AI Glossary

Artificial Intelligence (AI)

A field of science concerned with building computers and machines that can reason, learn, and act in such a way that would normally require human intelligence, or that involves data whose scale exceeds what humans can analyze.

AI is a broad field that encompasses many disciplines, including computer science, data analytics and statistics, hardware and software engineering, linguistics, neuroscience, and even philosophy and psychology. On an operational level for business use, AI is a set of technologies based primarily on machine learning and deep learning, used for data analytics, predictions and forecasting, object categorization, natural language processing, recommendations, intelligent data retrieval, and more.

Machine Learning

The use and development of computer systems that are able to learn and adapt without following explicit instructions, by using algorithms and statistical models to analyze and draw inferences from patterns in data.

Deep Learning

Deep Learning is a more complex subset of Machine Learning, inspired by the structure and function of the human brain. It uses artificial neural networks with multiple layers (hence "deep") to analyze various factors of data.

Neural Network

A neural network is a method in artificial intelligence (AI) that teaches computers to process data in a way that is inspired by the human brain. It is a type of machine learning (ML) process, called deep learning, that uses interconnected nodes or neurons in a layered structure that resembles the human brain.

Natural Language Processing

Natural Language Processing (NLP) is a field of Artificial Intelligence (AI) that enables computers to understand, interpret, and generate human language. It bridges the gap between human communication and computer processing, allowing machines to interact with us through text and speech.

Generative AI

Generative AI refers to AI systems that can create new content, such as images, text, or music. These systems learn patterns from existing data to generate new, original outputs.

Key Characteristics of Generative AI:

• Content Creation Generative AI models are designed to generate new data rather than just analyze or classify existing data.
• Learning from Data These models are trained on massive data sets of text, images, or other data types.
• Prompt-Based Generation Users can input prompts or instructions to guide the AI in creating specific outputs.
• Novelty Generative AI aims to produce outputs that are not simply repetitions of the training data, but rather original and creative creations.

Agentic AI

Agentic AI refers to artificial intelligence systems designed to operate autonomously, adapt in real-time, and solve multi-step problems based on context and objectives. These systems can make decisions, learn from experience, and act on their own to achieve goals.

Key Features of Agentic AI:

• Autonomy Agentic AI systems are designed to operate independently, making decisions and taking actions without direct human intervention.
• Adaptability They can adapt to changing environments and learn from their experiences, improving their performance over time.
• Goal-oriented Behavior Agentic AI systems are designed to achieve specific goals by creating and executing plans.
• Integration of Specialized Agents They often involve integrating specialized agents, each designed for a unique purpose, to perform complex tasks.
• Reasoning and Problem-Solving Agentic AI can use reasoning and problem-solving abilities to navigate complex situations and find solutions.
• Learning and Improvement They can continuously learn from their experiences and improve their performance through feedback loops.