The boom in Artificial Intelligence (AI) applications has resulted in the extensive use of Machine Learning (ML) models for various uses, introducing the rise of multimodal models. These models, which integrate numerous data sources like text and images, are gaining traction amongst researchers due to their ability to replicate the intricacy of human cognition. They provide numerous benefits across several domains.
The AI experts at Adept AI have developed a fresh multimodal model known as Fuyu-Heavy, currently the world’s third most competent model. The model exceeds the capabilities of Gemini Pro in Multimodal Language Understanding (MMLU) and Multimodal Model Understanding (MOU), yet still trails behind GPT4-V and Gemini Ultra. Despite being smaller than rival models, Fuyu-Heavy excels in a range of benchmarks. The researchers highlighted the need to balance language and image modeling tasks, which required the utilization of unique methodologies for excellent performance at scale.
According to the Adept AI researchers, creating Fuyu-Heavy posed significant challenges due to its large-scale nature. Additionally, the complicated process of training a brand-new architecture on both visual and text data presented obstacles. The model’s heavy reliance on image data during training put added pressure on systems, leading to difficulties managing data influx, memory usage, and cloud storage bandwidth.
As a solution, the researchers employed innovative dataset methods, utilizing existing resources and synthetically produced data to enhance the model’s image-processing abilities. However, managing the coordinate systems during training and inference stages and varying image formats posed further challenges. To overcome these, meticulous attention to detail and stringent quality assurance measures were required.
Upon testing, the team discovered that Fuyu-Heavy outperformed many larger models within its computing class, demonstrating its accuracy and capability. The Fuyu-Heavy Chat function was also found to be effective in conversational AI, matching the performance of larger models such as Claude 2.0 across numerous chat evaluation platforms.
Looking to the future, the researchers plan to focus on improving the base-model capabilities. They aim to convert these basic models into practical agents through reward modeling, self-play, and various inference-time search techniques. They also emphasize integrating these models to create dependable, useful products. Given its ability to merge text and image processing tasks, Fuyu-Heavy’s potential across various domains is evident, and with continued improvement, its applications will likely continue to grow.
