Glossary for Advanced Users
GPT is an advanced language model that uses Deep Learning techniques to generate human-like text. Built on the Transformer architecture, GPT models have been trained on vast amounts of text data and can generate coherent and contextually relevant sentences. GPT has applications in natural language generation, chatbots, and content creation.
Genetic Algorithms are optimisation techniques inspired by the principles of evolution. By mimicking natural selection, Genetic Algorithms explore a large search space and find optimal solutions to complex problems, making them valuable tools for businesses in various domains.
Graph Neural Networks are machine learning models designed to handle data structured as graphs. They can capture relationships and dependencies between entities and perform tasks such as node classification, link prediction, and graph generation. Graph Neural Networks are valuable for analysing social networks, biological networks, and recommendation systems.
Human-in-the-loop refers to a collaborative approach where humans and AI systems work together to achieve optimal results. It involves combining human expertise, judgement, and intervention with AI capabilities. By incorporating human feedback, AI models can learn and adapt, ensuring accuracy, fairness, and ethical decision-making.
Humanoid Robots are advanced machines designed to resemble and interact with humans in a human-like manner. These robots are equipped with AI capabilities that enable them to understand and respond to human commands. Humanoid Robots find applications in various industries, such as healthcare, customer service, and manufacturing.
Hyperparameters are parameters that are set before the training of an AI model. They control the behaviour and performance of the model, such as learning rate, batch size, and regularisation strength. Selecting appropriate Hyperparameters is crucial for optimising model performance and improving accuracy.
Incremental Learning is an AI technique that allows models to continuously learn from new data without retraining from scratch. Instead of training the model on the entire dataset, Incremental Learning updates the model using newly available data. This approach is efficient for handling evolving data and adapting to changing environments.
Instance-Based Learning is an AI approach where models make predictions based on similarity to previously seen examples. Instead of generalising from a predefined set of rules, Instance-Based Learning examines specific instances and their characteristics. This approach is effective in tasks such as pattern recognition and recommendation systems.
Intelligent Virtual Assistants, also known as Chatbots or Virtual Agents, are AI-powered software applications that can engage in conversations and perform tasks on behalf of users. These Virtual Assistants leverage Natural Language Processing and Machine Learning to understand user queries and provide relevant information. They enhance customer experiences and streamline operations by offering personalised and … Read more
