Practices for Governing Agentic AI Systems
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Understanding the Theory of Mind (ToM), the ability to grasp the thoughts and intentions of others, is crucial for developing machines with human-like social intelligence. Recent advancements in machine learning, especially with large language models, show some capability in ToM understanding. However, current ToM benchmarks primarily rely on either video or text datasets, neglecting the…
In the evolving landscape of artificial intelligence and natural language processing, utilizing large language models (LLMs) has become increasingly prevalent. However, one of the challenges that persist in this domain is enabling these models to engage in role-play effectively. This work requires a deep understanding of language and an ability to embody diverse characters consistently….
The struggle to balance training efficiency with performance has become increasingly pronounced within computer vision. Traditional training methodologies, often reliant on expansive datasets, substantially burden computational resources, creating a notable barrier for researchers with limited access to high-powered computing infrastructures. This issue is compounded by the fact that many existing solutions, while reducing the sample…
Foundational models are large deep-learning neural networks that are used as a starting point to develop effective ML models. They rely on large-scale training data and exhibit exceptional zero/few-shot performance in numerous tasks, making them invaluable in the field of natural language processing and computer vision. Foundational models are also used in Monocular Depth Estimation…
In the realm of artificial intelligence, Large Multimodal Models (LMMs) have exhibited remarkable problem-solving capabilities across diverse tasks, such as zero-shot image/video classification, zero-shot image/video-text retrieval, and multimodal question answering (QA). However, recent studies highlight a substantial gap between powerful LMMs and expert-level artificial intelligence, particularly in tasks involving complex perception and reasoning with domain-specific…
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