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In the artificial intelligence era, it is more important than ever for businesses to have a clear understanding of the jargon and terminology used in the field. This is where our generative artificial intelligence glossary comes in. A generative artificial intelligence glossary is a valuable resource that can help our customers to have clear and concise definitions of generative artificial intelligence terms, to better understand the services and products that our business offers.
A glossary can also help us to communicate more effectively with our partners. When everyone is on the same page with regard to the terminology used in generative artificial intelligence, it can lead to improved efficiency and productivity.
A technique used in deep learning models to selectively focus on certain parts of input data.
A type of neural network used for unsupervised learning and data compression.
A probabilistic graphical model that can be used for reasoning under uncertainty.
The ability of a computer to interpret and understand visual information from the world, including images and videos.
A type of neural network that can process image and video data, used in generative AI for image generation.
A technique used to increase the size and diversity of training data by creating new variations of existing data.
A subfield of machine learning that uses artificial neural networks with many layers to analyse and process data, which ChatGPT uses to generate responses.
A type of machine learning that involves training models to learn from only a few examples, similar to how humans can learn new concepts quickly.
Generative Artificial Intelligence refers to the subset of AI that involves creating or generating new and original content such as images, music, text or other media using algorithms.
Generative Adversarial Networks are a type of neural network that generates new content by learning from a dataset and generating similar content.
Machine learning models that generate new data based on a given set of input data, which ChatGPT is an example of.
The ability of machines to identify objects, people, and other elements in images, used in generative AI to create new images.
The process of generating new images using machine learning algorithms.
The process of generating new content from a generative AI model, used to create new content.
The process of using machine learning algorithms to predict the likelihood of a sequence of words or phrases, which ChatGPT is trained to do.
A type of neural network that can process sequential data, used in generative AI to create new content.
A type of artificial intelligence that enables systems to automatically learn and improve from experience without being explicitly programmed.
The field of AI that focuses on enabling machines to understand and generate human language, which ChatGPT is designed to do.
A type of machine learning model inspired by the structure and function of the human brain.
A technique that uses deep learning to transfer the style of one image to another, creating new and original art.
Mathematical functions that describe the likelihood of an event occurring, used in generative AI to model data distributions and create new content.
A type of neural network that can process variable-length sequences of data, used in generative AI to create new content.
A type of machine learning that involves training agents to make decisions based on rewards and punishments in a given environment.
A type of machine learning that involves training models using data that has been automatically labelled or augmented.
The process of converting spoken language into text, which ChatGPT can be used for with the appropriate training.
The conversion of written text into spoken words, used in generative AI for voice generation.
The process of using knowledge gained from one task to improve performance on another related task, which ChatGPT has benefited from through its training on a diverse range of text data.
A type of neural network architecture commonly used in natural language processing tasks.
A type of machine learning that involves training algorithms to find patterns and relationships in data without explicit labels or guidance.
A type of autoencoder that learns a compressed representation of data and can generate new samples from that representation.
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