{
  "docId": "019dd923-5e88-73ef-bd5c-f812573a947a",
  "docSlug": "eea7524c557036f4",
  "documentTitle": "2020 Air Street Capital The State of AI Report 2020",
  "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": 31,
  "pageCount": 177,
  "prevPage": 30,
  "nextPage": 32,
  "slideType": "case_study",
  "function": "illustrate_case",
  "density": "dense",
  "nDataPoints": 1,
  "notes": "The slide uses a visual comparison to show how techniques from computer vision are being applied to biological research.",
  "elementsJson": [
    "headline_text",
    "paragraph",
    "logo_grid",
    "infographic"
  ],
  "metadataConfidence": 0.95,
  "imagePath": null,
  "slideHref": "/slides/019dd923-5e88-73ef-bd5c-f812573a947a/31",
  "deckHref": "/decks/019dd923-5e88-73ef-bd5c-f812573a947a",
  "deckJsonHref": "/decks/019dd923-5e88-73ef-bd5c-f812573a947a.json",
  "deckAnchorHref": "/decks/019dd923-5e88-73ef-bd5c-f812573a947a#slide-31",
  "components": [
    {
      "bbox": null,
      "kind": "callout",
      "text": "Large labelled datasets offer huge potential for generating new biological knowledge about health and disease.",
      "attrs": null,
      "subkind": null,
      "toolName": "Visual emphasis",
      "toolSlug": "visual-emphasis",
      "confidence": null,
      "componentId": "019dd952-47c5-73ac-aa62-5e4b4629a753",
      "frameworkName": null,
      "frameworkSlug": null
    },
    {
      "bbox": {
        "h": 0.47,
        "w": 0.48,
        "x": 0.43,
        "y": 0.43
      },
      "kind": "image",
      "text": "Hexagonal grid of cell images",
      "attrs": null,
      "subkind": "illustration",
      "toolName": null,
      "toolSlug": null,
      "confidence": null,
      "componentId": "5c0872aa-13af-472e-9087-b6559f5062ba",
      "frameworkName": null,
      "frameworkSlug": null
    },
    {
      "bbox": {
        "h": 0.47,
        "w": 0.28,
        "x": 0.07,
        "y": 0.43
      },
      "kind": "image",
      "text": "Grid of ImageNet photos",
      "attrs": null,
      "subkind": "illustration",
      "toolName": null,
      "toolSlug": null,
      "confidence": null,
      "componentId": "94835bc5-c510-4d18-9385-44a1bb8868b9",
      "frameworkName": null,
      "frameworkSlug": null
    },
    {
      "bbox": {
        "h": 0.05,
        "w": 0.2,
        "x": 0.59,
        "y": 0.31
      },
      "kind": "image",
      "text": "RECURSION",
      "attrs": null,
      "subkind": "logo",
      "toolName": null,
      "toolSlug": null,
      "confidence": null,
      "componentId": "b7568aa2-5011-4324-a52e-aa7e1dfd8b11",
      "frameworkName": null,
      "frameworkSlug": null
    },
    {
      "bbox": {
        "h": 0.05,
        "w": 0.2,
        "x": 0.15,
        "y": 0.31
      },
      "kind": "image",
      "text": "IMAGENET",
      "attrs": null,
      "subkind": "logo",
      "toolName": null,
      "toolSlug": null,
      "confidence": null,
      "componentId": "bf4c6dca-2c51-441b-9865-3cd0c70b00ff",
      "frameworkName": null,
      "frameworkSlug": null
    },
    {
      "bbox": {
        "h": 0.03,
        "w": 0.2,
        "x": 0.15,
        "y": 0.38
      },
      "kind": "paragraph",
      "text": ">14M labeled images",
      "attrs": null,
      "subkind": "paragraph",
      "toolName": null,
      "toolSlug": null,
      "confidence": null,
      "componentId": "55ec3738-5b84-4ff9-b0d8-e38eb5b203eb",
      "frameworkName": null,
      "frameworkSlug": null
    },
    {
      "bbox": {
        "h": 0.03,
        "w": 0.55,
        "x": 0.38,
        "y": 0.38
      },
      "kind": "paragraph",
      "text": "RxRx.ai image datasets of cells treated with various chemical agents",
      "attrs": null,
      "subkind": "paragraph",
      "toolName": null,
      "toolSlug": null,
      "confidence": null,
      "componentId": "8ed39e73-f206-4157-97ac-843cb6d4f395",
      "frameworkName": null,
      "frameworkSlug": null
    },
    {
      "bbox": {
        "h": 0.04,
        "w": 0.9,
        "x": 0.05,
        "y": 0.21
      },
      "kind": "title",
      "text": "Large labelled datasets offer huge potential for generating new biological knowledge about health and disease.",
      "attrs": null,
      "subkind": "action-title",
      "toolName": null,
      "toolSlug": null,
      "confidence": null,
      "componentId": "268de28a-b35f-4f65-8367-5eae9c4dee99",
      "frameworkName": null,
      "frameworkSlug": null
    },
    {
      "bbox": {
        "h": 0.05,
        "w": 0.9,
        "x": 0.05,
        "y": 0.13
      },
      "kind": "title",
      "text": "From physical object recognition to “cell painting”: Decoding biology through images",
      "attrs": null,
      "subkind": "headline",
      "toolName": null,
      "toolSlug": null,
      "confidence": null,
      "componentId": "ffb1c576-e42f-4b0c-8d65-e92fc275acb4",
      "frameworkName": null,
      "frameworkSlug": null
    }
  ],
  "metrics": [],
  "tools": [
    {
      "name": "Picture Superiority Effect",
      "slug": "picture-superiority-effect",
      "agent": "Designer",
      "layer": "slide",
      "matchId": "019dd95a-0fd5-7148-8ece-a4f4504bdb06",
      "evidence": "Cell painting visuals carry the case.",
      "confidence": 70
    },
    {
      "name": "Visual Anchors",
      "slug": "visual-anchors",
      "agent": "Designer",
      "layer": "slide",
      "matchId": "f33822dd-0c36-4592-aada-6bce06aa2381",
      "evidence": "The slide features a hexagonal grid of cell images, using visual anchors to organize the information.",
      "confidence": 0.7
    }
  ],
  "frameworks": [],
  "arcBeats": [
    {
      "to": 129,
      "from": 10,
      "beatId": "019dd95a-0682-776c-8e34-ed2a8f38a754",
      "arcName": "The Triple Take",
      "arcSlug": "triple-take",
      "beatName": "The Facts (What)",
      "beatSlug": "triple-take-the-facts-what",
      "evidence": "Research, Talent, Industry sections inventory what happened in AI in 2020.",
      "position": 1,
      "confidence": 60,
      "parentBeatName": "Setup",
      "parentBeatSlug": "setup"
    },
    {
      "to": 129,
      "from": 10,
      "beatId": "019dd95a-0682-776c-8e34-fc384f439bba",
      "arcName": "The Mountain",
      "arcSlug": "mountain",
      "beatName": "Rising Action",
      "beatSlug": "mountain-rising-action",
      "evidence": "Research, Talent and Industry sections build momentum of AI progress.",
      "position": 2,
      "confidence": 40,
      "parentBeatName": "Development",
      "parentBeatSlug": "development"
    }
  ],
  "loops": [
    {
      "to": 46,
      "from": 30,
      "name": "Pattern Hunter",
      "slug": "02-pattern-hunter",
      "bestFor": "Time-pressed audiences, building consensus, when data is strong",
      "matchId": "019dd95a-07fe-70ce-8d3c-f421814661f3",
      "evidence": "p.30 'AI moment' framing then 16 case-study slides on bio/medical applications.",
      "position": 4,
      "objective": "Survey AI breakthroughs across biology, drug discovery and COVID",
      "structure": "Evidence A -> Evidence B -> Evidence C -> Pattern/Conclusion",
      "confidence": 75,
      "description": "Group multiple pieces of evidence that together point to a pattern or conclusion"
    }
  ],
  "imagePathAlt": null,
  "thumbSrc": null,
  "thumbSrcAlt": null,
  "locked": true
}