Practices for Governing Agentic AI Systems

Optical flow estimation, a cornerstone of computer vision, enables predicting per-pixel motion between consecutive images. This technology fuels advancements in numerous applications, from enhancing action recognition and video interpolation to improving autonomous navigation and object tracking systems. Traditionally, progress in this domain has been propelled by developing more complex models that promise higher accuracy. However,…

Language modeling, a critical component of natural language processing, involves the development of models to process and generate human language. This field has seen transformative advancements with the advent of large language models (LLMs). The primary challenge lies in efficiently optimizing these models. Distributed training with multiple devices faces communication latency hurdles, especially when varying…

The study diverges from previous approaches by concentrating on aligning long context, specifically by fine-tuning language models to interpret lengthy user prompts. Challenges include the absence of extensive datasets for supervised fine-tuning, difficulties in handling varied length distributions efficiently across multiple GPUs, and the necessity for robust benchmarks to assess the models’ capabilities with real-world…

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Understanding why things happen, known as causal inference, is a key part of human intelligence. There are two main ways we gain this ability: one is through what we’ve learned from experience, like knowing that touching a hot stove causes burns based on common sense; the other is through pure causal reasoning, where we formally…

Current multi-modal language models (LMs) face limitations in performing complex visual reasoning tasks. These tasks, such as compositional action recognition in videos, demand an intricate blend of low-level object motion and interaction analysis with high-level causal and compositional spatiotemporal reasoning. While these models excel in various areas, their effectiveness in tasks requiring detailed attention to…