AI Hallucination
When an AI model generates content or information that is completely fabricated or incorrect, often without any basis in the training data. This can happen in generative AI, such as text or image generation.

Algorithm
A set of instructions or rules followed by an AI system to solve a problem or complete a task. Algorithms help AI systems process data, make decisions, and generate results.

Artificial Intelligence (AI)
The field of computer science that focuses on creating systems capable of performing tasks that typically require human intelligence, such as recognizing speech, making decisions, or translating languages.

Bias in AI
The presence of unfair or prejudiced outcomes in AI systems, which may occur due to biased data or biased programming. This can result in discrimination or skewed results based on factors like race, gender, or socioeconomic status.

Chatbot
An AI system designed to simulate conversation with users. Chatbots can be rule-based or powered by more advanced natural language processing and machine learning to understand and respond to queries in a human-like way.

Computer Vision
A field of AI that enables machines to interpret and understand visual data from the world, such as images and videos. It’s used in tasks like facial recognition, object detection, and autonomous driving.

Deep Learning
A type of machine learning that uses artificial neural networks with multiple layers (hence "deep") to analyze large amounts of data. It's particularly effective for tasks like image recognition, speech processing, and natural language understanding.

Diffusion Models
A class of generative models used primarily for image generation, such as DALL·E or Stable Diffusion. These models work by progressively "denoising" random data to create images that match the desired prompt.

Ethics in AI
A field of study that examines the moral implications and responsibilities of creating and using AI systems. This includes addressing issues like fairness, privacy, accountability, and the potential impact of AI on society.

Generative AI
A type of AI designed to create new content—whether text, images, music, or video—by learning patterns from existing data and then generating original outputs.

Generative Pre-trained Transformer (GPT)
A type of large language model developed by OpenAI. GPT is trained on massive text datasets and can generate coherent and contextually relevant text, making it a powerful tool for writing, coding, and conversation.

Large Language Models (LLMs)
Advanced AI models trained on vast amounts of text data to understand and generate human language. Examples include GPT-3 and GPT-4, which are used in text generation, translation, and conversation.

Machine Learning (ML)
A subset of AI that involves teaching machines to recognize patterns in data and make predictions based on that data, without being explicitly programmed for specific tasks.

Model
In AI, a model refers to a trained algorithm that can make predictions or generate outputs. It’s the result of applying machine learning to training data, allowing the model to perform specific tasks like recognizing speech or translating text.

Natural Language Processing (NLP)
A branch of AI focused on enabling machines to understand, interpret, and generate human language. This is what allows AI to understand text, translate languages, or respond to user queries in a natural way.

Neural Networks
A computational model inspired by the human brain, consisting of layers of interconnected nodes (neurons). These networks process data and help AI systems learn to recognize patterns and make predictions.

Overfitting
A phenomenon in machine learning where an AI model becomes too tailored to its training data, leading to poor performance on new, unseen data. The model essentially "memorizes" the data instead of learning general patterns.

Reinforcement Learning
A type of machine learning where an AI system learns by interacting with its environment, receiving feedback, and adjusting its actions based on rewards or penalties. It’s often used for tasks like game playing or robotic navigation.

Training Data
The dataset used to teach an AI model. This data helps the model learn the patterns, relationships, and rules necessary to perform a task, such as recognizing images or generating text.

Transfer Learning
A machine learning technique where a model trained on one task is adapted to perform another related task. This allows AI systems to apply knowledge learned in one domain to solve problems in a new domain with less training data.