{
  "docId": "019dd923-5e88-73ef-bd5d-01cf0d8a8fbc",
  "docSlug": "cc8183ec02431b7a",
  "documentTitle": "2022 Air Street Capital The State of AI Report 2022",
  "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": 14,
  "pageCount": 114,
  "prevPage": 13,
  "nextPage": 15,
  "slideType": "case_study",
  "function": "illustrate_case",
  "density": "overcrowded",
  "nDataPoints": 12,
  "notes": "The slide tracks a previous year's prediction and provides a technical explanation of the breakthrough, supported by a bar chart comparing speed-ups.",
  "elementsJson": [
    "headline_text",
    "bullet_list",
    "bar_chart_grouped"
  ],
  "metadataConfidence": 1,
  "imagePath": null,
  "slideHref": "/slides/019dd923-5e88-73ef-bd5d-01cf0d8a8fbc/14",
  "deckHref": "/decks/019dd923-5e88-73ef-bd5d-01cf0d8a8fbc",
  "deckJsonHref": "/decks/019dd923-5e88-73ef-bd5d-01cf0d8a8fbc.json",
  "deckAnchorHref": "/decks/019dd923-5e88-73ef-bd5d-01cf0d8a8fbc#slide-14",
  "components": [
    {
      "bbox": null,
      "kind": "callout",
      "text": "DeepMind's approach not only helps speed up research in the field, but also boosts matrix multiplication based technology, that is AI, imaging, and essentially everything happening on our phones.",
      "attrs": null,
      "subkind": null,
      "toolName": "Visual emphasis",
      "toolSlug": "visual-emphasis",
      "confidence": null,
      "componentId": "019dd952-47c4-7719-a215-9c0192c7b63d",
      "frameworkName": null,
      "frameworkSlug": null
    },
    {
      "bbox": {
        "h": 0.5,
        "w": 0.3,
        "x": 0.65,
        "y": 0.35
      },
      "kind": "chart",
      "text": "Speed-up on Nvidia V100 GPU comparing AlphaTensor and Strassen-square across various matrix sizes.",
      "attrs": null,
      "subkind": "bar-grouped",
      "toolName": null,
      "toolSlug": null,
      "confidence": null,
      "componentId": "7e51d2c6-3470-406c-bd55-b58a1b835fe0",
      "frameworkName": null,
      "frameworkSlug": null
    },
    {
      "bbox": {
        "h": 0.05,
        "w": 0.1,
        "x": 0.85,
        "y": 0.85
      },
      "kind": "legend",
      "text": "Strassen-square; AlphaTensor",
      "attrs": null,
      "subkind": null,
      "toolName": null,
      "toolSlug": null,
      "confidence": null,
      "componentId": "163fd59d-87b6-4b4a-b85e-f24ef43d1907",
      "frameworkName": null,
      "frameworkSlug": null
    },
    {
      "bbox": {
        "h": 0.5,
        "w": 0.6,
        "x": 0.05,
        "y": 0.33
      },
      "kind": "list",
      "text": "DeepMind repurposed AlphaZero (their RL model trained to beat the best human players of Chess, Go and Shogi) to do matrix multiplication. This AlphaTensor model was able to find new deterministic algorithms to multiply two matrices. To use AlphaZero, the researchers recast the matrix multiplication problem as a single-player game where each move corresponds to an algorithm instruction and the goal is to zero-out a tensor measuring how far from correct the predicted algorithm is.\nFinding faster matrix multiplication algorithms, a seemingly simple and well-studied problem, has been stale for decades. DeepMind's approach not only helps speed up research in the field, but also boosts matrix multiplication based technology, that is AI, imaging, and essentially everything happening on our phones.",
      "attrs": null,
      "subkind": "bullet",
      "toolName": null,
      "toolSlug": null,
      "confidence": null,
      "componentId": "ffaba657-11c0-419c-b541-8465205f2458",
      "frameworkName": null,
      "frameworkSlug": null
    },
    {
      "bbox": null,
      "kind": "metric",
      "text": "Speed-up on Nvidia V100 GPU: 23.9%",
      "attrs": null,
      "subkind": "primary",
      "toolName": "Quantification",
      "toolSlug": "quantification",
      "confidence": null,
      "componentId": "019dd952-47c4-7719-a215-a0232c2ba89f",
      "frameworkName": null,
      "frameworkSlug": null
    },
    {
      "bbox": {
        "h": 0.1,
        "w": 0.6,
        "x": 0.05,
        "y": 0.22
      },
      "kind": "paragraph",
      "text": "In 2021, we predicted: \"DeepMind releases a major research breakthrough in the physical sciences.\" The company has since made significant advancements in both mathematics and materials science.",
      "attrs": null,
      "subkind": "paragraph",
      "toolName": null,
      "toolSlug": null,
      "confidence": null,
      "componentId": "f9e5f536-d82b-407d-8a85-a19e0905ac3b",
      "frameworkName": null,
      "frameworkSlug": null
    },
    {
      "bbox": {
        "h": 0.05,
        "w": 0.8,
        "x": 0.05,
        "y": 0.13
      },
      "kind": "title",
      "text": "2021 Prediction: DeepMind's breakthroughs in the physical sciences (3/3)",
      "attrs": null,
      "subkind": "headline",
      "toolName": null,
      "toolSlug": null,
      "confidence": null,
      "componentId": "61f06797-4846-4565-8f55-cf9df1fc2aab",
      "frameworkName": null,
      "frameworkSlug": null
    }
  ],
  "metrics": [],
  "tools": [
    {
      "name": "Waterfall chart",
      "slug": "waterfall-chart",
      "agent": null,
      "layer": "slide",
      "matchId": "c16adcd9-3cef-48e0-8f95-7c1f3a2b4efd",
      "evidence": "chart/bar-grouped showing speed-up across various matrix sizes",
      "confidence": 0.7
    }
  ],
  "frameworks": [],
  "arcBeats": [
    {
      "to": 80,
      "from": 5,
      "beatId": "019dd95a-0682-776c-8e35-2557e3799e96",
      "arcName": "The Triple Take",
      "arcSlug": "triple-take",
      "beatName": "The Facts (What)",
      "beatSlug": "triple-take-the-facts-what",
      "evidence": "Definitions, exec summary, Research and Industry sections inventory the state of AI.",
      "position": 1,
      "confidence": 78,
      "parentBeatName": "Setup",
      "parentBeatSlug": "setup"
    },
    {
      "to": 93,
      "from": 11,
      "beatId": "019dd95a-0682-776c-8e35-35abf646aa8b",
      "arcName": "The Mountain",
      "arcSlug": "mountain",
      "beatName": "Rising Action",
      "beatSlug": "mountain-rising-action",
      "evidence": "Research, Industry, Politics sections accumulate signals.",
      "position": 2,
      "confidence": 55,
      "parentBeatName": "Development",
      "parentBeatSlug": "development"
    }
  ],
  "loops": [
    {
      "to": 21,
      "from": 12,
      "name": "Pattern Hunter",
      "slug": "02-pattern-hunter",
      "bestFor": "Time-pressed audiences, building consensus, when data is strong",
      "matchId": "019dd95a-07fe-70ce-8d3d-9f07f56c535f",
      "evidence": "DeepMind math, fusion, AlphaFold, protein LMs, plastic enzymes, Minecraft agents — all evidence of one pattern.",
      "position": 2,
      "objective": "Compile case studies showing ML breakthroughs in physical/life sciences",
      "structure": "Evidence A -> Evidence B -> Evidence C -> Pattern/Conclusion",
      "confidence": 80,
      "description": "Group multiple pieces of evidence that together point to a pattern or conclusion"
    }
  ],
  "imagePathAlt": null,
  "thumbSrc": null,
  "thumbSrcAlt": null,
  "locked": true
}