Dictionary

A

• AI (Artificial Intelligence) (ay-eye): The simulation of human intelligence in machines that can perform tasks like learning, reasoning, and problem-solving.

• Algorithm (al-guh-rith-uhm): A set of rules or processes followed by a computer to solve a problem.

• API (Application Programming Interface) (ay-pee-eye): A set of protocols that allows different software applications to communicate.

• Automation (aw-tuh-may-shun): The use of technology to perform tasks without human intervention.

B

• Bot (baht): An automated program that can execute tasks without direct human input.

• Business Logic (biz-nis loj-ik): The underlying rules and workflows that dictate how an application functions.

C

• Chatbot (chat-bot): An AI-powered program that can simulate conversations with users.

• Computer Vision (kum-pyoo-ter vish-uhn): A field of AI that enables computers to interpret and analyze visual data.

• Citizen Developer (sit-uh-zuhn dih-vel-uh-per): A non-technical user who creates applications using no-code or low-code tools.

D

• Deep Learning (deep lur-ning): A subset of machine learning that uses neural networks with multiple layers to analyze complex patterns.

• Drag-and-Drop Interface (drag and drop in-ter-face): A visual approach to software development that allows users to build applications by moving elements on a screen.

E

• Embedding (em-bed-ing): A representation of data, such as words or images, in a numerical format that AI models can process.

F

• Fine-Tuning (fine too-ning): The process of training an AI model further to improve its performance on a specific task.

G

• Generative AI (jen-uh-ruh-tiv ay-eye): AI models that can create new content, such as text, images, or code, based on existing data.

• GPT (Generative Pre-trained Transformer) (jee-pee-tee): A type of AI model designed for generating human-like text.

H

• Hyperautomation (hy-per aw-tuh-may-shun): The use of AI, machine learning, and automation technologies to streamline business processes.

I

• Inference (in-fer-uhns): The process where an AI model makes predictions based on input data.

• Integration (in-tuh-gray-shun): Connecting different applications and systems to work together seamlessly.

L

• Low-Code (loh-kohd): A development approach that requires minimal coding, allowing faster application building with prebuilt components.

• LLM (Large Language Model) (el-el-em): A powerful AI model trained on vast amounts of text data to generate and understand natural language.

M

• Machine Learning (muh-sheen lur-ning): A subset of AI that enables systems to learn patterns from data and make predictions.

• Middleware (mid-uhl-wair): Software that connects different applications or systems, facilitating communication.

• Model Training (mah-dl tray-ning): The process of feeding data into an AI model to improve its accuracy and decision-making.

N

• Neural Network (nyoor-uhl net-wurk): A machine learning model inspired by the human brain, consisting of interconnected layers of nodes.

• No-Code (noh-kohd): A development approach that allows users to build applications without any coding, using visual interfaces and logic-based workflows.

O

• Optical Character Recognition (OCR) (op-ti-kuhl keh-rik-ter rek-uhg-nish-uhn): AI-driven technology that converts different types of documents into editable and searchable data.

P

• Predictive Analytics (pri-dik-tiv an-uh-lit-iks): The use of AI and statistical models to predict future outcomes based on historical data.

• Prompt Engineering (prompt en-juh-neer-ing): The practice of designing and refining input prompts to optimize AI-generated responses.

R

• Reinforcement Learning (ree-in-force-muhnt lur-ning): A type of machine learning where an AI model learns by receiving rewards or penalties based on its actions.

S

• Speech Recognition (spee-ch rek-uhg-nish-uhn): AI technology that converts spoken language into text.

• Supervised Learning (soo-per-vyzd lur-ning): A machine learning approach where the model is trained using labeled data.

T

• Text-to-Speech (TTS) (tekst too spee-ch): Technology that converts text into spoken audio.

• Transfer Learning (trans-fer lur-ning): A technique where an AI model trained on one task is adapted for another related task.

U

• Unsupervised Learning (uhn-soo-per-vyzd lur-ning): A type of machine learning where the model identifies patterns in data without labeled examples.

• UI Builder (yoo-eye bil-der): A no-code tool for designing and assembling user interfaces visually.

V

• Visual Programming (vizh-oo-uhl proh-gram-ing): A method of programming where users create software using graphical elements rather than text-based code.

W

• Workflow Automation (wurk-floh aw-tuh-may-shun): The process of using no-code tools or AI to automate repetitive tasks and business processes.