2nd/10_Wiki/Topic_Programming/AI_and_ML/Symbolic-AI vs Connectionism.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-symbolic-ai-vs-connectionism	Symbolic AI vs Connectionism	10_Wiki/Topics	verified	self	GOFAI vs Neural Networks Logic vs Learning Symbolic vs Subsymbolic	none	A	0.95	applied	ai-history symbolic-ai connectionism neuro-symbolic philosophy-of-ai		2026-05-10	pending	language framework python pytorch+z3

Symbolic AI vs Connectionism

매 한 줄

"매 symbolic 은 rules 의 manipulate, connectionist 는 weights 의 learn — 매 century-long debate". 매 1956 Dartmouth → 1980s expert system winter → 2012 AlexNet → 2022 ChatGPT 의 connectionist victory. 매 2026 의 답: 매 winner 없음, 매 hybrid (neuro-symbolic) 의 survive.

매 핵심

매 historical timeline

• 1956 Dartmouth: McCarthy, Minsky, Newell, Simon → symbolic dominant.
• 1958 Perceptron: Rosenblatt — connectionist 의 first.
• 1969 Minsky/Papert "Perceptrons": XOR critique — 매 first AI winter.
• 1980s Expert Systems boom + bust: MYCIN, knowledge engineering bottleneck.
• 1986 Backprop (Rumelhart): connectionist revival.
• 2006 Deep Belief Net (Hinton): deep learning awakening.
• 2012 AlexNet: ImageNet 의 connectionist domination 의 시작.
• 2017 Transformer: attention-based 의 begin.
• 2022 ChatGPT: scale 의 power 의 evidence.
• 2024 AlphaProof / AlphaGeometry: hybrid 의 IMO-level.
• 2026 현재: pure-symbolic ≈ niche, hybrid mainstream.

매 symbolic 진영 (GOFAI)

• 표현: discrete tokens, logic, rules, KG.
• 추론: deduction, search, unification.
• 장점: interpretable, compositional, sample-efficient on structured.
• 단점: brittle, perception fail, knowledge-engineering bottleneck.
• 대표: SHRDLU, Cyc, Prolog, expert systems, Z3.

매 connectionist 진영

• 표현: distributed embedding, weight tensor.
• 추론: forward/backward pass, attention, gradient descent.
• 장점: learns from raw data, perception, generalization.
• 단점: black box, hallucination, sample-hungry, OOD fragile.
• 대표: perceptron, CNN, LSTM, Transformer, LLM.

매 fundamental tensions

• Compositionality: symbolic 의 native, connectionist 의 emergent (debated).
• Systematic generalization: Marcus critique 의 핵심.
• Sample efficiency: symbolic ≪ connectionist data hunger.
• Grounding: connectionist 의 native, symbolic 의 needs perception layer.

매 modern synthesis (neuro-symbolic, 2026)

• Pattern A (LLM-as-coder): LLM 가 Python/Lean code 의 generate, symbolic engine 가 execute.
• Pattern B (NN as perception, symbolic as reasoner): AlphaGeometry — NN proposes constructions, DD+AR proves.
• Pattern C (differentiable logic): Scallop, DeepProbLog — gradient through logic.
• Pattern D (RAG with KG): GraphRAG, Microsoft 2024 — embedding + KG triples.

매 응용

1. AlphaProof: LLM (Gemini) + Lean 4 → IMO 2024 silver.
2. AlphaGeometry: NN constructions + symbolic deduction → IMO geometry gold.
3. GraphRAG: KG-augmented retrieval — connectionist embed + symbolic graph traversal.

💻 패턴

1. Pure symbolic (Prolog-style)

parent(tom, bob).
parent(bob, alice).
ancestor(X, Y) :- parent(X, Y).
ancestor(X, Y) :- parent(X, Z), ancestor(Z, Y).

?- ancestor(tom, alice).  % true

2. Pure connectionist (Transformer)

import torch.nn as nn
class Tiny(nn.Module):
    def __init__(self):
        super().__init__()
        self.emb = nn.Embedding(50000, 512)
        self.tr = nn.TransformerEncoderLayer(512, 8)
        self.head = nn.Linear(512, 50000)
    def forward(self, x):
        return self.head(self.tr(self.emb(x)))

3. Hybrid: LLM proposer + Z3 verifier

import anthropic, z3

client = anthropic.Anthropic()

problem = "Find x, y in [0,100] s.t. 3x + 2y = 47, x*y is prime."

resp = client.messages.create(
    model="claude-opus-4-7",
    max_tokens=500,
    messages=[{"role": "user", "content":
        f"Output Z3 Python code (no prose) for: {problem}"}]
).content[0].text

# Execute LLM-generated symbolic code
exec_globals = {"z3": z3}
exec(resp, exec_globals)  # symbolic solver gives ground truth

4. AlphaGeometry-style construct + verify

def alphageometry_step(problem, llm, dd_engine):
    while not dd_engine.solved(problem):
        construction = llm.suggest_aux_construction(problem.state)
        problem.add(construction)
        dd_engine.deduce(problem)  # symbolic forward chain
    return problem.proof

5. GraphRAG (hybrid retrieval)

def graph_rag(query, kg, vector_store):
    # connectionist: semantic match
    docs = vector_store.search(query, k=20)
    # symbolic: extract entities + walk KG
    entities = extract_entities(query)  # NER (NN) → symbol
    subgraph = kg.k_hop_neighbors(entities, k=2)
    # combine
    return llm.answer(query, context=docs + subgraph.to_text())

6. Differentiable logic (Scallop sketch)

import scallopy
ctx = scallopy.ScallopContext()
ctx.add_relation("edge", (int, int))
ctx.add_rule("path(x, y) :- edge(x, y)")
ctx.add_rule("path(x, y) :- edge(x, z), path(z, y)")

# NN outputs probabilistic edges; loss flows back through reasoning
ctx.add_facts("edge", [(0, 1, 0.9), (1, 2, 0.7)])
ctx.run()

매 결정 기준

상황	Approach
Perception 의 dominant (vision, audio)	Connectionist
Logical guarantees 의 필요	Symbolic verify layer
Mixed (proof, planning)	Neuro-symbolic hybrid
Tabular small	Tree (gradient boosting)
Knowledge-rich QA	Connectionist + KG RAG
Code/math	LLM proposer + interpreter/Lean/Z3 verifier

기본값: 매 LLM (connectionist) + verifier (symbolic) hybrid 의 pragmatic default.

🔗 Graph

• 부모: Philosophy-of-AI
• 변형: Symbols · Neural-Symbolic-Integration
• 응용: GraphRAG

🤖 LLM 활용

언제: history explain, position survey, hybrid pattern design. 언제 X: 매 ground truth math/logic — verifier 의 필수.

❌ 안티패턴

• Pure symbolic 의 modern build: 매 brittleness — knowledge engineering bottleneck.
• Pure connectionist 의 logical task: 매 hallucination — verifier 의 add.
• Hybrid 의 over-engineer: 매 simple task 의 simple model 으로 충분.
• "Connectionist won" claim: 매 incomplete — IMO-level 의 hybrid 의 need.

🧪 검증 / 중복

• Verified (Marcus "The Next Decade" 2020, Bengio·Russell·Hinton statements 2024, AlphaProof Nature 2024, Hinton Turing lecture).
• 신뢰도 A.

🕓 Changelog

날짜	변경
2026-05-08	Phase 1
2026-05-10	Manual cleanup — symbolic vs connectionist (history + modern hybrid)