AI Concepts — From Attention to AGI | AI Infrastructure Database

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Notas de campo

Las ideas también son infraestructura — un puñado de conceptos aquí explican la mayoría de los titulares que leerá este año. Empiece por los tres siguientes y el resto del sitio dejará de parecer jerga.

Empiece por estos tres

TR Transformer La idea de 2017 que todo en este sitio existe para ejecutar. SL Scaling Laws Las curvas que convencieron a los inversores de gastar un billón de dólares. A( AGI (Artificial General Intelligence) El destino que todos nombran y nadie define igual.

Técnico principal

11

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.

Entrenamiento e inferencia

13

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.

Hardware y cómputo

6

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.

Seguridad y alineamiento

4

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.

Gobernanza y política

2

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

Economía y sociedad

5

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.

Filosofía

4

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.

Futuro e IAG

4

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.