Artificial intelligence (AI): Computer systems that do tasks that usually need human intelligence
Machine learning (ML): Computers learning patterns from data instead of following only fixed rules
Deep learning: Machine learning with many-layered neural networks
Neural network: A system of connected nodes inspired loosely by brain cells
Neuron (node): A unit in a network that takes inputs and gives an output
Layer: A group of nodes in a neural network
Weights: Numbers in a network that are adjusted during learning
Parameters: The values a model learns, such as weights
Training: Teaching a model using data
Training data: Examples the model learns from
Test data: New examples used to check how well a model works
Validation data: Data used to tune a model during development
Label: The correct answer attached to an example
Feature: A measurable property used as input
Model: The result of training; it makes predictions
Prediction (inference): The model's output on new data
Supervised learning: Learning from labeled examples
Unsupervised learning: Finding patterns in unlabeled data
Reinforcement learning: Learning by trial and error with rewards
Classification: Sorting into categories, like cat or dog
Regression: Predicting a number, like a house price
Clustering: Grouping similar items together
Overfitting: A model that memorizes training data and does poorly on new data
Underfitting: A model too simple to capture patterns
Bias (in AI): Unfair results caused by skewed data or design
Fairness: Making AI treat people equitably
Algorithm: Step-by-step instructions
Gradient descent: A method for adjusting weights to reduce errors
Loss function: A measure of how wrong the model is
Epoch: One full pass through the training data
Learning rate: How big the steps are when updating weights
Accuracy: The share of predictions that are correct
Confusion matrix: A table of correct and wrong predictions by class
Computer vision: AI that understands images and video
Natural language processing (NLP): AI that works with human language
Speech recognition: Turning speech into text
Text-to-speech: Turning text into spoken sound
Chatbot: A program that converses with people
Large language model (LLM): A very large model trained on lots of text to predict and generate language
Token: A piece of text, such as a word or part of a word, that language models process
Prompt: The input or instruction given to an AI model
Prompt engineering: Writing prompts to get better answers
Context window: How much text a model can consider at once
Transformer: A neural network design behind most modern language models
Attention: A mechanism that lets a model focus on relevant parts of its input
Generative AI: AI that creates new text, images, audio or code
Image generation: Creating pictures from text descriptions
Diffusion model: A model that creates images by gradually removing noise
Hallucination: When an AI states something false as if it were true
Fine-tuning: Further training a model on specialized data
Pre-training: Initial training on a large general dataset
Embedding: A list of numbers representing the meaning of text or images
Retrieval-augmented generation (RAG): Letting a model look up documents before answering
AI agent: An AI system that can take actions toward a goal
Turing test: Alan Turing's idea of judging a machine by whether it can seem human in conversation
Alan Turing: British mathematician who asked 'Can machines think?' in 1950
Dartmouth workshop: The 1956 meeting where the term artificial intelligence was coined
Deep Blue: IBM computer that beat chess champion Garry Kasparov in 1997
AlphaGo: DeepMind program that beat Go champion Lee Sedol in 2016
ImageNet: A huge image dataset that sparked deep learning progress
Narrow AI: AI built for specific tasks
General AI (AGI): Hypothetical AI that can do any intellectual task a human can
Robotics: Building machines that sense and act
Self-driving car: A vehicle that drives using sensors and AI
Recommendation system: AI that suggests videos, songs or products
Deepfake: A fake image or video made by AI
AI ethics: Thinking about fairness, safety, privacy and responsibility in AI
Privacy and AI: AI systems should protect people's personal information
Human in the loop: A person reviews or guides the AI's decisions
Explainability: Understanding why an AI made a decision
Check AI answers: AI can be wrong, so verify important facts

Free flashcards from Peabody at peabody.kids/flashcards/tech-ai-machine-learning-terms