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Glossary

Short explainers for concepts in the world of artificial intelligence.

A

  • AI agent

    A system that pursues goals by observing an environment, planning, and taking actions (often via tools) rather than only producing static text.

  • AI alignment

    The problem of ensuring AI systems pursue intended goals and values, not just what they were literally optimized for on training data.

  • AI winter

    A period when AI research funding and public optimism dropped sharply after early hype exceeded practical results.

  • AlphaGo

    DeepMind's Go program that beat top professional Lee Sedol in 2016, a landmark mix of deep networks, self-play, and search.

  • Artificial general intelligence (AGI)

    Hypothetical AI that matches or exceeds human-level competence across many tasks, not the same as a narrow model or today's LLM chatbot.

  • Artificial intelligence (AI)

    The broad field of computer science focused on building systems that perform tasks typically requiring human intelligence, from pattern recognition to reasoning and language understanding.

C

  • Chatbot

    A conversational interface powered by rules, scripts, or, today often,a large language model; distinct from agents when it only replies without acting in external systems.

D

  • Dead Internet theory

    A fringe hypothesis (from online culture, circa 2021) that most visible web content and engagement is already bots, spam, or AI-generated, not authentic human activity.

  • Deep Blue

    IBM's 1997 chess supercomputer that beat world champion Garry Kasparov with brute-force search and hand-tuned evaluation, not learning.

  • Deep learning

    Neural networks with many successive layers and training methods tuned to stacked representations (often rebranded circa 2006–2012 revival).

E

  • ELIZA

    Joseph Weizenbaum's 1966 MIT program that mimicked therapy dialogue via keyword rules, famous for the ELIZA effect.

  • Expert system

    Rule-based AI program that encodes domain knowledge as if-then rules and an inference engine; commercial flagship of 1980s symbolic AI.

F

  • Fine-tuning

    Additional training starting from an existing model: usually smaller data and narrower objective than pretraining.

G

  • Guardrails

    Technical and policy layers that constrain what an AI system may say or do: filters, rules, tool permissions, and human review.

I

  • Inference

    Using a trained model to produce outputs on new inputs: after training is finished (also called deployment or forward pass in many setups).

J

  • Jailbreak

    Crafting prompts (or prompt chains) intended to bypass a model's safety rules, refusals, or usage policies, not the same as ordinary prompt engineering.

L

  • Large language model (LLM)

    A statistical language model trained on large text corpora, typically using a transformer architecture, used for generation and understanding tasks.

M

  • Machine learning (ML)

    Algorithms that generalize patterns from data to make predictions or decisions without hand-written rules for every case.

  • Model (machine learning model)

    A learned function with parameters fit on data: the artifact produced by training and used at inference time.

  • Model Context Protocol (MCP)

    An open protocol that standardises how AI applications connect to external data sources, tools, and services, enabling models to act on live context rather than static training data.

N

  • Natural language processing (NLP)

    The branch of AI focused on understanding, generating, and transforming human language with computers, from parsing and translation to modern LLMs.

  • Neural network (ANN)

    Parametric models organized in layers of simple units; strengths are learned from data via optimization.

P

  • Perceptron

    Early single-layer neural model that learns a linear decision boundary from examples; Rosenblatt (1958), later limited by Minsky and Papert's analysis.

  • PPO (Proximal Policy Optimization)

    A widely used reinforcement-learning algorithm that stabilizes policy updates with a clipped objective; common in RLHF fine-tuning stages.

  • Prompt

    The textual instruction or context given to an AI model to guide output style, scope, and task.

  • Prompt injection

    Hostile or hidden instructions in context that hijack an LLM's behavior, direct (user input) or indirect (RAG, tools, untrusted documents).

R

  • Reasoning

    Step-by-step inference behavior used to derive conclusions from context, rules, or intermediate states.

  • Red teaming

    Deliberately probing an AI system for failures, misuse, and bypasses before or after launch, structured adversarial testing, not casual chatting.

  • Reinforcement learning (RL)

    Learning policies via rewards or scores from interacting with an environment: core to games, robotics, and some RLHF-style LLM tuning.

  • Retrieval-augmented generation (RAG)

    Combining a retriever over documents or tools with a generator LLM so answers can cite fresher or private context.

  • RLHF (reinforcement learning from human feedback)

    Fine-tuning LLMs from human preference comparisons, often followed by reinforcement learning, widely used for alignment after pretraining.

S

  • Superintelligence (ASI)

    A hypothetical AI that far exceeds human capability across virtually all cognitive domains, not the same as a strong chatbot, today's LLM, or human-level AGI.

  • Supervised learning

    Machine learning from labeled input–output pairs: the model learns to map examples to known targets before deployment.

  • Symbolic AI (good old-fashioned AI)

    AI built from explicit symbols, logic rules, and structured knowledge: often contrasted with numeric learning from raw data.

T

  • Technological singularity

    A hypothetical future point where technological change (often via advanced AI) accelerates beyond reliable human forecasting or control.

  • Token

    The smallest text unit a language model processes (often a word piece, not a full word).

  • Training (model training)

    The phase where model parameters are optimized on data or feedback before deployment: distinct from inference at run time.

  • Transformer (architecture)

    Neural sequence model built mainly on attention: parallelizable and foundational for LLMs since "Attention Is All You Need" (2017).

  • Turing test (imitation game)

    Alan Turing’s proposed behavioral criterion: can dialogue from a machine be distinguished from a human’s?

V

  • Vector database

    A store optimized for similarity search over embedding vectors: the retrieval layer behind many RAG and semantic-search systems.