{
  "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": 10,
  "pageCount": 156,
  "prevPage": 9,
  "nextPage": 11,
  "slideType": "industry_trends",
  "function": "establish_context",
  "density": "dense",
  "nDataPoints": 6,
  "notes": "The slide uses two bar charts to demonstrate the performance advantage of GPUs in AI training.",
  "elementsJson": [
    "headline_text",
    "paragraph",
    "bar_chart_vertical",
    "bar_chart_vertical"
  ],
  "metadataConfidence": 1,
  "imagePath": null,
  "slideHref": "/slides/019dd923-5e88-73ef-bd5c-f341d4394195/10",
  "deckHref": "/decks/019dd923-5e88-73ef-bd5c-f341d4394195",
  "deckJsonHref": "/decks/019dd923-5e88-73ef-bd5c-f341d4394195.json",
  "deckAnchorHref": "/decks/019dd923-5e88-73ef-bd5c-f341d4394195#slide-10",
  "components": [
    {
      "bbox": null,
      "kind": "callout",
      "text": "Graphics processing units (GPUs) are today's workhorse chip for AI models largely because they offer immense computational parallelism over central processing units (CPUs).",
      "attrs": null,
      "subkind": null,
      "toolName": "Visual emphasis",
      "toolSlug": "visual-emphasis",
      "confidence": null,
      "componentId": "019dd952-41ac-7180-a80c-93fd0650af3d",
      "frameworkName": null,
      "frameworkSlug": null
    },
    {
      "bbox": {
        "h": 0.4,
        "w": 0.45,
        "x": 0.02,
        "y": 0.52
      },
      "kind": "chart",
      "text": "AI models run much faster on GPUs",
      "attrs": null,
      "subkind": "bar-vertical",
      "toolName": null,
      "toolSlug": null,
      "confidence": null,
      "componentId": "82ee007f-5d4e-4c03-9b37-39ef193f3385",
      "frameworkName": null,
      "frameworkSlug": null
    },
    {
      "bbox": {
        "h": 0.4,
        "w": 0.45,
        "x": 0.52,
        "y": 0.52
      },
      "kind": "chart",
      "text": "New GPU supercomputers eclipse older chips",
      "attrs": null,
      "subkind": "bar-vertical",
      "toolName": null,
      "toolSlug": null,
      "confidence": null,
      "componentId": "960abe1f-2985-4b82-982d-42feffefc212",
      "frameworkName": null,
      "frameworkSlug": null
    },
    {
      "bbox": null,
      "kind": "metric",
      "text": "Training time: 18 Mins",
      "attrs": null,
      "subkind": "primary",
      "toolName": "Quantification",
      "toolSlug": "quantification",
      "confidence": null,
      "componentId": "019dd952-41ac-7180-a80c-94c3b4d5a672",
      "frameworkName": null,
      "frameworkSlug": null
    },
    {
      "bbox": {
        "h": 0.15,
        "w": 0.95,
        "x": 0.02,
        "y": 0.27
      },
      "kind": "paragraph",
      "text": "Semiconductor (or 'chip') performance is a key driver behind progress in AI research and applications. This is because AI models often require huge amounts of training data to properly learn a task (e.g. image recognition). Graphics processing units (GPUs) are today's workhorse chip for AI models largely because they offer immense computational parallelism over central processing units (CPUs). This means faster training and model iteration.",
      "attrs": null,
      "subkind": "paragraph",
      "toolName": null,
      "toolSlug": null,
      "confidence": null,
      "componentId": "d00a760a-030f-4743-a08a-ab2cdc42576c",
      "frameworkName": null,
      "frameworkSlug": null
    },
    {
      "bbox": {
        "h": 0.03,
        "w": 0.45,
        "x": 0.02,
        "y": 0.22
      },
      "kind": "title",
      "text": "The role of semiconductors in driving AI performance",
      "attrs": null,
      "subkind": "action-title",
      "toolName": null,
      "toolSlug": null,
      "confidence": null,
      "componentId": "8e1cd0e4-c76f-4d6d-b2b4-c6db3978ebfd",
      "frameworkName": null,
      "frameworkSlug": null
    },
    {
      "bbox": {
        "h": 0.04,
        "w": 0.35,
        "x": 0.02,
        "y": 0.14
      },
      "kind": "title",
      "text": "AI hardware as the new frontier",
      "attrs": null,
      "subkind": "headline",
      "toolName": null,
      "toolSlug": null,
      "confidence": null,
      "componentId": "f2e19d57-e826-42fc-9c00-d9f8d8b11ae5",
      "frameworkName": null,
      "frameworkSlug": null
    }
  ],
  "metrics": [],
  "tools": [
    {
      "name": "Inductive Reasoning",
      "slug": "inductive-reasoning",
      "agent": "Architect",
      "layer": "loop",
      "matchId": "019dd95a-0fd5-7148-8eca-4c5539c9e63f",
      "evidence": "First slide of pattern_hunter loop assembling hardware evidence.",
      "confidence": 70
    },
    {
      "name": "Action Titles",
      "slug": "action-titles",
      "agent": "Architect",
      "layer": "slide",
      "matchId": "019dd95a-0fd5-7148-8eca-482fa470ac47",
      "evidence": "Title 'AI hardware as the new frontier' states position not topic.",
      "confidence": 88
    },
    {
      "name": "Action Titles",
      "slug": "action-titles",
      "agent": "Architect",
      "layer": "slide",
      "matchId": "dd662028-4ab2-4381-a97c-28b981f5ca63",
      "evidence": "title/action-title: The role of semiconductors in driving AI performance",
      "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": 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
}