{
  "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": 60,
  "pageCount": 177,
  "prevPage": 59,
  "nextPage": 61,
  "slideType": "industry_trends",
  "function": "present_framework",
  "density": "dense",
  "nDataPoints": 0,
  "notes": "The slide uses a diagram to illustrate the technical workflow of federated learning and encrypted inference.",
  "elementsJson": [
    "headline_text",
    "action_title",
    "bullet_list",
    "process_diagram",
    "logo_grid"
  ],
  "metadataConfidence": 0.95,
  "imagePath": null,
  "slideHref": "/slides/019dd923-5e88-73ef-bd5c-f812573a947a/60",
  "deckHref": "/decks/019dd923-5e88-73ef-bd5c-f812573a947a",
  "deckJsonHref": "/decks/019dd923-5e88-73ef-bd5c-f812573a947a.json",
  "deckAnchorHref": "/decks/019dd923-5e88-73ef-bd5c-f812573a947a#slide-60",
  "components": [
    {
      "bbox": null,
      "kind": "callout",
      "text": "While the pooling of medical data should lead to improved medical knowledge and clinical care, it is also an area with strong safeguards around privacy.",
      "attrs": null,
      "subkind": null,
      "toolName": "Visual emphasis",
      "toolSlug": "visual-emphasis",
      "confidence": null,
      "componentId": "019dd952-47c5-73ac-aa64-f6faf3b6ba8b",
      "frameworkName": null,
      "frameworkSlug": null
    },
    {
      "bbox": {
        "h": 0.55,
        "w": 0.4,
        "x": 0.55,
        "y": 0.3
      },
      "kind": "diagram",
      "text": "Technical workflow diagram showing Model Owner, Client, Network, and Medical Startup interactions with encryption/decryption.",
      "attrs": null,
      "subkind": "process",
      "toolName": null,
      "toolSlug": null,
      "confidence": null,
      "componentId": "49471839-2a4e-4223-9b27-f9ec038cfd44",
      "frameworkName": null,
      "frameworkSlug": null
    },
    {
      "bbox": {
        "h": 0.08,
        "w": 0.4,
        "x": 0.05,
        "y": 0.9
      },
      "kind": "image",
      "text": "OpenMined, TUM, Imperial College London, CrypTen logos",
      "attrs": null,
      "subkind": "logo-grid",
      "toolName": null,
      "toolSlug": null,
      "confidence": null,
      "componentId": "08b77c03-8253-4e14-be83-c43b9f6c433a",
      "frameworkName": null,
      "frameworkSlug": null
    },
    {
      "bbox": {
        "h": 0.45,
        "w": 0.5,
        "x": 0.05,
        "y": 0.31
      },
      "kind": "list",
      "text": "The 5P Project (Kaissis, Ziller, Passerat-Palmbach, Braren, Rueckert et al., Technical University of Munich, Imperial College London and OpenMined) demonstrates federated learning and encrypted inference on paediatric chest X-rays in a clinical setting.\nLarge academic consortia (German Cancer Consortium Joint Imaging Platform) and mixed consortia including startups and established industry (London Medical Imaging and AI Centre for Value Based Healthcare)\nProspective testing and first production roll-outs are expected within the next year.",
      "attrs": null,
      "subkind": "bullet",
      "toolName": null,
      "toolSlug": null,
      "confidence": null,
      "componentId": "d2d945e3-4916-47e3-b62e-1214f637cf0b",
      "frameworkName": null,
      "frameworkSlug": null
    },
    {
      "bbox": {
        "h": 0.08,
        "w": 0.9,
        "x": 0.05,
        "y": 0.21
      },
      "kind": "paragraph",
      "text": "While the pooling of medical data should lead to improved medical knowledge and clinical care, it is also an area with strong safeguards around privacy. New techniques enable privacy-preserving innovation.",
      "attrs": null,
      "subkind": "paragraph",
      "toolName": null,
      "toolSlug": null,
      "confidence": null,
      "componentId": "b412ac77-d7fe-41e1-8c57-78b5c5b0044a",
      "frameworkName": null,
      "frameworkSlug": null
    },
    {
      "bbox": {
        "h": 0.05,
        "w": 0.7,
        "x": 0.05,
        "y": 0.14
      },
      "kind": "title",
      "text": "Prospective testing begins for privacy-preserving AI applied to medical imaging",
      "attrs": null,
      "subkind": "headline",
      "toolName": null,
      "toolSlug": null,
      "confidence": null,
      "componentId": "047cd009-1534-4c78-9769-535ce769c1b6",
      "frameworkName": null,
      "frameworkSlug": null
    }
  ],
  "metrics": [],
  "tools": [],
  "frameworks": [
    {
      "name": "federated-learning-workflow",
      "slug": null,
      "matchId": "54022e5b-b551-4f18-98bd-af02d2e8bda7",
      "evidence": "Diagram illustrating the interaction between model owners, clients, and startups using encryption.",
      "confidence": 0.8
    }
  ],
  "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": 62,
      "from": 58,
      "name": "Pattern Hunter",
      "slug": "02-pattern-hunter",
      "bestFor": "Time-pressed audiences, building consensus, when data is strong",
      "matchId": "019dd95a-07fe-70ce-8d3c-fd470c64e823",
      "evidence": "Three short bursts of new research direction case studies.",
      "position": 6,
      "objective": "Map emerging research themes: federated learning, GPs, quantum",
      "structure": "Evidence A -> Evidence B -> Evidence C -> Pattern/Conclusion",
      "confidence": 65,
      "description": "Group multiple pieces of evidence that together point to a pattern or conclusion"
    }
  ],
  "imagePathAlt": null,
  "thumbSrc": null,
  "thumbSrcAlt": null,
  "locked": true
}