Advancing Deep Learning through Probability Engineering: A Pragmatic Paradigm for Modern AI

• URL: http://arxiv.org/abs/2503.18958v1
• Date: Wed, 19 Mar 2025 07:48:23 GMT
• Title: Advancing Deep Learning through Probability Engineering: A Pragmatic Paradigm for Modern AI
• Authors: Jianyi Zhang,
• Abstract summary: This paper proposes a novel concept, Probability Engineering, which treats the already-learned probability distributions within deep learning as engineering artifacts.<n>We introduce novel techniques and constraints to refine existing probability distributions, improving their robustness, efficiency, adaptability, or trustworthiness.<n>Case studies demonstrate how probability distributions once treated as static objects can be engineered to meet the diverse and evolving requirements of large-scale, data-intensive, and trustworthy AI systems.
• Score: 9.126527152752146
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Recent years have witnessed the rapid progression of deep learning, pushing us closer to the realization of AGI (Artificial General Intelligence). Probabilistic modeling is critical to many of these advancements, which provides a foundational framework for capturing data distributions. However, as the scale and complexity of AI applications grow, traditional probabilistic modeling faces escalating challenges, such as high-dimensional parameter spaces, heterogeneous data sources, and evolving real-world requirements often render classical approaches insufficiently flexible. This paper proposes a novel concept, Probability Engineering, which treats the already-learned probability distributions within deep learning as engineering artifacts. Rather than merely fitting or inferring distributions, we actively modify and reinforce them to better address the diverse and evolving demands of modern AI. Specifically, Probability Engineering introduces novel techniques and constraints to refine existing probability distributions, improving their robustness, efficiency, adaptability, or trustworthiness. We showcase this paradigm through a series of applications spanning Bayesian deep learning, Edge AI (including federated learning and knowledge distillation), and Generative AI (such as text-to-image generation with diffusion models and high-quality text generation with large language models). These case studies demonstrate how probability distributions once treated as static objects can be engineered to meet the diverse and evolving requirements of large-scale, data-intensive, and trustworthy AI systems. By systematically expanding and strengthening the role of probabilistic modeling, Probability Engineering paves the way for more robust, adaptive, efficient, and trustworthy deep learning solutions in today's fast-growing AI era.

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