AI Concepts — From Attention to AGI | AI Infrastructure Database

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데이터 및 API

사이트 캐시 데이터에 대한 무료 읽기 전용 JSON 접근.

다크 모드

가이드 보기
칩, 데이터 센터, 모델 용어가 생소하신가요? 브라우징하면서 핵심 용어를 알기 쉽게 설명해 드립니다. 동일한 데이터에 도움말이 내장되어 있습니다.

전문가 보기
스택은 이미 알고 계시죠. 데이터만 제공합니다: 깔끔하고, 빠르고, 간결하게, 불필요한 설명 없이. 이것이 기본 보기입니다.

인터페이스 언어

현장 노트

아이디어도 인프라다 — 여기의 몇 가지 개념들이 올해 읽게 될 대부분의 헤드라인을 설명해준다. 아래 세 가지부터 시작하면 이 사이트의 나머지 부분이 더 이상 전문 용어처럼 느껴지지 않는다.

이 세 가지부터 시작하세요

TR Transformer 이 사이트의 모든 것이 실행하기 위해 존재하는 2017년의 아이디어. SL Scaling Laws 투자자들이 수조 달러를 지출하도록 설득한 곡선들. A( AGI (Artificial General Intelligence) 모두가 이름을 대지만 아무도 같은 방식으로 정의하지 않는 목적지.

핵심 기술

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CW Context WindowThe model's working memory — from 2K tokens to millions in five years. DM Diffusion ModelsGeneration by denoising — the engine of images and video. MO Mixture of Experts (MoE)Huge capacity, small bills: only a few experts fire per token. MU MultimodalityOne model, many senses: text, image, audio, video in a single network. NN Neural Networks & Deep LearningThe substrate: layered functions tuned by gradient descent. OV Open vs Closed WeightsThe industry's defining strategic split. PA ParametersThe learned numbers — and why 'bigger' stopped being the whole story. SS State-Space Models (Mamba)The main architectural challenger: linear-time sequence processing. T& Tokens & TokenizationThe unit AI is bought, sold and measured in. TR TransformerThe 2017 architecture everything on this site exists to run. WM World ModelsLearning how reality behaves — the claimed next frontier.

학습 및 추론

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AA AI AgentsModels that act: plan, call tools, iterate — the 2025 product wave. DI DistillationTeaching a small model to imitate a big one. FT Fine-tuning & LoRAAdapting a trained model cheaply — the small-compute on-ramp. HA HallucinationConfident fabrication — the failure mode that shapes deployment. IN InferenceRunning the model — now the majority of AI compute and the focus of the buildout. PT Post-training (RLHF, RLVR and after)Where a raw predictor becomes an assistant — and, lately, a reasoner. QU QuantizationFewer bits per weight: how models shrink to fit real hardware. RL RLHFReinforcement learning from human feedback — the technique behind ChatGPT's manners. RA Retrieval-Augmented Generation (RAG)Look it up, then answer — how enterprises ground models in their data. SL Scaling LawsThe empirical curves that justify the capex on this site. SD Synthetic Data & the Data WallWhen the internet runs out, models train on model output. TT Test-time Compute & ReasoningThe 2024 discovery: models get smarter by thinking longer. T& Training & Pre-trainingMonths of thousands of GPUs predicting the next token, trillions of times.

하드웨어 & 컴퓨팅

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CH ChipletsMany small dies, one big chip — yield economics beat monoliths. EC Export Controls & the Chip WarCompute as statecraft: the US–China technology conflict. F& FLOPS & Precision FormatsHow compute is counted — and why the same chip has five different numbers. ML Moore's Law & Its AfterlifeTransistor scaling slowed; the industry routed around it. P& PUE & Data-Center EfficiencyPower Usage Effectiveness: how much electricity feeds chips vs overhead. SA Sovereign AINations buying their own stack: compute as critical infrastructure.

안전성 및 정렬

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AS AI SafetyThe umbrella: from jailbreaks and misuse to catastrophic risk. AL AlignmentMaking systems that pursue what we actually intend. ER Existential Risk (x-risk)The argued worst case — and the industry's strangest consensus statement. IN InterpretabilityOpening the black box: reading a model's internal features.

거버넌스 & 정책

2

AR AI RegulationThe EU legislates, the US pivots, everyone watches compute. CG Compute GovernanceRegulating AI by regulating FLOPs, chips and data centers.

경제 및 사회

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AE AI's Energy DemandFrom 1.5% of world electricity toward — how much? The load-growth question. JP Jevons ParadoxWhy efficiency can increase total consumption — the argument of 2025. J& Jobs & AutomationWhat the models do to work — early evidence, loud predictions. TB The Bubble QuestionRailroad boom, dot-com rerun, or the real thing? The arguments, attributed. TE The Economics of AITrillion-dollar capex against fast-falling token prices.

철학

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MC Machine ConsciousnessCould there be something it is like to be a model? Serious people now ask. MP Moravec's ParadoxHard things are easy, easy things are hard — still true, still shaping robotics. TC The Chinese RoomSearle's 1980 challenge: does symbol manipulation ever become understanding? TT The Turing Test1950's benchmark, quietly passed — and quietly retired.

미래 & AGI

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A( AGI (Artificial General Intelligence)The destination everyone names and no one defines the same way. AT AGI Timelines & PredictionsWho predicts what, by when — on the record. S( Superintelligence (ASI)Beyond human-level — the explicit goal several labs now name. TS The Singularity & Recursive Self-ImprovementThe feedback-loop hypothesis: AI improving AI, faster each cycle.