Apple has ultimately introduced MM1, their combined AI model for generating text and images. This AI model is also capable of making in-context predictions due to its extensive pre-training in different modes.
Following a period of conjecture and hearsay regarding their forthcoming AI initiatives and multimodal AI models, Apple's team of scientists have created a collection of extensive multimodal language models named MM1. These models are capable of both handling and producing textual and visual information, as detailed in a research paper released the previous week.
The research conducted at Apple's labs was focused on developing efficient and high-performing multimodal large language models (MLLMs). This was accomplished by meticulously analyzing and modifying different architectural elements, data sources, and training methods.
The study revealed that the quality of the image and the effectiveness of the visual encoder significantly influenced the model's performance. However, the particular technique used to integrate visual and text information had a lesser impact.
They also found out that a meticulous combination of various data forms was vital. Interspersed image-text documents aided with limited-shot learning, while conventional captioned images enhanced zero-shot performance. Incorporating text-only data helped sustain robust language comprehension abilities.
MM1 is capable of making predictions within a given context due to its extensive multimodal pre-training. This enables MM1 to enumerate objects and adhere to specific formatting, reference sections of pictures and carry out Optical Character Recognition (OCR), exhibit general knowledge and vocabulary regarding common objects, and execute fundamental mathematical operations.
Drawing from these observations, the group created the MM1 model series, which consists of three billion to 30 billion parameters, incorporating both dense and mixture-of-experts variants. Once they intensified the training, the MM1 managed to attain unparalleled outcomes on several multimodal benchmarks during the pre-training phase.
After additional adjustments made using a carefully selected dataset of 1 million examples, the ultimate MM1 models showed strong results across 12 different tasks involving multiple modes, like answering questions based on visuals and providing captions. Importantly, MM1 was able to reason using multiple images and learn from a few examples, both crucial skills made possible through the team's thoughtful approach to multimodal pre-training.
This study expands on earlier work in fields such as CLIP for acquiring visual depictions through natural language supervision, and autoregressive models, such as GPT, for generating text. Nonetheless, it stands out as one of the earliest comprehensive investigations specifically dedicated to large-scale multimodal pre-training.
The investigators are optimistic that their findings will hasten advancements, with rumors suggesting that Apple is negotiating to incorporate Google's Gemini generative AI models into future iPhone applications.
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