{ "kind": "all", "value": "all", "collectionKey": "slides:all:all:all-document-kinds:all-producers:all-orientations", "filters": { "documentKinds": [], "sourceTypes": [], "orientations": [] }, "total": 319975, "page": 894, "pageSize": 60, "pageCount": 5333, "rows": [ { "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": 5, "slideType": "other", "function": "establish_context", "notes": "The slide serves as a foundational reference for technical terms.", "imagePath": null, "matchCount": 1, "evidence": null, "slideHref": "/slides/019dd923-5e88-73ef-bd5c-f341d4394195/5", "deckHref": "/decks/019dd923-5e88-73ef-bd5c-f341d4394195", "deckAnchorHref": "/decks/019dd923-5e88-73ef-bd5c-f341d4394195#slide-5", "loopMatches": [], "arcBeatMatches": [ { "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" } ], "imagePathAlt": null, "thumbSrc": null, "thumbSrcAlt": null, "locked": true }, { "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": 6, "slideType": "section_divider", "function": "transition", "notes": null, "imagePath": null, "matchCount": 1, "evidence": null, "slideHref": "/slides/019dd923-5e88-73ef-bd5c-f341d4394195/6", "deckHref": "/decks/019dd923-5e88-73ef-bd5c-f341d4394195", "deckAnchorHref": "/decks/019dd923-5e88-73ef-bd5c-f341d4394195#slide-6", "loopMatches": [], "arcBeatMatches": [ { "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": 9, "from": 6, "beatId": "019dd95a-0682-776c-8e34-b9affb23a9a4", "arcName": "The Onion", "arcSlug": "onion", "beatName": "Surface Observation", "beatSlug": "onion-surface-observation", "evidence": "Transfer learning headline result on skin cancer.", "position": 1, "confidence": 55, "parentBeatName": "Setup", "parentBeatSlug": "setup" } ], "imagePathAlt": null, "thumbSrc": null, "thumbSrcAlt": null, "locked": true }, { "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": 7, "slideType": "other", "function": "present_framework", "notes": "The slide uses a process flow diagram to illustrate the concept of transfer learning.", "imagePath": null, "matchCount": 1, "evidence": null, "slideHref": "/slides/019dd923-5e88-73ef-bd5c-f341d4394195/7", "deckHref": "/decks/019dd923-5e88-73ef-bd5c-f341d4394195", "deckAnchorHref": "/decks/019dd923-5e88-73ef-bd5c-f341d4394195#slide-7", "loopMatches": [ { "to": 9, "from": 7, "name": "Aha Moment", "slug": "03-aha-moment", "bestFor": "Data-heavy sections, research findings, analytical arguments", "matchId": "019dd95a-07fe-70ce-8d3c-46cbef5e2b62", "evidence": "Defines transfer learning then reveals model beats 21 dermatologists with ROC charts.", "position": 1, "objective": "Show transfer learning enables superhuman skin-cancer detection", "structure": "The Problem/Question -> What the Data Shows -> The Insight", "confidence": 80, "description": "Establish a tension, present data, then deliver the insight that changes everything" } ], "arcBeatMatches": [ { "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": 9, "from": 6, "beatId": "019dd95a-0682-776c-8e34-b9affb23a9a4", "arcName": "The Onion", "arcSlug": "onion", "beatName": "Surface Observation", "beatSlug": "onion-surface-observation", "evidence": "Transfer learning headline result on skin cancer.", "position": 1, "confidence": 55, "parentBeatName": "Setup", "parentBeatSlug": "setup" } ], "imagePathAlt": null, "thumbSrc": null, "thumbSrcAlt": null, "locked": true }, { "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": 8, "slideType": "other", "function": "present_framework", "notes": "The slide uses a technical diagram to illustrate the concept of transfer learning in AI.", "imagePath": null, "matchCount": 1, "evidence": null, "slideHref": "/slides/019dd923-5e88-73ef-bd5c-f341d4394195/8", "deckHref": "/decks/019dd923-5e88-73ef-bd5c-f341d4394195", "deckAnchorHref": "/decks/019dd923-5e88-73ef-bd5c-f341d4394195#slide-8", "loopMatches": [ { "to": 9, "from": 7, "name": "Aha Moment", "slug": "03-aha-moment", "bestFor": "Data-heavy sections, research findings, analytical arguments", "matchId": "019dd95a-07fe-70ce-8d3c-46cbef5e2b62", "evidence": "Defines transfer learning then reveals model beats 21 dermatologists with ROC charts.", "position": 1, "objective": "Show transfer learning enables superhuman skin-cancer detection", "structure": "The Problem/Question -> What the Data Shows -> The Insight", "confidence": 80, "description": "Establish a tension, present data, then deliver the insight that changes everything" } ], "arcBeatMatches": [ { "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": 9, "from": 6, "beatId": "019dd95a-0682-776c-8e34-b9affb23a9a4", "arcName": "The Onion", "arcSlug": "onion", "beatName": "Surface Observation", "beatSlug": "onion-surface-observation", "evidence": "Transfer learning headline result on skin cancer.", "position": 1, "confidence": 55, "parentBeatName": "Setup", "parentBeatSlug": "setup" } ], "imagePathAlt": null, "thumbSrc": null, "thumbSrcAlt": null, "locked": true }, { "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": 9, "slideType": "case_study", "function": "illustrate_case", "notes": "The slide uses ROC curves to compare algorithm performance against human experts.", "imagePath": null, "matchCount": 1, "evidence": null, "slideHref": "/slides/019dd923-5e88-73ef-bd5c-f341d4394195/9", "deckHref": "/decks/019dd923-5e88-73ef-bd5c-f341d4394195", "deckAnchorHref": "/decks/019dd923-5e88-73ef-bd5c-f341d4394195#slide-9", "loopMatches": [ { "to": 9, "from": 7, "name": "Aha Moment", "slug": "03-aha-moment", "bestFor": "Data-heavy sections, research findings, analytical arguments", "matchId": "019dd95a-07fe-70ce-8d3c-46cbef5e2b62", "evidence": "Defines transfer learning then reveals model beats 21 dermatologists with ROC charts.", "position": 1, "objective": "Show transfer learning enables superhuman skin-cancer detection", "structure": "The Problem/Question -> What the Data Shows -> The Insight", "confidence": 80, "description": "Establish a tension, present data, then deliver the insight that changes everything" } ], "arcBeatMatches": [ { "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": 9, "from": 6, "beatId": "019dd95a-0682-776c-8e34-b9affb23a9a4", "arcName": "The Onion", "arcSlug": "onion", "beatName": "Surface Observation", "beatSlug": "onion-surface-observation", "evidence": "Transfer learning headline result on skin cancer.", "position": 1, "confidence": 55, "parentBeatName": "Setup", "parentBeatSlug": "setup" } ], "imagePathAlt": null, "thumbSrc": null, "thumbSrcAlt": null, "locked": true }, { "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, "slideType": "industry_trends", "function": "establish_context", "notes": "The slide uses two bar charts to demonstrate the performance advantage of GPUs in AI training.", "imagePath": null, "matchCount": 1, "evidence": null, "slideHref": "/slides/019dd923-5e88-73ef-bd5c-f341d4394195/10", "deckHref": "/decks/019dd923-5e88-73ef-bd5c-f341d4394195", "deckAnchorHref": "/decks/019dd923-5e88-73ef-bd5c-f341d4394195#slide-10", "loopMatches": [ { "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" } ], "arcBeatMatches": [ { "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" } ], "imagePathAlt": null, "thumbSrc": null, "thumbSrcAlt": null, "locked": true }, { "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": 11, "slideType": "industry_trends", "function": "analyze_data", "notes": "The slide uses two charts to demonstrate the scaling efficiency of GPU clusters in AI model training.", "imagePath": null, "matchCount": 1, "evidence": null, "slideHref": "/slides/019dd923-5e88-73ef-bd5c-f341d4394195/11", "deckHref": "/decks/019dd923-5e88-73ef-bd5c-f341d4394195", "deckAnchorHref": "/decks/019dd923-5e88-73ef-bd5c-f341d4394195#slide-11", "loopMatches": [ { "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" } ], "arcBeatMatches": [ { "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" } ], "imagePathAlt": null, "thumbSrc": null, "thumbSrcAlt": null, "locked": true }, { "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": 12, "slideType": "industry_trends", "function": "analyze_data", "notes": "The slide uses three distinct charts to demonstrate scaling laws: a conceptual framework, a loss-reduction chart, and a parameter-growth chart.", "imagePath": null, "matchCount": 1, "evidence": null, "slideHref": "/slides/019dd923-5e88-73ef-bd5c-f341d4394195/12", "deckHref": "/decks/019dd923-5e88-73ef-bd5c-f341d4394195", "deckAnchorHref": "/decks/019dd923-5e88-73ef-bd5c-f341d4394195#slide-12", "loopMatches": [ { "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" } ], "arcBeatMatches": [ { "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" } ], "imagePathAlt": null, "thumbSrc": null, "thumbSrcAlt": null, "locked": true }, { "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": 13, "slideType": "industry_trends", "function": "present_framework", "notes": "The slide illustrates the Information Bottleneck theory, showing the evolution of layers (L1-L5) through phases A-E.", "imagePath": null, "matchCount": 1, "evidence": null, "slideHref": "/slides/019dd923-5e88-73ef-bd5c-f341d4394195/13", "deckHref": "/decks/019dd923-5e88-73ef-bd5c-f341d4394195", "deckAnchorHref": "/decks/019dd923-5e88-73ef-bd5c-f341d4394195#slide-13", "loopMatches": [ { "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" } ], "arcBeatMatches": [ { "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" } ], "imagePathAlt": null, "thumbSrc": null, "thumbSrcAlt": null, "locked": true }, { "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": 14, "slideType": "industry_trends", "function": "analyze_data", "notes": "The chart shows a log-scale progression of compute usage for various AI models from 2013 to 2018. The right side depicts a flywheel effect.", "imagePath": null, "matchCount": 1, "evidence": null, "slideHref": "/slides/019dd923-5e88-73ef-bd5c-f341d4394195/14", "deckHref": "/decks/019dd923-5e88-73ef-bd5c-f341d4394195", "deckAnchorHref": "/decks/019dd923-5e88-73ef-bd5c-f341d4394195#slide-14", "loopMatches": [ { "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" } ], "arcBeatMatches": [ { "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" } ], "imagePathAlt": null, "thumbSrc": null, "thumbSrcAlt": null, "locked": true }, { "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": 15, "slideType": "data_table", "function": "quantify_impact", "notes": "The slide uses growth multipliers (+15X and +5X) to highlight the rapid adoption of GPU-based AI development.", "imagePath": null, "matchCount": 1, "evidence": null, "slideHref": "/slides/019dd923-5e88-73ef-bd5c-f341d4394195/15", "deckHref": "/decks/019dd923-5e88-73ef-bd5c-f341d4394195", "deckAnchorHref": "/decks/019dd923-5e88-73ef-bd5c-f341d4394195#slide-15", "loopMatches": [ { "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" } ], "arcBeatMatches": [ { "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" } ], "imagePathAlt": null, "thumbSrc": null, "thumbSrcAlt": null, "locked": true }, { "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": 16, "slideType": "industry_trends", "function": "analyze_data", "notes": "The slide illustrates that while GPUs are used for ML, a significant portion of their core area is dedicated to other tasks, yet they consume substantial power.", "imagePath": null, "matchCount": 1, "evidence": null, "slideHref": "/slides/019dd923-5e88-73ef-bd5c-f341d4394195/16", "deckHref": "/decks/019dd923-5e88-73ef-bd5c-f341d4394195", "deckAnchorHref": "/decks/019dd923-5e88-73ef-bd5c-f341d4394195#slide-16", "loopMatches": [ { "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" } ], "arcBeatMatches": [ { "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" } ], "imagePathAlt": null, "thumbSrc": null, "thumbSrcAlt": null, "locked": true }, { "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": 17, "slideType": "data_table", "function": "analyze_data", "notes": "Data presented in two tables (Table II and Table III) comparing resource allocation for training vs inference.", "imagePath": null, "matchCount": 1, "evidence": null, "slideHref": "/slides/019dd923-5e88-73ef-bd5c-f341d4394195/17", "deckHref": "/decks/019dd923-5e88-73ef-bd5c-f341d4394195", "deckAnchorHref": "/decks/019dd923-5e88-73ef-bd5c-f341d4394195#slide-17", "loopMatches": [ { "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" } ], "arcBeatMatches": [ { "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" } ], "imagePathAlt": null, "thumbSrc": null, "thumbSrcAlt": null, "locked": true }, { "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": 18, "slideType": "industry_trends", "function": "analyze_data", "notes": "The chart is a classic visualization of the end of Dennard scaling.", "imagePath": null, "matchCount": 1, "evidence": null, "slideHref": "/slides/019dd923-5e88-73ef-bd5c-f341d4394195/18", "deckHref": "/decks/019dd923-5e88-73ef-bd5c-f341d4394195", "deckAnchorHref": "/decks/019dd923-5e88-73ef-bd5c-f341d4394195#slide-18", "loopMatches": [ { "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" } ], "arcBeatMatches": [ { "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" } ], "imagePathAlt": null, "thumbSrc": null, "thumbSrcAlt": null, "locked": true }, { "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": 19, "slideType": "industry_trends", "function": "present_solution", "notes": "The slide highlights the architectural shift towards memory-centric design (75% RAM) and demonstrates significant performance gains in inference speed.", "imagePath": null, "matchCount": 1, "evidence": null, "slideHref": "/slides/019dd923-5e88-73ef-bd5c-f341d4394195/19", "deckHref": "/decks/019dd923-5e88-73ef-bd5c-f341d4394195", "deckAnchorHref": "/decks/019dd923-5e88-73ef-bd5c-f341d4394195#slide-19", "loopMatches": [ { "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" } ], "arcBeatMatches": [ { "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" } ], "imagePathAlt": null, "thumbSrc": null, "thumbSrcAlt": null, "locked": true }, { "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": 20, "slideType": "data_table", "function": "quantify_impact", "notes": "The chart uses multipliers (81x, 4x, 3.3x, 6.1x) to emphasize the price disparity between standard CPUs and specialized AI hardware.", "imagePath": null, "matchCount": 1, "evidence": null, "slideHref": "/slides/019dd923-5e88-73ef-bd5c-f341d4394195/20", "deckHref": "/decks/019dd923-5e88-73ef-bd5c-f341d4394195", "deckAnchorHref": "/decks/019dd923-5e88-73ef-bd5c-f341d4394195#slide-20", "loopMatches": [ { "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" } ], "arcBeatMatches": [ { "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" } ], "imagePathAlt": null, "thumbSrc": null, "thumbSrcAlt": null, "locked": true }, { "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": 21, "slideType": "data_table", "function": "quantify_impact", "notes": "The slide uses a line chart to show training convergence and a bar chart to show cost efficiency.", "imagePath": null, "matchCount": 1, "evidence": null, "slideHref": "/slides/019dd923-5e88-73ef-bd5c-f341d4394195/21", "deckHref": "/decks/019dd923-5e88-73ef-bd5c-f341d4394195", "deckAnchorHref": "/decks/019dd923-5e88-73ef-bd5c-f341d4394195#slide-21", "loopMatches": [ { "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" } ], "arcBeatMatches": [ { "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" } ], "imagePathAlt": null, "thumbSrc": null, "thumbSrcAlt": null, "locked": true }, { "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": 22, "slideType": "industry_trends", "function": "establish_context", "notes": "The slide features a photograph of a TPUv3 pod.", "imagePath": null, "matchCount": 1, "evidence": null, "slideHref": "/slides/019dd923-5e88-73ef-bd5c-f341d4394195/22", "deckHref": "/decks/019dd923-5e88-73ef-bd5c-f341d4394195", "deckAnchorHref": "/decks/019dd923-5e88-73ef-bd5c-f341d4394195#slide-22", "loopMatches": [ { "to": 29, "from": 22, "name": "Pattern Hunter", "slug": "02-pattern-hunter", "bestFor": "Time-pressed audiences, building consensus, when data is strong", "matchId": "019dd95a-07fe-70ce-8d3c-4c3e393d7324", "evidence": "TPUv3 specs, NVIDIA HGX-2, NVIDIA datacenter $2B, EV 10x, Intel DC, custom chip landscape, hyperscaler $76B capex.", "position": 3, "objective": "Demonstrate hardware vendors are scaling fast", "structure": "Evidence A -> Evidence B -> Evidence C -> Pattern/Conclusion", "confidence": 75, "description": "Group multiple pieces of evidence that together point to a pattern or conclusion" } ], "arcBeatMatches": [ { "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" } ], "imagePathAlt": null, "thumbSrc": null, "thumbSrcAlt": null, "locked": true }, { "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": 23, "slideType": "client_example", "function": "illustrate_case", "notes": "Slide from State of AI 2018 report.", "imagePath": null, "matchCount": 1, "evidence": null, "slideHref": "/slides/019dd923-5e88-73ef-bd5c-f341d4394195/23", "deckHref": "/decks/019dd923-5e88-73ef-bd5c-f341d4394195", "deckAnchorHref": "/decks/019dd923-5e88-73ef-bd5c-f341d4394195#slide-23", "loopMatches": [ { "to": 29, "from": 22, "name": "Pattern Hunter", "slug": "02-pattern-hunter", "bestFor": "Time-pressed audiences, building consensus, when data is strong", "matchId": "019dd95a-07fe-70ce-8d3c-4c3e393d7324", "evidence": "TPUv3 specs, NVIDIA HGX-2, NVIDIA datacenter $2B, EV 10x, Intel DC, custom chip landscape, hyperscaler $76B capex.", "position": 3, "objective": "Demonstrate hardware vendors are scaling fast", "structure": "Evidence A -> Evidence B -> Evidence C -> Pattern/Conclusion", "confidence": 75, "description": "Group multiple pieces of evidence that together point to a pattern or conclusion" } ], "arcBeatMatches": [ { "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" } ], "imagePathAlt": null, "thumbSrc": null, "thumbSrcAlt": null, "locked": true }, { "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": 24, "slideType": "data_table", "function": "quantify_impact", "notes": "The chart uses a combination of a bar chart for absolute revenue and a line chart for percentage share.", "imagePath": null, "matchCount": 1, "evidence": null, "slideHref": "/slides/019dd923-5e88-73ef-bd5c-f341d4394195/24", "deckHref": "/decks/019dd923-5e88-73ef-bd5c-f341d4394195", "deckAnchorHref": "/decks/019dd923-5e88-73ef-bd5c-f341d4394195#slide-24", "loopMatches": [ { "to": 29, "from": 22, "name": "Pattern Hunter", "slug": "02-pattern-hunter", "bestFor": "Time-pressed audiences, building consensus, when data is strong", "matchId": "019dd95a-07fe-70ce-8d3c-4c3e393d7324", "evidence": "TPUv3 specs, NVIDIA HGX-2, NVIDIA datacenter $2B, EV 10x, Intel DC, custom chip landscape, hyperscaler $76B capex.", "position": 3, "objective": "Demonstrate hardware vendors are scaling fast", "structure": "Evidence A -> Evidence B -> Evidence C -> Pattern/Conclusion", "confidence": 75, "description": "Group multiple pieces of evidence that together point to a pattern or conclusion" } ], "arcBeatMatches": [ { "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" } ], "imagePathAlt": null, "thumbSrc": null, "thumbSrcAlt": null, "locked": true }, { "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": 25, "slideType": "industry_trends", "function": "quantify_impact", "notes": "The chart uses a green line to represent stock price over time, with a specific annotation for the 'Deep learning works starts to work' inflection point.", "imagePath": null, "matchCount": 1, "evidence": null, "slideHref": "/slides/019dd923-5e88-73ef-bd5c-f341d4394195/25", "deckHref": "/decks/019dd923-5e88-73ef-bd5c-f341d4394195", "deckAnchorHref": "/decks/019dd923-5e88-73ef-bd5c-f341d4394195#slide-25", "loopMatches": [ { "to": 29, "from": 22, "name": "Pattern Hunter", "slug": "02-pattern-hunter", "bestFor": "Time-pressed audiences, building consensus, when data is strong", "matchId": "019dd95a-07fe-70ce-8d3c-4c3e393d7324", "evidence": "TPUv3 specs, NVIDIA HGX-2, NVIDIA datacenter $2B, EV 10x, Intel DC, custom chip landscape, hyperscaler $76B capex.", "position": 3, "objective": "Demonstrate hardware vendors are scaling fast", "structure": "Evidence A -> Evidence B -> Evidence C -> Pattern/Conclusion", "confidence": 75, "description": "Group multiple pieces of evidence that together point to a pattern or conclusion" } ], "arcBeatMatches": [ { "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" } ], "imagePathAlt": null, "thumbSrc": null, "thumbSrcAlt": null, "locked": true }, { "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": 26, "slideType": "financial_analysis", "function": "analyze_data", "notes": "The slide uses a combination of bulleted strategic highlights and a dual-axis bar/line chart to show financial performance.", "imagePath": null, "matchCount": 1, "evidence": null, "slideHref": "/slides/019dd923-5e88-73ef-bd5c-f341d4394195/26", "deckHref": "/decks/019dd923-5e88-73ef-bd5c-f341d4394195", "deckAnchorHref": "/decks/019dd923-5e88-73ef-bd5c-f341d4394195#slide-26", "loopMatches": [ { "to": 29, "from": 22, "name": "Pattern Hunter", "slug": "02-pattern-hunter", "bestFor": "Time-pressed audiences, building consensus, when data is strong", "matchId": "019dd95a-07fe-70ce-8d3c-4c3e393d7324", "evidence": "TPUv3 specs, NVIDIA HGX-2, NVIDIA datacenter $2B, EV 10x, Intel DC, custom chip landscape, hyperscaler $76B capex.", "position": 3, "objective": "Demonstrate hardware vendors are scaling fast", "structure": "Evidence A -> Evidence B -> Evidence C -> Pattern/Conclusion", "confidence": 75, "description": "Group multiple pieces of evidence that together point to a pattern or conclusion" } ], "arcBeatMatches": [ { "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" } ], "imagePathAlt": null, "thumbSrc": null, "thumbSrcAlt": null, "locked": true }, { "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": 27, "slideType": "market_landscape", "function": "summarize", "notes": "The slide uses a simple table format to segment the AI hardware market landscape.", "imagePath": null, "matchCount": 1, "evidence": null, "slideHref": "/slides/019dd923-5e88-73ef-bd5c-f341d4394195/27", "deckHref": "/decks/019dd923-5e88-73ef-bd5c-f341d4394195", "deckAnchorHref": "/decks/019dd923-5e88-73ef-bd5c-f341d4394195#slide-27", "loopMatches": [ { "to": 29, "from": 22, "name": "Pattern Hunter", "slug": "02-pattern-hunter", "bestFor": "Time-pressed audiences, building consensus, when data is strong", "matchId": "019dd95a-07fe-70ce-8d3c-4c3e393d7324", "evidence": "TPUv3 specs, NVIDIA HGX-2, NVIDIA datacenter $2B, EV 10x, Intel DC, custom chip landscape, hyperscaler $76B capex.", "position": 3, "objective": "Demonstrate hardware vendors are scaling fast", "structure": "Evidence A -> Evidence B -> Evidence C -> Pattern/Conclusion", "confidence": 75, "description": "Group multiple pieces of evidence that together point to a pattern or conclusion" } ], "arcBeatMatches": [ { "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" } ], "imagePathAlt": null, "thumbSrc": null, "thumbSrcAlt": null, "locked": true }, { "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": 28, "slideType": "comparison_table", "function": "compare_options", "notes": null, "imagePath": null, "matchCount": 1, "evidence": null, "slideHref": "/slides/019dd923-5e88-73ef-bd5c-f341d4394195/28", "deckHref": "/decks/019dd923-5e88-73ef-bd5c-f341d4394195", "deckAnchorHref": "/decks/019dd923-5e88-73ef-bd5c-f341d4394195#slide-28", "loopMatches": [ { "to": 29, "from": 22, "name": "Pattern Hunter", "slug": "02-pattern-hunter", "bestFor": "Time-pressed audiences, building consensus, when data is strong", "matchId": "019dd95a-07fe-70ce-8d3c-4c3e393d7324", "evidence": "TPUv3 specs, NVIDIA HGX-2, NVIDIA datacenter $2B, EV 10x, Intel DC, custom chip landscape, hyperscaler $76B capex.", "position": 3, "objective": "Demonstrate hardware vendors are scaling fast", "structure": "Evidence A -> Evidence B -> Evidence C -> Pattern/Conclusion", "confidence": 75, "description": "Group multiple pieces of evidence that together point to a pattern or conclusion" } ], "arcBeatMatches": [ { "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" } ], "imagePathAlt": null, "thumbSrc": null, "thumbSrcAlt": null, "locked": true }, { "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": 29, "slideType": "industry_trends", "function": "quantify_impact", "notes": "The slide uses a combination of qualitative news clippings and quantitative financial data to support the thesis.", "imagePath": null, "matchCount": 1, "evidence": null, "slideHref": "/slides/019dd923-5e88-73ef-bd5c-f341d4394195/29", "deckHref": "/decks/019dd923-5e88-73ef-bd5c-f341d4394195", "deckAnchorHref": "/decks/019dd923-5e88-73ef-bd5c-f341d4394195#slide-29", "loopMatches": [ { "to": 29, "from": 22, "name": "Pattern Hunter", "slug": "02-pattern-hunter", "bestFor": "Time-pressed audiences, building consensus, when data is strong", "matchId": "019dd95a-07fe-70ce-8d3c-4c3e393d7324", "evidence": "TPUv3 specs, NVIDIA HGX-2, NVIDIA datacenter $2B, EV 10x, Intel DC, custom chip landscape, hyperscaler $76B capex.", "position": 3, "objective": "Demonstrate hardware vendors are scaling fast", "structure": "Evidence A -> Evidence B -> Evidence C -> Pattern/Conclusion", "confidence": 75, "description": "Group multiple pieces of evidence that together point to a pattern or conclusion" } ], "arcBeatMatches": [ { "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" } ], "imagePathAlt": null, "thumbSrc": null, "thumbSrcAlt": null, "locked": true }, { "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": 30, "slideType": "diagnosis", "function": "present_framework", "notes": "The slide illustrates how AI models interpret visual data by identifying objects as 'nouns'.", "imagePath": null, "matchCount": 1, "evidence": null, "slideHref": "/slides/019dd923-5e88-73ef-bd5c-f341d4394195/30", "deckHref": "/decks/019dd923-5e88-73ef-bd5c-f341d4394195", "deckAnchorHref": "/decks/019dd923-5e88-73ef-bd5c-f341d4394195#slide-30", "loopMatches": [ { "to": 35, "from": 30, "name": "Zoom In", "slug": "06-zoom-in", "bestFor": "Technical deep-dives, case studies, detailed analysis", "matchId": "019dd95a-07fe-70ce-8d3c-514142d7981d", "evidence": "Big picture (CV detects nouns) -> focus on verbs/common sense -> specific case (multi-viewpoint scenes).", "position": 4, "objective": "Zoom from object detection to full scene understanding", "structure": "The Big Picture -> Key Area of Focus -> Specific Detail -> Implication", "confidence": 75, "description": "Start broad, then progressively focus on specific details that prove your point" } ], "arcBeatMatches": [ { "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" } ], "imagePathAlt": null, "thumbSrc": null, "thumbSrcAlt": null, "locked": true }, { "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": 31, "slideType": "problem_statement", "function": "frame_problem", "notes": "The slide uses four examples of AI mislabeling images to demonstrate the lack of 'common sense world models' in AI.", "imagePath": null, "matchCount": 1, "evidence": null, "slideHref": "/slides/019dd923-5e88-73ef-bd5c-f341d4394195/31", "deckHref": "/decks/019dd923-5e88-73ef-bd5c-f341d4394195", "deckAnchorHref": "/decks/019dd923-5e88-73ef-bd5c-f341d4394195#slide-31", "loopMatches": [ { "to": 35, "from": 30, "name": "Zoom In", "slug": "06-zoom-in", "bestFor": "Technical deep-dives, case studies, detailed analysis", "matchId": "019dd95a-07fe-70ce-8d3c-514142d7981d", "evidence": "Big picture (CV detects nouns) -> focus on verbs/common sense -> specific case (multi-viewpoint scenes).", "position": 4, "objective": "Zoom from object detection to full scene understanding", "structure": "The Big Picture -> Key Area of Focus -> Specific Detail -> Implication", "confidence": 75, "description": "Start broad, then progressively focus on specific details that prove your point" } ], "arcBeatMatches": [ { "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" } ], "imagePathAlt": null, "thumbSrc": null, "thumbSrcAlt": null, "locked": true }, { "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": 32, "slideType": "industry_trends", "function": "illustrate_case", "notes": "Slide from state.ai 2018 report.", "imagePath": null, "matchCount": 1, "evidence": null, "slideHref": "/slides/019dd923-5e88-73ef-bd5c-f341d4394195/32", "deckHref": "/decks/019dd923-5e88-73ef-bd5c-f341d4394195", "deckAnchorHref": "/decks/019dd923-5e88-73ef-bd5c-f341d4394195#slide-32", "loopMatches": [ { "to": 35, "from": 30, "name": "Zoom In", "slug": "06-zoom-in", "bestFor": "Technical deep-dives, case studies, detailed analysis", "matchId": "019dd95a-07fe-70ce-8d3c-514142d7981d", "evidence": "Big picture (CV detects nouns) -> focus on verbs/common sense -> specific case (multi-viewpoint scenes).", "position": 4, "objective": "Zoom from object detection to full scene understanding", "structure": "The Big Picture -> Key Area of Focus -> Specific Detail -> Implication", "confidence": 75, "description": "Start broad, then progressively focus on specific details that prove your point" } ], "arcBeatMatches": [ { "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" } ], "imagePathAlt": null, "thumbSrc": null, "thumbSrcAlt": null, "locked": true }, { "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": 33, "slideType": "implementation_plan", "function": "present_framework", "notes": "The diagram shows a cyclical process involving taxonomy creation, crowd-acting, model training, and customer testing, with feedback loops.", "imagePath": null, "matchCount": 1, "evidence": null, "slideHref": "/slides/019dd923-5e88-73ef-bd5c-f341d4394195/33", "deckHref": "/decks/019dd923-5e88-73ef-bd5c-f341d4394195", "deckAnchorHref": "/decks/019dd923-5e88-73ef-bd5c-f341d4394195#slide-33", "loopMatches": [ { "to": 35, "from": 30, "name": "Zoom In", "slug": "06-zoom-in", "bestFor": "Technical deep-dives, case studies, detailed analysis", "matchId": "019dd95a-07fe-70ce-8d3c-514142d7981d", "evidence": "Big picture (CV detects nouns) -> focus on verbs/common sense -> specific case (multi-viewpoint scenes).", "position": 4, "objective": "Zoom from object detection to full scene understanding", "structure": "The Big Picture -> Key Area of Focus -> Specific Detail -> Implication", "confidence": 75, "description": "Start broad, then progressively focus on specific details that prove your point" } ], "arcBeatMatches": [ { "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" } ], "imagePathAlt": null, "thumbSrc": null, "thumbSrcAlt": null, "locked": true }, { "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": 34, "slideType": "case_study", "function": "illustrate_case", "notes": "The slide highlights the capability of TwentyBN-trained networks compared to standard image-trained networks.", "imagePath": null, "matchCount": 1, "evidence": null, "slideHref": "/slides/019dd923-5e88-73ef-bd5c-f341d4394195/34", "deckHref": "/decks/019dd923-5e88-73ef-bd5c-f341d4394195", "deckAnchorHref": "/decks/019dd923-5e88-73ef-bd5c-f341d4394195#slide-34", "loopMatches": [ { "to": 35, "from": 30, "name": "Zoom In", "slug": "06-zoom-in", "bestFor": "Technical deep-dives, case studies, detailed analysis", "matchId": "019dd95a-07fe-70ce-8d3c-514142d7981d", "evidence": "Big picture (CV detects nouns) -> focus on verbs/common sense -> specific case (multi-viewpoint scenes).", "position": 4, "objective": "Zoom from object detection to full scene understanding", "structure": "The Big Picture -> Key Area of Focus -> Specific Detail -> Implication", "confidence": 75, "description": "Start broad, then progressively focus on specific details that prove your point" } ], "arcBeatMatches": [ { "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" } ], "imagePathAlt": null, "thumbSrc": null, "thumbSrcAlt": null, "locked": true }, { "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": 35, "slideType": "case_study", "function": "illustrate_case", "notes": "DeepMind research slide from 2018 State of AI report.", "imagePath": null, "matchCount": 1, "evidence": null, "slideHref": "/slides/019dd923-5e88-73ef-bd5c-f341d4394195/35", "deckHref": "/decks/019dd923-5e88-73ef-bd5c-f341d4394195", "deckAnchorHref": "/decks/019dd923-5e88-73ef-bd5c-f341d4394195#slide-35", "loopMatches": [ { "to": 35, "from": 30, "name": "Zoom In", "slug": "06-zoom-in", "bestFor": "Technical deep-dives, case studies, detailed analysis", "matchId": "019dd95a-07fe-70ce-8d3c-514142d7981d", "evidence": "Big picture (CV detects nouns) -> focus on verbs/common sense -> specific case (multi-viewpoint scenes).", "position": 4, "objective": "Zoom from object detection to full scene understanding", "structure": "The Big Picture -> Key Area of Focus -> Specific Detail -> Implication", "confidence": 75, "description": "Start broad, then progressively focus on specific details that prove your point" } ], "arcBeatMatches": [ { "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" } ], "imagePathAlt": null, "thumbSrc": null, "thumbSrcAlt": null, "locked": true }, { "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": 36, "slideType": "other", "function": "present_framework", "notes": "The diagram shows the standard RL feedback loop: Agent receives observations and rewards, and performs actions.", "imagePath": null, "matchCount": 1, "evidence": null, "slideHref": "/slides/019dd923-5e88-73ef-bd5c-f341d4394195/36", "deckHref": "/decks/019dd923-5e88-73ef-bd5c-f341d4394195", "deckAnchorHref": "/decks/019dd923-5e88-73ef-bd5c-f341d4394195#slide-36", "loopMatches": [ { "to": 39, "from": 36, "name": "Pattern Hunter", "slug": "02-pattern-hunter", "bestFor": "Time-pressed audiences, building consensus, when data is strong", "matchId": "019dd95a-07fe-70ce-8d3c-55bec48734a5", "evidence": "AlphaZero, OpenAI Dota2, world-model RL — three parallel evidence pieces of RL progress.", "position": 5, "objective": "Show RL agents now beat human experts across domains", "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" } ], "arcBeatMatches": [ { "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" } ], "imagePathAlt": null, "thumbSrc": null, "thumbSrcAlt": null, "locked": true }, { "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": 37, "slideType": "case_study", "function": "illustrate_case", "notes": "The slide uses two charts: a line chart showing training progress over 40 days and a bar chart comparing peak Elo ratings of various Go engines.", "imagePath": null, "matchCount": 1, "evidence": null, "slideHref": "/slides/019dd923-5e88-73ef-bd5c-f341d4394195/37", "deckHref": "/decks/019dd923-5e88-73ef-bd5c-f341d4394195", "deckAnchorHref": "/decks/019dd923-5e88-73ef-bd5c-f341d4394195#slide-37", "loopMatches": [ { "to": 39, "from": 36, "name": "Pattern Hunter", "slug": "02-pattern-hunter", "bestFor": "Time-pressed audiences, building consensus, when data is strong", "matchId": "019dd95a-07fe-70ce-8d3c-55bec48734a5", "evidence": "AlphaZero, OpenAI Dota2, world-model RL — three parallel evidence pieces of RL progress.", "position": 5, "objective": "Show RL agents now beat human experts across domains", "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" } ], "arcBeatMatches": [ { "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" } ], "imagePathAlt": null, "thumbSrc": null, "thumbSrcAlt": null, "locked": true }, { "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": 38, "slideType": "case_study", "function": "illustrate_case", "notes": "The slide includes a screenshot of the game interface and a comparison table of training infrastructure and performance metrics.", "imagePath": null, "matchCount": 1, "evidence": null, "slideHref": "/slides/019dd923-5e88-73ef-bd5c-f341d4394195/38", "deckHref": "/decks/019dd923-5e88-73ef-bd5c-f341d4394195", "deckAnchorHref": "/decks/019dd923-5e88-73ef-bd5c-f341d4394195#slide-38", "loopMatches": [ { "to": 39, "from": 36, "name": "Pattern Hunter", "slug": "02-pattern-hunter", "bestFor": "Time-pressed audiences, building consensus, when data is strong", "matchId": "019dd95a-07fe-70ce-8d3c-55bec48734a5", "evidence": "AlphaZero, OpenAI Dota2, world-model RL — three parallel evidence pieces of RL progress.", "position": 5, "objective": "Show RL agents now beat human experts across domains", "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" } ], "arcBeatMatches": [ { "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" } ], "imagePathAlt": null, "thumbSrc": null, "thumbSrcAlt": null, "locked": true }, { "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": 39, "slideType": "case_study", "function": "illustrate_case", "notes": "The slide illustrates the 'World Models' architecture (Ha & Schmidhuber, 2018).", "imagePath": null, "matchCount": 1, "evidence": null, "slideHref": "/slides/019dd923-5e88-73ef-bd5c-f341d4394195/39", "deckHref": "/decks/019dd923-5e88-73ef-bd5c-f341d4394195", "deckAnchorHref": "/decks/019dd923-5e88-73ef-bd5c-f341d4394195#slide-39", "loopMatches": [ { "to": 39, "from": 36, "name": "Pattern Hunter", "slug": "02-pattern-hunter", "bestFor": "Time-pressed audiences, building consensus, when data is strong", "matchId": "019dd95a-07fe-70ce-8d3c-55bec48734a5", "evidence": "AlphaZero, OpenAI Dota2, world-model RL — three parallel evidence pieces of RL progress.", "position": 5, "objective": "Show RL agents now beat human experts across domains", "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" } ], "arcBeatMatches": [ { "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" } ], "imagePathAlt": null, "thumbSrc": null, "thumbSrcAlt": null, "locked": true }, { "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": 40, "slideType": "industry_trends", "function": "establish_context", "notes": "The chart uses a humorous, hand-drawn style to illustrate the sudden surge in research interest.", "imagePath": null, "matchCount": 1, "evidence": null, "slideHref": "/slides/019dd923-5e88-73ef-bd5c-f341d4394195/40", "deckHref": "/decks/019dd923-5e88-73ef-bd5c-f341d4394195", "deckAnchorHref": "/decks/019dd923-5e88-73ef-bd5c-f341d4394195#slide-40", "loopMatches": [ { "to": 43, "from": 40, "name": "Zoom In", "slug": "06-zoom-in", "bestFor": "Technical deep-dives, case studies, detailed analysis", "matchId": "019dd95a-07fe-70ce-8d3c-5930175dc2d9", "evidence": "Big picture (fairness) -> specific examples (Turkish gender, racial) -> 5-type allocation bias table.", "position": 6, "objective": "Diagnose ML bias by isolating its sources", "structure": "The Big Picture -> Key Area of Focus -> Specific Detail -> Implication", "confidence": 75, "description": "Start broad, then progressively focus on specific details that prove your point" } ], "arcBeatMatches": [ { "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" } ], "imagePathAlt": null, "thumbSrc": null, "thumbSrcAlt": null, "locked": true }, { "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": 41, "slideType": "client_example", "function": "illustrate_case", "notes": "The slide uses screenshots of Google Translate to show how machine learning models can perpetuate societal stereotypes.", "imagePath": null, "matchCount": 1, "evidence": null, "slideHref": "/slides/019dd923-5e88-73ef-bd5c-f341d4394195/41", "deckHref": "/decks/019dd923-5e88-73ef-bd5c-f341d4394195", "deckAnchorHref": "/decks/019dd923-5e88-73ef-bd5c-f341d4394195#slide-41", "loopMatches": [ { "to": 43, "from": 40, "name": "Zoom In", "slug": "06-zoom-in", "bestFor": "Technical deep-dives, case studies, detailed analysis", "matchId": "019dd95a-07fe-70ce-8d3c-5930175dc2d9", "evidence": "Big picture (fairness) -> specific examples (Turkish gender, racial) -> 5-type allocation bias table.", "position": 6, "objective": "Diagnose ML bias by isolating its sources", "structure": "The Big Picture -> Key Area of Focus -> Specific Detail -> Implication", "confidence": 75, "description": "Start broad, then progressively focus on specific details that prove your point" } ], "arcBeatMatches": [ { "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" } ], "imagePathAlt": null, "thumbSrc": null, "thumbSrcAlt": null, "locked": true }, { "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": 42, "slideType": "case_study", "function": "illustrate_case", "notes": "The slide uses external media headlines and a personal anecdote to illustrate the problem of algorithmic bias.", "imagePath": null, "matchCount": 1, "evidence": null, "slideHref": "/slides/019dd923-5e88-73ef-bd5c-f341d4394195/42", "deckHref": "/decks/019dd923-5e88-73ef-bd5c-f341d4394195", "deckAnchorHref": "/decks/019dd923-5e88-73ef-bd5c-f341d4394195#slide-42", "loopMatches": [ { "to": 43, "from": 40, "name": "Zoom In", "slug": "06-zoom-in", "bestFor": "Technical deep-dives, case studies, detailed analysis", "matchId": "019dd95a-07fe-70ce-8d3c-5930175dc2d9", "evidence": "Big picture (fairness) -> specific examples (Turkish gender, racial) -> 5-type allocation bias table.", "position": 6, "objective": "Diagnose ML bias by isolating its sources", "structure": "The Big Picture -> Key Area of Focus -> Specific Detail -> Implication", "confidence": 75, "description": "Start broad, then progressively focus on specific details that prove your point" } ], "arcBeatMatches": [ { "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" } ], "imagePathAlt": null, "thumbSrc": null, "thumbSrcAlt": null, "locked": true }, { "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": 43, "slideType": "diagnosis", "function": "diagnose", "notes": "The slide uses a matrix to categorize different types of algorithmic bias observed in various tech products.", "imagePath": null, "matchCount": 1, "evidence": null, "slideHref": "/slides/019dd923-5e88-73ef-bd5c-f341d4394195/43", "deckHref": "/decks/019dd923-5e88-73ef-bd5c-f341d4394195", "deckAnchorHref": "/decks/019dd923-5e88-73ef-bd5c-f341d4394195#slide-43", "loopMatches": [ { "to": 43, "from": 40, "name": "Zoom In", "slug": "06-zoom-in", "bestFor": "Technical deep-dives, case studies, detailed analysis", "matchId": "019dd95a-07fe-70ce-8d3c-5930175dc2d9", "evidence": "Big picture (fairness) -> specific examples (Turkish gender, racial) -> 5-type allocation bias table.", "position": 6, "objective": "Diagnose ML bias by isolating its sources", "structure": "The Big Picture -> Key Area of Focus -> Specific Detail -> Implication", "confidence": 75, "description": "Start broad, then progressively focus on specific details that prove your point" } ], "arcBeatMatches": [ { "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" } ], "imagePathAlt": null, "thumbSrc": null, "thumbSrcAlt": null, "locked": true }, { "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": 44, "slideType": "filler", "function": "illustrate_case", "notes": "The comic is a reference to the 'AI-Box Experiment' thought experiment.", "imagePath": null, "matchCount": 1, "evidence": null, "slideHref": "/slides/019dd923-5e88-73ef-bd5c-f341d4394195/44", "deckHref": "/decks/019dd923-5e88-73ef-bd5c-f341d4394195", "deckAnchorHref": "/decks/019dd923-5e88-73ef-bd5c-f341d4394195#slide-44", "loopMatches": [ { "to": 50, "from": 44, "name": "Iceberg", "slug": "10-iceberg", "bestFor": "Consulting, complex problem solving, organizational change", "matchId": "019dd95a-07fe-70ce-8d3c-5ed4c9b352e3", "evidence": "Symptom (black box) -> hidden system (feature importance) -> root issues (adversarial attacks fool models in real world).", "position": 7, "objective": "Surface hidden risks beneath model accuracy", "structure": "The Symptom (Visible) -> The System (Hidden) -> The Root Cause", "confidence": 72, "description": "Reveal that the visible problem is merely a symptom of a deeper root cause" } ], "arcBeatMatches": [ { "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" } ], "imagePathAlt": null, "thumbSrc": null, "thumbSrcAlt": null, "locked": true }, { "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": 45, "slideType": "other", "function": "illustrate_case", "notes": "The slide uses a visual example of a dog playing a guitar to demonstrate how explainability techniques (like saliency maps or feature attribution) reveal what a model is 'looking at' to make a prediction.", "imagePath": null, "matchCount": 1, "evidence": null, "slideHref": "/slides/019dd923-5e88-73ef-bd5c-f341d4394195/45", "deckHref": "/decks/019dd923-5e88-73ef-bd5c-f341d4394195", "deckAnchorHref": "/decks/019dd923-5e88-73ef-bd5c-f341d4394195#slide-45", "loopMatches": [ { "to": 50, "from": 44, "name": "Iceberg", "slug": "10-iceberg", "bestFor": "Consulting, complex problem solving, organizational change", "matchId": "019dd95a-07fe-70ce-8d3c-5ed4c9b352e3", "evidence": "Symptom (black box) -> hidden system (feature importance) -> root issues (adversarial attacks fool models in real world).", "position": 7, "objective": "Surface hidden risks beneath model accuracy", "structure": "The Symptom (Visible) -> The System (Hidden) -> The Root Cause", "confidence": 72, "description": "Reveal that the visible problem is merely a symptom of a deeper root cause" } ], "arcBeatMatches": [ { "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" } ], "imagePathAlt": null, "thumbSrc": null, "thumbSrcAlt": null, "locked": true }, { "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": 46, "slideType": "case_study", "function": "illustrate_case", "notes": "The slide showcases four examples of AI-generated explanations for image-based questions, using heatmaps to highlight visual evidence.", "imagePath": null, "matchCount": 1, "evidence": null, "slideHref": "/slides/019dd923-5e88-73ef-bd5c-f341d4394195/46", "deckHref": "/decks/019dd923-5e88-73ef-bd5c-f341d4394195", "deckAnchorHref": "/decks/019dd923-5e88-73ef-bd5c-f341d4394195#slide-46", "loopMatches": [ { "to": 50, "from": 44, "name": "Iceberg", "slug": "10-iceberg", "bestFor": "Consulting, complex problem solving, organizational change", "matchId": "019dd95a-07fe-70ce-8d3c-5ed4c9b352e3", "evidence": "Symptom (black box) -> hidden system (feature importance) -> root issues (adversarial attacks fool models in real world).", "position": 7, "objective": "Surface hidden risks beneath model accuracy", "structure": "The Symptom (Visible) -> The System (Hidden) -> The Root Cause", "confidence": 72, "description": "Reveal that the visible problem is merely a symptom of a deeper root cause" } ], "arcBeatMatches": [ { "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" } ], "imagePathAlt": null, "thumbSrc": null, "thumbSrcAlt": null, "locked": true }, { "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": 47, "slideType": "diagnosis", "function": "diagnose", "notes": "The chart is a lollipop chart showing feature importance.", "imagePath": null, "matchCount": 1, "evidence": null, "slideHref": "/slides/019dd923-5e88-73ef-bd5c-f341d4394195/47", "deckHref": "/decks/019dd923-5e88-73ef-bd5c-f341d4394195", "deckAnchorHref": "/decks/019dd923-5e88-73ef-bd5c-f341d4394195#slide-47", "loopMatches": [ { "to": 50, "from": 44, "name": "Iceberg", "slug": "10-iceberg", "bestFor": "Consulting, complex problem solving, organizational change", "matchId": "019dd95a-07fe-70ce-8d3c-5ed4c9b352e3", "evidence": "Symptom (black box) -> hidden system (feature importance) -> root issues (adversarial attacks fool models in real world).", "position": 7, "objective": "Surface hidden risks beneath model accuracy", "structure": "The Symptom (Visible) -> The System (Hidden) -> The Root Cause", "confidence": 72, "description": "Reveal that the visible problem is merely a symptom of a deeper root cause" } ], "arcBeatMatches": [ { "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" } ], "imagePathAlt": null, "thumbSrc": null, "thumbSrcAlt": null, "locked": true }, { "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": 48, "slideType": "case_study", "function": "illustrate_case", "notes": "Shows two examples: a panda misclassified as a gibbon, and a macaw misclassified as a bookcase.", "imagePath": null, "matchCount": 1, "evidence": null, "slideHref": "/slides/019dd923-5e88-73ef-bd5c-f341d4394195/48", "deckHref": "/decks/019dd923-5e88-73ef-bd5c-f341d4394195", "deckAnchorHref": "/decks/019dd923-5e88-73ef-bd5c-f341d4394195#slide-48", "loopMatches": [ { "to": 50, "from": 44, "name": "Iceberg", "slug": "10-iceberg", "bestFor": "Consulting, complex problem solving, organizational change", "matchId": "019dd95a-07fe-70ce-8d3c-5ed4c9b352e3", "evidence": "Symptom (black box) -> hidden system (feature importance) -> root issues (adversarial attacks fool models in real world).", "position": 7, "objective": "Surface hidden risks beneath model accuracy", "structure": "The Symptom (Visible) -> The System (Hidden) -> The Root Cause", "confidence": 72, "description": "Reveal that the visible problem is merely a symptom of a deeper root cause" } ], "arcBeatMatches": [ { "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" } ], "imagePathAlt": null, "thumbSrc": null, "thumbSrcAlt": null, "locked": true }, { "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": 49, "slideType": "case_study", "function": "illustrate_case", "notes": "The slide demonstrates adversarial machine learning, specifically targeted adversarial patches.", "imagePath": null, "matchCount": 1, "evidence": null, "slideHref": "/slides/019dd923-5e88-73ef-bd5c-f341d4394195/49", "deckHref": "/decks/019dd923-5e88-73ef-bd5c-f341d4394195", "deckAnchorHref": "/decks/019dd923-5e88-73ef-bd5c-f341d4394195#slide-49", "loopMatches": [ { "to": 50, "from": 44, "name": "Iceberg", "slug": "10-iceberg", "bestFor": "Consulting, complex problem solving, organizational change", "matchId": "019dd95a-07fe-70ce-8d3c-5ed4c9b352e3", "evidence": "Symptom (black box) -> hidden system (feature importance) -> root issues (adversarial attacks fool models in real world).", "position": 7, "objective": "Surface hidden risks beneath model accuracy", "structure": "The Symptom (Visible) -> The System (Hidden) -> The Root Cause", "confidence": 72, "description": "Reveal that the visible problem is merely a symptom of a deeper root cause" } ], "arcBeatMatches": [ { "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" } ], "imagePathAlt": null, "thumbSrc": null, "thumbSrcAlt": null, "locked": true }, { "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": 50, "slideType": "case_study", "function": "illustrate_case", "notes": "The slide uses a visual comparison to demonstrate a security vulnerability in AI.", "imagePath": null, "matchCount": 1, "evidence": null, "slideHref": "/slides/019dd923-5e88-73ef-bd5c-f341d4394195/50", "deckHref": "/decks/019dd923-5e88-73ef-bd5c-f341d4394195", "deckAnchorHref": "/decks/019dd923-5e88-73ef-bd5c-f341d4394195#slide-50", "loopMatches": [ { "to": 50, "from": 44, "name": "Iceberg", "slug": "10-iceberg", "bestFor": "Consulting, complex problem solving, organizational change", "matchId": "019dd95a-07fe-70ce-8d3c-5ed4c9b352e3", "evidence": "Symptom (black box) -> hidden system (feature importance) -> root issues (adversarial attacks fool models in real world).", "position": 7, "objective": "Surface hidden risks beneath model accuracy", "structure": "The Symptom (Visible) -> The System (Hidden) -> The Root Cause", "confidence": 72, "description": "Reveal that the visible problem is merely a symptom of a deeper root cause" } ], "arcBeatMatches": [ { "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" } ], "imagePathAlt": null, "thumbSrc": null, "thumbSrcAlt": null, "locked": true }, { "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": 51, "slideType": "industry_trends", "function": "illustrate_case", "notes": "The slide uses a chronological sequence of technical diagrams to show the progression of AI model complexity.", "imagePath": null, "matchCount": 1, "evidence": null, "slideHref": "/slides/019dd923-5e88-73ef-bd5c-f341d4394195/51", "deckHref": "/decks/019dd923-5e88-73ef-bd5c-f341d4394195", "deckAnchorHref": "/decks/019dd923-5e88-73ef-bd5c-f341d4394195#slide-51", "loopMatches": [ { "to": 55, "from": 51, "name": "Pattern Hunter", "slug": "02-pattern-hunter", "bestFor": "Time-pressed audiences, building consensus, when data is strong", "matchId": "019dd95a-07fe-70ce-8d3c-619c71c15583", "evidence": "Architecture iteration -> AutoML automating engineers -> federated learning -> consensus mechanism.", "position": 8, "objective": "Catalogue frontier research trends (AutoML, federated)", "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" } ], "arcBeatMatches": [ { "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" } ], "imagePathAlt": null, "thumbSrc": null, "thumbSrcAlt": null, "locked": true }, { "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": 52, "slideType": "industry_trends", "function": "analyze_data", "notes": "The slide shows a bar chart of model accuracy over time (2013-2016) and a bubble chart showing accuracy vs. operations (complexity).", "imagePath": null, "matchCount": 1, "evidence": null, "slideHref": "/slides/019dd923-5e88-73ef-bd5c-f341d4394195/52", "deckHref": "/decks/019dd923-5e88-73ef-bd5c-f341d4394195", "deckAnchorHref": "/decks/019dd923-5e88-73ef-bd5c-f341d4394195#slide-52", "loopMatches": [ { "to": 55, "from": 51, "name": "Pattern Hunter", "slug": "02-pattern-hunter", "bestFor": "Time-pressed audiences, building consensus, when data is strong", "matchId": "019dd95a-07fe-70ce-8d3c-619c71c15583", "evidence": "Architecture iteration -> AutoML automating engineers -> federated learning -> consensus mechanism.", "position": 8, "objective": "Catalogue frontier research trends (AutoML, federated)", "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" } ], "arcBeatMatches": [ { "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" } ], "imagePathAlt": null, "thumbSrc": null, "thumbSrcAlt": null, "locked": true }, { "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": 53, "slideType": "case_study", "function": "illustrate_case", "notes": "The slide shows two neural network cell architectures and a scatter plot comparing accuracy vs. computational cost.", "imagePath": null, "matchCount": 1, "evidence": null, "slideHref": "/slides/019dd923-5e88-73ef-bd5c-f341d4394195/53", "deckHref": "/decks/019dd923-5e88-73ef-bd5c-f341d4394195", "deckAnchorHref": "/decks/019dd923-5e88-73ef-bd5c-f341d4394195#slide-53", "loopMatches": [ { "to": 55, "from": 51, "name": "Pattern Hunter", "slug": "02-pattern-hunter", "bestFor": "Time-pressed audiences, building consensus, when data is strong", "matchId": "019dd95a-07fe-70ce-8d3c-619c71c15583", "evidence": "Architecture iteration -> AutoML automating engineers -> federated learning -> consensus mechanism.", "position": 8, "objective": "Catalogue frontier research trends (AutoML, federated)", "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" } ], "arcBeatMatches": [ { "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" } ], "imagePathAlt": null, "thumbSrc": null, "thumbSrcAlt": null, "locked": true }, { "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": 54, "slideType": "case_study", "function": "illustrate_case", "notes": "The slide explains the technical mechanism of federated learning using OpenMined's framework.", "imagePath": null, "matchCount": 1, "evidence": null, "slideHref": "/slides/019dd923-5e88-73ef-bd5c-f341d4394195/54", "deckHref": "/decks/019dd923-5e88-73ef-bd5c-f341d4394195", "deckAnchorHref": "/decks/019dd923-5e88-73ef-bd5c-f341d4394195#slide-54", "loopMatches": [ { "to": 55, "from": 51, "name": "Pattern Hunter", "slug": "02-pattern-hunter", "bestFor": "Time-pressed audiences, building consensus, when data is strong", "matchId": "019dd95a-07fe-70ce-8d3c-619c71c15583", "evidence": "Architecture iteration -> AutoML automating engineers -> federated learning -> consensus mechanism.", "position": 8, "objective": "Catalogue frontier research trends (AutoML, federated)", "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" } ], "arcBeatMatches": [ { "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" } ], "imagePathAlt": null, "thumbSrc": null, "thumbSrcAlt": null, "locked": true }, { "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": 55, "slideType": "industry_trends", "function": "present_framework", "notes": "The slide uses a process flow diagram to explain a technical concept.", "imagePath": null, "matchCount": 1, "evidence": null, "slideHref": "/slides/019dd923-5e88-73ef-bd5c-f341d4394195/55", "deckHref": "/decks/019dd923-5e88-73ef-bd5c-f341d4394195", "deckAnchorHref": "/decks/019dd923-5e88-73ef-bd5c-f341d4394195#slide-55", "loopMatches": [ { "to": 55, "from": 51, "name": "Pattern Hunter", "slug": "02-pattern-hunter", "bestFor": "Time-pressed audiences, building consensus, when data is strong", "matchId": "019dd95a-07fe-70ce-8d3c-619c71c15583", "evidence": "Architecture iteration -> AutoML automating engineers -> federated learning -> consensus mechanism.", "position": 8, "objective": "Catalogue frontier research trends (AutoML, federated)", "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" } ], "arcBeatMatches": [ { "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" } ], "imagePathAlt": null, "thumbSrc": null, "thumbSrcAlt": null, "locked": true }, { "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": 56, "slideType": "section_divider", "function": "transition", "notes": null, "imagePath": null, "matchCount": 1, "evidence": null, "slideHref": "/slides/019dd923-5e88-73ef-bd5c-f341d4394195/56", "deckHref": "/decks/019dd923-5e88-73ef-bd5c-f341d4394195", "deckAnchorHref": "/decks/019dd923-5e88-73ef-bd5c-f341d4394195#slide-56", "loopMatches": [], "arcBeatMatches": [ { "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": 108, "from": 56, "beatId": "019dd95a-0682-776c-8e34-c00b5f60e426", "arcName": "The Onion", "arcSlug": "onion", "beatName": "Deeper Layer", "beatSlug": "onion-deeper-layer", "evidence": "Talent and industry deployment across verticals.", "position": 3, "confidence": 55, "parentBeatName": "Development", "parentBeatSlug": "development" } ], "imagePathAlt": null, "thumbSrc": null, "thumbSrcAlt": null, "locked": true }, { "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": 57, "slideType": "geographic_map", "function": "size_opportunity", "notes": "The slide uses a choropleth map to represent talent density, supplemented by callouts for specific regional insights.", "imagePath": null, "matchCount": 1, "evidence": null, "slideHref": "/slides/019dd923-5e88-73ef-bd5c-f341d4394195/57", "deckHref": "/decks/019dd923-5e88-73ef-bd5c-f341d4394195", "deckAnchorHref": "/decks/019dd923-5e88-73ef-bd5c-f341d4394195#slide-57", "loopMatches": [ { "to": 64, "from": 57, "name": "Pattern Hunter", "slug": "02-pattern-hunter", "bestFor": "Time-pressed audiences, building consensus, when data is strong", "matchId": "019dd95a-07fe-70ce-8d3c-6709b420daf9", "evidence": "Element AI counts (22k, 5k) -> talent flows -> Google headcount -> NIPS/ICML authorship dominance.", "position": 9, "objective": "Prove AI talent is scarce and concentrated at Google", "structure": "Evidence A -> Evidence B -> Evidence C -> Pattern/Conclusion", "confidence": 82, "description": "Group multiple pieces of evidence that together point to a pattern or conclusion" } ], "arcBeatMatches": [ { "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": 108, "from": 56, "beatId": "019dd95a-0682-776c-8e34-c00b5f60e426", "arcName": "The Onion", "arcSlug": "onion", "beatName": "Deeper Layer", "beatSlug": "onion-deeper-layer", "evidence": "Talent and industry deployment across verticals.", "position": 3, "confidence": 55, "parentBeatName": "Development", "parentBeatSlug": "development" } ], "imagePathAlt": null, "thumbSrc": null, "thumbSrcAlt": null, "locked": true }, { "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": 58, "slideType": "geographic_map", "function": "size_opportunity", "notes": "The slide uses a color-coded choropleth map to visualize talent density.", "imagePath": null, "matchCount": 1, "evidence": null, "slideHref": "/slides/019dd923-5e88-73ef-bd5c-f341d4394195/58", "deckHref": "/decks/019dd923-5e88-73ef-bd5c-f341d4394195", "deckAnchorHref": "/decks/019dd923-5e88-73ef-bd5c-f341d4394195#slide-58", "loopMatches": [ { "to": 64, "from": 57, "name": "Pattern Hunter", "slug": "02-pattern-hunter", "bestFor": "Time-pressed audiences, building consensus, when data is strong", "matchId": "019dd95a-07fe-70ce-8d3c-6709b420daf9", "evidence": "Element AI counts (22k, 5k) -> talent flows -> Google headcount -> NIPS/ICML authorship dominance.", "position": 9, "objective": "Prove AI talent is scarce and concentrated at Google", "structure": "Evidence A -> Evidence B -> Evidence C -> Pattern/Conclusion", "confidence": 82, "description": "Group multiple pieces of evidence that together point to a pattern or conclusion" } ], "arcBeatMatches": [ { "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": 108, "from": 56, "beatId": "019dd95a-0682-776c-8e34-c00b5f60e426", "arcName": "The Onion", "arcSlug": "onion", "beatName": "Deeper Layer", "beatSlug": "onion-deeper-layer", "evidence": "Talent and industry deployment across verticals.", "position": 3, "confidence": 55, "parentBeatName": "Development", "parentBeatSlug": "development" } ], "imagePathAlt": null, "thumbSrc": null, "thumbSrcAlt": null, "locked": true }, { "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": 59, "slideType": "geographic_map", "function": "illustrate_case", "notes": "The map highlights the US, Canada, UK, and China in red, with flow lines connecting the US to the other three countries.", "imagePath": null, "matchCount": 1, "evidence": null, "slideHref": "/slides/019dd923-5e88-73ef-bd5c-f341d4394195/59", "deckHref": "/decks/019dd923-5e88-73ef-bd5c-f341d4394195", "deckAnchorHref": "/decks/019dd923-5e88-73ef-bd5c-f341d4394195#slide-59", "loopMatches": [ { "to": 64, "from": 57, "name": "Pattern Hunter", "slug": "02-pattern-hunter", "bestFor": "Time-pressed audiences, building consensus, when data is strong", "matchId": "019dd95a-07fe-70ce-8d3c-6709b420daf9", "evidence": "Element AI counts (22k, 5k) -> talent flows -> Google headcount -> NIPS/ICML authorship dominance.", "position": 9, "objective": "Prove AI talent is scarce and concentrated at Google", "structure": "Evidence A -> Evidence B -> Evidence C -> Pattern/Conclusion", "confidence": 82, "description": "Group multiple pieces of evidence that together point to a pattern or conclusion" } ], "arcBeatMatches": [ { "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": 108, "from": 56, "beatId": "019dd95a-0682-776c-8e34-c00b5f60e426", "arcName": "The Onion", "arcSlug": "onion", "beatName": "Deeper Layer", "beatSlug": "onion-deeper-layer", "evidence": "Talent and industry deployment across verticals.", "position": 3, "confidence": 55, "parentBeatName": "Development", "parentBeatSlug": "development" } ], "imagePathAlt": null, "thumbSrc": null, "thumbSrcAlt": null, "locked": true }, { "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": 60, "slideType": "competitive_analysis", "function": "compare_peers", "notes": "The chart uses house-shaped bars to represent headcount. Data points: Facebook (300), Tencent (400), Baidu (450), Google (1400), IBM (900), Microsoft (1000).", "imagePath": null, "matchCount": 1, "evidence": null, "slideHref": "/slides/019dd923-5e88-73ef-bd5c-f341d4394195/60", "deckHref": "/decks/019dd923-5e88-73ef-bd5c-f341d4394195", "deckAnchorHref": "/decks/019dd923-5e88-73ef-bd5c-f341d4394195#slide-60", "loopMatches": [ { "to": 64, "from": 57, "name": "Pattern Hunter", "slug": "02-pattern-hunter", "bestFor": "Time-pressed audiences, building consensus, when data is strong", "matchId": "019dd95a-07fe-70ce-8d3c-6709b420daf9", "evidence": "Element AI counts (22k, 5k) -> talent flows -> Google headcount -> NIPS/ICML authorship dominance.", "position": 9, "objective": "Prove AI talent is scarce and concentrated at Google", "structure": "Evidence A -> Evidence B -> Evidence C -> Pattern/Conclusion", "confidence": 82, "description": "Group multiple pieces of evidence that together point to a pattern or conclusion" } ], "arcBeatMatches": [ { "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": 108, "from": 56, "beatId": "019dd95a-0682-776c-8e34-c00b5f60e426", "arcName": "The Onion", "arcSlug": "onion", "beatName": "Deeper Layer", "beatSlug": "onion-deeper-layer", "evidence": "Talent and industry deployment across verticals.", "position": 3, "confidence": 55, "parentBeatName": "Development", "parentBeatSlug": "development" } ], "imagePathAlt": null, "thumbSrc": null, "thumbSrcAlt": null, "locked": true }, { "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": 61, "slideType": "data_table", "function": "analyze_data", "notes": "The data is presented as a simple list of counts and institution names.", "imagePath": null, "matchCount": 1, "evidence": null, "slideHref": "/slides/019dd923-5e88-73ef-bd5c-f341d4394195/61", "deckHref": "/decks/019dd923-5e88-73ef-bd5c-f341d4394195", "deckAnchorHref": "/decks/019dd923-5e88-73ef-bd5c-f341d4394195#slide-61", "loopMatches": [ { "to": 64, "from": 57, "name": "Pattern Hunter", "slug": "02-pattern-hunter", "bestFor": "Time-pressed audiences, building consensus, when data is strong", "matchId": "019dd95a-07fe-70ce-8d3c-6709b420daf9", "evidence": "Element AI counts (22k, 5k) -> talent flows -> Google headcount -> NIPS/ICML authorship dominance.", "position": 9, "objective": "Prove AI talent is scarce and concentrated at Google", "structure": "Evidence A -> Evidence B -> Evidence C -> Pattern/Conclusion", "confidence": 82, "description": "Group multiple pieces of evidence that together point to a pattern or conclusion" } ], "arcBeatMatches": [ { "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": 108, "from": 56, "beatId": "019dd95a-0682-776c-8e34-c00b5f60e426", "arcName": "The Onion", "arcSlug": "onion", "beatName": "Deeper Layer", "beatSlug": "onion-deeper-layer", "evidence": "Talent and industry deployment across verticals.", "position": 3, "confidence": 55, "parentBeatName": "Development", "parentBeatSlug": "development" } ], "imagePathAlt": null, "thumbSrc": null, "thumbSrcAlt": null, "locked": true }, { "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": 62, "slideType": "industry_trends", "function": "quantify_impact", "notes": "The chart illustrates the increasing concentration of AI research talent at Google/DeepMind.", "imagePath": null, "matchCount": 1, "evidence": null, "slideHref": "/slides/019dd923-5e88-73ef-bd5c-f341d4394195/62", "deckHref": "/decks/019dd923-5e88-73ef-bd5c-f341d4394195", "deckAnchorHref": "/decks/019dd923-5e88-73ef-bd5c-f341d4394195#slide-62", "loopMatches": [ { "to": 64, "from": 57, "name": "Pattern Hunter", "slug": "02-pattern-hunter", "bestFor": "Time-pressed audiences, building consensus, when data is strong", "matchId": "019dd95a-07fe-70ce-8d3c-6709b420daf9", "evidence": "Element AI counts (22k, 5k) -> talent flows -> Google headcount -> NIPS/ICML authorship dominance.", "position": 9, "objective": "Prove AI talent is scarce and concentrated at Google", "structure": "Evidence A -> Evidence B -> Evidence C -> Pattern/Conclusion", "confidence": 82, "description": "Group multiple pieces of evidence that together point to a pattern or conclusion" } ], "arcBeatMatches": [ { "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": 108, "from": 56, "beatId": "019dd95a-0682-776c-8e34-c00b5f60e426", "arcName": "The Onion", "arcSlug": "onion", "beatName": "Deeper Layer", "beatSlug": "onion-deeper-layer", "evidence": "Talent and industry deployment across verticals.", "position": 3, "confidence": 55, "parentBeatName": "Development", "parentBeatSlug": "development" } ], "imagePathAlt": null, "thumbSrc": null, "thumbSrcAlt": null, "locked": true }, { "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": 63, "slideType": "data_table", "function": "analyze_data", "notes": null, "imagePath": null, "matchCount": 1, "evidence": null, "slideHref": "/slides/019dd923-5e88-73ef-bd5c-f341d4394195/63", "deckHref": "/decks/019dd923-5e88-73ef-bd5c-f341d4394195", "deckAnchorHref": "/decks/019dd923-5e88-73ef-bd5c-f341d4394195#slide-63", "loopMatches": [ { "to": 64, "from": 57, "name": "Pattern Hunter", "slug": "02-pattern-hunter", "bestFor": "Time-pressed audiences, building consensus, when data is strong", "matchId": "019dd95a-07fe-70ce-8d3c-6709b420daf9", "evidence": "Element AI counts (22k, 5k) -> talent flows -> Google headcount -> NIPS/ICML authorship dominance.", "position": 9, "objective": "Prove AI talent is scarce and concentrated at Google", "structure": "Evidence A -> Evidence B -> Evidence C -> Pattern/Conclusion", "confidence": 82, "description": "Group multiple pieces of evidence that together point to a pattern or conclusion" } ], "arcBeatMatches": [ { "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": 108, "from": 56, "beatId": "019dd95a-0682-776c-8e34-c00b5f60e426", "arcName": "The Onion", "arcSlug": "onion", "beatName": "Deeper Layer", "beatSlug": "onion-deeper-layer", "evidence": "Talent and industry deployment across verticals.", "position": 3, "confidence": 55, "parentBeatName": "Development", "parentBeatSlug": "development" } ], "imagePathAlt": null, "thumbSrc": null, "thumbSrcAlt": null, "locked": true }, { "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": 64, "slideType": "data_table", "function": "analyze_data", "notes": "The slide highlights the concentration of AI research talent in a few top-tier academic institutions and major tech corporations.", "imagePath": null, "matchCount": 1, "evidence": null, "slideHref": "/slides/019dd923-5e88-73ef-bd5c-f341d4394195/64", "deckHref": "/decks/019dd923-5e88-73ef-bd5c-f341d4394195", "deckAnchorHref": "/decks/019dd923-5e88-73ef-bd5c-f341d4394195#slide-64", "loopMatches": [ { "to": 64, "from": 57, "name": "Pattern Hunter", "slug": "02-pattern-hunter", "bestFor": "Time-pressed audiences, building consensus, when data is strong", "matchId": "019dd95a-07fe-70ce-8d3c-6709b420daf9", "evidence": "Element AI counts (22k, 5k) -> talent flows -> Google headcount -> NIPS/ICML authorship dominance.", "position": 9, "objective": "Prove AI talent is scarce and concentrated at Google", "structure": "Evidence A -> Evidence B -> Evidence C -> Pattern/Conclusion", "confidence": 82, "description": "Group multiple pieces of evidence that together point to a pattern or conclusion" } ], "arcBeatMatches": [ { "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": 108, "from": 56, "beatId": "019dd95a-0682-776c-8e34-c00b5f60e426", "arcName": "The Onion", "arcSlug": "onion", "beatName": "Deeper Layer", "beatSlug": "onion-deeper-layer", "evidence": "Talent and industry deployment across verticals.", "position": 3, "confidence": 55, "parentBeatName": "Development", "parentBeatSlug": "development" } ], "imagePathAlt": null, "thumbSrc": null, "thumbSrcAlt": null, "locked": true } ] }