{
  "docId": "019dd923-5e88-73ef-bd5d-0d0f98caffe1",
  "docSlug": "5df6bd1b0447b5f6",
  "documentTitle": "2025 Air Street Capital The State of AI Report 2025",
  "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": 74,
  "pageCount": 313,
  "prevPage": 73,
  "nextPage": 75,
  "slideType": "case_study",
  "function": "illustrate_case",
  "density": "dense",
  "nDataPoints": 4,
  "notes": "The slide highlights the technical architecture (CNN, Transformer, Language Model) and the current limitations compared to invasive BCIs.",
  "elementsJson": [
    "headline_text",
    "bullet_list",
    "process_diagram"
  ],
  "metadataConfidence": 1,
  "imagePath": null,
  "slideHref": "/slides/019dd923-5e88-73ef-bd5d-0d0f98caffe1/74",
  "deckHref": "/decks/019dd923-5e88-73ef-bd5d-0d0f98caffe1",
  "deckJsonHref": "/decks/019dd923-5e88-73ef-bd5d-0d0f98caffe1.json",
  "deckAnchorHref": "/decks/019dd923-5e88-73ef-bd5d-0d0f98caffe1#slide-74",
  "components": [
    {
      "bbox": null,
      "kind": "callout",
      "text": "Meta AI researchers developed Brain2Qwerty, a system that decodes what people are typing by reading brain signals from outside the skull, achieving a 19% character error rate for the best participants.",
      "attrs": null,
      "subkind": null,
      "toolName": "Visual emphasis",
      "toolSlug": "visual-emphasis",
      "confidence": null,
      "componentId": "019dd952-41ac-7180-a821-bb0d4a938ebd",
      "frameworkName": null,
      "frameworkSlug": null
    },
    {
      "bbox": {
        "h": 0.45,
        "w": 0.3,
        "x": 0.65,
        "y": 0.38
      },
      "kind": "image",
      "text": "Diagram showing brain signal input (MEG/EEG) to a processing pipeline (CNN, Transformer, Language Module) resulting in text output.",
      "attrs": null,
      "subkind": "infographic",
      "toolName": null,
      "toolSlug": null,
      "confidence": null,
      "componentId": "36197501-44e7-4323-bab4-c45d4775c89d",
      "frameworkName": null,
      "frameworkSlug": null
    },
    {
      "bbox": {
        "h": 0.55,
        "w": 0.6,
        "x": 0.02,
        "y": 0.36
      },
      "kind": "list",
      "text": "35 Spanish-speaking participants memorised sentences, then typed them “blind” on a keyboard. Their brain activity was recorded using either electro- (EEG) or magneto-encephalography (MEG).\nBrain2Qwerty has three-stages: a CNN analyses input from hundreds of sensors, a transformer refines predictions using sentence context, and a Spanish language model fixes obvious errors.\nWhile an average error rate of 32% remains far from invasive Brain Computer Interfaces (<6% CER), down the line this kind of work could have applications in restoring communication for individuals with speech impairments, and contributes to understanding the neural basis of language.\nThe system's errors show it's tracking finger movements rather than understanding language: when it mistakes a letter, it picks physically adjacent keys 73% of the time.",
      "attrs": null,
      "subkind": "bullet",
      "toolName": null,
      "toolSlug": null,
      "confidence": null,
      "componentId": "49816889-4502-4eb1-8f38-686cdbbd05b8",
      "frameworkName": null,
      "frameworkSlug": null
    },
    {
      "bbox": null,
      "kind": "metric",
      "text": "Character error rate: 19%",
      "attrs": null,
      "subkind": "primary",
      "toolName": "Quantification",
      "toolSlug": "quantification",
      "confidence": null,
      "componentId": "019dd952-41ac-7180-a821-be90ba6ea1d8",
      "frameworkName": null,
      "frameworkSlug": null
    },
    {
      "bbox": {
        "h": 0.12,
        "w": 0.96,
        "x": 0.02,
        "y": 0.22
      },
      "kind": "paragraph",
      "text": "Meta AI researchers developed Brain2Qwerty, a system that decodes what people are typing by reading brain signals from outside the skull, achieving a 19% character error rate for the best participants. This is a substantial improvement on previous non-invasive approaches (but is still far from clinical viability).",
      "attrs": null,
      "subkind": "paragraph",
      "toolName": null,
      "toolSlug": null,
      "confidence": null,
      "componentId": "2444eba0-9f9c-47f4-8dfb-44bc64dd3131",
      "frameworkName": null,
      "frameworkSlug": null
    },
    {
      "bbox": {
        "h": 0.05,
        "w": 0.6,
        "x": 0.02,
        "y": 0.14
      },
      "kind": "title",
      "text": "Brain-to-text decoding: decoding brain activity during typing",
      "attrs": null,
      "subkind": "headline",
      "toolName": null,
      "toolSlug": null,
      "confidence": null,
      "componentId": "2b31effb-3734-42c9-9a85-c502dbaa8c19",
      "frameworkName": null,
      "frameworkSlug": null
    }
  ],
  "metrics": [],
  "tools": [
    {
      "name": "Data Story Arc",
      "slug": "data-story-arc",
      "agent": "Storyteller",
      "layer": "slide",
      "matchId": "7e959012-5ee6-4e40-a7a5-e4e621075771",
      "evidence": "achieving a 19% character error rate for the best participant",
      "confidence": 0.5
    }
  ],
  "frameworks": [],
  "arcBeats": [
    {
      "to": 189,
      "from": 12,
      "beatId": "019dd95a-0682-776c-8e35-9f380673831f",
      "arcName": "The Triple Take",
      "arcSlug": "triple-take",
      "beatName": "The Facts (What)",
      "beatSlug": "triple-take-the-facts-what",
      "evidence": "Sections 1-2 lay out research findings and industry data with charts and case studies.",
      "position": 1,
      "confidence": 70,
      "parentBeatName": "Setup",
      "parentBeatSlug": "setup"
    },
    {
      "to": 89,
      "from": 12,
      "beatId": "019dd95a-0682-776c-8e35-ac68557d958b",
      "arcName": "The Onion",
      "arcSlug": "onion",
      "beatName": "First Layer",
      "beatSlug": "onion-first-layer",
      "evidence": "Research section unpacks technical layer beneath the headlines.",
      "position": 2,
      "confidence": 45,
      "parentBeatName": "Development",
      "parentBeatSlug": "development"
    }
  ],
  "loops": [],
  "imagePathAlt": null,
  "thumbSrc": null,
  "thumbSrcAlt": null,
  "locked": true
}