Model Interpretability refers to the degree to which a human can understand the reasoning behind a model’s decisions or predictions. It plays a crucial role in evaluating whether an AI model behaves reliably, fairly, and transparently—especially in high-stakes or regulated...

API (Application Programming Interface) is a set of defined rules and protocols that enable different software applications to communicate with each other. APIs simplify software development by abstracting complex operations and exposing only necessary functionality. They allow systems to interact,...

AIaaS (Artificial Intelligence as a Service) is a cloud-based service model that provides AI tools and infrastructure on demand. It enables businesses to access machine learning models, natural language processing, and other AI capabilities without building or maintaining their own...

Cloud AI refers to the delivery of artificial intelligence services through cloud computing platforms. It enables organizations to access powerful AI capabilities—such as machine learning, natural language processing, and computer vision—without building or maintaining their own infrastructure. As a result,...

Edge AI refers to the deployment of artificial intelligence directly on edge devices such as smartphones, sensors, or IoT hardware—rather than relying on centralized cloud servers. This approach enables real-time data processing, reduces latency, and enhances privacy by keeping data...

Quantum AI refers to the integration of quantum computing and artificial intelligence to enhance the performance and scalability of AI systems. By leveraging quantum principles such as superposition and entanglement, quantum AI aims to accelerate tasks like optimization, pattern recognition,...

DALL·E is a generative AI model developed by OpenAI that creates images from natural language prompts. Introduced in 2021, DALL·E gained attention for its ability to produce imaginative and contextually accurate visuals from abstract or unusual descriptions. It marked a...

Long Short-Term Memory (LSTM) is a type of recurrent neural network (RNN) architecture specifically designed to model sequences and handle long-range dependencies. Introduced by Hochreiter and Schmidhuber in 1997, LSTM overcomes the limitations of traditional RNNs by preventing the vanishing...

A Bayesian Network is a probabilistic graphical model that represents a set of variables and their conditional dependencies using a directed acyclic graph (DAG). It combines principles from graph theory and probability theory to model uncertainty, causality, and inference in...