Foundational models in gen AI are a type of machine learning (ML) model that is pretrained to perform a range of tasks. They have two defining characteristics: transfer learning and scale. Transfer learning refers to the ability of a model to apply information about one situation to another and build upon its knowledge. Scale refers to hardware, specifically, graphics processing units (GPUs), that allow the model to perform multiple computations simultaneously, also known as parallel processing. Deep learning, a technology that underpins many foundation models, teaches computers to learn through observation, imitating the way humans gain knowledge. Transformers, a type of artificial neural network (ANN), are often used in foundation models that involve text, allowing them to capture contextual relationships and dependencies between words. After a foundation model has been trained, it can rely on the knowledge gained from the huge pools of data to help solve problems, providing valuable assistance in tasks such as natural language processing (NLP), computer vision, and audio processing.
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21 апр 2024
