Base Foundation Model Slide | Overview
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Here are some key characteristics of foundation models: Massive Training Data: These models are trained on enormous datasets of text, code, images, or a combination of these. This data can range from books and articles to code repositories and social media feeds. The vast amount of data allows the model to learn complex patterns and relationships. Transfer Learning: A defining feature of foundation models is their ability to transfer knowledge gained on one task to other tasks. This is achieved through techniques like pre-training on a general-purpose objective. Essentially, the model learns foundational skills like recognizing patterns and relationships in data, which can then be adapted for specific tasks. Generative Capabilities: Many foundation models can generate different creative text formats, translate languages, or even create images. This stems from their ability to identify patterns and predict the next element in a sequence, whether it's the next word in a sentence or the next pixel in an image. Scalability: Foundation models are built on powerful hardware like GPUs (Graphics Processing Units) that allow them to handle the massive amounts of data and complex computations involved in training and running the models. Unlabeled Data: A significant portion of the training data is often unlabeled, meaning it doesn't have specific tags or categories assigned. The model learns to identify patterns within this data itself. Self-Supervised Learning: In addition to transfer learning, foundation models often leverage self-supervised learning techniques. This involves creating training tasks from the unlabeled data itself, allowing the model to learn meaningful representations from the data. Broad Applicability: Due to their ability to transfer learning and handle various data types, foundation models have the potential to be applied to a wide range of tasks across different industries. |