Empowering Small Businesses with AI
AI Glossary-Key Concepts
AI and ChatGPT terminology is a whole new ballgame. Here are some AI key concepts to help you become familiar. AI changes almost daily, so we’ll do our best to keep you up to date!
Common AI Terms for Laypersons
Artificial Intelligence (AI)
A branch of computer science focused on creating systems capable of performing tasks that typically require human intelligence, such as learning, problem-solving, and decision-making.
Machine Learning (ML)
A subset of AI that allows computers to learn and improve from experience without being explicitly programmed. It uses algorithms to analyze data and make predictions or decisions.
Deep Learning
A more advanced form of machine learning that uses neural networks with many layers (hence “deep”) to analyze complex patterns in large datasets. It powers technologies like facial recognition and speech processing.
Natural Language Processing (NLP)
A field of AI that enables machines to understand, interpret, and respond to human language. Examples include chatbots, translation apps, and virtual assistants.
Chatbot
An AI-powered tool designed to simulate human conversation. It can answer questions, provide customer support, or guide users through processes.
Neural Network
A computer system inspired by the human brain’s structure. It consists of interconnected nodes (neurons) that process and analyze data.
Algorithm
A set of rules or instructions that a computer follows to perform a task. In AI, algorithms are used to identify patterns and make decisions based on data.
Training Data
The dataset used to teach an AI model how to perform specific tasks. Quality and diversity of training data significantly impact the AI’s performance.
Model
The result of training an AI system using data and algorithms. The model is what performs tasks such as recognizing images or generating text.
Inference
The process of using a trained AI model to make predictions or decisions based on new data.
Supervised Learning
A type of machine learning where the model is trained on labeled data. The system learns to predict outcomes based on examples with known answers.
Unsupervised Learning
A machine learning approach where the model works with unlabeled data to find patterns or groupings without predefined answers.
Reinforcement Learning
An area of machine learning where an AI learns by trial and error, receiving rewards or penalties for its actions, much like training a pet.
Prompt
A user-provided input or question given to an AI system like ChatGPT to generate a response.
Generative AI
A type of AI that creates new content, such as text, images, music, or code, based on the data it was trained on.
Bias in AI
Unintended favoritism or discrimination in AI models caused by biased training data or flawed algorithms.
Overfitting
A scenario in machine learning where a model learns the training data too well, making it less effective on new, unseen data.
API (Application Programming Interface)
A set of tools and protocols that allow different software systems to communicate. Many AI tools, like ChatGPT, provide APIs for integration into other applications.
Computer Vision
An AI field focused on enabling machines to interpret and make decisions based on visual data, such as images or videos.
Edge AI
AI computation performed locally on a device (like a smartphone) rather than relying on cloud servers. It’s used for real-time, on-device processing.
Cloud AI
AI services and tools hosted on remote servers, accessible via the internet. Examples include Google Cloud AI and AWS AI services.
Data Mining
The process of analyzing large datasets to uncover hidden patterns, trends, and insights.
Tokenization
In NLP, breaking down text into smaller units, like words or phrases, for easier analysis by AI systems.
Embedding
A numerical representation of words, phrases, or concepts that AI systems use to understand relationships and context in data.
Overparameterization
A model with more parameters (variables) than needed, which can lead to inefficiency or overfitting.
Ethical AI
The practice of developing and deploying AI systems responsibly, ensuring fairness, privacy, and accountability.
AI Ethics
A branch of study concerned with the ethical implications and societal impact of AI technologies.
Turing Test
A test to measure a machine’s ability to exhibit intelligent behavior indistinguishable from that of a human.