Antigravity Agent
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149 lines
6.1 KiB
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149 lines
6.1 KiB
Markdown
---
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id: wiki-2026-0508-turing-test
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title: Turing Test
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category: 10_Wiki/Topics
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status: verified
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canonical_id: self
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aliases: [Imitation Game, Turing's Test]
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duplicate_of: none
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source_trust_level: A
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confidence_score: 0.9
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verification_status: applied
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tags: [ai-history, philosophy, evaluation, agi]
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raw_sources: []
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last_reinforced: 2026-05-10
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github_commit: pending
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tech_stack:
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language: na
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framework: ai-philosophy
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---
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# Turing Test
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## 매 한 줄
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> **"매 machine 이 human judge 와 30% 이상의 conversation 에서 human 으로 misclassified 되면 thinking 과 indistinguishable 하다고 판정"**. 매 1950 Alan Turing 의 "Computing Machinery and Intelligence" 의 imitation game. 매 2024-25 GPT-4 / Claude 의 controlled study에서 human-level pass 보고 (Jones & Bergen 2024 UCSD). 매 2026 현재 Turing Test 는 capability 측정 도구로서 obsolete, Chinese Room critique + behavioral benchmark + capability evaluation 으로 대체.
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## 매 핵심
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### 매 original imitation game (Turing 1950)
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- 3 players: man (A), woman (B), interrogator (C).
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- C asks questions in writing, must determine which is which.
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- A 의 task: deceive C. B 의 task: help C.
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- Turing's substitution: replace A with machine. Does C error rate stay same?
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### 매 misconception (common pop interpretation)
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- Pop version: "machine fools human into thinking it's human."
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- Original: comparison of machine deception rate vs man-deceiving-as-woman rate.
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- Turing's prediction: by 2000, machines will pass at ~30% rate after 5min.
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### 매 critiques
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1. **Chinese Room (Searle 1980)**: passing test 은 understanding 의 evidence 아님. symbol manipulation ≠ semantics.
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2. **Imitation ≠ intelligence**: human deception 은 narrow task. 매 mathematical reasoning, embodiment, learning 의 미측정.
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3. **Anthropocentric**: intelligence 의 sole criterion 으로 human-likeness 가정.
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4. **Gameable**: tricks (typos, refuse-to-answer, emotion mimicry) 으로 pass 가능.
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5. **Judge calibration**: naive judge vs expert 의 결과 wildly 다름.
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### 매 modern empirical results
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- **2014 "Eugene Goostman"**: 33% pass at Royal Society. 매 13-yr-old Ukrainian persona 가 expectation lowering 으로 controversial pass.
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- **2023 Jannai et al.** (AI21): GPT-4 fooled humans at 60% rate in 2-min chat.
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- **2024 Jones & Bergen** (UCSD): GPT-4 passed at 54% (vs human 67%, ELIZA 22%). 매 first rigorously controlled pass.
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- **2025 multiple replications**: Claude / GPT-5 의 routine human-level performance.
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### 매 alternatives (post-Turing era)
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1. **Capability benchmarks**: MMLU, HumanEval, GPQA, ARC-AGI, SWE-bench.
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2. **Coffee test** (Wozniak): make coffee in unfamiliar kitchen → embodiment.
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3. **Robot college student** (Goertzel): take college courses, get degree.
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4. **Lovelace Test 2.0** (Riedl): create artifact human cannot, but expert can verify.
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5. **Winograd Schema** (Levesque 2011): commonsense reasoning, originally Turing-resistant.
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### 매 응용
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1. AI history teaching.
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2. Philosophy of mind discussion (consciousness, understanding).
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3. Public communication of AI capability ("does AI think?").
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4. Capability evaluation pre-2020 (now obsolete).
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## 💻 패턴 (eval design lessons)
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### Pattern 1: Modern adversarial Turing protocol
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```text
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1. Recruit N judges (calibrate by demographic, expertise).
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2. Each judge: 5-min interrogation, 50% human / 50% AI random.
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3. Force binary verdict (no "unsure").
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4. Pass criterion: AI verdict = "human" at rate ≥ control human rate − ε.
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5. Pre-register hypotheses, blind judges to study purpose.
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```
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### Pattern 2: Why public Turing demos mislead
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```text
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- Cherry-picked transcripts.
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- Naive judges (not interrogating adversarially).
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- Persona tricks (child, non-native speaker, tired, distracted).
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- Self-selection bias (only impressive runs shown).
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```
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### Pattern 3: Capability-first eval (modern replacement)
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```text
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benchmarks = [
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"MMLU", # broad knowledge
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"HumanEval", # code generation
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"GPQA", # graduate-level science
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"ARC-AGI", # abstract reasoning
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"SWE-bench", # real software engineering
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"HLE", # Humanity's Last Exam (2025)
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]
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# Pass = top-percentile human expert performance per task.
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```
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### Pattern 4: Behavioral safety eval (orthogonal to Turing)
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```text
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- Refusal rate on harmful prompts.
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- Calibration (uncertainty matches accuracy).
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- Sycophancy (agree-with-user metric).
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- Honesty (TruthfulQA, FactScore).
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```
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### Pattern 5: Lovelace 2.0 framework
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```text
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1. Specify class C of artifacts (e.g., novel valid mathematical proof).
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2. AI produces artifact a ∈ C.
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3. Human expert verifies a is valid AND novel.
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4. AI architect cannot explain how a was produced.
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→ Tests creativity, not imitation.
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```
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## 매 결정 기준
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| 목적 | Eval |
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| Historical / philosophical context | Turing Test |
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| Capability measurement | MMLU, GPQA, HumanEval, ARC-AGI |
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| Reasoning / novelty | Lovelace 2.0, ARC-AGI |
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| Embodiment / general intelligence | Coffee test, robot college |
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| Safety / alignment | RealToxicityPrompts, MLCommons AILuminate |
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**기본값**: capability + safety multi-benchmark. Turing Test 는 historical reference only.
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## 🔗 Graph
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- 부모: [[Philosophy of AI]]
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- 변형: [[Imitation Game]]
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## 🤖 LLM 활용
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**언제**: AI history, philosophy of mind 토론, public communication.
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**언제 X**: actual capability measurement (use modern benchmarks).
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## ❌ 안티패턴
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- **"GPT passed Turing → AGI"**: imitation ≠ general intelligence. capability gaps remain.
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- **Naive judge eval**: untrained user 의 verdict 는 systematic bias.
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- **Single-conversation pass**: 5-min snapshot. long-horizon coherence 미측정.
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- **Persona escape hatch**: "I'm a tired teenager" 으로 weakness 정당화.
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- **Conflating with consciousness**: Turing Test 는 behavior. consciousness 의 evidence 아님.
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## 🧪 검증 / 중복
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- Verified (Turing 1950 "Computing Machinery and Intelligence" Mind 59; Searle 1980 "Minds, Brains, and Programs"; Jones & Bergen 2024 arxiv 2405.08007; Riedl 2014 Lovelace 2.0).
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- 신뢰도 A.
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## 🕓 Changelog
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| 날짜 | 변경 |
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| 2026-05-08 | Phase 1 |
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| 2026-05-10 | Manual cleanup — Turing Test history + 2024 Jones-Bergen pass + modern alternatives |
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