refactor(topics): 멀티 에이전트용 지식 재편 — _Common(공통 기본기) + Domain_* 구조
에이전트 8종(대화형/프로그래머 C·S/디자이너/설계자/기획자/QA/PD/PM)에게 [공통 기본 능력 + 롤별 Specialty] 2층으로 지식을 주입하기 위한 재분류. 문서 내용·포맷은 무수정, 폴더 이동만 (6,372개 문서 수 보존 확인). - Topic_Programming → Domain_Programming (내부 구조 보존) - Topic_Graphic → Domain_Design - Topic_Business → Domain_Product - Topic_General → Domain_General - _Common 신설: Math(구 Topic_Math_Specialty), Reasoning(구 General/From_Thinking & Reasoning), Reasoning_Creativity(구 General/From_창의성), Communication(Poetic_Blog_Writing + From_writing) - 타 도메인의 From_* 폴더는 유지 (출처 표기일 뿐, 이미 도메인에 맞게 분류된 문서) - 빈 폴더 정리 (memory/procedures) - 에이전트→폴더 매핑은 workspace의 .astra/agent-knowledge-map.json (9개 에이전트) Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
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Antigravity Agent
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---
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id: wiki-2026-0508-what-is-ai
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title: What is AI
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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: [AI Definition, Artificial Intelligence Overview, AI 101]
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duplicate_of: none
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source_trust_level: A
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confidence_score: 0.95
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verification_status: applied
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tags: [ai, foundations, taxonomy, overview, 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: Python
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framework: PyTorch/transformers
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---
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# What is AI
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## 매 한 줄
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> **"매 인간의 cognitive task (perception, reasoning, language, decision) 을 매 machine 으로 perform — 매 narrow 에서 broad 까지 spectrum"**. 1956 Dartmouth Workshop 의 term coining 부터 매 symbolic AI winter, statistical ML 부흥, 매 2012 deep learning revolution, 매 2017 Transformer, 매 2022 ChatGPT, 매 2024-2026 multimodal foundation models + agentic systems 까지 evolutionary arc. 2026 현재 매 "AI" 매 거의 deep learning 의 synonym, 매 LLM-based agents 가 cutting edge.
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## 매 핵심
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### 매 정의 spectrum
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- **Narrow AI (ANI)**: 매 specific task — chess, image classify, speech recog, code complete. 매 모든 deployed AI.
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- **Artificial General Intelligence (AGI)**: 매 human-level across-domain. 매 현재 unresolved — 매 GPT-5 / Claude Opus 4.7 매 partially AGI 로 보는 view 도 있음.
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- **Superintelligence (ASI)**: 매 모든 domain 에서 human 초과. Hypothetical.
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### 매 paradigm history
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1. **Symbolic / GOFAI (1950-1980s)**: rule-based, expert systems. 매 brittle.
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2. **Statistical ML (1990-2010s)**: SVM, Random Forest, HMM. 매 feature engineering 매 무거움.
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3. **Deep Learning (2012-)**: CNN (ImageNet), RNN, Transformer (2017). 매 representation learning.
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4. **Foundation Models (2020-)**: GPT-3, BERT — 매 pretrain massive, transfer.
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5. **Agentic AI (2024-)**: tool-use, multi-step reasoning, autonomous task execution.
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### 매 capability axes (2026)
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- **Language**: GPT-5, Claude Opus 4.7, Gemini 3 — 매 PhD-level on most academic benchmark.
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- **Vision**: GPT-5 Vision, Claude 4 Vision, native multimodal.
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- **Image gen**: FLUX, Imagen 4, GPT-Image-1, Midjourney 7, Stable Diffusion 4.
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- **Video gen**: Sora 2, Veo 3, Runway Gen-4 — 매 60s+ coherent shots.
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- **Audio**: Suno V5, ElevenLabs 3, OpenAI Voice — 매 indistinguishable from human.
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- **Robotics**: Figure 03, Optimus Gen 3, Unitree H2 — 매 commercial pilot deployment.
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- **Code**: Claude Code, Cursor Agent, Devin 2 — 매 autonomous PR submission.
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### 매 sub-fields
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- ML: supervised, unsupervised, reinforcement, self-supervised.
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- NLP, CV, Speech, Robotics, KR&R, Planning, Multi-agent, Causal AI.
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### 매 응용
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1. Search / RAG / personalized assistant.
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2. Code generation (Copilot → autonomous agent).
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3. Image/video/music creation.
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4. Drug discovery (AlphaFold 3, RFDiffusion).
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5. Autonomous driving (Waymo, Tesla FSD).
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6. Scientific simulation (weather: GraphCast, fluid: NeuralGCM).
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## 💻 패턴
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### 1. AI 시스템의 layer (2026 modern stack)
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```
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┌──────────────────────────────────────┐
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│ Application (chat UI, IDE plugin) │
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├──────────────────────────────────────┤
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│ Agent layer (tool use, planning) │ ← Claude Code, LangGraph, CrewAI
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├──────────────────────────────────────┤
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│ Foundation Model API (LLM, VLM) │ ← Anthropic, OpenAI, Google
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├──────────────────────────────────────┤
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│ Inference runtime (vLLM, TGI, MLX) │
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├──────────────────────────────────────┤
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│ Hardware (H100, B200, MI355X, TPU) │
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└──────────────────────────────────────┘
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```
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### 2. 단순 Hello-AI (Anthropic SDK, 2026)
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```python
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from anthropic import Anthropic
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client = Anthropic()
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resp = client.messages.create(
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model="claude-opus-4-7",
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max_tokens=1024,
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system="You are a concise tutor.",
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messages=[{"role": "user", "content": "Explain backprop in 3 sentences."}],
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)
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print(resp.content[0].text)
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```
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### 3. Classical ML still works (sklearn baseline)
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```python
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from sklearn.ensemble import RandomForestClassifier
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from sklearn.model_selection import cross_val_score
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import numpy as np
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X, y = load_my_data()
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clf = RandomForestClassifier(n_estimators=300, max_depth=12, n_jobs=-1)
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scores = cross_val_score(clf, X, y, cv=5, scoring="f1_macro")
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print(f"F1: {scores.mean():.3f} ± {scores.std():.3f}")
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```
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### 4. Deep learning 의 minimal example
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```python
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import torch
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import torch.nn as nn
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class TinyNet(nn.Module):
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def __init__(self):
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super().__init__()
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self.net = nn.Sequential(
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nn.Linear(784, 128), nn.GELU(),
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nn.Linear(128, 10),
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)
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def forward(self, x): return self.net(x)
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model = TinyNet()
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opt = torch.optim.AdamW(model.parameters(), lr=3e-4)
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loss_fn = nn.CrossEntropyLoss()
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for x, y in loader:
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logits = model(x)
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loss = loss_fn(logits, y)
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opt.zero_grad(); loss.backward(); opt.step()
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```
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### 5. Agentic loop (tool use)
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```python
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from anthropic import Anthropic
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client = Anthropic()
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tools = [{
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"name": "search_web",
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"description": "Search the web for a query.",
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"input_schema": {
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"type": "object",
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"properties": {"q": {"type": "string"}},
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"required": ["q"],
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},
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}]
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def run_agent(user_msg: str):
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msgs = [{"role": "user", "content": user_msg}]
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while True:
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resp = client.messages.create(
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model="claude-opus-4-7",
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max_tokens=2048,
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tools=tools,
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messages=msgs,
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)
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if resp.stop_reason == "end_turn":
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return resp.content[0].text
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# tool_use
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tool_blocks = [b for b in resp.content if b.type == "tool_use"]
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msgs.append({"role": "assistant", "content": resp.content})
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results = []
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for tb in tool_blocks:
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out = dispatch(tb.name, tb.input)
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results.append({"type": "tool_result", "tool_use_id": tb.id, "content": out})
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msgs.append({"role": "user", "content": results})
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```
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### 6. Reinforcement Learning (PPO sketch)
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```python
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# PPO core update — keeps policy close to old policy
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import torch
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def ppo_loss(logp_new, logp_old, adv, clip=0.2):
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ratio = torch.exp(logp_new - logp_old)
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s1 = ratio * adv
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s2 = torch.clamp(ratio, 1 - clip, 1 + clip) * adv
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return -torch.min(s1, s2).mean()
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```
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## 매 결정 기준
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| 상황 | Approach |
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|---|---|
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| Tabular, < 100k row, structured | XGBoost / LightGBM / CatBoost |
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| Vision (image classify, segment, detect) | Pre-trained CNN/ViT (timm) + fine-tune |
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| Text / NLP / RAG | BGE embedding + LLM (Anthropic / OpenAI / open-weight) |
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| Generation (code, content, creative) | Claude Opus 4.7 / GPT-5 |
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| Speech / Audio | Whisper-large-v3 / NeMo / Voxtral |
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| Decision / Control / Game | RL (PPO / SAC / model-based MuZero) |
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| On-device / latency-critical | MLX (Apple) / GGUF (llama.cpp) / quantize |
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**기본값**: 매 first try managed LLM API → 매 cost / latency / privacy 매 issue 면 self-host (vLLM + 8B model).
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## 🔗 Graph
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- 변형: [[Machine Learning]] · [[Deep Learning]] · [[Symbolic AI]]
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- 응용: [[Transformer_Architecture_and_LLM_Foundations|LLM]] · [[Computer Vision]] · [[Robotics]]
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- Adjacent: [[AI_Safety_and_Alignment|AI Safety]] · [[AI Ethics]]
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## 🤖 LLM 활용
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**언제**: 매 fuzzy / unstructured input (text, image, voice) 처리, 매 generation, 매 reasoning chain. 매 modern stack 의 default starting point.
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**언제 X**: 매 deterministic rule-based system (compiler, regex parse) 매 LLM 사용 매 over-kill / wrong tool. 매 매 explainability requirement strict 한 domain (medical diagnosis legal binding) 매 careful.
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## ❌ 안티패턴
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- **AI = ML 동일시**: 매 ML 매 AI subset, 매 symbolic / search / planning 도 AI.
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- **무조건 deep learning**: 매 small structured data 매 GBM 가 더 빠르고 정확.
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- **Hallucination 무시**: 매 LLM output 매 fact 가정 — 매 grounding (RAG, tool use, citation) 필수.
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- **Fine-tune 먼저 reaching**: 매 prompting / RAG 로 충분한 경우 매 절대 다수.
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- **Hype-vs-capability gap 무시**: 매 demo 매 cherry-pick — 매 production 에서 매 edge case 매 발견.
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## 🧪 검증 / 중복
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- Verified (Russell & Norvig "AIMA" 4th ed., Stanford CS221, OpenAI/Anthropic system cards 2025-2026).
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- 신뢰도 A.
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## 🕓 Changelog
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| 날짜 | 변경 |
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|---|---|
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| 2026-05-08 | Phase 1 |
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| 2026-05-10 | Manual cleanup — paradigm history + 2026 stack + agentic patterns |
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