Glossary

Foundational AI Terms

Agentic AI

Agentic systems autonomously plan and execute complex tasks without requiring prompts from end-users. Agentic is widely viewed as the “third wave” of AI. According to Gartner, agentic AI will autonomously resolve 80% of common customer service issues by 2029.¹⁷

AIOps

When AI is used to make IT operations more efficient and reliable. For example, companies use AIOps to fix server issues, manage network performance, and predict outages before they occur.

Artificial intelligence (AI)

AI refers to the field of computer science that focuses on creating intelligent machines capable of performing tasks that typically require human intelligence. It involves the development of algorithms and systems that can acquire knowledge, reason, learn, understand natural language, perceive the environment, and make decisions or take actions based on that understanding.

Automated machine learning (AutoML)

AutoML streamlines the end-to-end process of developing and deploying ML models. By leveraging AutoML, anyone can quickly train or deploy ML models regardless of technical ability. This approach saves time and helps improve results. Some popular tools include Google Cloud AutoML and Microsoft Azure AutoML.

Cloud AI

Cloud AI involves deploying AI models and algorithms on cloud servers to leverage higher processing power, data storage, reliability, and flexibility. Cloud security provider Wiz found that 74% of organizations are now using managed AI services – a 70% YoY increase.¹⁸

Deep Learning

Deep learning is a subset of ML that uses multi-layered artificial neural networks to automatically learn patterns from large volumes of data. These models improve as they are exposed to more data, making them powerful for complex tasks involving images, audio, language, and unstructured data.

Edge AI

Edge AI involves AI model and algorithm deployment on local devices for faster data processing and analysis. Gartner predicts that by 2026, at least half of edge computing deployments will involve ML.¹⁹

Large Language Models (LLM)

LLMs represent a specialized application of ML focused on processing and generating human language at a large scale. While they share commonalities with traditional ML techniques, LLMs are uniquely designed to tackle the complexities of natural language processing tasks.

Machine Learning (ML)

A subset of AI, ML enables computers to learn from data and make predictions or decisions without explicit programming. ML models include facial recognition to unlock devices, medical imaging analysis to detect abnormalities, predictive analytics to forecast sales or estimate stock prices, and fraud detection.

MLOps

A discipline that streamlines the entire ML lifecycle, from developing and deploying models to monitoring and governance. MLOps aims to align data scientists and IT and ensure that models are production ready.

Natural Language Processing (NLP)

NLP focuses on enabling computers to understand, interpret, and generate human language, powering applications like chatbots and language translation. Additionally, there are distinctions between general AI (AGI) and narrow AI (ANI). AGI, often depicted in science fiction, refers to AI systems with human-like cognitive abilities and the capacity to understand and perform any intellectual task that a human can. In contrast, ANI is designed for specific tasks or domains, such as playing chess or diagnosing medical conditions.

←Go back to Section 1