---
id: wiki-2026-0508-artificial-intelligence
title: Artificial Intelligence (AI)
category: 10_Wiki/Topics
status: verified
canonical_id: self
aliases: [AI, 인공지능, ANI, AGI, ASI, machine learning, deep learning, neuro-symbolic]
duplicate_of: none
source_trust_level: A
confidence_score: 0.93
verification_status: applied
tags: [ai, ml, deep-learning, agi, history, paradigm, neuro-symbolic, foundation-model]
raw_sources: []
last_reinforced: 2026-05-10
github_commit: pending
tech_stack:
  language: Python / Various
  framework: PyTorch / TensorFlow / Transformers
---

# Artificial Intelligence (AI)

## 📌 한 줄 통찰
> **"매 thinking 의 X — 매 data 의 compression + 매 prediction"**. 매 hidden pattern 의 statistical inference. 매 narrow ANI (chess, GPT) 의 dominate 가, 매 AGI 의 frontier 의 race. 매 Data + Compute + Algorithm 의 3 element 의 explosion.

## 📖 핵심

### 매 AI 의 종류
| 종류 | Scope | 예 |
|---|---|---|
| ANI (Narrow) | 매 single domain | Chess, GPT, AlphaFold |
| AGI (General) | 매 human-level cross-domain | 매 not yet (debated) |
| ASI (Super) | 매 human 의 surpass | 매 hypothetical |

### 매 paradigm history

#### 1. Symbolic AI (1950s-80s)
- 매 rule + 매 logic.
- 매 expert system (MYCIN, DENDRAL).
- 매 GOFAI (Good Old-Fashioned AI).
- ❄️ AI Winter (knowledge bottleneck).

#### 2. Statistical / ML (1990s-2010s)
- 매 SVM, 매 Bayesian, 매 random forest.
- 매 feature engineering.
- 매 ImageNet 2012 → 매 deep learning.

#### 3. Deep Learning (2012-)
- 매 NN with many layers.
- 매 GPU explosion.
- 매 representation learning.

#### 4. Foundation Model / LLM (2018-)
- 매 BERT (2018), 매 GPT-3 (2020), 매 ChatGPT (2022).
- 매 transfer learning.
- 매 emergent capability.

#### 5. Agentic / Multimodal (2024-)
- 매 tool use.
- 매 reasoning (o1 / R1).
- 매 multimodal (vision, audio).
- 매 robotics fusion.

### 매 핵심 paradigm
- **Supervised**: 매 label.
- **Unsupervised**: 매 structure.
- **Self-supervised**: 매 pretext task (BERT, GPT, MAE).
- **Reinforcement**: 매 reward.
- **Imitation**: 매 expert demo.
- **Multi-task / meta-learning**: 매 few-shot.

### 매 3 element (Sutton's "Bitter Lesson")
- 매 general method + 매 compute > 매 hand-crafted feature.
- **Data**: 매 internet-scale.
- **Compute**: 매 GPU / TPU exponential.
- **Algorithm**: 매 transformer / RL.

→ "Most of the AI research has wasted on human knowledge insertion."

### 매 limitation (current)
1. **Hallucination**: 매 generation 의 fact 의 X.
2. **Reasoning**: 매 multi-step 의 weak (improving with o1).
3. **Generalization**: 매 OOD 의 fail.
4. **Sample efficiency**: 매 human 의 few-shot vs 매 LLM 의 trillion.
5. **Embodiment**: 매 robot 의 transfer 의 challenge.
6. **Energy**: 매 GW-scale.

### 매 neuro-symbolic
- 매 neural (pattern) + 매 symbolic (logic).
- 매 AlphaProof, 매 AlphaGeometry.
- 매 hallucination 의 reduce.
- 매 verifiable.

### 매 societal impact
- **Labor**: 매 automation (cognitive).
- **Creativity**: 매 augmentation.
- **Decision**: 매 personalization + bias.
- **Power**: 매 concentration.
- **Truth**: 매 deepfake.
- **Education**: 매 tutor.
- **Health**: 매 diagnostic / drug.

### 매 milestone (selected)
- 1956: Dartmouth Conference (term "AI" coined).
- 1997: Deep Blue beats Kasparov.
- 2012: AlexNet ImageNet win.
- 2016: AlphaGo beats Lee Sedol.
- 2020: AlphaFold solves protein folding.
- 2022: ChatGPT launch.
- 2024: o1 reasoning, Sora video.

## 💻 패턴 (응용 — 빅 picture)

### Stack overview
```
Application
  ├─ Agent (LangChain, LlamaIndex, AutoGen)
  ├─ Vector DB (Pinecone, Weaviate, Chroma)
  └─ LLM API (OpenAI, Anthropic, Bedrock)
        │
Foundation model
  ├─ Pretraining (compute-heavy)
  ├─ Fine-tuning (LoRA, RLHF)
  └─ Inference (vLLM, TensorRT-LLM)
        │
Hardware
  ├─ NVIDIA H100 / B200
  ├─ Google TPU
  └─ Custom (Cerebras, Groq, AWS Trainium)
```

### Training pipeline (simplified)
```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, Trainer, TrainingArguments

# 1. Load
model = AutoModelForCausalLM.from_pretrained('meta-llama/Llama-3-8B')
tokenizer = AutoTokenizer.from_pretrained('meta-llama/Llama-3-8B')

# 2. Fine-tune (LoRA)
from peft import LoraConfig, get_peft_model
lora = LoraConfig(r=16, lora_alpha=32, target_modules=['q_proj', 'v_proj'])
model = get_peft_model(model, lora)

# 3. Train
args = TrainingArguments(
    output_dir='./out', num_train_epochs=3,
    per_device_train_batch_size=4, learning_rate=2e-4,
    bf16=True, gradient_accumulation_steps=4,
)
trainer = Trainer(model=model, args=args, train_dataset=dataset)
trainer.train()
```

### Inference (production)
```python
# vLLM (continuous batching)
from vllm import LLM, SamplingParams

llm = LLM(model='meta-llama/Llama-3-8B', tensor_parallel_size=2)
outputs = llm.generate(prompts, SamplingParams(temperature=0.7, max_tokens=512))
```

### RAG (real-world)
```python
from langchain.vectorstores import Chroma
from langchain.embeddings import OpenAIEmbeddings

vectordb = Chroma.from_documents(docs, OpenAIEmbeddings())

def answer(question):
    relevant = vectordb.similarity_search(question, k=5)
    context = '\n\n'.join(d.page_content for d in relevant)
    return llm.generate(f"Context:\n{context}\n\nQuestion: {question}")
```

### Agent (tool use)
```python
from langchain.agents import create_react_agent
from langchain_community.tools import DuckDuckGoSearchRun, PythonREPLTool

tools = [DuckDuckGoSearchRun(), PythonREPLTool()]
agent = create_react_agent(llm, tools, prompt=react_prompt)
result = agent.invoke({'input': 'What is 2026 + 1, and search what happened then?'})
```

## 🤔 결정 기준
| 문제 | Tool |
|---|---|
| Classification | scikit-learn / PyTorch |
| NLP understanding | BERT / RoBERTa |
| NLP generation | GPT / Claude / Llama |
| Vision | ViT / YOLO / CLIP |
| Speech | Whisper / Wav2Vec |
| RL | PPO / SAC / DreamerV3 |
| Robotics | RL + sim2real |
| Math / proof | Lean + LLM |
| Drug | AlphaFold |
| RAG | LangChain + vectordb |
| Agent | LangGraph / OpenAI Agents SDK |

**기본값**: LLM (general) + RAG (knowledge) + agent (tool). 매 specific = 매 specialized model.

## 🔗 Graph
- 부모: [[Statistics]]
- 변형: [[Machine-Learning]] · [[Deep Learning]] · [[Reinforcement-Learning]] · [[NLP]] · [[Computer Vision|Computer-Vision]]
- 응용: [[Transformer_Architecture_and_LLM_Foundations|LLM]] · [[Agent]] · [[RAG]] · [[Foundation-Model]]
- 비판: [[AI Safety]] · [[AI-Ethics]] · [[AI_Safety_and_Alignment|AI-Alignment]] · [[Hallucination]]
- Adjacent: [[Neural-Symbolic-Integration|Neuro-Symbolic-AI]] · [[AGI]] · [[Scaling-Laws]]

## 🤖 LLM 활용
**언제**: 매 AI strategy. 매 paradigm choice. 매 history overview. 매 stack design.
**언제 X**: 매 specific implementation detail (sub-page reference).

## ❌ 안티패턴
- **AI 의 magic 의 expectation**: 매 limitation 의 ignore.
- **Hand-craft feature 의 over-invest**: 매 Bitter Lesson.
- **No baseline**: 매 fancy model 의 simple 대비 X.
- **Hallucination 의 trust**: 매 fact verify 의 X.
- **Compute 의 cost 의 underestimate**: 매 budget overrun.
- **Single model 의 monoculture**: 매 vendor lock-in / robustness.

## 🧪 검증 / 중복
- Verified (Russell-Norvig, Goodfellow DL, Sutton RL, OpenAI / DeepMind / Anthropic papers).
- 신뢰도 A.
- Related: [[Machine-Learning]] · [[Transformer_Architecture_and_LLM_Foundations|LLM]] · [[Deep Learning]] · [[AGI]] · [[Bitter-Lesson]].

## 🕓 Changelog
| 날짜 | 변경 |
|---|---|
| 2026-05-08 | Phase 1 |
| 2026-05-10 | Manual cleanup — paradigm history + 3 element + stack + 매 training / inference / RAG / agent code |