{
  "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": 45,
  "pageCount": 156,
  "prevPage": 44,
  "nextPage": 46,
  "slideType": "other",
  "function": "illustrate_case",
  "density": "dense",
  "nDataPoints": 3,
  "notes": "The slide uses a visual example of a dog playing a guitar to demonstrate how explainability techniques (like saliency maps or feature attribution) reveal what a model is 'looking at' to make a prediction.",
  "elementsJson": [
    "headline_text",
    "paragraph",
    "photo",
    "footnote"
  ],
  "metadataConfidence": 0.95,
  "imagePath": null,
  "slideHref": "/slides/019dd923-5e88-73ef-bd5c-f341d4394195/45",
  "deckHref": "/decks/019dd923-5e88-73ef-bd5c-f341d4394195",
  "deckJsonHref": "/decks/019dd923-5e88-73ef-bd5c-f341d4394195.json",
  "deckAnchorHref": "/decks/019dd923-5e88-73ef-bd5c-f341d4394195#slide-45",
  "components": [
    {
      "bbox": null,
      "kind": "callout",
      "text": "In computer vision, a model can show us which pixels it used to infer a specific label (e.g. which pixels = “dog”) This way, we understand that the model has “learned” properly vs. predicted the right label for the wrong reason.",
      "attrs": null,
      "subkind": null,
      "toolName": "Visual emphasis",
      "toolSlug": "visual-emphasis",
      "confidence": null,
      "componentId": "019dd952-41ac-7180-a810-4d30fe8a423e",
      "frameworkName": null,
      "frameworkSlug": null
    },
    {
      "bbox": {
        "h": 0.4,
        "w": 0.8,
        "x": 0.1,
        "y": 0.35
      },
      "kind": "image",
      "text": "Four-part image showing original dog-guitar photo and three segmented explanations.",
      "attrs": null,
      "subkind": "illustration",
      "toolName": null,
      "toolSlug": null,
      "confidence": null,
      "componentId": "f9ca01da-1d3d-4cf6-87e9-39d573df4591",
      "frameworkName": null,
      "frameworkSlug": null
    },
    {
      "bbox": {
        "h": 0.1,
        "w": 0.9,
        "x": 0.05,
        "y": 0.22
      },
      "kind": "paragraph",
      "text": "In computer vision, a model can show us which pixels it used to infer a specific label (e.g. which pixels = “dog”) This way, we understand that the model has “learned” properly vs. predicted the right label for the wrong reason.",
      "attrs": null,
      "subkind": "paragraph",
      "toolName": null,
      "toolSlug": null,
      "confidence": null,
      "componentId": "03e99f06-36ef-440f-b5f0-67903ff84420",
      "frameworkName": null,
      "frameworkSlug": null
    },
    {
      "bbox": {
        "h": 0.1,
        "w": 0.8,
        "x": 0.1,
        "y": 0.78
      },
      "kind": "source-note",
      "text": "Figure 4: Explaining an image classification prediction made by Google’s Inception neural network. The top 3 classes predicted are “Electric Guitar” (p = 0.32), “Acoustic guitar” (p = 0.24) and “Labrador” (p = 0.21)",
      "attrs": null,
      "subkind": null,
      "toolName": null,
      "toolSlug": null,
      "confidence": null,
      "componentId": "9cee3e5e-83cd-4b46-afff-ce1e0bc3a07d",
      "frameworkName": null,
      "frameworkSlug": null
    },
    {
      "bbox": {
        "h": 0.05,
        "w": 0.8,
        "x": 0.05,
        "y": 0.15
      },
      "kind": "title",
      "text": "Explainability helps validate that ML models perform well for the “right” reasons",
      "attrs": null,
      "subkind": "headline",
      "toolName": null,
      "toolSlug": null,
      "confidence": null,
      "componentId": "70f4aa0b-1607-4f86-abed-82467b690068",
      "frameworkName": null,
      "frameworkSlug": null
    }
  ],
  "metrics": [],
  "tools": [
    {
      "name": "Action Titles",
      "slug": "action-titles",
      "agent": "Architect",
      "layer": "slide",
      "matchId": "019dd95a-0fd5-7148-8eca-f307bd88bbaf",
      "evidence": "Title 'Explainability helps validate that ML models perform well for the right reasons'.",
      "confidence": 85
    },
    {
      "name": "Visual Anchors",
      "slug": "visual-anchors",
      "agent": "Designer",
      "layer": "slide",
      "matchId": "a60d6033-b99e-40e4-a048-9da3377ae645",
      "evidence": "Figure 4: Explaining an image classification prediction made by Google’s Inception neural network",
      "confidence": 0.8
    }
  ],
  "frameworks": [],
  "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": 50,
      "from": 44,
      "name": "Iceberg",
      "slug": "10-iceberg",
      "bestFor": "Consulting, complex problem solving, organizational change",
      "matchId": "019dd95a-07fe-70ce-8d3c-5ed4c9b352e3",
      "evidence": "Symptom (black box) -> hidden system (feature importance) -> root issues (adversarial attacks fool models in real world).",
      "position": 7,
      "objective": "Surface hidden risks beneath model accuracy",
      "structure": "The Symptom (Visible) -> The System (Hidden) -> The Root Cause",
      "confidence": 72,
      "description": "Reveal that the visible problem is merely a symptom of a deeper root cause"
    }
  ],
  "imagePathAlt": null,
  "thumbSrc": null,
  "thumbSrcAlt": null,
  "locked": true
}