AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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1,737 papers
This paper describes a novel AI-driven predictive maintenance approach in the context of industrial systems that overcomes the limitations of the existing approaches including high dependency on the availability of data, model complexity and generalisation. With the integration of transformer-based structures, hybrid l...
Rising cyber risks require intelligent, flexible and transparent risk management solutions. In this paper, we introduced an AI enabled framework for cyber risk assessment and insurance modeling for the age of cloud computing, which incorporates transparent AI, real-time threat intelligence, and federated learning to ta...
This research release develops sharp mathematical results for Byzantine-robust aggregation, exact communication complexity, and compressed delayed momentum in federated and distributed optimization. The work studies a central question in robust distributed learning: how accurately can a server aggregate vectors when up...
The role of unlearning in privacy protection and its dual role in security is examined, where it serves as a defense against poisoning and backdoors but also becomes an attack surface and verifiable unlearning in federated and decentralized environments remains a central open problem.
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This research release develops sharp mathematical results for Byzantine-robust aggregation, exact communication complexity, and compressed delayed momentum in federated and distributed optimization. The work studies a central question in robust distributed learning: how accurately can a server aggregate vectors when up...
An open-access curriculum module exploring the architectural frameworks, communication bounds, and machine learning topologies that govern distributed robotics. Covers three-tier cyber-physical hierarchies (embedded edge devices, local fog/edge servers, centralized hyper-scale cloud), non-minimum phase transfer functio...
An open-access curriculum module exploring the architectural frameworks, communication bounds, and machine learning topologies that govern distributed robotics. Covers three-tier cyber-physical hierarchies (embedded edge devices, local fog/edge servers, centralized hyper-scale cloud), non-minimum phase transfer functio...
FedMuse is proposed, a privacy-aware federated framework for multi-style text-to-image art generation, in which distributed clients collaboratively train a shared generative model while keeping their private art data local, and an adaptive privacy regularizer is introduced to reduce memorization risk while maintaining...
An evidence-informed AI-to-Carbon Value Chain conceptual synthesis comprising data observability, model credibility, decision executability, system coordination, and carbon verification is advanced, although this synthesis is interpretive rather than a validated causal framework.
StudentSim is a research system for evaluating and improving AI tutoring tools before they are tested with learners. It models how individuals may engage with tutors, giving feedback to improve tutor performance across subjects like chess, writing, and mathematics. The post Simulating student learning for better AI tutoring appeared first on Microsoft Research.
Students in MIT’s Concourse program delve deeply into the human condition, debate challenging questions, and learn to develop judgment about issues that can’t be quantified.
Training AI agents with reinforcement learning can be challenging because their tools, context, and decision-making are managed by complex frameworks. Agent Lightning connects existing agents to RL training, making it easier to improve them without rebuilding them. The post Agent Lightning v1.0: A 3,500-Line Lightweight Agentic RL Framework for Training Agents with Real Harnesses appeared first on Microsoft Research.
The effort aims to help U.S. learners from kindergarten to community college, with an emphasis on math, making, and the constructive use of AI.
Associate Professor Cathy Wu uses reinforcement learning to help map out improvements to transportation and other multifaceted systems.
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