Large language models (LLMs) for text are advanced machine learning models, specifically deep learning models, designed to understand, generate, and interact with human language. They are trained on vast amounts of textual data to capture linguistic patterns, nuances, and context, allowing them to perform a wide range of tasks, from simple text classification to sophisticated content generation and comprehension.
A question and answer with the GPT-4 chatbot
A prominent performant example of a large language model is GPT-3 (Generative Pre-trained Transformer 3) developed by OpenAI. With 175 billion parameters, GPT-3 is one of the largest and most powerful LLMs, known for generating coherent and contextually relevant text across a multitude of tasks without task-specific training.
An even more powerful (but also more expensive model) is GPT-4, released in March of 2023, which has over 1.7 trillion parameters. This makes GPT-4 nearly 1000 times larger than GPT-3.
Not Eliza …. (Created by Midjourney)
Large image models function by processing visual data using layers of artificial neurons. These layers can detect patterns, shapes, textures, and other features within images. The models are trained on vast datasets, with each image labeled, allowing the model to learn to recognize and categorize various objects and scenes. Deep neural networks, especially Convolutional Neural Networks (CNNs), have been particularly influential in this domain because they are designed to automatically and adaptively learn spatial hierarchies of features from images.
<aside> 💡 Three of the most publicly recognizable image generation models in market today are DALL-E 2 by OpenAI, Stable Diffusion, and Midjourney (which basically functions as a bot on Discord).
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AI models for audio process sound data, typically converting sound waves into spectrograms or other numerical representations. These representations can then be processed by neural networks to detect patterns, recognize features, or generate new sounds.