AI Basics
(7 terms)Foundational concepts everyone should know
Artificial Intelligence (AI)
Computer systems that can perform tasks normally requiring human intelligence.
Machine Learning
AI that improves automatically through experience without being explicitly programmed.
Neural Network
AI systems loosely inspired by how the human brain processes information.
Deep Learning
Machine learning using neural networks with many layers for complex pattern recognition.
Algorithm
A step-by-step set of instructions for solving a problem or completing a task.
Training
The process of teaching an AI system by exposing it to large amounts of data.
Training Data
The examples used to teach an AI system what to learn.
AI Models
(8 terms)Types of AI systems and how they work
Large Language Model (LLM)
AI systems trained on massive text data that can understand and generate human language.
GPT (Generative Pre-trained Transformer)
OpenAI's series of language models that power ChatGPT.
Transformer
The neural network architecture that powers most modern AI language models.
Chatbot
An AI program designed to have conversations with humans.
Multimodal AI
AI that can understand and work with multiple types of content like text, images, and audio.
Foundation Model
A large AI model trained on broad data that can be adapted for many different tasks.
Open Source AI
AI models whose code and weights are publicly available for anyone to use and modify.
Parameters
The adjustable settings inside an AI model that determine how it processes information.
Techniques
(12 terms)Methods and approaches used in AI
Prompt
The text input you give to an AI to tell it what you want.
Vibe Coding
Building software by describing what you want to an AI in plain English and accepting most of what it generates, without closely reviewing the code.
Prompt Engineering
The skill of crafting effective instructions to get better results from AI.
Fine-tuning
Additional training to specialize a general AI model for a specific task or domain.
Context Window
The maximum amount of text an AI can consider at once when generating a response.
Tokens
The units of text that AI models process — roughly equivalent to word pieces.
Temperature
A setting that controls how creative or predictable an AI's responses are.
RAG (Retrieval-Augmented Generation)
A technique that lets AI access external documents to provide more accurate answers.
Few-shot Learning
Teaching AI a task by showing it just a few examples in your prompt.
Chain-of-Thought
A prompting technique that asks AI to show its reasoning step by step.
Inference
When a trained AI model processes new inputs and generates outputs.
Embedding
Converting text into numbers that capture its meaning for AI processing.
Applications
(9 terms)Real-world uses of AI technology
Generative AI
AI that creates new content like text, images, code, or music.
Natural Language Processing (NLP)
AI technology that helps computers understand and work with human language.
Computer Vision
AI that can understand and analyze images and video.
AI Agent
An AI system that can take actions and make decisions to accomplish goals.
Copilot
AI that assists humans with tasks rather than replacing them entirely.
Text-to-Image
AI that generates images based on text descriptions.
Speech-to-Text
AI that converts spoken words into written text.
Sentiment Analysis
AI that detects emotions and opinions in text.
Recommendation System
AI that suggests products, content, or actions based on your preferences.
Ethics & Safety
(10 terms)Important considerations for responsible AI
Hallucination
When AI confidently generates false or made-up information.
AI Bias
When AI systems produce unfair results due to biased training data or design.
AI Safety
Efforts to ensure AI systems behave safely and as intended.
Alignment
Ensuring AI systems pursue goals that match human intentions and values.
RLHF (Reinforcement Learning from Human Feedback)
A training technique that improves AI using human ratings of its responses.
Deepfake
AI-generated fake video or audio that makes people appear to say or do things they didn't.
AI Transparency
Being open about how AI systems work and make decisions.
AI Regulation
Laws and rules governing how AI can be developed and used.
Explainability
The ability to understand and explain why an AI made a particular decision.
Synthetic Data
Artificially generated data used to train AI when real data is scarce or sensitive.