The Future of Language Models: Embracing Multi-Modality for Enhanced User Experiences

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Artificial Intelligence is advancing, thanks to the introduction of super beneficial and efficient Large Language Models. Based on the concepts of Natural Language Processing, Natural Language Generation, and Natural Language Understanding, these models have been able to make lives easier. From text generation and question answering to code completion, language translation, and text summarization, LLMs have come a long way. With the development of the latest version of LLM by OpenAI, i.e., GPT 4, this advancement has opened the way for the progress of the multi-modal nature of models. Unlike the previous versions, GPT 4 can take textual as well as inputs in the form of images.

The future is becoming more multi-modal, which means that these models can now understand and process various types of data in a manner akin to that of people. This change reflects how we communicate in real life, which involves combining text, visuals, music, and diagrams to express meaning effectively. This invention is viewed as a crucial improvement in the user experience, comparable to the revolutionary effects that chat functionality had earlier.

In a recent tweet, the author emphasized the significance of multi-modality in terms of user experience and technical difficulties in the context of language models. ByteDance has taken the lead in realizing the promise of multi-modal models thanks to its well-known platform, TikTok. They use a combination of text and image data as part of their technique, and a variety of applications, such as object detection and text-based image retrieval, are powered by this combination. Their method’s main component is offline batch inference, which produces embeddings for 200 terabytes of image and text data, which makes it possible to process various data kinds in an integrated vector space without any issues.

Some of the limitations that accompany the implementation of multi-modal systems include inference optimization, resource scheduling, elasticity, and the amount of data and models involved is enormous. ByteDance has used Ray, a flexible computing framework that provides a number of tools to solve the complexities of multi-modal processing to address the problems. Ray’s capabilities provide the flexibility and scalability needed for large-scale model parallel inference, especially Ray Data. The technology supports effective model sharding, which permits the spread of computing jobs over various GPUs or even various regions of the same GPU, which guarantees efficient processing of even models that are too huge to fit on a single GPU.

The move towards multi-modal language models heralds a new era in AI-driven interactions. ByteDance uses Ray to provide effective and scalable multi-modal inference, showcasing the enormous potential of this method. The capacity of AI systems to comprehend, interpret, and react to multi-modal input will surely influence how people interact with technology as the digital world grows more complex and varied. Innovative businesses working with cutting-edge frameworks like Ray are paving the way for a time when AI systems can comprehend not just our speech but also our visual cues, enabling richer and more human-like interactions.

Check out the Reference 1 and Reference 2. All Credit For This Research Goes To the Researchers on This Project. Also, don’t forget to join our 29k+ ML SubReddit, 40k+ Facebook Community, Discord Channel, and Email Newsletter, where we share the latest AI research news, cool AI projects, and more.

Tanya Malhotra is a final year undergrad from the University of Petroleum & Energy Studies, Dehradun, pursuing BTech in Computer Science Engineering with a specialization in Artificial Intelligence and Machine Learning.
She is a Data Science enthusiast with good analytical and critical thinking, along with an ardent interest in acquiring new skills, leading groups, and managing work in an organized manner.

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