{
  "docId": "019dd923-5e88-73ef-bd5c-fd5012384ae3",
  "docSlug": "bf350dd574c19997",
  "documentTitle": "2021 Air Street Capital The State of AI Report 2021",
  "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": 101,
  "pageCount": 188,
  "prevPage": 100,
  "nextPage": 102,
  "slideType": "case_study",
  "function": "illustrate_case",
  "density": "dense",
  "nDataPoints": 4,
  "notes": "The slide features a line chart comparing antibiotic usage and two heatmaps comparing prediction error rates between industry standards and AI models.",
  "elementsJson": [
    "headline_text",
    "paragraph",
    "line_chart",
    "heatmap"
  ],
  "metadataConfidence": 1,
  "imagePath": null,
  "slideHref": "/slides/019dd923-5e88-73ef-bd5c-fd5012384ae3/101",
  "deckHref": "/decks/019dd923-5e88-73ef-bd5c-fd5012384ae3",
  "deckJsonHref": "/decks/019dd923-5e88-73ef-bd5c-fd5012384ae3.json",
  "deckAnchorHref": "/decks/019dd923-5e88-73ef-bd5c-fd5012384ae3#slide-101",
  "components": [
    {
      "bbox": null,
      "kind": "callout",
      "text": "Connecterra is able to predict health issues 2-3 days prior to human observation.",
      "attrs": null,
      "subkind": null,
      "toolName": "Visual emphasis",
      "toolSlug": "visual-emphasis",
      "confidence": null,
      "componentId": "019dd952-47c4-7719-a209-eaaca856f738",
      "frameworkName": null,
      "frameworkSlug": null
    },
    {
      "bbox": {
        "h": 0.4,
        "w": 0.45,
        "x": 0.52,
        "y": 0.48
      },
      "kind": "chart",
      "text": "Industry-standard vs AI milk predictor error heatmaps",
      "attrs": null,
      "subkind": "heatmap",
      "toolName": null,
      "toolSlug": null,
      "confidence": null,
      "componentId": "ede1297f-f95a-4a72-8fb1-98b8c814d4ee",
      "frameworkName": null,
      "frameworkSlug": null
    },
    {
      "bbox": {
        "h": 0.4,
        "w": 0.4,
        "x": 0.02,
        "y": 0.48
      },
      "kind": "chart",
      "text": "Average number of days animals are treated with antibiotics",
      "attrs": null,
      "subkind": "line",
      "toolName": null,
      "toolSlug": null,
      "confidence": null,
      "componentId": "c3ed2463-dbc5-47a9-a31b-12e222bda380",
      "frameworkName": null,
      "frameworkSlug": null
    },
    {
      "bbox": null,
      "kind": "metric",
      "text": "Antibiotic usage reduction: 50%",
      "attrs": null,
      "subkind": "primary",
      "toolName": "Quantification",
      "toolSlug": "quantification",
      "confidence": null,
      "componentId": "019dd952-47c4-7719-a209-eec20b02549b",
      "frameworkName": null,
      "frameworkSlug": null
    },
    {
      "bbox": {
        "h": 0.18,
        "w": 0.96,
        "x": 0.02,
        "y": 0.2
      },
      "kind": "paragraph",
      "text": "Dairy cow farmers monitor their livestock to for health issues and the onset of calving. Using deep learning to analyse accelerometer data from a neck-worn sensor, Connecterra is able to predict health issues 2-3 days prior to human observation. They can also predict the onset of calving, which reduces the number of days that pregnant cows are treated with antibiotics by 50% (left graph). Connecterra can predict milk yield with <1% margin of error up to 200 days in the future (right graph, blue = less error), which could reduce CO2 emissions.",
      "attrs": null,
      "subkind": "paragraph",
      "toolName": null,
      "toolSlug": null,
      "confidence": null,
      "componentId": "18397d37-6ec7-4f36-9c7a-f0fa9c0a9e2c",
      "frameworkName": null,
      "frameworkSlug": null
    },
    {
      "bbox": {
        "h": 0.04,
        "w": 0.88,
        "x": 0.02,
        "y": 0.14
      },
      "kind": "title",
      "text": "Improving the sustainability and carbon efficiency of farms using predictive models",
      "attrs": null,
      "subkind": "headline",
      "toolName": null,
      "toolSlug": null,
      "confidence": null,
      "componentId": "b3420372-801a-4e08-a701-66d3320bab41",
      "frameworkName": null,
      "frameworkSlug": null
    }
  ],
  "metrics": [],
  "tools": [
    {
      "name": "Audience Definition",
      "slug": "audience-definition",
      "agent": "Storyteller",
      "layer": "slide",
      "matchId": "14ec5332-9983-4768-8a31-4a3094432216",
      "evidence": "The slide presents a case study on improving the sustainability and carbon efficiency of farms, indicating that the audience is likely interested in AI applications in agriculture or sustainability.",
      "confidence": 0.7
    },
    {
      "name": "Data Story Arc",
      "slug": "data-story-arc",
      "agent": "Storyteller",
      "layer": "slide",
      "matchId": "f4542fb8-67c9-4370-833b-e811b55a08f4",
      "evidence": "The slide presents data and metrics (e.g., antibiotic usage reduction: 50%) to tell a story about the effectiveness of predictive models in improving farm sustainability.",
      "confidence": 0.7
    },
    {
      "name": "Visual Anchors",
      "slug": "visual-anchors",
      "agent": "Designer",
      "layer": "slide",
      "matchId": "1b16aa6e-050b-4137-b411-7a375837d0ab",
      "evidence": "The slide uses visual elements such as charts (line, heatmap), callouts, and metrics to draw attention to key points.",
      "confidence": 0.6
    }
  ],
  "frameworks": [],
  "arcBeats": [
    {
      "to": 152,
      "from": 5,
      "beatId": "019dd95a-0682-776c-8e35-0affbadec38e",
      "arcName": "The Triple Take",
      "arcSlug": "triple-take",
      "beatName": "The Facts (What)",
      "beatSlug": "triple-take-the-facts-what",
      "evidence": "Definitions p.5-6, Exec Summary p.7, then Sections 1-3 catalog evidence.",
      "position": 1,
      "confidence": 78,
      "parentBeatName": "Setup",
      "parentBeatSlug": "setup"
    },
    {
      "to": 152,
      "from": 10,
      "beatId": "019dd95a-0682-776c-8e35-1b3ee3a1c012",
      "arcName": "The Mountain",
      "arcSlug": "mountain",
      "beatName": "Rising Action",
      "beatSlug": "mountain-rising-action",
      "evidence": "Three sections accumulating evidence of accelerating progress.",
      "position": 2,
      "confidence": 55,
      "parentBeatName": "Development",
      "parentBeatSlug": "development"
    }
  ],
  "loops": [
    {
      "to": 105,
      "from": 98,
      "name": "Pattern Hunter",
      "slug": "02-pattern-hunter",
      "bestFor": "Time-pressed audiences, building consensus, when data is strong",
      "matchId": "019dd95a-07fe-70ce-8d3d-7275ca71fecb",
      "evidence": "Workplace safety, disasters, energy grid, agriculture, microbiome, medical imaging, stroke detection.",
      "position": 13,
      "objective": "Catalogue real-world AI deployments across industries",
      "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
}