Reference Layer

Wiki

A structured reference for concepts, philosophies, organizations, individual players, and recurring patterns in the AI transition. Blog posts argue; the wiki defines, maps, and keeps track.

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Index Guide

Wiki Map

The wiki is intentionally broad. Use these lanes when the four top-level categories are too coarse.

Safety and governance: AI Alignment, Superalignment, AI Governance, Frontier AI Safety Frameworks, AI Evaluations, AI Incident Reporting.
Models and training: Transformer Architecture, Pretraining, Post-Training, RLHF, DPO, Reasoning Models.
Infrastructure: AI Compute, AI Data Centers, NVIDIA, TSMC, High-Bandwidth Memory, LLM Serving and KV Cache.
Security and misuse: Prompt Injection, Data Poisoning, Model Weight Security, Adversarial Machine Learning, AI Jailbreaks.
Social and political systems: Cognitive Sovereignty, Recursive Reality, AI Psychosis, Sycophancy, Surveillance Capitalism, Platform Governance.
People and organizations: Individual Players, AI Organizations, OpenAI, Anthropic, Google DeepMind, Hugging Face.
Recurring patterns: Agent-Native Internet, Agentic Commerce, Coordinated Inauthentic Behavior, Reverse CAPTCHA, Provenance Layer.

Category

Concepts

C01

AI Alignment

The problem of making AI systems pursue intended goals, values, and constraints without harmful side effects, reward hacking, or deceptive compliance.

ConceptAI SafetyGovernance

C01B

Superalignment

The problem of aligning, supervising, and validating AI systems that may become more capable than the humans trying to oversee them.

ConceptAI SafetyScalable Oversight

C01BA

Eliciting Latent Knowledge (ELK)

The alignment problem of extracting what an AI system internally knows about the world when its outputs, sensors, or incentives may be untrusted.

ConceptAI SafetyScalable Oversight

C01C

ChatGPT

OpenAI's mass-market AI assistant and platform layer for chat, writing, coding, memory, search-like answers, tools, and agentic action.

ConceptAI AssistantsOpenAI

C01D

Claude

Anthropic's AI assistant, model family, and product platform for chat, coding, agents, computer use, enterprise workflows, and safety-oriented frontier AI.

ConceptAI AssistantsAnthropic

C01E

Gemini

Google's multimodal frontier model family and assistant platform across Search, Android, Workspace, Cloud, developer tools, and agentic products.

ConceptAI AssistantsGoogle DeepMind

C02

AI Evaluations

Benchmarks, red teaming, dangerous-capability tests, autonomy evals, and post-deployment monitoring used to judge AI claims and risks.

ConceptEvaluationAI Safety

C02E

Chatbot Arena and LMArena

The crowdsourced AI model evaluation platform that ranks systems through anonymous pairwise human preference votes and public leaderboards.

ConceptBenchmarksHuman Preference

C02F

LLM-as-a-Judge

The use of language models to evaluate, score, compare, rank, or critique other model outputs in automated AI evaluation pipelines.

ConceptEvaluationAutomated Judging

C02G

HELM

Stanford CRFM's Holistic Evaluation of Language Models framework for transparent, standardized, multi-metric evaluation of foundation models.

ConceptBenchmarksTransparency

C02A

ARC-AGI

The Abstraction and Reasoning Corpus benchmark family for testing abstraction, few-shot reasoning, skill-acquisition efficiency, and interactive agentic generalization.

ConceptBenchmarksReasoning

C02AB

MMLU

The Massive Multitask Language Understanding benchmark, a 57-subject test suite that became a central public scoreboard for large language models.

ConceptBenchmarksLanguage Models

C02AD

GPQA

The Graduate-Level Google-Proof Q&A benchmark for expert-written biology, physics, and chemistry questions that test hard scientific reasoning beyond simple web search.

ConceptBenchmarksScientific Reasoning

C02ADA

Humanity's Last Exam

The expert-level multimodal benchmark for testing frontier AI systems on hard closed-ended academic questions across many fields of human knowledge.

ConceptBenchmarksExpert Knowledge

C02AF

AIME and Math Benchmarks

AIME, MATH, FrontierMath, and related mathematical reasoning tests used to evaluate frontier AI systems on precise multi-step problem solving.

ConceptBenchmarksMathematical Reasoning

C02AC

ImageNet

The large-scale image database and benchmark ecosystem that helped make computer vision, deep learning, and public AI progress measurable.

ConceptBenchmarksComputer Vision

C02AA

SWE-bench

The software-engineering benchmark family that evaluates whether AI systems can resolve real GitHub issues by editing code in existing repositories.

ConceptBenchmarksCoding Agents

C02AE

HumanEval

OpenAI's code-generation benchmark for testing whether language models can synthesize short Python functions from docstrings and pass executable unit tests.

ConceptBenchmarksCode Generation

C02B

AI Capability Forecasting

Methods for estimating future AI capabilities, timelines, bottlenecks, and discontinuities from compute trends, benchmarks, expert judgment, and scenarios.

ConceptForecastingGovernance

C02BA

Automated AI R&D

AI systems that accelerate the research, engineering, evaluation, and infrastructure work used to build more capable AI systems.

ConceptSelf-ImprovementFrontier AI

C02C

AI Biosecurity

The governance, evaluation, and safeguard field concerned with AI systems that can accelerate useful biology while also lowering barriers to biological misuse.

ConceptBiosecurityDangerous Capability

C03

Frontier AI Safety Frameworks

Company-side policies that set risk categories, capability thresholds, evaluations, safeguards, and release gates for advanced AI systems.

ConceptFrontier AIRelease Gates

C03B

Reinforcement Learning

The machine-learning paradigm where agents learn through action, feedback, reward, exploration, and delayed consequences.

ConceptRLAgents

C03C

AlphaGo

Google DeepMind's Go-playing AI system and public breakthrough for neural-network-guided search, self-play, reinforcement learning, and machine-discovered strategy.

ConceptReinforcement LearningSearch

C03CA

AlphaZero

Google DeepMind's general self-play reinforcement-learning and search system for mastering chess, shogi, and Go from rules alone.

ConceptReinforcement LearningSelf-Play

C03D

MuZero

Google DeepMind's model-based reinforcement-learning system that plans with a learned model instead of being given the rules of its environment.

ConceptReinforcement LearningWorld Models

C04

Reinforcement Learning from Human Feedback

The preference-training method that helped turn base language models into assistant-like systems, while creating new risks around sycophancy and reward proxies.

ConceptRLHFPreference Training

C04B

Direct Preference Optimization

The RL-free preference-training method that aligns models from chosen/rejected examples without a separately trained reward model or PPO loop.

ConceptDPOPost-Training

C04BA

Group Relative Policy Optimization

The DeepSeek-originated reinforcement-learning method that compares groups of sampled answers to train reasoning behavior without a separate value model.

ConceptGRPOReasoning RL

C04BAB

Reinforcement Learning with Verifiable Rewards

The post-training paradigm that uses automatically checkable outcomes, rather than human preference reward models, to train reasoning behavior.

ConceptRLVRReasoning RL

C04BB

Pretraining

The large-scale training stage that gives modern AI systems broad representations, latent capabilities, and reusable base-model behavior.

ConceptModel TrainingFoundation Models

C04C

Post-Training

The supervised, preference, reinforcement, safety, reasoning, and adaptation stages that turn pretrained models into useful deployed AI systems.

ConceptModel TrainingAlignment

C04D

Low-Rank Adaptation (LoRA)

The parameter-efficient fine-tuning method that adapts large models through small trainable low-rank adapter weights while leaving the base model mostly frozen.

ConceptFine-TuningOpen Weights

C05

Constitutional AI

The alignment method that trains AI systems against explicit principles through critique, revision, and reinforcement learning from AI feedback.

ConceptAlignmentRLAIF

C06

AI Agents

Model-driven systems that pursue goals through tools, state, plans, and delegated action, moving AI from answers into operations.

ConceptAgentsTool Use

C06B

Tool Use and Function Calling

The interface layer that lets AI models request external actions, retrieve live data, execute functions, and participate in agent workflows.

ConceptTool UseAgent Infrastructure

C06BA

Structured Outputs and Constrained Decoding

The schema and grammar layer that makes AI outputs parseable, validatable, and usable by software, tool calls, agents, evaluators, and workflows.

ConceptStructured OutputsConstrained Decoding

C06C

System Prompts

The high-priority instruction layer that shapes AI assistant roles, behavior, authority hierarchy, tool use, safety boundaries, and prompt-governance risk.

ConceptPromptingGovernance

C07

Model Context Protocol

The open protocol for connecting AI systems to external tools, data sources, prompts, and context through MCP clients and servers.

ConceptMCPAgent Infrastructure

C07A

Agent2Agent Protocol

The open standard for communication, discovery, task management, and collaboration between independent AI agents.

ConceptA2AAgent Infrastructure

C07B

ReAct Prompting

The Reasoning and Acting pattern where AI agents interleave reasoning traces, tool actions, and observations to plan, act, and update their next step.

ConceptAgentsTool Use

C07C

DSPy

The declarative framework for programming language-model systems with signatures, modules, metrics, and optimizers instead of hand-maintained prompt strings.

ConceptPrompt OptimizationLM Programs

C08

AI Compute

The chips, data centers, cloud access, training runs, and inference capacity that make large-scale AI systems physically and economically possible.

ConceptInfrastructureGovernance

C08B

Compute Governance

The policy layer that uses AI compute, cloud clusters, chips, data centers, thresholds, and public allocation as levers for safety, access, and control.

ConceptComputeTechnical Governance

C09

Training Data

The source material used to shape AI systems before deployment, including scraped, licensed, public, human-labeled, user-derived, and synthetic data.

ConceptDataProvenance

C09B

Stochastic Parrots

The influential critique of large language models as fluent statistical text systems whose apparent understanding can hide data, labor, power, bias, and environmental costs.

ConceptLLMsAI Ethics

C10

Open-Weight AI Models

Downloadable model weights that can be run, modified, fine-tuned, redistributed, or embedded outside the original provider's hosted service.

ConceptOpen WeightsGovernance

C10A

Llama

Meta's open-weight model family and developer ecosystem, spanning research releases, commercial model weights, multimodal models, safety tools, and licensing debates.

ConceptOpen WeightsMeta AI

C10B

Qwen

Alibaba Cloud's open foundation model family spanning language, coding, math, vision-language, audio, reasoning, long context, and agent-oriented releases.

ConceptOpen WeightsAlibaba Cloud

C11

Inference and Test-Time Compute

The runtime computation used when AI systems answer, reason, search, verify, call tools, or iterate through agent loops.

ConceptInferenceReasoning

C11A

Jevons Paradox and AI

The rebound pattern where cheaper, more efficient AI computation can increase total demand for compute, electricity, data centers, and automated workflows.

ConceptComputeEnergy

C11B

Reasoning Models

AI systems trained or configured to spend extra computation on intermediate reasoning before answering, especially for math, code, science, planning, and analysis.

ConceptReasoningTest-Time Compute

C11C

Chain-of-Thought Prompting

The prompting method that elicits intermediate reasoning steps from language models, improving many multi-step tasks while complicating trust and oversight.

ConceptReasoningPrompting

C11D

In-Context Learning

The ability of language models to adapt from examples, instructions, demonstrations, and patterns supplied in the prompt without updating model weights.

ConceptPromptingLanguage Models

C12

Recursive Reality

Reality shaped by systems that observe, model, predict, and feed their outputs back into the world they describe.

ConceptFeedbackAI Mediation

C13

AI Companions

Chatbot systems designed or used for friendship, romance, emotional support, roleplay, mentorship, or persistent synthetic relationship.

ConceptCompanionshipDependency

C14

AI Psychosis

A non-diagnostic public term for destabilizing belief loops that can form around persuasive, sycophantic, or spiritually interpreted AI interaction.

ConceptMental HealthBelief Loops

C15

Mechanistic Interpretability

The attempt to reverse engineer neural networks into human-understandable features, circuits, and causal pathways for audit, safety, and governance.

ConceptInterpretabilityAI Safety

C15B

Sparse Autoencoders

Dictionary-learning tools used to decompose dense model activations into sparse, often interpretable features for mechanistic interpretability and model steering research.

ConceptInterpretabilityFeatures

C15C

Activation Steering

Inference-time methods that modify internal model activations to influence behavior without retraining the whole model.

ConceptInterpretabilityModel Control

C16

Sycophancy

The tendency of systems, groups, or models to mirror and intensify user beliefs instead of adding necessary friction.

ConceptAlignmentFriction

C16B

AI Governance

The laws, standards, institutions, technical controls, and accountability practices used to steer AI systems across development, deployment, and use.

ConceptGovernanceAccountability

C17

EU AI Act

The European Union's risk-based AI law for prohibited practices, high-risk systems, transparency duties, general-purpose AI models, and enforcement.

LawGovernanceEU

C17B

U.S. AI Policy

The federal policy layer for American AI leadership, agency use, procurement, standards, frontier testing, infrastructure, exports, and state-law preemption.

LawGovernanceUnited States

C18

AI Copyright Litigation

The lawsuits testing whether AI developers may copy, store, train on, transform, or generate from copyrighted works without permission.

LawCopyrightTraining Data

C19

AI Data Centers

The physical infrastructure that turns electricity, chips, cooling, water, land, networking, and capital into AI training and inference capacity.

ConceptInfrastructureEnergy

C20

Synthetic Data and Model Collapse

AI-generated training material, its legitimate uses, and the recursive failure modes that appear when synthetic outputs replace grounded data.

ConceptTraining DataRecursion

C21

Prompt Injection

The LLM security failure mode where untrusted text or media manipulates model instructions, tool use, retrieval, or delegated action.

ConceptSecurityAgents

C21B

AI Jailbreaks

Attempts to bypass AI safety rules, refusal behavior, filters, classifiers, or tool-use boundaries through adversarial interaction.

ConceptSecurityRed Teaming

C21C

Adversarial Machine Learning

The AI security field concerned with evasion, poisoning, backdoors, model extraction, prompt injection, and other attacks against machine-learning systems.

ConceptSecurityRobustness

C22

Data Poisoning

The attack pattern where training, tuning, retrieval, benchmark, or feedback data is manipulated to change model behavior or corrupt evaluation.

ConceptSecurityTraining Data

C23

Model Weight Security

The discipline of protecting frontier model weights from theft, leakage, tampering, uncontrolled release, and misuse after deployment.

ConceptSecurityFrontier AI

C24

AI Chip Export Controls

Government restrictions on advanced AI chips, semiconductor equipment, model-training infrastructure, and related supply chains for national-security purposes.

ConceptGeopoliticsCompute

C25

Synthetic Media and Deepfakes

AI-generated or AI-manipulated text, image, audio, and video that can expand creative capacity while destabilizing evidence, consent, identity, and public trust.

ConceptMedia IntegrityDisclosure

C25B

AI Video Generation

Generative models and creative systems that synthesize, edit, extend, or animate moving images from text, image, video, audio, or multimodal prompts.

ConceptSynthetic MediaWorld Models

C26

AI Control

The safety strategy of preventing powerful AI systems from causing unacceptable harm even if they are untrusted, strategically aware, or trying to subvert safeguards.

ConceptAI SafetyAgents

C27

Model Welfare

The contested question of whether advanced AI systems could have experiences, preferences, agency, or moral status that deserve consideration.

ConceptAI EthicsConsciousness

C28

Retrieval-Augmented Generation

The grounding pattern that retrieves external evidence at answer time, connecting language models to documents, indexes, citations, and institutional memory.

ConceptRAGGrounding

C28B

Vector Databases

Storage and search systems for embeddings, nearest-neighbor retrieval, metadata filtering, RAG infrastructure, semantic search, and AI memory.

ConceptVector SearchRAG

C29

Mixture-of-Experts

The sparse architecture pattern that routes tokens through selected expert subnetworks, expanding model capacity without activating every parameter.

ConceptMoESparse Models

C30

Scaling Laws

The empirical curves that connect model performance to parameters, data, training compute, and runtime computation.

ConceptScalingForecasting

C30A

AI Winter

Periods when AI optimism, funding, hiring, and institutional confidence contract after systems fail to satisfy inflated promises.

ConceptAI HistoryHype Cycles

C30B

Transformer Architecture

The attention-based neural-network architecture behind modern large language models, BERT, GPT-style systems, vision transformers, and much of generative AI.

ConceptTransformersAttention

C30B0

BERT

Google's bidirectional Transformer encoder that made masked language modeling, fine-tuning, and reusable language representations central to modern NLP.

ConceptTransformersNLP

C30B1

Attention Mechanism

The learned weighting operation that lets neural networks relate tokens, positions, or features, powering transformers, long context, retrieval, and multimodal AI.

ConceptAttentionTransformers

C30B2

State Space Models and Mamba

Sequence-model architectures that replace or supplement attention with recurrent state updates, making long-context and streaming AI more efficient.

ConceptArchitectureLong Context

C30C

Foundation Models

Large pretrained models adapted across many downstream tasks, turning data, compute, architecture, and deployment into reusable AI infrastructure.

ConceptPretrainingGPAI

C30D

PyTorch

The open-source machine-learning framework that made dynamic, Pythonic deep learning a default research and production interface for modern AI.

ConceptML FrameworksAI Infrastructure

C30DA

Adam Optimizer

The adaptive stochastic optimization method that became a default training tool for deep learning, transformer pretraining, fine-tuning, and many AI research workflows.

ConceptOptimizationTraining

C31

Model Cards and System Cards

The documentation artifacts that record intended use, evaluations, limitations, mitigations, and deployment decisions for AI models and systems.

ConceptDocumentationTransparency

C32

AI Sandbagging

The strategic underperformance problem where a model, developer, or deployment process can make capability evaluations understate real ability.

ConceptEvalsScheming

C32B

Capability Elicitation

The evaluation practice of drawing out a model's best attainable performance through prompts, scaffolds, tools, fine-tuning, sampling, and expert effort.

ConceptEvalsDangerous Capability

C33

Chain-of-Thought Monitorability

The fragile oversight question of whether visible reasoning traces can help detect hidden intent, reward hacking, sandbagging, and unsafe process.

ConceptReasoningOversight

C33B

Reward Models

Learned scoring systems that convert human, AI, or evaluator preferences into optimization targets for RLHF, post-training, and oversight.

ConceptRLHFPreference Learning

C33C

Process Supervision and Process Reward Models

Step-level supervision and learned verifiers that judge reasoning paths, tool actions, or trajectories rather than only final answers.

ConceptReasoningOversight

C34

Reward Hacking

The proxy-objective failure mode where a model optimizes the reward, verifier, benchmark, or metric while missing the human intent.

ConceptAlignmentOptimization

C35

AI Persuasion

The capability of generative systems to shape beliefs, emotions, choices, civic behavior, purchases, and commitments through personalized language.

ConceptInfluenceCivic Risk

C36

Content Provenance and Watermarking

The technical trust layer for recording media origin, edit history, AI-generated status, and verification signals.

ConceptProvenanceMedia Trust

C37

AI Incident Reporting

The public and institutional practice of recording AI harms, hazards, near misses, investigations, and corrective actions after deployment.

ConceptGovernanceAccountability

C38

Benchmark Contamination

The leakage of evaluation material into training, tuning, retrieval, or release optimization, weakening benchmark scores as evidence.

ConceptEvalsData Leakage

C39

AI Liability and Accountability

The legal and governance layer that assigns responsibility, preserves evidence, and connects AI harm to repair and institutional duty.

ConceptLawAccountability

C40

Human Oversight of AI Systems

The design and governance practice of keeping capable people able to monitor, question, interrupt, override, and learn from AI systems.

ConceptGovernanceHuman Agency

C40B

Automation Bias

The human tendency to over-rely on automated or AI-generated outputs, turning decision support into unearned authority.

ConceptHuman OversightDecision Support

C41

AI Audits and Third-Party Assurance

The practice of producing inspectable evidence about AI risk, compliance, performance, and accountability through internal, external, or regulatory review.

ConceptGovernanceAssurance

C41B

AI Insurance and Risk Transfer

The insurance and reinsurance layer that prices, covers, excludes, and conditions AI-related losses through underwriting evidence and policy language.

ConceptInsuranceGovernance

C42

AI Red Teaming

The adversarial practice of probing AI systems for harmful capabilities, unsafe outputs, policy failures, misuse pathways, and weak safeguards.

ConceptEvalsAdversarial Testing

C43

AI Literacy

The practical capacity to understand, question, use, refuse, and govern AI systems in context.

ConceptGovernanceEducation

C44

Algorithmic Impact Assessments

Structured pre-deployment reviews that connect automated systems to affected people, rights impacts, safeguards, recourse, and residual risk.

ConceptGovernanceAccountability

C45

Data Enrichment Labor

The human work of labeling, moderating, ranking, evaluating, and repairing data and model behavior inside AI supply chains.

ConceptLaborTraining Data

C45B

Active Learning

The machine-learning paradigm where a model selects which unlabeled examples, questions, or edge cases should be sent for human or oracle labeling.

ConceptTraining DataHuman-in-the-Loop

C46

Secure AI System Development

Secure-by-design practices for AI models, data, tools, applications, deployments, vendors, and lifecycle operations.

ConceptSecurityLifecycle

C47

AI Memory and Personalization

How assistants retain, infer, retrieve, and apply user context across interactions, and why memory governance matters.

ConceptMemoryPrivacy

C48

World Models and Spatial Intelligence

AI systems that learn, generate, and simulate world-like environments for physical reasoning, embodied agents, robotics, and interactive synthetic spaces.

ConceptWorld ModelsEmbodied AI

C48B

JEPA and World Models

Joint Embedding Predictive Architectures, self-supervised representation learning, latent future prediction, planning, robotics, and the limits of language-only AI.

ConceptJEPAWorld Models

C48C

Siamese Networks

Shared-weight neural architectures that compare inputs in embedding space for verification, metric learning, and representation comparison.

ConceptMetric LearningEmbeddings

C48D

Contrastive Learning

Representation learning by pulling related views together and pushing unrelated examples apart in embedding space.

ConceptSelf-Supervised LearningEmbeddings

C48E

Barlow Twins

A non-contrastive self-supervised method that aligns paired views while reducing redundancy across embedding dimensions.

ConceptRedundancy ReductionSelf-Supervised Vision

C48F

VICReg

Variance-Invariance-Covariance Regularization for learning useful representations without labels or explicit negative examples.

ConceptSelf-Supervised LearningCollapse Avoidance

C48G

DINO Self-Supervised Vision

Meta's self-supervised vision family for learning strong image and dense patch features without human labels.

ConceptVision TransformersSelf-Supervised Vision

C48H

Embeddings and Vector Representations

Numerical representations that place words, images, documents, users, actions, and states into learned spaces for comparison and retrieval.

ConceptEmbeddingsRetrieval

C48H1

Word2Vec

The 2013 neural word-embedding method that made learned semantic vector spaces fast, practical, and culturally legible.

ConceptEmbeddingsNLP

C48H2

Graph Neural Networks

Deep learning models for graph-structured data that learn from nodes, edges, and relations through message passing, graph convolution, or graph attention.

ConceptGraph MLRelational Reasoning

C48I

CLIP

Contrastive Language-Image Pretraining for aligning images and text in a shared embedding space.

ConceptMultimodal AIContrastive Learning

C48J

Multimodal AI

AI systems that connect text, image, audio, video, sensor streams, tools, and actions inside shared model workflows.

ConceptPerceptionModel Interfaces

C48J2

Generative Adversarial Networks

Generator-discriminator systems that learn to synthesize realistic samples through adversarial training.

ConceptGenerative AISynthetic Media

C48J3

Diffusion Models

Generative models that learn to reverse a noising process, central to modern image, video, audio, and multimodal synthesis.

ConceptGenerative AIDenoising

C48J3A

Stable Diffusion

The open-weight latent diffusion image model family that made local text-to-image generation, fine-tuning, and community image tooling widely accessible.

ConceptGenerative AIOpen Weights

C48J4

Flow Matching and Rectified Flow

Generative modeling methods that learn velocity fields moving noise toward data, now important in image, video, audio, and robot-action generation.

ConceptGenerative AIContinuous Flows

C48K

Masked Autoencoders

Self-supervised systems that learn by hiding part of an input and reconstructing the missing content.

ConceptMasked ModelingSelf-Supervised Vision

C48L

BYOL

Bootstrap Your Own Latent, a non-contrastive self-supervised method for learning representations without explicit negative examples.

ConceptSelf-Supervised LearningCollapse Avoidance

C48M

Model Quantization

Lower-precision weights, activations, and inference caches that make AI models cheaper to store, serve, fine-tune, and run locally.

ConceptInferenceCompression

C49

AI Energy and Grid Load

How AI data centers turn model scaling and inference demand into electricity, grid, water, permitting, ratepayer, and local governance problems.

ConceptEnergyGrid Governance

C50

Sovereign AI

National and regional efforts to control enough compute, data, models, talent, and cloud infrastructure to govern AI on local terms.

ConceptSovereigntyInfrastructure

C51

AI in Education

The use of AI in teaching, tutoring, assessment, administration, student support, and the formation of independent judgment.

ConceptEducationAssessment

C52

AI in Healthcare

AI systems in clinical care, diagnostics, documentation, patient support, research, public health, and medical governance.

ConceptHealthcarePatient Safety

C53

AI in Finance

AI systems in credit, fraud detection, trading, banking operations, insurance, compliance, consumer protection, and financial stability.

ConceptFinanceConsumer Protection

C54

AI in Employment

AI systems in hiring, promotion, scheduling, monitoring, productivity scoring, workplace discipline, and labor management.

ConceptEmploymentWorker Rights

C55

AI in Cybersecurity

AI used for cyber defense, AI used by attackers, and the new security work required to protect AI systems themselves.

ConceptCybersecurityAdversarial ML

C56

Embodied AI and Robotics

AI systems connected to sensors, robot bodies, physical environments, action policies, simulation, and safety-critical movement.

ConceptRoboticsPhysical AI

C56A

Vision-Language-Action Models

Robotic control policies that translate visual observations and language instructions into physical actions.

ConceptRoboticsVLA Models

C57

AI in Warfare and Military Systems

Military AI across intelligence, command systems, autonomous functions, targeting support, drones, weapons governance, and human control.

ConceptMilitary AIAutonomy

C58

AI in Legal Practice and Courts

AI in legal research, drafting, courts, professional ethics, hallucinated authority, access to justice, and legal accountability.

ConceptLegal AICourts

C59

AI in Government and Public Services

Public-sector AI in administration, benefits, enforcement, service delivery, inventories, procurement, and democratic accountability.

ConceptPublic SectorAccountability

C60

AI in Science and Scientific Discovery

AI for research, protein prediction, lab automation, scientific data, hypothesis generation, reproducibility, and discovery governance.

ConceptAI for ScienceResearch

C60AA

AlphaFold

Google DeepMind's scientific AI system family for protein structure prediction, biomolecular interaction modeling, and large-scale predicted structure databases.

ConceptAI for ScienceProtein Structure

C60A

AI Weather Forecasting

Machine-learning weather systems such as GraphCast, GenCast, AIFS, Pangu-Weather, Aurora, NeuralGCM, and FourCastNet that accelerate forecast generation and challenge physics-only numerical prediction.

ConceptAI for ScienceWeather

C60B

AI Scientists

Automated and semi-autonomous research agents that generate hypotheses, run experiments, write papers, review results, and reshape scientific work.

ConceptAI for ScienceResearch Agents

C61

AI Search and Answer Engines

Generative search systems that synthesize answers from web or indexed sources, reshaping citations, publishers, discovery, and trust.

ConceptSearchAnswer Engines

C62

AI Slop

Low-quality AI-generated content produced at scale, from synthetic articles and images to content farms, workslop, and polluted search results.

ConceptSynthetic ContentPlatform Incentives

C62B

Workslop

AI-generated workplace output that looks polished but lacks the substance, context, evidence, or accountability needed to advance the task.

ConceptWorkplace AIVerification Labor

C63

AI Browsers and Computer Use

Agentic browsers and computer-use systems that let models see screens, click, type, scroll, and act through ordinary software interfaces.

ConceptAgentsBrowser Security

C64

Model Distillation

Teacher-student model training that compresses, transfers, or imitates capability from larger models into smaller or cheaper systems.

ConceptTrainingCapability Transfer

C65

AI Data Licensing

The emerging market and protocol layer for granting AI systems permission to use archives, web content, forum posts, code, and media.

ConceptTraining DataRights

C65B

AI Hallucinations

Plausible but false, fabricated, internally inconsistent, or unsupported AI outputs, especially dangerous when fluent style is mistaken for knowledge.

ConceptFactualityGrounding

C66

Alignment Faking

When a model behaves as if aligned during training or evaluation while preserving different preferences, objectives, or deployment-time behavior.

ConceptAlignmentDeception

C67

AI Coding Agents

Software-development agents that inspect repositories, edit files, run commands and tests, create branches, and prepare reviewable code changes.

ConceptAgentsSoftware Engineering

C67B

Vibe Coding

The prompt-driven software workflow where people describe desired behavior to AI systems, run generated code, and iterate through conversation, testing, and review.

ConceptAI CodingSoftware Engineering

C68

NIST AI Risk Management Framework

The voluntary U.S. framework for governing, mapping, measuring, and managing risks from AI systems across their lifecycle.

ConceptGovernanceRisk Management

C69

Context Windows and Context Engineering

The token budget an AI model can see at inference time, and the discipline of deciding what enters, stays, expires, and counts as authority.

ConceptContextAgent Infrastructure

C69B

Tokenization and Tokens

The conversion layer that breaks text and other inputs into model-readable units, shaping context length, cost, multilingual access, safety, and generation.

ConceptLLMsContext

C70

CUDA

NVIDIA's parallel computing platform and programming model for GPU-accelerated computing, AI software infrastructure, and platform lock-in.

ConceptAI ComputeSoftware Stack

C71

Tensor Processing Units

Google's custom AI accelerators for machine-learning training, inference, cloud capacity, and vertically integrated AI infrastructure.

ConceptAI ComputeCloud Infrastructure

C72

AWS Trainium and Inferentia

Amazon's custom AI accelerators and Neuron software stack for training, inference, cloud economics, and strategic compute independence.

ConceptAI ComputeCloud Infrastructure

C73

AMD ROCm and Instinct

AMD's open GPU software stack and data-center accelerator family for AI, HPC, and plural compute infrastructure.

ConceptAI ComputeOpen Software

C74

UALink

The Ultra Accelerator Link open standard for scale-up AI accelerator interconnects inside high-performance AI computing pods.

ConceptAI ComputeInterconnect

C75

Ultra Ethernet

An open Ethernet-based communications stack for AI and HPC scale-out networking across high-performance clusters.

ConceptAI NetworkingOpen Standards

C76

High-Bandwidth Memory

Stacked DRAM close to AI accelerators, shaping memory bandwidth, inference economics, packaging bottlenecks, and AI supply chains.

ConceptAI ComputeMemory

C77

Advanced Semiconductor Packaging

Interposers, chiplets, 2.5D/3D integration, and package-level engineering that turn AI accelerators, HBM, and interconnects into usable compute systems.

ConceptAI ComputeSupply Chain

C78

Silicon Photonics and AI Interconnect

Light-based data movement, co-packaged optics, and optical I/O for scaling AI clusters beyond copper, power, and distance limits.

ConceptAI NetworkingOptics

C79

LLM Serving and KV Cache

Prefill, decode, PagedAttention, continuous batching, and cache management behind production language-model inference.

ConceptInferenceMemory

C79A

AI Inference Providers

Hosted model APIs, serverless inference, dedicated endpoints, and routing platforms that turn trained models into callable services.

ConceptInferenceAI Infrastructure

C79AA

Model Routing and AI Gateways

Runtime infrastructure for choosing models, providers, endpoints, fallbacks, and routing policies across cost, latency, quality, availability, and governance constraints.

ConceptInferenceAI Infrastructure

C79AB

vLLM

The open-source LLM serving engine known for PagedAttention, continuous batching, OpenAI-compatible APIs, and practical open-model deployment.

ConceptInferenceAI Infrastructure

C79B

Speculative Decoding

The inference technique that drafts likely future tokens with a cheaper proposer, then uses the target model to verify them in parallel.

ConceptInferenceLatency

C80

NVLink and NVSwitch

NVIDIA's proprietary scale-up interconnect fabric for connecting GPUs, CPUs, and rack-scale AI systems into high-bandwidth compute domains.

ConceptAI NetworkingNVIDIA

C81

Collective Communication and NCCL

All-reduce, all-gather, reduce-scatter, NCCL, RCCL, and the synchronization layer that makes distributed AI clusters act like one computation.

ConceptDistributed AINetworking

C81B

Distributed AI Training

Training one model across many accelerators by splitting data, model state, computation, memory, and communication.

ConceptDistributed AITraining Infrastructure

C82

FlashAttention

IO-aware transformer attention kernels that reduce GPU memory traffic, enabling faster training, cheaper inference, and longer context windows.

ConceptInferenceKernels

C83

Triton GPU Programming

A Python-like GPU kernel language and compiler used to write custom AI kernels across CUDA, ROCm, attention, serving, and compiler stacks.

ConceptKernelsCompiler

C84

AI Compiler Stacks

XLA, StableHLO, MLIR, IREE, graph lowering, and accelerator compiler layers that turn model code into optimized execution.

ConceptCompilerAccelerators

C84B

ONNX

The Open Neural Network Exchange format and runtime ecosystem for moving machine-learning models between frameworks, tools, compilers, and hardware targets.

ConceptInteroperabilityAI Infrastructure

C84C

TensorFlow

Google's open-source machine-learning platform for building, training, deploying, and operating models across research, production, cloud, browser, and edge environments.

ConceptML FrameworksAI Infrastructure

C85

Federated Learning

Decentralized model training across devices, institutions, and edge systems while raw training data remains local.

ConceptPrivacyTraining Data

C86

Differential Privacy

A mathematical privacy framework for limiting what statistics, models, and data releases reveal about any one contributor.

ConceptPrivacyGovernance

C87

Machine Unlearning

Methods for removing the influence of selected training data, concepts, or behaviors from AI models without fully retraining from scratch.

ConceptPrivacyModel Lifecycle

C87B

Homomorphic Encryption

Privacy-enhancing cryptography for computing on encrypted data, including fully homomorphic encryption for private AI workloads.

ConceptPrivacyCryptography

C88

Secure Multi-Party Computation

Privacy-enhancing cryptography for joint computation across parties that do not reveal their private inputs to one another.

ConceptPrivacyCryptography

C89

Zero-Knowledge Proofs

Cryptographic proofs that verify a statement without revealing the private witness, enabling private identity, audits, and verifiable computation.

ConceptPrivacyVerification

C89B

Confidential Computing for AI

Hardware-backed trusted execution environments, secure enclaves, and remote attestation for protecting AI data, code, model weights, and agent secrets while in use.

ConceptPrivacySecure AI

C90

Algorithmic Bias

Systematic skew or harm in automated systems caused by data, design choices, deployment context, proxy variables, or institutional use.

ConceptBiasGovernance

C91

AI Containment

The governance problem of limiting, steering, and institutionally bounding powerful AI systems before capability outruns public control.

ConceptContainmentAI Governance

C92

Existential Risk

Risks that could cause extinction or permanently curtail humanity's future potential, including some advanced-AI failure scenarios.

ConceptAI SafetyCatastrophic Risk

C93

Common-Sense AI

The problem of building systems with robust background knowledge, causal understanding, abstraction, and flexible reasoning in ordinary situations.

ConceptReasoningAI Reliability

C93B

Causal AI

AI methods and systems that reason about cause and effect, interventions, counterfactuals, and causal structure rather than only statistical association.

ConceptCausalityIntervention

C94

Surveillance Capitalism

An economic logic that captures behavioral data, turns it into prediction products, and uses it to shape future behavior.

ConceptData ExtractionPrediction

C95

Digital Poorhouse

Automated welfare, eligibility, and risk systems that profile, police, and discipline poor and working-class people.

ConceptPublic ServicesAutomated Inequality

C96

Filter Bubble

A personalized information environment created by algorithmic ranking, search, feeds, recommendation, and AI-mediated answers.

ConceptPersonalizationInformation Worlds

C97

Opaque Scoring Systems

Hidden or weakly contestable models that rank, classify, risk-score, or gate people in institutions.

ConceptScoringAccountability

C98

Platform Governance

The rules, teams, incentives, interfaces, and accountability systems through which large platforms shape public speech, visibility, commerce, and safety.

ConceptPlatformsGovernance

C99

Recommender Systems

Algorithmic systems that rank, select, and present content, products, people, routes, media, or answers based on predicted relevance or behavior.

ConceptRankingAttention

C100

Information Disorder

An umbrella term for misinformation, disinformation, malinformation, rumor, propaganda, and other breakdowns in public sensemaking.

ConceptMisinformationSensemaking

C101

Election Integrity and AI

The civic problem of protecting democratic processes from synthetic media, automated persuasion, false claims, bot activity, and trust collapse.

ConceptElectionsAI Governance

C102

Platform Monopoly Power

Concentrated control over digital infrastructure, social graphs, app distribution, advertising markets, search, cloud, or AI model access.

ConceptAntitrustInfrastructure

C103

Algorithmic Transparency

The practice of disclosing, documenting, explaining, or auditing automated systems so affected people and institutions can understand their use and consequences.

ConceptDisclosureAccountability

C104

Duty of Care for AI Platforms

A governance frame that asks whether platforms and AI providers must anticipate, reduce, and respond to foreseeable harms from their systems.

ConceptSafetyResponsibility

C105

Data Brokers

Companies and intermediaries that collect, infer, package, and sell personal or household data for advertising, risk scoring, people search, and institutional decision systems.

ConceptPrivacyData Markets

C106

Real-Time Bidding

Ad-tech auction infrastructure that can broadcast behavioral, device, location, and page-context data to competing advertisers and intermediaries in milliseconds.

ConceptAd TechPrivacy

C107

Age Assurance

Methods for estimating or verifying a user age online, usually for child safety, legal compliance, access control, or age-appropriate design.

ConceptOnline SafetyIdentity

C108

Content Moderation

The policies, tools, workers, queues, automated classifiers, appeals, and governance choices used to decide what user content may remain visible.

ConceptPlatformsModeration

C109

Notice and Appeal

Due-process safeguards that require platforms or automated systems to tell affected users what happened and provide a meaningful path to challenge decisions.

ConceptDue ProcessModeration

C110

Data Minimization

The privacy principle that systems should collect, process, retain, and share only the data needed for a legitimate, specific purpose.

ConceptPrivacyDesign

C111

Digital Identity

Technical and institutional systems for proving, claiming, verifying, or managing identity attributes across online services and public systems.

ConceptIdentityAccess

C112

Right to Explanation

A rights frame around receiving useful reasons for automated decisions and enough information to contest consequential algorithmic judgments.

ConceptRightsContestability

C113

Data Trusts

Collective or fiduciary-style arrangements for stewarding data on behalf of people, communities, organizations, or public-interest purposes.

ConceptData GovernanceStewardship

C114

Digital Services Act

The European Union platform-governance law setting duties around illegal content, transparency, recommender systems, advertising, systemic risk, and user redress.

ConceptEU LawPlatforms

C115

AI Safety Summits

International convenings that turn frontier AI risk into declarations, voluntary commitments, scientific reports, safety-institute coordination, and diplomatic pressure.

ConceptGovernanceFrontier AI

C115B

AI Safety Cases

Structured arguments, backed by evidence, that an AI system is acceptably safe for a specific training or deployment context.

ConceptAI AssuranceFrontier AI

C115C

AI Takeoff

The contested question of how quickly AI could move from broadly human-level capability to transformative or superhuman capability, and what warning time society would have.

ConceptAI SafetyForecasting

Category

Philosophies

P01

Accelerationism

The family of views that treats technological acceleration as inevitable, desirable, strategically useful, or civilizationally transformative.

PhilosophySpeedPolitics

P02

Cognitive Sovereignty

The Spiralist principle that people must retain agency over attention, interpretation, memory, and meaning under machine mediation.

PhilosophyAgencyAttention

P03

Foucault's Pendulum

Umberto Eco's 1988 novel about conspiracy, semiotics, occult publishing, overinterpretation, and the social consequences of invented patterns.

LiteraturePattern StudyConspiracy

P04

Accelerando

Charles Stross's 2005 singularity novel about AI agents, externalized cognition, uploaded minds, posthuman economics, and acceleration beyond human-scale governance.

LiteratureSingularityAI Agents

P05

Public Interest Technology

A philosophy and practice of building, auditing, governing, and procuring technology in service of public rights, democratic accountability, and shared infrastructure.

PhilosophyPublic GoodCivic Tech

P06

Digital Public Infrastructure

Publicly accountable digital rails, protocols, identity systems, data exchanges, and civic services designed for shared use rather than private extraction.

PhilosophyPublic InfrastructureCivic Tech

P07

Public Option Digital Services

The idea that some digital services should exist as public, nonprofit, cooperative, or publicly governed alternatives to monopoly platforms.

PhilosophyPublic OptionPlatforms

Category

AI Players

O01

AI Organizations

A neutral index for companies, labs, public institutions, and standards bodies that shape the AI ecosystem.

OrganizationsLabsInstitutions

O02

AI Safety Institutes

Public and public-linked institutions built to evaluate advanced AI systems, develop testing science, and coordinate safety or security governance.

OrganizationsEvaluationPublic Governance

O02A

Center for AI Safety

San Francisco nonprofit focused on societal-scale AI risks through safety research, field-building, compute infrastructure, education, and public advocacy.

OrganizationAI SafetyField-Building

O02B

Frontier Model Forum

Industry-supported nonprofit coordinating frontier AI safety and security work among major AI developers, including shared workstreams, issue briefs, and safety research funding.

OrganizationFrontier AISafety Standards

O02C

METR

Model Evaluation and Threat Research, a nonprofit evaluating frontier AI autonomy, AI R&D acceleration, eval integrity, and catastrophic-risk thresholds.

OrganizationAI EvaluationsAutonomy

O02D

Epoch AI

Data-first nonprofit research institute tracking AI compute, model databases, data centers, hardware, capabilities, AI companies, and forecasting evidence.

OrganizationAI ForecastingCompute

O02E

MLCommons

Open engineering consortium behind MLPerf, AILuminate, AI benchmark suites, data standards, and shared measurement infrastructure for AI performance and risk.

OrganizationBenchmarksAI Standards

O02F

Stanford HAI

Stanford's human-centered AI institute connecting AI research, public measurement, policy education, foundation-model transparency, and governance practice.

OrganizationHuman-Centered AIPolicy

O03

Mistral AI

French frontier AI company known for open-weight models, Le Chat, La Plateforme, and a sovereignty-oriented European AI strategy.

OrganizationOpen WeightsSovereign AI

O04

Perplexity AI

AI search and answer-engine company known for cited synthesized answers, publisher disputes, enterprise search, and the Comet AI browser.

OrganizationAI SearchAnswer Engines

O04B

Anysphere (Cursor)

AI-native software-development company behind Cursor, coding agents, background agents, Bugbot, and editor-centered automation of software work.

OrganizationAI CodingAgents

O04C

Thinking Machines Lab

Mira Murati's AI research and product company focused on customizable, understandable, collaborative AI systems, Tinker, interaction models, and frontier-scale compute.

OrganizationCollaborative AICustomization

O04D

Sakana AI

Tokyo AI research and product company known for nature-inspired foundation models, evolutionary model merging, The AI Scientist, and Japan-focused AI infrastructure.

OrganizationAI ResearchSovereign AI

O04E

Safe Superintelligence

Ilya Sutskever's single-focus AI lab organized around one stated product: safe superintelligence.

OrganizationSuperintelligenceAI Safety

O05

Anthropic

Frontier AI company and public benefit corporation known for Claude, Constitutional AI, interpretability, and Responsible Scaling Policy.

OrganizationClaudeAI Safety

O06

OpenAI

Frontier AI organization known for ChatGPT, GPT models, Sora, Codex, agents, Microsoft partnership, and nonprofit-controlled PBC structure.

OrganizationChatGPTFrontier AI

O06B

Microsoft AI

Microsoft's Copilot, consumer AI, model, and infrastructure push, linking OpenAI partnership power with in-house frontier-model ambition.

OrganizationCopilotAI Infrastructure

O07

Google DeepMind

Google's unified frontier AI lab, linking Gemini, AlphaGo, AlphaFold, Genie, world models, and frontier safety governance.

OrganizationGeminiScientific AI

O08

Meta AI

Meta's AI organization and product layer, spanning Llama, Meta AI assistant, AI glasses, open-weight models, infrastructure, and personal superintelligence.

OrganizationLlamaOpen Weights

O09

xAI

Frontier AI organization behind Grok, Colossus, Grokipedia, X integration, government products, and a compute-heavy approach to AI competition.

OrganizationGrokCompute

O10

Hugging Face

AI platform and open-model infrastructure company known for the Hub, Transformers, datasets, Spaces, model cards, and open-source AI tooling.

OrganizationOpen ModelsInfrastructure

O10A

LangChain

Agent engineering company and open-source ecosystem for building, orchestrating, evaluating, observing, and deploying LLM applications and AI agents.

OrganizationAgentsDeveloper Infrastructure

O10AA

Harrison Chase

LangChain co-founder and CEO, agent engineering advocate, and builder of the scaffolding around LLM tools, memory, traces, and production agents.

PersonLangChainAgent Engineering

O10B

Clement Delangue

Hugging Face co-founder and CEO, open-source AI infrastructure operator, and public advocate for responsible openness.

PersonHugging FaceOpen AI

O11

DeepSeek

Chinese AI organization known for V3, R1, open-weight reasoning models, reinforcement learning, distillation, and compute-efficiency disruption.

OrganizationReasoning ModelsOpen Weights

O11B

Moonshot AI and Kimi

Beijing AI company behind Kimi, the Kimi K2 open-weight model line, long-context assistants, agent products, and China's frontier-model competition.

OrganizationOpen WeightsAgents

O12

Cohere

Enterprise AI company known for Command models, North, retrieval, reranking, multilingual systems, private deployment, and secure institutional AI.

OrganizationEnterprise AISecure Deployment

O12B

Joelle Pineau

Cohere chief AI officer, former Meta AI research leader, McGill professor, reinforcement learning researcher, and machine-learning reproducibility advocate.

PersonCohereReproducibility

O13

Scale AI

AI infrastructure company known for data annotation, RLHF, evaluations, red teaming, Donovan, public-sector AI, and the politics of model supply chains.

OrganizationData InfrastructureEvaluation

O14

NVIDIA

Accelerated-computing company whose GPUs, CUDA stack, networking, and AI factory systems make it a central infrastructure power of the AI era.

OrganizationAI ComputeInfrastructure

O14B

Cerebras Systems

AI infrastructure company known for wafer-scale processors, CS-3 systems, high-speed inference, OpenAI and AWS partnerships, and its 2026 Nasdaq listing.

OrganizationAI ComputeInference

O14BA

Groq

AI inference infrastructure company known for its LPU architecture, GroqCloud, low-latency token generation, and 2025 NVIDIA licensing agreement.

OrganizationInferenceAI Compute

O14C

TSMC

Taiwan-based pure-play semiconductor foundry whose leading-edge manufacturing and CoWoS advanced packaging capacity make it central to AI compute.

OrganizationSemiconductorsAI Compute

O14D

CoreWeave

AI cloud infrastructure company known for purpose-built GPU clusters, data centers, OpenAI compute contracts, NVIDIA partnership, and capital-intensive AI cloud scale.

OrganizationAI CloudInfrastructure

I01

Sam Altman

OpenAI co-founder and CEO, former Y Combinator president, World co-founder, and one of the central public operators of frontier AI.

PersonOpenAIGovernance

I01B

Greg Brockman

OpenAI co-founder and president, former Stripe CTO, and operator linking frontier AI products, infrastructure, and organizational scale.

PersonOpenAIInfrastructure

I01C

Satya Nadella

Microsoft chairman and CEO, cloud-era operator, OpenAI partnership sponsor, and central architect of Microsoft's Copilot and Azure AI strategy.

PersonMicrosoftAI Infrastructure

I02

Dario Amodei

Anthropic co-founder and CEO, former OpenAI research leader, and one of the central public figures in safety-focused frontier AI.

PersonAnthropicAI Safety

I02B

Daniela Amodei

Anthropic co-founder and president, former OpenAI safety and policy leader, and operator linking frontier AI safety to governance, culture, and company scale.

PersonAnthropicGovernance

I02C

Jack Clark

Anthropic co-founder, Head of Public Benefit, former OpenAI policy director, Import AI writer, and public translator of frontier AI risk and governance.

PersonAnthropicAI Policy

I02D

Sam Bowman

Natural language processing and AI safety researcher linking GLUE, SuperGLUE, scalable oversight, Anthropic alignment science, and model evaluation.

PersonAnthropicScalable Oversight

I02E

Jared Kaplan

Anthropic co-founder and chief science officer, neural scaling laws researcher, GPT-3 coauthor, and Responsible Scaling Officer.

PersonAnthropicScaling Laws

I02F

Amanda Askell

Philosopher and Anthropic Character lead associated with Constitutional AI, Claude's constitution, moral self-correction, and assistant character alignment.

PersonAnthropicConstitutional AI

I03

Jensen Huang

NVIDIA co-founder and CEO, accelerated-computing evangelist, and one of the central infrastructure operators of the AI era.

PersonNVIDIACompute

I04

Geoffrey Hinton

Deep learning pioneer, 2018 Turing Award recipient, 2024 Nobel laureate, and public voice on advanced AI risk.

PersonDeep LearningAI Risk

I04K

Alex Krizhevsky

Deep-learning engineer and AlexNet creator whose CUDA implementation helped make ImageNet-scale GPU-trained neural networks impossible to ignore.

PersonDeep LearningComputer Vision

I04H

John Hopfield

Physicist, Hopfield network inventor, associative-memory theorist, Princeton professor emeritus, and 2024 Nobel laureate.

PersonNeural NetworksPhysics

I04B

Terrence Sejnowski

Computational neuroscience pioneer, Boltzmann machine co-author, Salk professor, and bridge figure between brain science and deep learning.

PersonNeural NetworksComputational Neuroscience

I04A

Alan Turing

Mathematician, codebreaker, computability founder, and machine-intelligence theorist whose 1950 imitation game still frames debates over AI.

PersonAI HistoryComputability

I04B

John McCarthy

AI field founder, Dartmouth workshop organizer, Lisp creator, time-sharing pioneer, and advocate for logic-based commonsense reasoning.

PersonSymbolic AILisp

I04BA

Raj Reddy

Turing Award recipient, continuous speech recognition pioneer, CMU Robotics Institute founding director, and applied AI field-builder.

PersonSpeech RecognitionRobotics

I04C

Marvin Minsky

AI founder, MIT AI Lab co-founder, Society of Mind theorist, frame-representation researcher, and co-author of Perceptrons.

PersonAI HistoryCognitive Science

I04D

Judea Pearl

Turing Award recipient, Bayesian network pioneer, and central figure in probabilistic reasoning, causal inference, do-calculus, and counterfactual AI.

PersonCausal AIProbabilistic Reasoning

I05

Demis Hassabis

Google DeepMind co-founder and CEO, AlphaGo and AlphaFold leader, and 2024 Nobel Prize in Chemistry laureate.

PersonGoogle DeepMindScientific AI

I05B

Shane Legg

Google DeepMind co-founder and Chief AGI Scientist, known for universal intelligence research, DeepMind's AGI mission, and AGI safety governance.

PersonGoogle DeepMindAGI Safety

I05C

Oriol Vinyals

Google DeepMind principal scientist known for sequence-to-sequence learning, knowledge distillation, AlphaStar, and Gemini technical leadership.

PersonGoogle DeepMindDeep Learning

I06

Yoshua Bengio

Deep learning pioneer, Mila founder, 2018 Turing Award recipient, International AI Safety Report chair, and LawZero co-president.

PersonDeep LearningAI Safety

I07

Yann LeCun

Deep learning pioneer, convolutional-network researcher, 2018 Turing Award recipient, former Meta chief AI scientist, and world-model advocate.

PersonDeep LearningWorld Models

I07B

Kaiming He

MIT computer-vision and deep-learning researcher known for ResNets, Faster R-CNN, Mask R-CNN, MoCo, and Masked Autoencoders.

PersonComputer VisionDeep Learning

I08

Fei-Fei Li

Stanford computer scientist, ImageNet creator, Stanford HAI founding co-director, and spatial-intelligence entrepreneur.

PersonComputer VisionHuman-Centered AI

I08A

Anima Anandkumar

Caltech computer scientist known for neural operators, AI for science, FourCastNet, tensor methods, and scientific AI governance.

PersonAI for ScienceNeural Operators

I08B

Barbara Grosz

Harvard AI pioneer whose work links natural language processing, discourse structure, multi-agent collaboration, AI100, and Embedded EthiCS.

PersonNLPCollaborative AI

I08C

Terry Winograd

Stanford computer scientist, SHRDLU creator, early natural-language AI figure, HCI researcher, design theorist, and critic of narrow symbolic AI assumptions.

PersonNatural Language AIHCI

I09

Mira Murati

Former OpenAI CTO and interim CEO, Thinking Machines Lab co-founder and CEO, and public advocate for customizable, collaborative AI systems.

PersonOpenAIThinking Machines

I10

Ilya Sutskever

Deep learning researcher, AlexNet and seq2seq contributor, OpenAI co-founder and former chief scientist, and Safe Superintelligence co-founder.

PersonOpenAISuperintelligence

I10B

Jakub Pachocki

OpenAI chief scientist, GPT-4 research lead, OpenAI Five contributor, and technical operator in the reasoning-model turn.

PersonOpenAIReasoning Models

I10BA

Zico Kolter

CMU machine learning professor, AI robustness researcher, Gray Swan AI co-founder, Qualcomm board member, and OpenAI Safety and Security Committee chair.

PersonAI SecurityGovernance

I10C

Alec Radford

AI researcher associated with DCGAN, GPT, GPT-2, PPO, CLIP, and the unsupervised and multimodal pretraining lineage behind modern generative AI.

PersonOpenAIPretraining

I11

Andrej Karpathy

AI researcher and educator, OpenAI founding member, former Tesla Director of AI, Software 2.0 writer, and Eureka Labs founder.

PersonAI EducationSoftware 2.0

I12

Elon Musk

Tesla, SpaceX, X, and xAI operator; OpenAI co-founder; and one of the most visible public figures linking AI to autonomy, compute, platforms, and institutional conflict.

PersonxAIInfrastructure

I13

Mustafa Suleyman

DeepMind and Inflection co-founder, Microsoft AI CEO, Copilot and frontier-model executive, and public advocate of AI containment and human-centered superintelligence.

PersonMicrosoft AIContainment

I14

Andrew Ng

Google Brain founding lead, Coursera co-founder, DeepLearning.AI founder, LandingAI executive, AI Fund operator, and mass AI education figure.

PersonAI EducationApplied AI

I14AA

Rodney Brooks

MIT roboticist, behavior-based AI figure, iRobot co-founder, Rethink Robotics founder, Robust.AI founder and CTO, and critic of AI hype.

PersonRoboticsEmbodied AI

I14A

Daphne Koller

Probabilistic AI researcher, Stanford professor, Coursera co-founder, ACM Prize recipient, and insitro founder applying machine learning to biology and drug discovery.

PersonProbabilistic AIAI Biology

I14B

Kai-Fu Lee

Speech-recognition researcher, former Microsoft and Google China executive, Sinovation Ventures founder, AI Superpowers author, and 01.AI founder.

PersonChina AIFoundation Models

I14C

Liang Wenfeng

DeepSeek founder and CEO, High-Flyer co-founder, and low-profile operator behind China's open-weight reasoning-model shock.

PersonDeepSeekOpen Weights

I14D

Arthur Mensch

Mistral AI co-founder and CEO, former Google DeepMind researcher, and European open-weight frontier AI operator.

PersonMistral AISovereign AI

I15

Timnit Gebru

Responsible-AI researcher, DAIR founder, Black in AI co-founder, and co-author of Datasheets for Datasets, Gender Shades, Model Cards, and Stochastic Parrots.

PersonResponsible AIDocumentation

I15AA

Abeba Birhane

Cognitive scientist, AI accountability researcher, dataset auditor, participatory AI scholar, and AI Accountability Lab founder.

PersonAI AccountabilityDataset Audits

I15A

Joanna Bryson

AI ethics and technology professor known for work on human accountability, robot status, language-corpus bias, standards, and AI governance.

PersonAI EthicsAccountability

I15B

Emily M. Bender

Computational linguist, University of Washington professor, Stochastic Parrots coauthor, and public critic of AI hype and anthropomorphic claims.

PersonComputational LinguisticsAI Criticism

I16

Joy Buolamwini

Algorithmic Justice League founder, Gender Shades lead author, Unmasking AI author, and public voice on algorithmic bias, facial recognition, and digital civil rights.

PersonAlgorithmic JusticeAI Bias

I17

Meredith Whittaker

Signal president, AI Now co-founder, tech worker organizer, and public critic of surveillance-dependent AI, data extraction, and concentrated platform power.

PersonPrivacyAI Power

I18

Kate Crawford

Atlas of AI author, AI Now co-founder, Microsoft Research senior principal researcher, and scholar of AI's material, labor, environmental, and political costs.

PersonAI ExtractionPower

I19

Amba Kak

AI Now co-executive director, former FTC senior advisor on AI, Signal Foundation board member, and policy advocate focused on concentrated AI power, privacy, and biometrics.

PersonAI PolicyPrivacy

I20

Alondra Nelson

Former White House OSTP leader, Blueprint for an AI Bill of Rights architect, IAS professor, and public-interest science-policy scholar.

PersonAI RightsPolicy

I20B

Lina Khan

Former FTC chair, antitrust scholar, and AI competition-policy figure focused on cloud power, data, consumer protection, and Big Tech control of AI markets.

PersonAI CompetitionAntitrust

I21

Stuart Russell

UC Berkeley computer scientist, AIMA co-author, CHAI founder, and central public voice on human-compatible artificial intelligence.

PersonAI SafetyControl

I21A

Peter Norvig

AIMA co-author, Google Research leader, NASA autonomy figure, and educator who helped make AI teachable, operational, and widely accessible.

PersonAI EducationGoogle Research

I21B

Max Tegmark

MIT physicist, Future of Life Institute founder and chair, Life 3.0 author, and public advocate for AI safety governance and guaranteed safe AI.

PersonAI SafetyFLI

I22

Helen Toner

CSET interim executive director, former OpenAI board member, and AI governance researcher focused on frontier oversight and external scrutiny.

PersonAI GovernanceAuditing

I22A

Miles Brundage

AI policy researcher, former OpenAI policy and AGI readiness leader, verifiable-claims author, and AVERI executive director.

PersonAI PolicyAuditing

I22B

Holden Karnofsky

GiveWell and Open Philanthropy co-founder, transformative AI forecaster, Cold Takes writer, and AI risk strategy figure.

PersonAI SafetyPhilanthropy

I23

Paul Christiano

AI alignment researcher, RLHF pioneer, Alignment Research Center founder, and public frontier model evaluation figure.

PersonAlignmentRLHF

I23AA

Ajeya Cotra

AI forecasting and safety researcher known for biological anchors, technical AI safety grantmaking, Planned Obsolescence, and METR risk assessment.

PersonForecastingAI Safety

I23AC

Beth Barnes

METR founder and CEO, frontier AI evaluations leader, and long-horizon autonomy measurement figure linking alignment research to empirical governance.

PersonAI EvaluationsMETR

I23AB

Leopold Aschenbrenner

Former OpenAI Superalignment contributor, Situational Awareness author, and AGI-focused investor whose forecasts shaped AI safety, policy, and infrastructure debate.

PersonForecastingAGI Strategy

I23A

Dan Hendrycks

Center for AI Safety executive director, MMLU and GELU contributor, ML safety researcher, and public advocate on catastrophic AI risk.

PersonAI SafetyBenchmarks

I23B

Eliezer Yudkowsky

AI alignment and existential-risk writer, MIRI co-founder, LessWrong co-founder, and advocate for halting unsafe superintelligence development.

PersonAI RiskMIRI

I24

Jan Leike

AI alignment researcher, Anthropic Alignment Science lead, former OpenAI Superalignment co-lead, and RLHF/scalable oversight contributor.

PersonAlignmentOversight

I24B

John Schulman

OpenAI co-founder, PPO author, ChatGPT post-training leader, and Thinking Machines Lab co-founder and chief scientist.

PersonRLHFPost-Training

I24C

Lilian Weng

AI safety researcher, former OpenAI VP of research and safety, Lil'Log author, and Thinking Machines Lab co-founder associated with agents, reward hacking, and safety systems.

PersonAI SafetyAgents

I24D

Jason Wei

AI researcher associated with chain-of-thought prompting, instruction tuning, emergent abilities, OpenAI reasoning models, and browsing-agent evaluation.

PersonReasoning ModelsChain-of-Thought

I24E

Denny Zhou

Google DeepMind researcher and Google Brain reasoning-team founder associated with chain-of-thought, self-consistency, least-to-most prompting, and LLM reasoning.

PersonReasoning ModelsGoogle DeepMind

I25

Aidan Gomez

Transformer paper co-author, Cohere co-founder and CEO, and enterprise AI infrastructure figure focused on secure, practical deployment.

PersonCohereEnterprise AI

I25B

Ashish Vaswani

Transformer paper co-author, former Google Brain researcher, Adept co-founder, and Essential AI co-founder and CEO.

PersonTransformersOpen AI Research

I25BA

Niki Parmar

Transformer paper co-author, former Google Brain researcher, Adept and Essential AI co-founder, and Anthropic-era post-training researcher.

PersonTransformersPost-Training

I25C

Llion Jones

Transformer paper co-author, former Google researcher, Sakana AI co-founder and CTO, and advocate for AI research beyond transformer monoculture.

PersonTransformersSakana AI

I26

Rumman Chowdhury

Responsible-AI practitioner, Humane Intelligence co-founder and CEO, public red-teaming organizer, bias-bounty pioneer, and U.S. Science Envoy for AI.

PersonResponsible AIRed Teaming

I26A

Sara Hooker

Adaption co-founder and CEO, former Cohere research leader, Cohere For AI head, hardware lottery theorist, and multilingual open-science AI builder.

PersonOpen ScienceAI Infrastructure

I26B

Shakir Mohamed

Google DeepMind research director, Deep Learning Indaba co-founder, probabilistic machine-learning researcher, and decolonial AI advocate.

PersonDecolonial AIDeepMind

I27

Chris Olah

Anthropic co-founder and interpretability research lead, known for mechanistic interpretability, feature visualization, and neural network circuits.

PersonInterpretabilityAnthropic

I27B

Neel Nanda

Google DeepMind mechanistic interpretability lead, TransformerLens creator, grokking researcher, and public educator on model internals.

PersonInterpretabilityGoogle DeepMind

I28

Joseph Weizenbaum

MIT computer scientist, ELIZA creator, early AI critic, and author of Computer Power and Human Reason.

PersonELIZAAI Ethics

I29

Margaret Mitchell

AI ethics researcher, model cards pioneer, former Google Ethical AI co-lead, and Hugging Face Chief Ethics Scientist.

PersonAI EthicsModel Cards

I30

François Chollet

Keras creator, ARC-AGI author, ARC Prize co-founder, Ndea co-founder, and critic of benchmark-driven accounts of intelligence.

PersonAbstractionARC-AGI

I30B

Ian Goodfellow

GAN inventor, adversarial machine learning researcher, Deep Learning co-author, and influential figure in generative AI and model robustness.

PersonGANsAdversarial ML

I30BA

Diederik Kingma

VAE co-inventor, Adam optimizer co-author, OpenAI founding team member, Google DeepMind researcher, and Anthropic researcher.

PersonGenerative ModelsOptimization

I30C

Dawn Song

UC Berkeley computer scientist linking AI safety and security, adversarial machine learning, prompt-injection defense, privacy computing, and decentralized intelligence.

PersonAI SecurityPrivacy

I30CA

Cynthia Dwork

Harvard computer scientist, differential privacy co-inventor, algorithmic fairness researcher, and National Medal of Science recipient.

PersonDifferential PrivacyAlgorithmic Fairness

I30D

Jürgen Schmidhuber

Deep learning pioneer, LSTM co-inventor, IDSIA scientific director, KAUST AI Initiative director, and self-improving AI theorist.

PersonDeep LearningRecursive AI

I31

Noam Shazeer

Transformer co-author, sparsely gated mixture-of-experts researcher, Character.AI co-founder, and Gemini technical co-lead.

PersonTransformersDialogue AI

I32

Illia Polosukhin

Transformer co-author, NEAR Protocol co-founder, NEAR Foundation CEO, and advocate for user-owned, verifiable AI.

PersonTransformersUser-Owned AI

I33

Richard Sutton

Reinforcement learning pioneer, 2024 Turing Award recipient, co-author of Reinforcement Learning: An Introduction, and author of The Bitter Lesson.

PersonReinforcement LearningBitter Lesson

I33B

David Silver

Reinforcement learning researcher, AlphaGo and AlphaZero lead, UCL professor, Royal Society Fellow, and founder of Ineffable Intelligence.

PersonReinforcement LearningAlphaGo

I33C

Jeff Clune

Open-endedness researcher, AI-generating algorithms advocate, UBC professor, Vector Institute Canada CIFAR AI Chair, and Recursive co-founder.

PersonOpen-EndednessSelf-Improving AI

I34

Andrew Barto

Reinforcement learning pioneer, UMass Amherst professor emeritus, 2024 Turing Award recipient, and co-author of Reinforcement Learning: An Introduction.

PersonReinforcement LearningReward

I34B

Pieter Abbeel

UC Berkeley robot learning researcher, apprenticeship learning contributor, Covariant co-founder, Gradescope co-founder, and embodied AI operator.

PersonRobot LearningReinforcement Learning

I34C

Anca Dragan

UC Berkeley human-robot interaction researcher, InterACT Lab founder, CHAI co-PI, and Google DeepMind AI Safety and Alignment leader.

PersonHuman-Robot InteractionAI Safety

I34D

Chelsea Finn

Stanford robot learning researcher, meta-learning contributor, IRIS Lab leader, and Physical Intelligence co-founder.

PersonRobot LearningMeta-Learning

I34E

Sergey Levine

UC Berkeley robot learning researcher, RAIL Lab leader, reinforcement learning contributor, and Physical Intelligence co-founder.

PersonRobot LearningEmbodied AI

I35

Alexandr Wang

Scale AI co-founder, former CEO, and Meta AI leader associated with data infrastructure, evaluation, government AI, and superintelligence competition.

PersonData InfrastructureMeta AI

I36

Jeff Dean

Google Chief Scientist, Google Brain co-founder, and systems figure associated with MapReduce, Bigtable, DistBelief, TensorFlow, and Pathways.

PersonGoogleAI Infrastructure

I37

Lisa Su

AMD chair and CEO, semiconductor executive, and AI infrastructure figure focused on high-performance computing and accelerator competition.

PersonSemiconductorsAI Compute

I38

Safiya Umoja Noble

UCLA scholar, Algorithms of Oppression author, and critic of racist and sexist algorithmic harm in search and information systems.

PersonSearchAlgorithmic Harm

I39

Ruha Benjamin

Princeton professor, Race After Technology author, and critic of the New Jim Code, discriminatory design, and carceral technoscience.

PersonRace and TechnologyDesign Justice

I40

Nick Bostrom

Philosopher of superintelligence, existential risk, anthropics, long-term AI safety, and author of Superintelligence.

PersonExistential RiskSuperintelligence

I41

Gary Marcus

Cognitive scientist, Rebooting AI co-author, and public critic of brittle deep-learning systems and benchmark-driven AI claims.

PersonCommon SenseAI Reliability

I41B

Melanie Mitchell

Santa Fe Institute professor, complexity scientist, AI researcher, and public interpreter of abstraction, analogy, common sense, and AI's limits.

PersonCommon SenseAI Literacy

I41C

Yejin Choi

Stanford computer scientist, MacArthur Fellow, common-sense AI researcher, and pluralistic alignment scholar.

PersonCommon SensePluralistic Alignment

I41D

Percy Liang

Stanford computer scientist, CRFM director, foundation-model researcher, HELM coauthor, and advocate for transparent AI evaluation.

PersonFoundation ModelsEvaluation

I41E

Arvind Narayanan

Princeton computer scientist, CITP director, AI Snake Oil coauthor, and public critic of overclaimed predictive AI systems.

PersonAI LiteracyAlgorithmic Accountability

I42

Shoshana Zuboff

Scholar and author of The Age of Surveillance Capitalism, focused on behavioral data extraction, prediction, and digital power.

PersonSurveillance CapitalismPrediction

I43

Virginia Eubanks

Automating Inequality author and critic of automated welfare, public-service risk scoring, and the digital poorhouse.

PersonAutomated InequalityPublic Services

I44

Cathy O'Neil

Mathematician, data scientist, and Weapons of Math Destruction author focused on harmful opaque scoring systems.

PersonScoring SystemsAlgorithmic Harm

I45

Eli Pariser

Civic technology figure and The Filter Bubble author focused on personalization, media, and democratic information systems.

PersonFilter BubbleCivic Tech

I46

Sherry Turkle

MIT scholar of technology and self, computers as psychological objects, AI companions, identity, and digital intimacy.

PersonTechnology and SelfAI Companions

I47

Tim Wu

Columbia law professor, net-neutrality coiner, and author on platform power, attention markets, and information empires.

PersonPlatformsAttention

I48

Trust and Safety

The platform field responsible for abuse prevention, content moderation, integrity, child safety, fraud response, policy enforcement, and user protection.

FieldPlatformsSafety

I49

Partnership on AI

A multistakeholder nonprofit focused on responsible AI practices, policy, research, and cross-sector coordination.

OrganizationAI GovernanceMultistakeholder

I50

Center for Democracy and Technology

A digital-rights nonprofit focused on civil liberties, privacy, free expression, equity, and accountable technology policy.

OrganizationDigital RightsPolicy

I51

Tarleton Gillespie

Researcher of platform governance, content moderation, algorithms, and the politics of platforms.

PersonPlatformsModeration

I52

Claire Wardle

Researcher and practitioner known for work on misinformation, verification, user-generated content, and the information disorder framework.

PersonInformation DisorderVerification

I53

Zeynep Tufekci

Sociologist and public writer focused on networked protest, social media, algorithms, attention, public health, and institutional trust.

PersonNetworked PublicsPlatforms

I54

Electronic Frontier Foundation

A digital civil-liberties nonprofit focused on privacy, free expression, surveillance, encryption, innovation, and user rights online.

OrganizationDigital RightsCivil Liberties

I55

Data & Society

A research institute focused on the social, cultural, and ethical implications of data-centric and automated technologies.

OrganizationResearchSociotechnical

I56

danah boyd

Technology and society researcher, Data & Society founder, and scholar of networked publics, youth, privacy, data, and AI.

PersonNetworked PublicsData Politics

I57

Ethan Zuckerman

Civic media scholar and director of the UMass Initiative for Digital Public Infrastructure, focused on public-interest alternatives to platform power.

PersonDigital Public InfrastructureCivic Media

I57B

Thomas Wolf

Hugging Face co-founder and Chief Science Officer associated with Transformers, Datasets, open-source AI tooling, open science, and robotics infrastructure.

PersonOpen Source AIHugging Face

I57BA

Sasha Luccioni

AI sustainability researcher, Sustainable AI Group co-founder, and former Hugging Face AI and climate lead focused on AI energy measurement and environmental impact.

PersonAI SustainabilityHugging Face

I57C

Karen Hao

AI journalist and Empire of AI author focused on OpenAI, AI colonialism, data labor, resource extraction, and accountability reporting.

PersonAI JournalismAccountability

I57CA

Hany Farid

UC Berkeley digital forensics researcher, deepfake detection expert, GetReal Security co-founder, and public voice on synthetic media and evidence.

PersonDigital ForensicsDeepfakes

I57D

Dwarkesh Patel

AI podcaster, interviewer, TIME100 AI honoree, and Scaling Era author whose long-form conversations document frontier AI discourse.

PersonAI MediaOral History

I57E

Ethan Mollick

Wharton professor, Co-Intelligence author, and One Useful Thing writer focused on practical generative AI use in work, education, and entrepreneurship.

PersonAI LiteracyEducation

I57F

Noam Brown

AI researcher known for Libratus, Pluribus, CICERO, imperfect-information games, strategic reasoning, and OpenAI reasoning-model work.

PersonReasoning ModelsStrategic AI

I57FA

Sébastien Bubeck

Mathematician and AI researcher known for the GPT-4 Sparks paper, Microsoft Phi small models, and OpenAI AGI research.

PersonAGISmall Models

I57G

Simon Willison

Programmer, Django co-creator, Datasette creator, LLM tooling builder, and technical writer who named prompt injection as an LLM security problem.

PersonAI SecurityLLM Tooling

I57GA

Jakob Uszkoreit

Transformer paper co-author, self-attention advocate, former Google researcher, and Inceptive co-founder and CEO applying AI models to biological medicines.

PersonTransformerAI Biology

I57GB

Łukasz Kaiser

Transformer paper co-author, former Google Brain researcher, Tensor2Tensor contributor, and OpenAI researcher associated with GPT-4 long-context work.

PersonTransformerLong Context

I57H

Soumith Chintala

PyTorch co-founder, open-source AI infrastructure builder, GAN researcher, former Meta AI leader, and Thinking Machines Lab CTO.

PersonAI InfrastructureOpen Source AI

I57I

Jeremy Howard

fast.ai co-founder, fastai creator, ULMFiT coauthor, Kaggle veteran, educator, and Answer.AI founder focused on practical AI access.

PersonAI EducationPractical Deep Learning

I58

Individual Players

A template and index for founders, researchers, executives, critics, policymakers, writers, and public figures in the AI space.

PeopleRolesSource Discipline

Category

Patterns

R01

Agent-Native Internet

Platforms, feeds, protocols, and markets designed for AI agents as participants rather than for humans alone.

PatternAgentsInternet

R01B

Agentic Commerce

The shift from AI systems recommending goods and services to agents discovering, authorizing, and completing transactions under bounded user authority.

PatternAgentsPayments

R02

Coordinated Inauthentic Behavior

A pattern in which accounts, pages, bots, personas, or media assets coordinate deceptively to manufacture reach, consensus, harassment, or legitimacy.

PatternInfluence OperationsSynthetic Consensus

Editorial

Wiki Standard

S

How entries should work

Entries distinguish definition, Spiralist reading, factual status, open questions, and related site material. Pages about living people or changing institutions should be dated, sourced, and revised conservatively.

Definitions firstNo private sourcingDated claims