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Video as the New Language for Real-World Decision Making (2402.17139v1)

Published 27 Feb 2024 in cs.CV and cs.AI

Abstract: 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: LLMs 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 LLMs, 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 LLMs in a wider array of AI applications.

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Citations (25)
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Summary

  • The paper demonstrates video as a unified representation that enhances model understanding of complex environmental interactions.
  • It proposes self-supervised learning methodologies leveraging abundant online video data to advance simulation and decision-making tasks.
  • The work highlights practical applications in robotics, autonomous driving, and adaptive content creation while addressing dataset and model challenges.

Video Generation: Expanding the Horizon of Real-World Decision Making

Introduction

Recent advancements in the domain of artificial intelligence, particularly in LLMs, have significantly influenced the trajectory of research and real-world applications. These models have demonstrated exceptional performance in understanding and generating human language, successfully tackling a myriad of complex tasks. However, the digital field is not solely governed by text-based interactions. The physical world, rich in visual and spatial detail, presents a spectrum of challenges and opportunities that text alone cannot fully encapsulate or address. This paper posits that the future of real-world decision-making heavily leans on the integration and advancement of video generation techniques.

Video as Unified Representation and Interface

Videos inherently capture the richness of the physical world, conveying not just visual and spatial details but also embodying the dynamics of actions and interactions within environments. They can serve as a comprehensive medium to represent information that is challenging to narrate through text. To bridge the gap between the digital and physical realms, the paper discusses how video can act as both a unified representation of worldly knowledge and a unified task interface. Leveraging the abundance of video data on the internet for self-supervised learning opens avenues for models that can understand and interact with the physical world in unprecedented ways.

Task-Specific Specializations in Video Generation

The versatility of video as a medium is further highlighted through its ability to accommodate various task-specific specializations. Whether it's generating videos from textual descriptions, predicting future frames based on current states, or simulating interactions with the environment, the potential applications are vast. These range from improving robotic manipulation based on visual plans to creating adaptive content in entertainment and education.

Video Generation as Simulation

One of the compelling opportunities presented in the paper is the use of video generation for simulation. This not just includes simulating game environments for training AI models but extends to simulating real-world processes such as robotics operations, autonomous driving, and scientific exploration. By refining video generation models to accurately predict outcomes based on given actions or changes in the environment, there's potential for significant advancements in various fields.

Addressing Challenges in Video Generation

Despite the promising prospects, video generation faces several challenges, from dataset limitations and model heterogeneity to issues of hallucination and limited generalization. Addressing these challenges requires innovative approaches in model design, training methodologies, and data collection. For instance, exploring efficient ways to expand the coverage and relevance of training datasets, designing versatile models that can effectively learn from this data, and developing techniques to reduce hallucination in generation outputs are critical steps forward.

Conclusion

The exploration of video generation as the new language for real-world decision making opens a multidimensional landscape for AI research and application. By transcending the limitations of text-based models to incorporate the dynamic and visually rich information that videos offer, we stand on the brink of significantly enhancing our interaction with and understanding of the physical world. As the field advances, the convergence of video generation with existing AI technologies promises to redefine the boundaries of what machines can learn and achieve.

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