Reference

Glossary

Key terms and definitions for enterprise AI in regulated environments.

AI: Stands for artificial intelligence, which is the simulation of human intelligence processes by machines or computer systems. AI can mimic human capabilities such as communication, learning, and decision-making.
Custom AI: Is the process of developing a company-specific AI solution targeting a particular problem. Since custom AI software is developed for a single business, it needs to satisfy the business's specifications and expectations.
Generative AI: Generative AI refers to deep-learning models that can generate high-quality text, images, and other content based on the data they were trained on.
Large Language Models (LLMs): Type of language model notable for its ability to achieve general-purpose language understanding and generation.
Ensemble AI: Is the process by which multiple models, such as classifiers or experts, are strategically generated and combined to solve a particular computational intelligence problem.
Cloud Computing: The delivery of computing services — including servers, storage, databases, networking, software, analytics, and intelligence — over the internet ("the cloud") to offer faster innovation, flexible resources, and economies of scale.
Machine Learning (ML): A subset of AI that incorporates aspects of computer science, mathematics, and coding. Machine learning focuses on developing algorithms and models that help machines learn from data and predict trends and behaviors, without human assistance.
Deep Learning: A function of AI that imitates the human brain by learning from how it structures and processes information to make decisions. Instead of relying on an algorithm that can only perform one specific task, this subset of machine learning can learn from unstructured data without supervision.
Natural Language Processing (NLP): A type of AI that enables computers to understand spoken and written human language. NLP enables features like text and speech recognition on devices.
Hallucination: When an artificial intelligence model detects non-existent patterns or incorrectly analyzes data, leading to inaccurate or misleading outputs.
Neural Networks: A deep learning technique designed to resemble the human brain's structure. Neural networks require large data sets to perform calculations and create outputs, which enables features like speech and vision recognition.
Quantum Computing: The process of using quantum-mechanical phenomena such as entanglement and superposition to perform calculations. Quantum machine learning uses these algorithms on quantum computers to expedite work because it performs much faster than a classic machine learning program and computer.