AI Beginner's Glossary: Demystifying the Jargon

This glossary offers straightforward, easy-to-understand definitions for the most common AI terms, specifically designed for beginners who want to grasp the core concepts without getting lost in technical details.

Algorithm: A set of step-by-step instructions that a computer follows to solve a problem or complete a task. In AI, an algorithm is the recipe for how a model learns and makes decisions.
Artificial Intelligence (AI): The broad field of computer science focused on creating machines that can perform tasks that typically require human intelligence, such as learning, reasoning, and problem-solving.
Automation: Using technology to perform tasks with minimal human input. AI is often used to automate complex tasks that were previously done manually.

Chatbot: An AI program designed to simulate conversation with human users, especially over the internet. They can answer questions, provide information, and handle basic customer service inquiries.

Data: The essential ingredient for AI. It's the information (text, images, numbers, etc.) that AI models are trained on to learn patterns and make predictions.
Data Set: A collection of related data that is used to train an AI model. For example, a data set for a real estate AI might include thousands of property listings with photos, prices, and descriptions.
Deep Learning: A more advanced type of machine learning that uses complex, multi-layered "neural networks" to find hidden patterns in data. This is what powers most of today's impressive AI tools like ChatGPT.

Generative AI: A type of AI that can create new, original content, such as text, images, music, or video. This is the technology behind tools like ChatGPT and Midjourney.
GPT (Generative Pre-trained Transformer): A specific type of large language model (LLM) developed by OpenAI. It is known for its ability to generate human-like text. ChatGPT is built on a GPT model.

Large Language Model (LLM): A powerful type of AI model that has been trained on a massive amount of text data. LLMs can understand and generate human-like language, making them useful for tasks like writing, summarizing, and translating.

Machine Learning (ML): A subfield of AI that focuses on enabling computers to learn from data without being explicitly programmed. Instead of following a rigid script, ML models learn patterns and improve their performance over time.
- Simplified Explanation: Imagine a super-smart financial advisor. You don't program them with a rigid script. Instead, you give them access to all of a company's past financial data — every transaction, every sale, every market trend. The advisor studies all this information to find hidden patterns and predict future outcomes. That’s machine learning in a nutshell: giving computers data so they can learn to make predictions and decisions on their own.
Model: In AI, a model is the trained "brain" of the system. It's the result of an algorithm learning from a data set. The model is what makes predictions or performs a task after the training is complete. Think of a baby born with infinite possibilities; it needs to be trained. It will learn and develop a worldview. In the same way, an AI Model has to be “trained”

Natural Language Processing (NLP): A field of AI that gives computers the ability to understand, interpret, and generate human language. This is what enables chatbots and voice assistants to function effectively.
- Analogy: The AI Translator: Imagine you have a super-smart translator who can not only translate languages but also understand the meaning and feeling behind the words.
- Humans talk or write in a messy, "natural" way. We use slang, shortcuts, sarcasm, and grammatical errors. A computer can't understand this on its own because it only speaks in code (zeros and ones).
- NLP is a special translator. Its job is to take your messy human language and translate it into a structured, organized format that a computer can understand and process.
- What it does:
  - It listens to the words. It breaks down your sentence into individual words and phrases.
  - It understands the context. It figures out if "bank" means a riverbank or a financial institution.
  - It gets the feeling. It determines if the message is positive, negative, or neutral (this is called sentiment analysis).
  - It takes action. Once it understands the meaning, it can either answer a question, sort the message, or generate a response.
- In a nutshell, NLP is the technology that allows a computer to "read" and "understand" human language, so it can do useful things like translate text, answer your questions (Siri, Alexa), or filter your emails.
Neural Network: A computer system inspired by the human brain. It consists of layers of interconnected "nodes" that process information and learn from it. Neural networks are the foundation of deep learning.

Predictive Analytics: The use of AI and data to predict future outcomes or trends. In real estate, this can be used to forecast property values, identify neighborhoods with growth potential, or predict which homeowners are likely to sell their homes.
Prompt: The text or command a user gives to an AI model to tell it what to do. For example, "Write a social media post about a new listing." The quality of the output often depends on how well the prompt is written.

Regression Analysis: A statistical method used in AI to find the relationship between variables. It's a key technique used in predictive analytics to forecast things like home prices based on factors like square footage and location.

Training: The process of feeding a data set to an AI model so it can learn and identify patterns. The more relevant and diverse the data, the better the model becomes at its task.

Unstructured Data: Information that is not organized in a predefined way. Examples include emails, social media posts, videos, and photos. AI is particularly good at making sense of this type of data.

Virtual Assistant: An AI-powered tool that can perform tasks, provide information, and interact with users through voice or text commands, like Siri or Alexa.