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Recently, Apple's AI research team launched their next-generation family of Multimodal Large Language Models (MLLMs) - MM1.5. This series of models can integrate various data types such as text and images, showcasing new capabilities of AI in understanding complex tasks. Tasks like visual question answering, image generation, and multimodal data interpretation can be better addressed with the help of these models. A major challenge for multimodal models is how to achieve effective interaction between different data types. Previous models often struggled in this aspect.

2023 has been one of the most disruptive years in the field of artificial intelligence, with a plethora of generative AI products entering the mainstream. Continuing its transformative journey, generative AI is expected to transition from an exciting topic to real-world applications in 2024. The field of generative AI is rapidly evolving, giving rise to a series of widespread trends that will facilitate the adoption of AI across various industries and its presence in our daily lives. Generative AI models are transcending text creation through the integration of multimodal functionalities.

Unified-IO2 marks a significant breakthrough in the field of artificial intelligence, featuring autoregressive capabilities that can handle various data types including text, images, audio, and video. The innovative single encoder-decoder transformer model overcomes the limitations of previous models in multimodal data processing. It excels in performance, setting new records in GRIT assessments across 35 datasets, particularly surpassing competitors in image generation. Unified-IO2 employs complex and innovative methods, including shared representation spaces and pretrained visual transformers.