Critical Framework · v0.4

Humor Genome

Building AI That Truly Gets The Joke

Core Premise

Humor has identifiable patterns like music's rhythm and melody. By systematically mapping these patterns, we can build AI systems that truly understand humor.

Our approach: Combine structured taxonomy, measurable dimensions, curated data, and rigorous evaluation.

🚨 The Problem

Current AI systems fail at humor because they lack systematic understanding of comedic mechanisms, cultural context, and audience dynamics—leading to awkward, inappropriate, or simply unfunny responses.

🎯 Success Looks Like

  • AI assistants that can engage in natural comedic banter
  • Content platforms that automatically adapt humor to audience preferences
  • Creative tools that help comedians test and refine material
Foundation
🧬

Taxonomy

  • Linguistic: Puns, wordplay, ambiguity/double-entendre, rhyme/alliteration, malapropism
  • Conceptual: Absurdity, misdirection/surprise, paradox/self-reference, analogy, superiority/roast
  • Contextual: Satire, parody, cultural/reference & stereotypes, Dark/Gallows, era sensitivity
  • Performance: Delivery/cadence/timing, impressions/“funny voice”, act-outs, physical/slapstick, props
  • Format / Schema: Knock-knock, “X walks into a bar…”, lightbulb jokes, set-templates, memes/remixes
  • Cognitive / Logical: Puzzle-like jokes, cognitive-load payoff, deadpan/understatement
Example
"I told my wife she was drawing her eyebrows too high. She looked surprised." → Linguistic (wordplay) + Conceptual (surprise)
Data
📚

Corpus

  • Short-text: Jokes, Tweets, Captions
  • Long-text: Stand-up Transcripts, Scripts, Literature
  • Multi-modal: Memes, Images, Video, Audio
  • Dialogue: Conversational exchanges, Improv, Chat logs
  • Annotation: Type + Dimension scores (1–5), Marking delivery features (cadence, emphasis, “funny voice”), Labels for humor blur/overlap (e.g., sarcasm vs irony)
  • Context: Culture, Age, Medium, Era, Historical shifts (e.g., “gay” = happy → sexuality), Dark vs light humor segmentation (coping vs defiance), Taste analogy: audience “palates” differ like food preferences
Meme Example
Distracted Boyfriend → Format + Cultural Reference, High Relatability
Measurement
📊

Dimensions

  • Incongruity: How unexpected the punchline
  • Relatability: Audience connection level
  • Cleverness: Intellectual sophistication required
  • Benign Violation: Safe boundary‑pushing
  • Taboo Intensity: Controversial content level
  • Timing: Effect of pacing or pause on humor
  • Surprise Factor: Degree of unexpectedness in punchline
  • Emotional Resonance: Level of vulnerability or shared feeling
  • Cultural Specificity: Dependence on shared cultural knowledge
Dad Joke Profile
High Relatability, Low Complexity, Minimal Taboo
Validation
🎯

Benchmark

  • Binary Humor Detection: Funny vs. Not funny (baseline)
  • Classification: Identify humor types (F1 score)
  • Generation: Create targeted humor (human ratings)
  • Prediction: Forecast audience reactions (accuracy)
  • Portability: Cross‑cultural transfer testing
  • Multi-Objective Optimization: Balance humor quality, appropriateness, and audience engagement using reinforcement learning methods
🎭 Comedians 🎬 Creators 🤖 AI Systems 🔬 Research
⚡️ Taxonomy + Corpus + Dimensions + Benchmark = Humor Genome

🚀 Development Phases

Phase 1: Core Taxonomy + Basic Corpus
Phase 2: Dimension Framework + Annotation
Phase 3: Benchmark Suite + Model Training
Phase 4: Real-world Applications