Home AI News Advancements in Multi-Modal AI: Language Models and the Power of Images

Advancements in Multi-Modal AI: Language Models and the Power of Images

Advancements in Multi-Modal AI: Language Models and the Power of Images

The Significance of Multi-Modal Language Models in AI

Artificial Intelligence is advancing with the introduction of Large Language Models (LLMs) that are highly beneficial and efficient. These models, based on Natural Language Processing, Natural Language Generation, and Natural Language Understanding, have made our lives easier. They can generate text, answer questions, complete code, translate languages, and summarize text. The latest version of LLM, GPT 4 by OpenAI, takes this advancement a step further by allowing inputs in the form of images as well as text.

This advancement towards multi-modal language models reflects the future, where models can understand and process various types of data just like humans. Our communication in real life involves the combination of text, visuals, music, and diagrams to convey meaning effectively. This new feature in LLMs is seen as a crucial improvement in user experience, similar to the revolutionary impact that chat functionality had earlier.

ByteDance, the company behind TikTok, is leading the way in realizing the potential of multi-modal models. They use a combination of text and image data in their technique, which powers various applications like object detection and text-based image retrieval. Their method involves offline batch inference, which can process large amounts of image and text data in an integrated vector space without any issues.

However, there are certain limitations in implementing multi-modal systems, such as optimizing inference, resource scheduling, and handling enormous amounts of data and models. ByteDance uses Ray, a flexible computing framework, to tackle these challenges. Ray provides the necessary flexibility and scalability for large-scale model parallel inference, especially with its Ray Data feature. This technology supports efficient model sharding, allowing computing jobs to be distributed over multiple GPUs. This ensures efficient processing, even for models that are too large to fit on a single GPU.

The move towards multi-modal language models marks a new era in AI-driven interactions. ByteDance’s use of Ray showcases the immense potential of multi-modal inference. As AI systems become capable of understanding and responding to multi-modal input, it will significantly impact how people interact with technology in the increasingly complex and diverse digital world. Innovative companies like ByteDance, working with cutting-edge frameworks like Ray, are paving the way for AI systems that can comprehend not only our speech but also our visual cues, enabling more human-like interactions.

For more information, check out the references ([1](https://www.anyscale.com/blog/how-bytedance-scales-offline-inference-with-multi-modal-llms-to-200TB-data) and [2](https://mp.weixin.qq.com/s/R_N1AbQuMF3q186MQQLeBw)). Credit goes to the researchers involved in this project.

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