Multi-modal models that can process both text and images are a growing area of research in artificial intelligence. However, training these models presents a unique challenge: language models deal with discrete values (words and tokens), while image generation models must handle continuous pixel values.
Current multi-modal models use techniques that reduce the quality of representing data. In a new research paper, scientists from Meta and the University of Southern California introduce Transfusion, a novel technique that enables a single model to seamlessly handle both discrete and continuous modalities. — Read More
Monthly Archives: September 2024
Introducing AI21 Labs Jamba 1.5
The AI21 Jamba 1.5 family of models is state-of-the-art, hybrid SSM-Transformer instruction following foundation models. The Jamba models are the most powerful & efficient long-context models on the market, which deliver up to 2.5X faster inference than leading models of comparable sizes.
The models demonstrate superior long context handling, speed, and quality. They mark the first time a non-Transformer model has been successfully scaled to the quality and strength of the market’s leading models. — Read More
The Paper
Galileo LLM Hallucination Index
Many enterprise teams have already successfully deployed LLMs in production, and many others have committed to deploying Generative AI products in 2024. However, for enterprise AI teams, the biggest hurdle to deploying production-ready Generative AI products remains the fear of model hallucinations – a catch-all phrase for when the model generates text that is incorrect or fabricated. There can be several reasons for this, such as a lack of the model’s capacity to memorize all of the information it was fed, training data errors, and outdated training data. — Read More
The Index
Rick's Cafe AI 