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Multimodal interactions refer to the ability of artificial intelligence systems to understand and respond to multiple types of input, such as text, voice, images, video, and gestures, at the same time. This mirrors how humans naturally communicate, using several senses…
Hugging Face is an open-source platform and community that provides tools, models, and datasets for building modern artificial intelligence systems. It began with chatbot technology but has since become one of the most widely used ecosystems for machine learning and natural language processing (NLP). Developers use it to access state-of-the-art models, collaborate on research, and accelerate AI development.
Natural Language Processing (NLP) is a branch of artificial intelligence that enables computers to understand, interpret, and generate human language. It bridges the gap between human communication and machine understanding, powering everything from chatbots and virtual assistants to sentiment analysis…
A hyperparameter is a configuration setting used to control the learning process of a machine learning model. Unlike model parameters, which are learned automatically during training, hyperparameters are defined manually before the training begins and directly influence how efficiently and…
Inference in artificial intelligence refers to the process of applying a trained machine learning model to new data to generate predictions, classifications, or decisions. It’s the stage where a model moves from learning patterns in training data to making real-world…
A Knowledge Graph is a structured representation of information that connects data points through relationships, enabling artificial intelligence systems to understand context and meaning. Instead of storing data in isolated tables, a knowledge graph organises it as nodes (entities) and…
A Large Language Model (LLM) is a type of artificial intelligence designed to understand, generate, and interact with human language. Trained on massive amounts of text data, LLMs use deep learning techniques to identify linguistic patterns, predict text sequences, and…
Latency refers to the time delay between an input and its corresponding output in a system. In artificial intelligence and machine learning, latency measures how long it takes for a model to process data and return a prediction, response, or…
Latent space refers to the hidden, multidimensional representation of data that a machine learning model learns during training. It’s where complex, high-dimensional information such as images, text, or sounds is compressed into numerical vectors that capture underlying patterns, relationships, and…
Explainability in AI refers to the ability to understand and interpret how an artificial intelligence or machine learning model arrives at its predictions or decisions. It answers the question: “Why did the AI make that choice?” As AI becomes more…