{
  "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,
  "pageCount": 156,
  "prevPage": 12,
  "nextPage": 14,
  "slideType": "industry_trends",
  "function": "present_framework",
  "density": "dense",
  "nDataPoints": 0,
  "notes": "The slide illustrates the Information Bottleneck theory, showing the evolution of layers (L1-L5) through phases A-E.",
  "elementsJson": [
    "headline_text",
    "line_chart",
    "numbered_list"
  ],
  "metadataConfidence": 1,
  "imagePath": null,
  "slideHref": "/slides/019dd923-5e88-73ef-bd5c-f341d4394195/13",
  "deckHref": "/decks/019dd923-5e88-73ef-bd5c-f341d4394195",
  "deckJsonHref": "/decks/019dd923-5e88-73ef-bd5c-f341d4394195.json",
  "deckAnchorHref": "/decks/019dd923-5e88-73ef-bd5c-f341d4394195#slide-13",
  "components": [
    {
      "bbox": null,
      "kind": "callout",
      "text": "First memorise the data, then forget what doesn't help the model make predictions.",
      "attrs": null,
      "subkind": null,
      "toolName": "Visual emphasis",
      "toolSlug": "visual-emphasis",
      "confidence": null,
      "componentId": "019dd952-41ac-7180-a80c-28ca6c40c530",
      "frameworkName": null,
      "frameworkSlug": null
    },
    {
      "bbox": {
        "h": 0.6,
        "w": 0.45,
        "x": 0.05,
        "y": 0.32
      },
      "kind": "chart",
      "text": "Inside Deep Learning: New experiments reveal how deep neural networks evolve as they learn.",
      "attrs": null,
      "subkind": "line",
      "toolName": null,
      "toolSlug": null,
      "confidence": null,
      "componentId": "155f700b-5a59-4b6b-b4c2-4f81baaba9db",
      "frameworkName": null,
      "frameworkSlug": null
    },
    {
      "bbox": {
        "h": 0.6,
        "w": 0.45,
        "x": 0.52,
        "y": 0.32
      },
      "kind": "list",
      "text": "A INITIAL STATE: Neurons in Layer 1 encode everything about the input data, including all information about its label. Neurons in the highest layers are in a nearly random state bearing little to no relationship to the data or its label.\nB FITTING PHASE: As deep learning begins, neurons in higher layers gain information about the input and get better at fitting labels to it.\nC PHASE CHANGE: The layers suddenly shift gears and start to \"forget\" information about the input.\nD COMPRESSION PHASE: Higher layers compress their representation of the input data, keeping what is most relevant to the output label. They get better at predicting the label.\nE FINAL STATE: The last layer achieves an optimal balance of accuracy and compression, retaining only what is needed to predict the label.",
      "attrs": null,
      "subkind": "bullet",
      "toolName": null,
      "toolSlug": null,
      "confidence": null,
      "componentId": "9bc7a207-89ce-4cf6-8bc1-eb225b836e46",
      "frameworkName": null,
      "frameworkSlug": null
    },
    {
      "bbox": {
        "h": 0.03,
        "w": 0.9,
        "x": 0.02,
        "y": 0.22
      },
      "kind": "title",
      "text": "The Information Theory: First memorise the data, then forget what doesn't help the model make predictions",
      "attrs": null,
      "subkind": "action-title",
      "toolName": null,
      "toolSlug": null,
      "confidence": null,
      "componentId": "b59ffc7d-1a58-44e3-bcd6-e7273d63488a",
      "frameworkName": null,
      "frameworkSlug": null
    },
    {
      "bbox": {
        "h": 0.04,
        "w": 0.5,
        "x": 0.02,
        "y": 0.14
      },
      "kind": "title",
      "text": "AI hardware is especially helpful for deep learning",
      "attrs": null,
      "subkind": "headline",
      "toolName": null,
      "toolSlug": null,
      "confidence": null,
      "componentId": "e3108e8a-7cf2-4a47-9058-ced032b9019a",
      "frameworkName": null,
      "frameworkSlug": null
    }
  ],
  "metrics": [],
  "tools": [],
  "frameworks": [
    {
      "name": "Information Bottleneck Theory",
      "slug": null,
      "matchId": "7a8a46ae-f108-4b15-aebe-a15ca8d09a13",
      "evidence": "The slide describes the process of memorizing data and then compressing it to retain only relevant information for prediction.",
      "confidence": 1
    }
  ],
  "arcBeats": [
    {
      "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"
    }
  ],
  "loops": [
    {
      "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"
    }
  ],
  "imagePathAlt": null,
  "thumbSrc": null,
  "thumbSrcAlt": null,
  "locked": true
}