A
Algorithm
A set of step-by-step instructions that tells a computer how to solve a problem or complete a task. Like a recipe for computers.
Artificial Intelligence (AI)
Technology that enables computers and machines to simulate human intelligence, including learning, reasoning, and problem-solving.
Artificial Neural Network (ANN)
A computing system inspired by biological neural networks in the human brain. Consists of interconnected nodes that process information.
B
Backpropagation
A method used to train neural networks by adjusting weights based on the error of predictions. Helps the network learn from mistakes.
Bias (AI)
Systematic errors in AI predictions caused by prejudiced training data or flawed algorithms. Can lead to unfair or discriminatory outcomes.
Big Data
Extremely large datasets that are too complex for traditional data processing. AI systems often require big data for training.
C
ChatGPT
An AI chatbot developed by OpenAI that can have natural conversations, answer questions, and generate text based on prompts.
Classification
A machine learning task where the AI categorizes data into predefined classes. Example: identifying if an email is spam or not spam.
Computer Vision
AI technology that enables computers to interpret and understand visual information from images and videos, like facial recognition.
Convolutional Neural Network (CNN)
A type of deep learning network especially good at processing images. Commonly used in image recognition and computer vision tasks.
D
Data Science
The field of extracting knowledge and insights from data using statistics, programming, and AI techniques.
Dataset
A collection of data used to train and test AI models. Larger, high-quality datasets typically lead to better AI performance.
Deep Learning
A subset of machine learning using neural networks with multiple layers. Capable of learning complex patterns from large amounts of data.
F - G
Feature
An individual measurable property or characteristic of data that AI uses to make predictions. Example: in house pricing, features include size and location.
GPT (Generative Pre-trained Transformer)
A type of AI model that can generate human-like text. Powers systems like ChatGPT and other language models.
L - M
Large Language Model (LLM)
An AI model trained on massive amounts of text data that can understand and generate human language. Examples include GPT-4 and Claude.
Machine Learning (ML)
A branch of AI where computers learn from data and improve their performance without being explicitly programmed for every scenario.
Model
The result of training an AI system on data. A trained model can make predictions or decisions on new, unseen data.
N
Natural Language Processing (NLP)
AI technology that helps computers understand, interpret, and generate human language. Used in chatbots, translation, and text analysis.
Neural Network
A computing system modeled after the human brain, consisting of interconnected nodes (neurons) that process and transmit information.
O - P
Overfitting
When an AI model learns the training data too well, including noise and outliers, making it perform poorly on new data.
Parameters
The internal variables that an AI model learns during training. More parameters generally mean more complex models.
Prompt
The input text or instruction given to an AI system (especially language models) to generate a response or complete a task.
Python
The most popular programming language for AI and machine learning. Known for its simplicity and powerful AI libraries.
R - S
Reinforcement Learning
A type of machine learning where an AI learns by trial and error, receiving rewards for good actions and penalties for bad ones.
Supervised Learning
Machine learning where the AI is trained on labeled data - you tell it what the correct answers are. Like learning with a teacher.
T
TensorFlow
An open-source machine learning framework developed by Google. Widely used for building and training AI models.
Training Data
The dataset used to teach an AI system. The AI learns patterns from this data to make predictions on new data.
Transformer
A neural network architecture excellent at processing sequential data like text. Powers modern language models like GPT and BERT.
U - Z
Unsupervised Learning
Machine learning where the AI finds patterns in unlabeled data without being told what to look for. Like learning without a teacher.
Validation
The process of testing an AI model's performance on data it hasn't seen during training to ensure it works correctly.