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In the realm of deep learning, much emphasis has been placed on deciphering digital media files that resonate with human understanding. Yet, amidst this pursuit, the ubiquitous presence of native binary data in the digital landscape often goes unnoticed.
Bytes, the elemental units of digital information, form the bedrock of all data, devices, and software, permeating everything from computer processors to the operating systems of everyday electronics. Thus, the potential for training models geared towards next-byte prediction heralds a transformative paradigm shift in deep learning, promising a comprehensive comprehension and emulation of all digital phenomena.
In a new paper Beyond Language Models: Byte Models are Digital World Simulators, a research team from Microsoft Research Asia, Central Conservatory of Music, and Tsinghua University introduces bGPT, a pioneering model engineered explicitly for processing binary data and simulating the digital world through next-byte prediction. bGPT transcends conventional boundaries of deep learning by directly engaging with and manipulating binary data, fostering a deeper and more holistic understanding of the digital realm.
Operating at the byte level not only empowers models to discern intricate patterns within digital systems but also furnishes a unified methodology for amalgamating diverse data types within a singular framework. Inspired by this vision, the bGPT framework endeavors to simulate digital systems by harnessing native binary data and seamlessly integrating disparate data modalities into a cohesive byte sequence. This approach not only streamlines integration processes but also broadens the horizons of application within the digital domain.