AI Glossary

Batch Normalization is a deep learning technique that normalizes activations within each mini-batch, improving training speed, stability, and model performance by reducing internal covariate shift.

Bayesian programming is an AI approach that uses probability theory to represent and update beliefs, enabling systems to reason under uncertainty and adapt to new data.

The Bees Algorithm is a swarm intelligence optimization method modeled after the foraging behavior of honey bees. It balances exploration and exploitation, making it effective for complex AI and machine learning problems.

Behavior Informatics is an AI field focused on modeling and analyzing complex behaviors. By turning raw behavioral data into structured insights, it helps understand, predict, and influence actions in areas like healthcare, robotics, and marketing.

A Behavior Tree is a modular, hierarchical structure used in AI to manage complex decision-making in agents. Popular in robotics and gaming, behavior trees break down tasks into simple, reusable components for flexible and scalable intelligence.

The Belief-Desire-Intention (BDI) Software Model is a core AI framework for building intelligent agents that reason like humans, balancing their knowledge, goals, and actions to operate adaptively in complex environments.

BERT is a transformer-based language model that captures context from both directions in text, enabling state-of-the-art performance in natural language processing tasks through pre-training and fine-tuning.

Bias in AI refers to systematic errors or preferences that cause models to make unfair or unbalanced decisions. Learn how bias arises, why it matters, and how to tackle it for more ethical AI.

A bias term in AI and machine learning is a trainable constant added to a model's output, giving it flexibility to better fit data. It's essential in neural networks and linear models for accurate predictions.

Bias mitigation encompasses the methods used to reduce or remove unfair biases from AI and machine learning systems, ensuring fairer, more ethical outcomes.

The bias-variance tradeoff describes the balance between a model's ability to learn patterns (bias) and its sensitivity to data fluctuations (variance). Finding the right tradeoff helps build models that perform well on both training and unseen data.