Video as the New Language for Real-World Decision Making
Sherry Yang Jacob Walker Jack Parker-Holder Yilun Du Jake Bruce Andre Barreto Pieter Abbeel Dale Schuurmans
Both text and video data are abundant on the internet and support large-scale
self-supervised learning through next token or frame prediction. However, they
have not been equally leveraged: language models have had significant
real-world impact, whereas video generation has remained largely limited to
media entertainment. Yet video data captures important information about the
physical world that is difficult to express in language. To address this gap,
we discuss an under-appreciated opportunity to extend video generation to solve
tasks in the real world. We observe how, akin to language, video can serve as a
unified interface that can absorb internet knowledge and represent diverse
tasks. Moreover, we demonstrate how, like language models, video generation can
serve as planners, agents, compute engines, and environment simulators through
techniques such as in-context learning, planning and reinforcement learning. We
identify major impact opportunities in domains such as robotics, self-driving,
and science, supported by recent work that demonstrates how such advanced
capabilities in video generation are plausibly within reach. Lastly, we
identify key challenges in video generation that mitigate progress. Addressing
these challenges will enable video generation models to demonstrate unique
value alongside language models in a wider array of AI applications.