What is Artificial Intelligence?
Artificial intelligence (AI) is the simulation of human intelligence processes by machines, especially computer systems. AI employs sophisticated analysis and logic-based techniques—including machine learning—to interpret events, facilitate and automate decisions, and execute actions. This technology enables machines to understand, respond to, and learn with human-comparable levels of intelligence.
Why it matters
AI underpins modern decision intelligence by letting machines interpret data, automate routine choices, and surface insights at a speed and scale humans cannot match on their own.
See how Diwo operationalizes Artificial Intelligence.
Read the decision-intelligence playbooks that put this concept to work at Fortune 50 scale.
Related concepts
Machine learning represents a data analytics approach that harnesses artificial intelligence to emulate human learning from experience. Rather than depending on preset formulas, machine learning algorithms extract "knowledge" directly from data through computational methods. These algorithms identify inherent patterns within datasets that generate actionable insights and support improved forecasting and decision-making.
Natural Language Processing (NLP) is a user interface (UI) advancement that allows business users to query and interact with their data using their own natural language. The system leverages AI to comprehend both verbal and text-based communications, responding with analytics output in matching formats.
Explainable artificial intelligence (XAI) comprises processes and methods enabling users to comprehend and trust machine learning algorithm outputs. It describes AI models, their expected impact, and potential biases while characterizing model accuracy, fairness, transparency, and decision-making outcomes.
Automated machine learning (AutoML) is the process of automating the tasks of applying machine learning to real-world problems. The automation potentially encompasses every stage from beginning with raw datasets through feature engineering, model selection, deployment, and solution monitoring. More specifically, AutoML automates the selection, composition, and parameterization of machine learning models.