AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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2,613 papers
This paper explores the application of adaptive chaos theory, leveraging reinforcement learning to dynamically adjust parameters of chaotic systems. Traditional approaches often rely on fixed, static parameter sets, limiting the system's flexibility and potential for robust behavior. This research introduces a novel sy...
This paper presents a novel approach to digital circuit synthesis leveraging the power of Evolutionary Reinforcement Learning (ERL). Traditional circuit synthesis methods often rely on deterministic algorithms that struggle to effectively manage the complexity and dynamism inherent in modern design requirements. This r...
Neuro-hacking, understood here as the deliberate application of neuroscience-informed techniques to regulate attention, memory formation and cognitive load, has begun to migrate from clinical and self-improvement contexts into mainstream higher-education pedagogy. This review synthesises evidence from five primary stud...
This research investigates the application of reinforcement learning to automatically generate and optimize geometric patterns, focusing on identifying novel patterns exhibiting complex, self-organizing behavior. Traditional geometric pattern generation often relies on predefined rules, limiting the potential for truly...
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This paper explores the application of reinforcement learning to dynamically optimize topology in parallel computing systems. The core claim is to develop a system capable of automatically adjusting the topology of parallel environments—specifically, the number of threads and processes—to maximize throughput and minimi...
This paper introduces the Neuro-Symbolic Symbiosis Architecture (NSSA), a novel framework designed to enhance reasoning and learning capabilities by dynamically adjusting the interaction between neural networks and symbolic knowledge bases. The core claim is that by modulating the interaction strength between these two...
This paper investigates the application of reinforcement learning to automatically optimize lattice parameterization for complex systems, particularly in materials science and molecular modeling. The core claim is to develop a system that dynamically adjusts lattice parameters based on real-time feedback, aiming to imp...
Traditional cognitive computing systems often struggle with robust reasoning and adaptability, particularly in dynamic and uncertain environments. This research proposes a novel approach to cognitive computing by integrating Bayesian belief networks (BBNs) and reinforcement learning (RL). BBNs provide a framework for r...
The design of effective feedback loops is a fundamental challenge in many engineering and scientific disciplines. Traditional approaches often involve manual tuning of parameters to achieve desired outcomes, which is a time-consuming and potentially error-prone process. This paper introduces the Self-Organizing Feedbac...
This paper introduces a novel approach to large-scale code analysis leveraging hierarchical abstraction learning. The core idea is to accelerate code analysis by learning hierarchical representations of code structures, effectively capturing semantic relationships across multiple levels of granularity. A reinforcement...
This paper presents a novel approach to program code optimization leveraging reinforcement learning (RL). Traditional static code analysis techniques often fall short in achieving optimal performance due to their lack of adaptability and global optimization capabilities. Our proposed system utilizes an RL agent to dyna...
This paper investigates the application of Multi-Agent Reinforcement Learning (MARL) to the problem of distributed resource management. Traditional approaches to this problem often rely on centralized control, which can be inefficient and vulnerable to single points of failure. This research proposes a decentralized so...
A Blog post by Ai2 on Hugging Face
Research Manager Mike Walker reflects on a career spanning hacker culture, cybersecurity, and AI, sharing lessons about trust, teamwork, leadership, and why solving difficult problems requires curiosity, judgment, agility—and an understanding of the rules. The post Learn the rules: The hacker mindset appeared first on Microsoft Research.
Learn how Google Antigravity and our Cheminformatics Skills accelerate drug discovery workflows, search compounds, and propose novel rigidified macrocycles.
Prabha Jha used her experience with MIT Open Learning’s program for senior executives to develop AI solutions for her employer, Verizon, and to write a book.
Education Innovation
Assistant Professor Benjamin Lindquist studies how engineers deliberately injected randomness into early AI, giving rise to deep-learning models that sometimes hallucinate.
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