{
  "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": 16,
  "pageCount": 177,
  "prevPage": 15,
  "nextPage": 17,
  "slideType": "industry_trends",
  "function": "analyze_data",
  "density": "dense",
  "nDataPoints": 3,
  "notes": "The charts demonstrate power-law relationships between model performance (test loss) and three key scaling factors: compute, dataset size, and parameter count.",
  "elementsJson": [
    "headline_text",
    "paragraph",
    "line_chart"
  ],
  "metadataConfidence": 1,
  "imagePath": null,
  "slideHref": "/slides/019dd923-5e88-73ef-bd5c-f812573a947a/16",
  "deckHref": "/decks/019dd923-5e88-73ef-bd5c-f812573a947a",
  "deckJsonHref": "/decks/019dd923-5e88-73ef-bd5c-f812573a947a.json",
  "deckAnchorHref": "/decks/019dd923-5e88-73ef-bd5c-f812573a947a#slide-16",
  "components": [
    {
      "bbox": null,
      "kind": "callout",
      "text": "Empirical scaling laws of neural language models show smooth power-law relationships, which means that as model performance increases, the model size and amount of computation has to increase more rapidly.",
      "attrs": null,
      "subkind": null,
      "toolName": "Visual emphasis",
      "toolSlug": "visual-emphasis",
      "confidence": null,
      "componentId": "019dd952-47c5-73ac-aa61-2eed3a28a5ea",
      "frameworkName": null,
      "frameworkSlug": null
    },
    {
      "bbox": {
        "h": 0.5,
        "w": 0.32,
        "x": 0.35,
        "y": 0.35
      },
      "kind": "chart",
      "text": "Test Loss vs Dataset Size",
      "attrs": null,
      "subkind": "line",
      "toolName": null,
      "toolSlug": null,
      "confidence": null,
      "componentId": "388d5180-8846-4537-bc08-82971101c7d2",
      "frameworkName": null,
      "frameworkSlug": null
    },
    {
      "bbox": {
        "h": 0.5,
        "w": 0.32,
        "x": 0.68,
        "y": 0.35
      },
      "kind": "chart",
      "text": "Test Loss vs Parameters",
      "attrs": null,
      "subkind": "line",
      "toolName": null,
      "toolSlug": null,
      "confidence": null,
      "componentId": "96bed9b2-138f-4248-8886-1c9b4308b298",
      "frameworkName": null,
      "frameworkSlug": null
    },
    {
      "bbox": {
        "h": 0.5,
        "w": 0.32,
        "x": 0.02,
        "y": 0.35
      },
      "kind": "chart",
      "text": "Test Loss vs Compute",
      "attrs": null,
      "subkind": "line",
      "toolName": null,
      "toolSlug": null,
      "confidence": null,
      "componentId": "9d9395b9-d136-4922-8ca2-85705c45f600",
      "frameworkName": null,
      "frameworkSlug": null
    },
    {
      "bbox": null,
      "kind": "metric",
      "text": "Test Loss",
      "attrs": null,
      "subkind": "primary",
      "toolName": "Quantification",
      "toolSlug": "quantification",
      "confidence": null,
      "componentId": "019dd952-47c5-73ac-aa61-305665383bee",
      "frameworkName": null,
      "frameworkSlug": null
    },
    {
      "bbox": {
        "h": 0.08,
        "w": 0.95,
        "x": 0.02,
        "y": 0.22
      },
      "kind": "paragraph",
      "text": "Empirical scaling laws of neural language models show smooth power-law relationships, which means that as model performance increases, the model size and amount of computation has to increase more rapidly.",
      "attrs": null,
      "subkind": "paragraph",
      "toolName": null,
      "toolSlug": null,
      "confidence": null,
      "componentId": "2dd67ee1-9b98-410c-b764-8d4aba45748b",
      "frameworkName": null,
      "frameworkSlug": null
    },
    {
      "bbox": {
        "h": 0.05,
        "w": 0.7,
        "x": 0.02,
        "y": 0.14
      },
      "kind": "title",
      "text": "Bigger models, datasets and compute budgets clearly drive performance",
      "attrs": null,
      "subkind": "headline",
      "toolName": null,
      "toolSlug": null,
      "confidence": null,
      "componentId": "f6779238-49ef-4e86-be48-9ee9f0beae4d",
      "frameworkName": null,
      "frameworkSlug": null
    }
  ],
  "metrics": [],
  "tools": [
    {
      "name": "Action Titles",
      "slug": "action-titles",
      "agent": "Architect",
      "layer": "slide",
      "matchId": "019dd95a-0fd5-7148-8ece-37fcd4f23f9f",
      "evidence": "Title states the empirical scaling law conclusion.",
      "confidence": 90
    },
    {
      "name": "Causal Chain",
      "slug": "causal-chain",
      "agent": "Architect",
      "layer": "slide",
      "matchId": "f27d948d-0b39-42d9-bbeb-7d16a1de85a8",
      "evidence": "The description of how bigger models, datasets, and compute budgets drive performance implies a causal relationship.",
      "confidence": 0.7
    },
    {
      "name": "Chart Selection Guide",
      "slug": "chart-selection-guide",
      "agent": "Designer",
      "layer": "slide",
      "matchId": "019dd95a-0fd5-7148-8ece-3a10cdcb86ec",
      "evidence": "Power-law scaling chart appropriate for log relationship.",
      "confidence": 70
    }
  ],
  "frameworks": [
    {
      "name": "Neural Scaling Laws",
      "slug": null,
      "matchId": "019dd95a-1ca5-70bb-bba0-7887504640e9",
      "evidence": "Title cites 'empirical scaling laws of neural language models'.",
      "confidence": 80
    },
    {
      "name": "Scaling Laws",
      "slug": null,
      "matchId": "76731a61-e1bb-4626-81f7-a4a472910e0a",
      "evidence": "Empirical scaling laws of neural language models",
      "confidence": 1
    }
  ],
  "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": 23,
      "from": 15,
      "name": "Pattern Hunter",
      "slug": "02-pattern-hunter",
      "bestFor": "Time-pressed audiences, building consensus, when data is strong",
      "matchId": "019dd95a-07fe-70ce-8d3c-ee7c2907ccd3",
      "evidence": "Slides 15-19 stack scaling-law evidence; 20-23 add caveats.",
      "position": 2,
      "objective": "Establish pattern that bigger models + more compute drive performance at rising cost",
      "structure": "Evidence A -> Evidence B -> Evidence C -> Pattern/Conclusion",
      "confidence": 70,
      "description": "Group multiple pieces of evidence that together point to a pattern or conclusion"
    }
  ],
  "imagePathAlt": null,
  "thumbSrc": null,
  "thumbSrcAlt": null,
  "locked": true
}