2nd/10_Wiki/Topics/AI_and_ML/Artificial-Intelligence.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-artificial-intelligence	Artificial Intelligence (AI)	10_Wiki/Topics	verified	self	AI 인공지능 ANI AGI ASI machine learning deep learning neuro-symbolic	none	A	0.93	applied	ai ml deep-learning agi history paradigm neuro-symbolic foundation-model		2026-05-10	pending	language framework Python / Various 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)

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)

# 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)

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)

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
• 응용: Transformer_Architecture_and_LLM_Foundations · Agent · RAG · Foundation-Model
• 비판: AI Safety · AI-Ethics · AI_Safety_and_Alignment · Hallucination
• Adjacent: Neural-Symbolic-Integration · 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 · 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