AI model families, architectures, capabilities and optimization techniques.
A theoretical AI system capable of understanding, learning, and applying intelligence across any cognitive task humans can perform.
A type of generative AI model that learns to create data by reversing a process of adding random noise.
A massive AI model trained on broad data that can be adapted for a wide variety of downstream tasks.
A deep learning model trained on vast amounts of text to understand, generate, and predict human language.
An AI system designed to simultaneously understand and process multiple forms of data, such as text, images, and audio.
An AI model optimized to spend additional computation time generating intermediate steps before answering complex problems.
A compact version of a language model designed to run efficiently on devices with limited computational power.
A neural network architecture that relies on an attention mechanism to process sequential data efficiently.