YOLO is a real-time object detection algorithm that simultaneously identifies and localizes objects within an image. Instead of performing multiple steps, YOLO uses a single convolutional neural network pass, making it remarkably fast. Although it may sacrifice a bit of...

VLM (Vision-Language Model) refers to a class of AI systems that can process and understand both visual and textual information. These models learn to align images with corresponding text, enabling tasks such as image captioning, visual question answering, and multimodal...

A Vector Database is a specialized type of database that stores and searches high-dimensional vector embeddings. These embeddings represent data—such as text, images, or audio—in numeric form to capture semantic meaning. As a result, vector databases support tasks like semantic...

Unsupervised Learning is a type of machine learning where models learn patterns from data without labeled outcomes. Unlike supervised learning, it doesn’t rely on input-output pairs. Instead, the algorithm explores the data to find hidden structures, groupings, or relationships. This...

Trustworthy AI refers to artificial intelligence systems that are ethical, transparent, and reliable. These systems align with human values and meet societal expectations. As a result, they ensure safety, fairness, and accountability throughout their lifecycle. Trustworthy AI supports responsible adoption...

Tree of Thoughts is a reasoning framework designed for large language models (LLMs) that guides them to explore multiple reasoning paths before settling on a final answer. Rather than following a single, linear chain of thought, the model branches out...

Transformer Model is a neural network architecture introduced in the 2017 paper “Attention is All You Need.” It serves as the foundation for most modern AI systems. Unlike older recurrent models, transformers process input in parallel. They use self-attention to...

Transfer Learning is a machine learning technique that allows a model trained on one task or domain to be repurposed for another, often related, task or domain. Instead of starting from scratch, the model leverages the knowledge and patterns it...

Training datasets are collections of data used to teach machine learning models the patterns and relationships needed to perform specific tasks. These datasets consist of input features along with corresponding labels or target values, allowing algorithms to learn from examples....

Tokenization is the process of breaking down text, speech, or other inputs into smaller units called tokens. These tokens serve as the basic building blocks that AI models use to understand and generate language. Importantly, tokenization plays a critical role...