Sezione del database · 49 concetti
Concetti
Il livello delle idee: tecnologia di base, training e inference, sicurezza, governance, economia, filosofia e futuro — ciascuno spiegato in modo chiaro e collegato al resto del database.
Note sul campo
Anche le idee sono infrastruttura — una manciata di concetti qui spiega la maggior parte dei titoli che leggerai quest'anno. Inizia dai tre qui sotto e il resto del sito smette di sembrare gergo.
Inizia da questi tre
TR
Transformer
L'idea del 2017 per cui esiste tutto ciò che è in questo sito.
SL
Scaling Laws
Le curve che hanno convinto gli investitori a spendere un trilione di dollari.
A(
AGI (Artificial General Intelligence)
La destinazione che tutti nominano e nessuno definisce allo stesso modo.
Tecnico di base
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.
Addestramento e inferenza
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 e calcolo
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.
Sicurezza e allineamento
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.
Governance e politiche
2
AR
AI RegulationThe EU legislates, the US pivots, everyone watches compute.
CG
Compute GovernanceRegulating AI by regulating FLOPs, chips and data centers.
Economia e società
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.
Filosofia
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 AGI
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.