AI Glossary
Welcome to the Future – One Term at a Time!
The world of Artificial Intelligence is full of buzzwords that sound like they’re straight out of a sci-fi movie. From „Black Box“ to „Zero-shot Learning“ – it’s easy to get lost in translation.
But fear not: This AI Glossary is your trusty Compass through the jargon jungle. Whether you’re a curious newbie, a legal eagle trying to make sense of machine logic, or an AI enthusiast brushing up your vocabulary – this list brings clarity.
Let’s decode the future, one term at a time.
A
AGI (Artificial General Intelligence)
The theoretical holy grail of AI: a machine with the cognitive abilities of a human.
Algorithm
A set of rules or instructions that a computer follows to solve problems.
Artificial Intelligence (AI)
The umbrella term for machines mimicking human intelligence.
Autonomous System
A system that operates independently without human intervention.
B
Bias
When an AI reflects or amplifies human prejudices from skewed data.
Black Box
A model whose inner workings are not transparent. Mysterious, eh?
C
Clustering
Grouping similar data points together in machine learning.
Computer Vision
AI’s ability to “see” and interpret visual input.
Concept Drift
When an AI model becomes less accurate over time due to real-world changes.
D
Data Labeling
Tagging data (like images or text) to train AI models.
Deep Learning
A type of ML using neural networks with many layers.
Diffusion Models
Used in image generation by reversing noise — the secret sauce behind AI art.
Digital Markets Act (DMA)
Aimed at curbing the power of Big Tech “gatekeepers.” Important if your AI operates in digital marketplaces or uses large datasets from dominant platforms.
Digital Services Act (DSA)
This law regulates online platforms and content moderation, with a focus on transparency and accountability. Relevant for AI systems used in recommender systems or content filtering.
E
Edge AI
AI that runs locally on devices (not in the cloud).
EU AI Act
The first major attempt to regulate AI in the EU. It classifies AI systems based on risk levels (minimal, limited, high, and unacceptable) and imposes stricter rules the higher the risk. Think GDPR, but for robots with ambition.
Embedding
Turning data (like words) into numerical vectors with meaning.
Ethical AI
Developing AI responsibly and without harming society.
Explainability Gap
The disconnect between AI behavior and human understanding.
Explainable AI (XAI)
Efforts to make AI decisions understandable to humans.
F
Federated Learning
Training models across decentralized devices while keeping data private.
Fine-Tuning
Adapting a pre-trained model to a specific task or domain.
G
GAN (Generative Adversarial Network)
Two neural nets battling to create ultra-realistic content.
GDPR (General Data Protection Regulation)
The EU’s iconic data protection law that governs how personal data is collected, stored, and used. If your AI handles personal data, GDPR is the big boss — with fines to match.
Generative AI
AI that creates — whether it’s text, music, or cat memes.
H
Hallucination
When an AI makes stuff up with confidence.
I
Inference
The moment a model puts its learning to work.
J
Coming soon
K
Knowledge Graph
A structured map of facts and their relationships.
L
Latency
Time delay between user input and AI output. Shorter = better.
Large Language Model (LLM)
A model trained on massive datasets for language tasks.
M
Machine Learning (ML)
A subset of AI where machines learn from data.
Model
The result of training — used to make predictions or generate content.
Multimodal AI
AI that combines text, images, audio, and more.
N
Natural Language Processing (NLP)
AI’s ability to understand and produce human language.
Neural Network
A brain-inspired system of interconnected “neurons.”
O
Overfitting
When a model learns training data too well and fails on new input.
P
Pre-trained Model
A model trained on a large dataset, ready for adaptation.
Prompt
Your input to an AI — the digital “go fetch!”
Prompt Engineering
Crafting clever prompts to get better results from AI.
Q
coming soon.
R
RAG (Retrieval-Augmented Generation)
Combines search with generative AI for fact-based output.
Reinforcement Learning
Training a model using rewards and penalties — just like training a dog, but nerdier.
S
Safety Alignment
Making sure AI systems behave in line with human values.
Strong AI
Also known as AGI — AI that can think and generalize like a human.
Swarm Intelligence
Modeling AI behavior on natural systems like ant colonies.
Synthetic Data
Artificial data created to train or test AI systems.
Synthetic Media
Digitally generated or altered content created using AI. This includes AI-generated images, videos, voices, and text — from deepfakes to digital influencers. It’s the future of creativity… and misinformation, depending on who’s behind the keyboard.
T
Token
A small piece of input (like a word fragment) that models use.
Token Limit / Context Window
The max number of tokens an AI model can handle at once.
Training Data
The data used to “teach” AI models.
Transformer
The architecture behind powerful language models like GPT.
Tuning Parameters (Hyperparameters)
Settings adjusted during training to improve performance.
Turing Test
Alan Turing’s idea: if you can’t tell a machine from a human, the machine wins.
U
Underfitting
When a model fails to learn enough from the data.
V
Verification
Confirming AI output is correct, especially critical in legal/medical contexts.
Virtual Agent
An AI-powered chatbot or assistant, often used in customer service or HR bots. Like Clippy 2.0, but smarter.
W
Weak AI
AI specialized for a narrow task — not as scary, but very useful.
X
coming soon.
Y
coming soon.
Z
Zero-shot Learning
When a model successfully handles tasks it wasn’t explicitly trained for.