AI for Business Glossary

Systematic errors in AI outputs that arise from prejudiced assumptions in training data, algorithm design, or deployment context.

A centralized organizational unit that develops AI strategy, standards, talent, and shared resources to accelerate enterprise AI adoption.

Frameworks and policies ensuring AI systems are developed and deployed responsibly, ethically, and in regulatory compliance.

The simulation of human intelligence by computer systems, including learning, reasoning, problem-solving, perception, and language understanding.

Using predictive models to identify customers who are likely to stop using a product or service in the near future.

An AI field that trains computers to interpret and act on visual information from images and video.

The end-to-end infrastructure that collects, cleans, transforms, and delivers data to AI models and analytics systems.

A subset of machine learning using multi-layered neural networks to model complex, non-linear relationships in data.

Deploying AI algorithms on local devices such as phones, sensors, and IoT hardware rather than relying on cloud computing.

Methods and techniques that make the decisions or predictions of AI systems understandable and interpretable to humans.

AI systems capable of creating new content such as text, images, code, and audio based on patterns learned from training data.

A neural network trained on vast amounts of text data that can understand context and generate human-like language.

A subset of AI where algorithms learn patterns from data to make predictions or decisions without being explicitly programmed.

Practices combining machine learning, DevOps, and data engineering for reliable deployment and maintenance of ML models in production.

The field of AI focused on enabling computers to understand, interpret, and generate human language.

A computing architecture inspired by the human brain, consisting of interconnected nodes (neurons) organized in layers that process information.

The use of data, statistical algorithms, and machine learning to forecast future outcomes based on historical patterns.

The practice of designing and refining inputs (prompts) to elicit desired outputs from generative AI and large language models.

A small-scale project to test whether a proposed AI solution is technically feasible and can deliver business value.

An AI system that predicts and suggests items or content a user is likely to prefer based on past behavior and similar profiles.

Software robots that automate repetitive, rule-based digital tasks by mimicking human actions in applications.

An NLP technique that identifies and categorizes the emotional tone expressed in text as positive, negative, or neutral.

A machine learning approach where the model is trained on labeled data with known inputs and correct outputs.

A technique where a model trained on one task is reused as the starting point for a related task, reducing data and training requirements.

A machine learning approach where the model finds hidden patterns in data without pre-labeled outputs.