923-5e88-73ef-bd5c-f341d4394195#slide-13",
      "loopMatches": [
        {
          "to": 21,
          "from": 10,
          "name": "Pattern Hunter",
          "slug": "02-pattern-hunter",
          "bestFor": "Time-pressed audiences, building consensus, when data is strong",
          "matchId": "019dd95a-07fe-70ce-8d3c-4a0086ef4670",
          "evidence": "Twelve contiguous slides on GPU growth, Moore's Law, new architectures, hourly cost, ending in TPUv2 vs V100 cost case.",
          "position": 2,
          "objective": "Stack evidence that AI hardware is the binding constraint",
          "structure": "Evidence A -> Evidence B -> Evidence C -> Pattern/Conclusion",
          "confidence": 78,
          "description": "Group multiple pieces of evidence that together point to a pattern or conclusion"
        }
      ],
      "arcBeatMatches": [
        {
          "to": 69,
          "from": 4,
          "beatId": "019dd95a-0682-776c-8e34-ad4df4fe3ce7",
          "arcName": "The Triple Take",
          "arcSlug": "triple-take",
          "beatName": "The Facts (What)",
          "beatSlug": "triple-take-the-facts-what",
          "evidence": "Definitions then research breakthroughs (transfer learning, hardware, RL) and talent supply data.",
          "position": 1,
          "confidence": 78,
          "parentBeatName": "Setup",
          "parentBeatSlug": "setup"
        },
        {
          "to": 55,
          "from": 10,
          "beatId": "019dd95a-0682-776c-8e34-be65c7627fe7",
          "arcName": "The Onion",
          "arcSlug": "onion",
          "beatName": "First Layer",
          "beatSlug": "onion-first-layer",
          "evidence": "Hardware, vision, RL, bias - technical research layer.",
          "position": 2,
          "confidence": 55,
          "parentBeatName": "Development",
          "parentBeatSlug": "development"
        }
      ],
      "imagePathAlt": null,
      "thumbSrc": null,
      "thumbSrcAlt": null,
      "locked": true
    }
  ]
}