{ "docId": "019dd923-5e88-73ef-bd5d-06b04d219fea", "docSlug": "dd91c78f6570bf29", "documentTitle": "2023 Air Street Capital The State of AI Report 2023", "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, "pageCount": 163, "prevPage": 5, "nextPage": 7, "slideType": "other", "function": "establish_context", "density": "overcrowded", "nDataPoints": 0, "notes": "This slide serves as a foundational reference for the rest of the deck.", "elementsJson": [ "paragraph" ], "metadataConfidence": 1, "imagePath": null, "slideHref": "/slides/019dd923-5e88-73ef-bd5d-06b04d219fea/6", "deckHref": "/decks/019dd923-5e88-73ef-bd5d-06b04d219fea", "deckJsonHref": "/decks/019dd923-5e88-73ef-bd5d-06b04d219fea.json", "deckAnchorHref": "/decks/019dd923-5e88-73ef-bd5d-06b04d219fea#slide-6", "components": [ { "bbox": { "h": 0.08, "w": 0.96, "x": 0.02, "y": 0.77 }, "kind": "list", "text": "Transformer: a model architecture at the core of most state of the art (SOTA) ML research. It is composed of multiple “attention” layers which learn which parts of the input data are the most important for a given task. Transformers started in NLP (specifically machine translation) and subsequently were expanded into computer vision, audio, and other modalities.", "attrs": null, "subkind": "bullet", "toolName": null, "toolSlug": null, "confidence": null, "componentId": "18bfa51c-f2e7-412a-a060-e741b0b344e9", "frameworkName": null, "frameworkSlug": null }, { "bbox": { "h": 0.08, "w": 0.96, "x": 0.02, "y": 0.67 }, "kind": "list", "text": "Self-supervised learning (SSL): a form of unsupervised learning, where manually labeled data is not needed. Raw data is instead modified in an automated way to create artificial labels to learn from. An example of SSL is learning to complete text by masking random words in a sentence and trying to predict the missing ones.", "attrs": null, "subkind": "bullet", "toolName": null, "toolSlug": null, "confidence": null, "componentId": "266f0a36-cfa0-40a9-8ebd-70d2869d3071", "frameworkName": null, "frameworkSlug": null }, { "bbox": { "h": 0.08, "w": 0.96, "x": 0.02, "y": 0.21 }, "kind": "list", "text": "(Large) Language model (LM, LLM): a model trained on vast amounts of (often) textual data to predict the next word in a self-supervised manner. The term “LLM” is used to designate multi-billion parameter LMs, but this is a moving definition.", "attrs": null, "subkind": "bullet", "toolName": null, "toolSlug": null, "confidence": null, "componentId": "63f3856d-e1ad-48dc-909c-2f5e51b24a54", "frameworkName": null, "frameworkSlug": null }, { "bbox": { "h": 0.03, "w": 0.96, "x": 0.02, "y": 0.52 }, "kind": "list", "text": "Prompt: a user input often written in natural language that is used to instruct an LLM to generate something or take action.", "attrs": null, "subkind": "bullet", "toolName": null, "toolSlug": null, "confidence": null, "componentId": "6a31544f-d508-4013-b470-e39b40ebcc11", "frameworkName": null, "frameworkSlug": null }, { "bbox": { "h": 0.08, "w": 0.96, "x": 0.02, "y": 0.57 }, "kind": "list", "text": "Reinforcement learning (RL): an area of ML in which software agents learn goal-oriented behavior by trial and error in an environment that provides rewards or penalties in response to their actions (called a \"policy\") towards achieving that goal.", "attrs": null, "subkind": "bullet", "toolName": null, "toolSlug": null, "confidence": null, "componentId": "6d551293-e0dd-4af9-8d6d-4eb9373c035d", "frameworkName": null, "frameworkSlug": null }, { "bbox": { "h": 0.11, "w": 0.96, "x": 0.02, "y": 0.3 }, "kind": "list", "text": "Machine learning (ML): a subset of AI that often uses statistical techniques to give machines the ability to \"learn\" from data without being explicitly given the instructions for how to do so. This process is known as \"training\" a \"model\" using a learning \"algorithm\" that progressively improves model performance on a specific task.", "attrs": null, "subkind": "bullet", "toolName": null, "toolSlug": null, "confidence": null, "componentId": "883484ce-eb65-42d1-9d91-ce987f4d42fb", "frameworkName": null, "frameworkSlug": null }, { "bbox": { "h": 0.03, "w": 0.96, "x": 0.02, "y": 0.42 }, "kind": "list", "text": "Model: a ML algorithm trained on data and used to make predictions.", "attrs": null, "subkind": "bullet", "toolName": null, "toolSlug": null, "confidence": null, "componentId": "cd850bd4-68b4-4db1-a635-e4c9c2fa4511", "frameworkName": null, "frameworkSlug": null }, { "bbox": { "h": 0.03, "w": 0.96, "x": 0.02, "y": 0.47 }, "kind": "list", "text": "Natural language processing (NLP): the ability of a program to understand human language as it is spoken and written.", "attrs": null, "subkind": "bullet", "toolName": null, "toolSlug": null, "confidence": null, "componentId": "e0e1590e-3903-4da7-896f-de2070937553", "frameworkName": null, "frameworkSlug": null }, { "bbox": { "h": 0.05, "w": 0.15, "x": 0.02, "y": 0.14 }, "kind": "title", "text": "Definitions", "attrs": null, "subkind": "headline", "toolName": null, "toolSlug": null, "confidence": null, "componentId": "fd85f0f5-848b-41c9-bb93-8d940d6ef4c8", "frameworkName": null, "frameworkSlug": null } ], "metrics": [], "tools": [], "frameworks": [], "arcBeats": [ { "to": 10, "from": 4, "beatId": "019dd95a-0682-776c-8e35-4dec34eefca8", "arcName": "The Mountain", "arcSlug": "mountain", "beatName": "Setup & Stakes", "beatSlug": "mountain-setup-stakes", "evidence": "Definitions, agenda, exec summary set scope and prior-prediction stakes.", "position": 1, "confidence": 45, "parentBeatName": "Setup", "parentBeatSlug": "setup" } ], "loops": [], "imagePathAlt": null, "thumbSrc": null, "thumbSrcAlt": null, "locked": true }