AI Literacy
Understanding the terminology and concepts of artificial intelligence is essential as we navigate the AI age. This guide will help you get up to speed with key terms and their meanings.
Artificial Intelligence (AI)
The simulation of human intelligence in machines that are programmed to think and learn like humans. AI systems can perform tasks that typically require human intelligence.
Example: Virtual assistants like Siri or Alexa, recommendation systems, and autonomous vehicles.
Machine Learning (ML)
A subset of AI that enables systems to learn and improve from experience without being explicitly programmed. ML algorithms use statistical techniques to identify patterns in data.
Example: Email spam filters that improve based on which emails you mark as spam.
Deep Learning
A subfield of machine learning that uses artificial neural networks with multiple layers to progressively extract higher-level features from raw input.
Example: Image recognition systems that can identify objects in photos with high accuracy.
Neural Network
Computing systems inspired by the biological neural networks in human brains. They consist of interconnected nodes or 'neurons' that process and transmit information.
Example: A neural network trained to recognize handwritten digits by analyzing thousands of example images.
Natural Language Processing (NLP)
A field of AI that focuses on the interaction between computers and human language. NLP enables computers to understand, interpret, and generate human language.
Example: Language translation services like Google Translate, or chatbots that can maintain a conversation.
Computer Vision
A field of AI that enables computers to derive meaningful information from digital images, videos, and other visual inputs and take actions based on that information.
Example: Facial recognition systems or autonomous vehicles that can 'see' and interpret their surroundings.
Supervised Learning
A type of machine learning where the algorithm is trained on labeled data. The model learns to map inputs to known output labels.
Example: Training a model to classify emails as spam or not spam based on previously labeled examples.
Unsupervised Learning
A type of machine learning where the algorithm is trained on unlabeled data and finds patterns or relationships on its own.
Example: Customer segmentation based on purchasing behaviors without predefined categories.
Reinforcement Learning
A type of machine learning where an agent learns to behave in an environment by performing actions and receiving rewards or penalties.
Example: An AI system learning to play chess by receiving positive feedback for winning moves.
Training Data
The dataset used to teach a machine learning model. It contains the input data and, in supervised learning, the corresponding labels.
Example: A collection of labeled images used to train an image recognition system.
Algorithm
A step-by-step procedure or formula for solving a problem. In AI, algorithms are the sets of rules that guide a system in performing tasks.
Example: The algorithm behind a recommendation system that suggests products based on past purchases.
Model
A representation of knowledge extracted from data. In machine learning, a model is the output of a training process and is used to make predictions on new data.
Example: A trained model that can predict house prices based on features like location, size, and age.
Large Language Model (LLM)
AI systems trained on vast amounts of text data that can generate human-like text, translate languages, and answer questions in an informative way.
Example: Models like GPT-4, Claude, or Llama that can write essays, code, and engage in conversation.
Generative AI
AI systems that can create new content including text, images, audio, code, and more based on patterns learned from training data.
Example: DALL-E generating unique images based on text descriptions or ChatGPT writing original content.
Transformer Architecture
A neural network architecture that uses self-attention mechanisms to process sequential data, revolutionizing natural language processing and other AI applications.
Example: The architecture underlying models like BERT and GPT that enables them to understand context in language.
Fine-tuning
The process of taking a pre-trained model and further training it on a specific dataset for a specialized task.
Example: Taking a general-purpose language model and fine-tuning it to generate legal documents.
Prompt Engineering
The practice of designing and refining input prompts to effectively communicate with and get desired outputs from AI systems, particularly large language models.
Example: Creating well-structured prompts for DALL-E to generate specific types of images.
Bias in AI
The presence of unfair or prejudiced outputs in AI systems, often reflecting biases present in training data or algorithm design.
Example: A recruitment AI that favors male candidates due to being trained on historically male-dominated hiring data.
Overfitting
When a machine learning model learns the training data too well, including its noise and outliers, making it perform poorly on new, unseen data.
Example: A model that achieves 99% accuracy on training data but only 70% on test data.
Feature
An individual measurable property or characteristic of a phenomenon being observed. Features are the inputs used by machine learning models.
Example: For a house price prediction model, features might include square footage, number of bedrooms, and location.
Inference
The process of using a trained model to make predictions or decisions based on new data inputs.
Example: Using a trained language model to generate text responses to user queries in real-time.
Token
In language models, a token is a unit of text that the model processes. It can be a word, part of a word, or even a single character depending on the tokenization method.
Example: The word 'hamburger' might be split into tokens like 'ham', 'bur', and 'ger' by a language model.
Multimodal AI
AI systems that can process and understand multiple types of information or 'modalities' such as text, images, audio, and video.
Example: A model that can generate images based on text descriptions and also add text captions to images.
Explainable AI (XAI)
AI systems designed to be transparent and understandable to humans, allowing users to comprehend how and why a particular decision was made.
Example: An AI medical diagnosis system that explains which symptoms led to its conclusion.
Responsible AI
The practice of designing, developing, and deploying AI in a manner that is ethical, transparent, fair, and accountable.
Example: Implementing transparency reports, bias audits, and ethical guidelines for AI development.