2nd/10_Wiki/Topic_Programming/AI_and_ML/What-is-AI.md

id, title, category, status, canonical_id, aliases, duplicate_of, source_trust_level, confidence_score, verification_status, tags, raw_sources, last_reinforced, github_commit, tech_stack

id	title	category	status	canonical_id	aliases	duplicate_of	source_trust_level	confidence_score	verification_status	tags	raw_sources	last_reinforced	github_commit	tech_stack
wiki-2026-0508-what-is-ai	What is AI	10_Wiki/Topics	verified	self	AI Definition Artificial Intelligence Overview AI 101	none	A	0.95	applied	ai foundations taxonomy overview agi		2026-05-10	pending	language framework Python PyTorch/transformers

What is AI

매 한 줄

"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.

매 핵심

매 정의 spectrum

• Narrow AI (ANI): 매 specific task — chess, image classify, speech recog, code complete. 매 모든 deployed AI.
• 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.
• Superintelligence (ASI): 매 모든 domain 에서 human 초과. Hypothetical.

매 paradigm history

1. Symbolic / GOFAI (1950-1980s): rule-based, expert systems. 매 brittle.
2. Statistical ML (1990-2010s): SVM, Random Forest, HMM. 매 feature engineering 매 무거움.
3. Deep Learning (2012-): CNN (ImageNet), RNN, Transformer (2017). 매 representation learning.
4. Foundation Models (2020-): GPT-3, BERT — 매 pretrain massive, transfer.
5. Agentic AI (2024-): tool-use, multi-step reasoning, autonomous task execution.

매 capability axes (2026)

• Language: GPT-5, Claude Opus 4.7, Gemini 3 — 매 PhD-level on most academic benchmark.
• Vision: GPT-5 Vision, Claude 4 Vision, native multimodal.
• Image gen: FLUX, Imagen 4, GPT-Image-1, Midjourney 7, Stable Diffusion 4.
• Video gen: Sora 2, Veo 3, Runway Gen-4 — 매 60s+ coherent shots.
• Audio: Suno V5, ElevenLabs 3, OpenAI Voice — 매 indistinguishable from human.
• Robotics: Figure 03, Optimus Gen 3, Unitree H2 — 매 commercial pilot deployment.
• Code: Claude Code, Cursor Agent, Devin 2 — 매 autonomous PR submission.

매 sub-fields

• ML: supervised, unsupervised, reinforcement, self-supervised.
• NLP, CV, Speech, Robotics, KR&R, Planning, Multi-agent, Causal AI.

매 응용

1. Search / RAG / personalized assistant.
2. Code generation (Copilot → autonomous agent).
3. Image/video/music creation.
4. Drug discovery (AlphaFold 3, RFDiffusion).
5. Autonomous driving (Waymo, Tesla FSD).
6. Scientific simulation (weather: GraphCast, fluid: NeuralGCM).

💻 패턴

1. AI 시스템의 layer (2026 modern stack)

┌──────────────────────────────────────┐
│ Application (chat UI, IDE plugin)   │
├──────────────────────────────────────┤
│ Agent layer (tool use, planning)    │  ← Claude Code, LangGraph, CrewAI
├──────────────────────────────────────┤
│ Foundation Model API (LLM, VLM)     │  ← Anthropic, OpenAI, Google
├──────────────────────────────────────┤
│ Inference runtime (vLLM, TGI, MLX)  │
├──────────────────────────────────────┤
│ Hardware (H100, B200, MI355X, TPU)  │
└──────────────────────────────────────┘

2. 단순 Hello-AI (Anthropic SDK, 2026)

from anthropic import Anthropic

client = Anthropic()
resp = client.messages.create(
    model="claude-opus-4-7",
    max_tokens=1024,
    system="You are a concise tutor.",
    messages=[{"role": "user", "content": "Explain backprop in 3 sentences."}],
)
print(resp.content[0].text)

3. Classical ML still works (sklearn baseline)

from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import cross_val_score
import numpy as np

X, y = load_my_data()
clf = RandomForestClassifier(n_estimators=300, max_depth=12, n_jobs=-1)
scores = cross_val_score(clf, X, y, cv=5, scoring="f1_macro")
print(f"F1: {scores.mean():.3f} ± {scores.std():.3f}")

4. Deep learning 의 minimal example

import torch
import torch.nn as nn

class TinyNet(nn.Module):
    def __init__(self):
        super().__init__()
        self.net = nn.Sequential(
            nn.Linear(784, 128), nn.GELU(),
            nn.Linear(128, 10),
        )
    def forward(self, x): return self.net(x)

model = TinyNet()
opt = torch.optim.AdamW(model.parameters(), lr=3e-4)
loss_fn = nn.CrossEntropyLoss()

for x, y in loader:
    logits = model(x)
    loss = loss_fn(logits, y)
    opt.zero_grad(); loss.backward(); opt.step()

5. Agentic loop (tool use)

from anthropic import Anthropic

client = Anthropic()
tools = [{
    "name": "search_web",
    "description": "Search the web for a query.",
    "input_schema": {
        "type": "object",
        "properties": {"q": {"type": "string"}},
        "required": ["q"],
    },
}]

def run_agent(user_msg: str):
    msgs = [{"role": "user", "content": user_msg}]
    while True:
        resp = client.messages.create(
            model="claude-opus-4-7",
            max_tokens=2048,
            tools=tools,
            messages=msgs,
        )
        if resp.stop_reason == "end_turn":
            return resp.content[0].text
        # tool_use
        tool_blocks = [b for b in resp.content if b.type == "tool_use"]
        msgs.append({"role": "assistant", "content": resp.content})
        results = []
        for tb in tool_blocks:
            out = dispatch(tb.name, tb.input)
            results.append({"type": "tool_result", "tool_use_id": tb.id, "content": out})
        msgs.append({"role": "user", "content": results})

6. Reinforcement Learning (PPO sketch)

# PPO core update — keeps policy close to old policy
import torch
def ppo_loss(logp_new, logp_old, adv, clip=0.2):
    ratio = torch.exp(logp_new - logp_old)
    s1 = ratio * adv
    s2 = torch.clamp(ratio, 1 - clip, 1 + clip) * adv
    return -torch.min(s1, s2).mean()

매 결정 기준

상황	Approach
Tabular, < 100k row, structured	XGBoost / LightGBM / CatBoost
Vision (image classify, segment, detect)	Pre-trained CNN/ViT (timm) + fine-tune
Text / NLP / RAG	BGE embedding + LLM (Anthropic / OpenAI / open-weight)
Generation (code, content, creative)	Claude Opus 4.7 / GPT-5
Speech / Audio	Whisper-large-v3 / NeMo / Voxtral
Decision / Control / Game	RL (PPO / SAC / model-based MuZero)
On-device / latency-critical	MLX (Apple) / GGUF (llama.cpp) / quantize

기본값: 매 first try managed LLM API → 매 cost / latency / privacy 매 issue 면 self-host (vLLM + 8B model).

🔗 Graph

• 변형: Machine Learning · Deep Learning · Symbolic AI
• 응용: Transformer_Architecture_and_LLM_Foundations · Computer Vision · Robotics
• Adjacent: AI_Safety_and_Alignment · AI Ethics

🤖 LLM 활용

언제: 매 fuzzy / unstructured input (text, image, voice) 처리, 매 generation, 매 reasoning chain. 매 modern stack 의 default starting point. 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.

❌ 안티패턴

• AI = ML 동일시: 매 ML 매 AI subset, 매 symbolic / search / planning 도 AI.
• 무조건 deep learning: 매 small structured data 매 GBM 가 더 빠르고 정확.
• Hallucination 무시: 매 LLM output 매 fact 가정 — 매 grounding (RAG, tool use, citation) 필수.
• Fine-tune 먼저 reaching: 매 prompting / RAG 로 충분한 경우 매 절대 다수.
• Hype-vs-capability gap 무시: 매 demo 매 cherry-pick — 매 production 에서 매 edge case 매 발견.

🧪 검증 / 중복

• Verified (Russell & Norvig "AIMA" 4th ed., Stanford CS221, OpenAI/Anthropic system cards 2025-2026).
• 신뢰도 A.

🕓 Changelog

날짜	변경
2026-05-08	Phase 1
2026-05-10	Manual cleanup — paradigm history + 2026 stack + agentic patterns