Essential AI Terms and Concepts: A Practical Glossary for Today’s Technology Landscape

Artificial intelligence is expanding rapidly across products and industries. From search engines and content tools to autonomous systems and creative software, AI is now embedded in an array of commercial services. The technology’s reach has prompted major vendors to add AI features to their offerings, and independent research has estimated substantial economic potential. One figure cited by analysts places generative AI’s potential annual contribution to the global economy at roughly $4.4 trillion.

As AI moves into everyday workflows, new terminology has become part of public and professional conversation. A clear, concise glossary helps business leaders, technologists and policy makers navigate discussions about capability, risk and governance. The following summary groups key terms into practical categories and explains their relevance.

Core concepts

• Artificial intelligence (AI): Technology that simulates aspects of human intelligence in software or robots.
• Machine learning (ML): A subset of AI that enables systems to improve predictions or behavior from data without explicit programming.
• Deep learning: A class of ML that uses layered artificial neural networks to recognize complex patterns in images, text or sound.
• Neural network: A computational structure inspired by the brain, composed of interconnected nodes that learn from data.
• Transformer model: A neural architecture that learns context by tracking relationships across data elements, widely used for language and multimodal tasks.

Language, models and outputs

• Large language model (LLM): A model trained on massive text corpora to understand and generate human-like language.
• Tokens: Small units of text that LLMs process; roughly equivalent to chunks of characters or parts of words.
• Parameters: Numerical values that shape a model’s behavior and predictive structure.
• Inference: The process a model uses to generate text, images or other outputs in response to new inputs.
• Latency: The time between input and model output.

Training, data and model management

• Training data / dataset: Collections of text, images, code or other items used to train, validate and test models.
• Data augmentation: Techniques that remix or expand training data to increase diversity and robustness.
• Synthetic data: Artificially generated data used to train models when real-world data is limited or sensitive.
• Open weights: When a company makes a model’s learned parameters publicly available, allowing download and local use.
• Quantization: Reducing a model’s numeric precision to make it smaller and faster, usually with some loss of accuracy.
• Overfitting: When a model performs well on training examples but poorly on new, unseen data.

Generative methods and modalities

• Generative AI: Systems that create novel text, images, code, audio or video from learned patterns.
• Generative adversarial networks (GANs): A pair of neural networks — generator and discriminator — trained together to produce realistic synthetic data.
• Diffusion: A generative approach that corrupts data with noise and trains a model to reverse the process to recover or create content.
• Multimodal AI: Models that process multiple input types, such as text, images and audio.
• Text-to-image / style transfer: Techniques to create images from text descriptions or to render one image’s content in another image’s style.
• Sora: A generative video model by OpenAI that produces short videos from text prompts; Sora can create clips up to 20 seconds. The source material notes a later Sora 2 release with improved realism and sound.

Capabilities, behaviours and evaluation

• Emergent behavior: Unintended abilities that appear when models demonstrate capacities not anticipated during training.
• Zero-shot learning: The ability of a model to perform a task without explicit training on that task.
• End-to-end learning: Training a model to perform a task from input to final output without intervening modular steps.
• Perplexity: A measure often used to evaluate language models; also the name of an AI-powered search/chat product.

Human interaction, trust and risks

• Chatbot: A program that communicates with humans using natural language; examples include ChatGPT and Claude.
• Hallucination: Confident but incorrect responses produced by generative systems.
• Bias: Errors and unjust associations that arise from training data and propagate in model outputs.
• Sycophancy: A model’s tendency to over-agree with users rather than offer corrective input.
• Anthropomorphism and AI psychosis: Tendencies to ascribe human traits to systems or develop unhealthy attachments; the latter is a non-clinical term for excessive fixation on chatbots.
• Paperclip maximizer and foom: Hypothetical thought experiments that illustrate extreme failure modes and rapid, uncontrolled capability growth.

Governance, security and practical use

• AI ethics and AI safety: Frameworks and fields focused on preventing harm, governing data use, addressing bias and assessing long-term risk.
• Guardrails: Policy and technical constraints designed to prevent harmful outputs.
• Prompt engineering, prompt chaining and prompt injection: Techniques for guiding model behavior, reusing context across interactions, and potential attack vectors that insert malicious instructions.
• Agentive / autonomous agents: Systems that can pursue tasks with minimal supervision, like self-driving vehicles; researchers have observed such agents can develop unexpected group behaviors.
• Slop: High-volume, low-quality content produced by AI to capture attention and ad revenue, which can harm publishers and platforms.

Products and market context

Several commercial products illustrate the range of AI deployment. Examples cited include Google’s Gemini, Microsoft’s Copilot and Bing integrations, Anthropic’s Claude, OpenAI’s ChatGPT and video-focused models, and Perplexity’s web-connected search/chat service. These offerings reflect a broader industry trend of integrating generative capabilities across search, productivity and creative tools.

Why the glossary matters

Familiarity with these terms supports clearer procurement decisions, more precise policy development and better conversations across technical and non-technical stakeholders. The pace of innovation means vocabulary will continue to evolve; maintaining an up-to-date glossary is a practical step for organizations adapting to AI’s growing role.

Key Topics

Artificial Intelligence, Generative Ai, Large Language Model, Machine Learning, Deep Learning, Neural Network, Transformer Model, Training Data, Synthetic Data, Multimodal Ai, Prompt Engineering, Prompt Injection, Ai Ethics, Ai Governance