AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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2,754 papers
The University of Florida (UF) and Epoca International jointly transformed the flood of everyday social-media posts into a precise, AI-driven demand-forecasting engine. UF leveraged its HiPerGator supercomputer and interdisciplinary faculty to build large-scale models that sifted billions of unprompted consumer convers...
The assessment of vibration signals in automotive generators is crucial for ensuring operational efficiency, reliability and predictive maintenance. Excessive vibrations can lead to mechanical failures, energy losses and reduced generator lifespan. However, traditional vibration monitoring methods often rely on periodi...
Abstract With an increasing number of devices being installed in agriculture, factories, hospitals, and cities, the need for intelligent, trustworthy, and energy-efficient communication systems is rising. This is one such system: Narrow Band Internet of Things, or NB-IoT, which connects devices at a long range with low...
The integration of artificial intelligence (AI) and edge computing creates new opportunities for effective and real-time data processing in various fields, such as healthcare, autonomous systems, the Internet of Things, and smart environments. In this chapter, a thorough examination of implementing deep learning (DL) a...
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—Fires in electric vehicle battery packs, whether they begin with thermal runaway, overcharging, or an internal short circuit, continue to erode public confidence in electric mobility. At the same time, most conventional battery management systems respond only after the damage has started: they isolate the pack once a...
Tiny machine learning (TinyML) represents a transformative frontier in artificial intelligence, enabling the deployment of machine learning models on resource-constrained devices. This technology embeds deep learning capabilities directly into billions of Internet of Things (IoT) devices and microcontrollers (MCUs), si...
Two tools for cellular graph embeddings into closed surfaces given by rotation-system codes. general_codes.py enumerates all canonical codes with k edges, computes faces, Euler characteristic and genus, builds equivalence classes (rooted graphs, f-atoms, atoms) and saves the results as JSON. atoms_viewer.html is a sing...
ABSTRACT The von Neumann bottleneck has driven the development of low‐power neuromorphic devices such as vertical organic synaptic transistors (VOSTs), which offer ultrashort charge transport pathways and high integration density. However, their performance is still limited by poor molecular ordering and the difficulty...
For over fifty years, computing has operated under an amnesic, bipartite abstraction: source code is an ungrounded string, and execution is an ephemeral process whose evidence is destroyed upon termination. In the era of autonomous AI agents, this produces an epistemic void where claims of execution cannot be adjudicat...
The integration of machine learning (ML) and embedded systems is referred to as tiny machine learning (TinyML), which aims to achieve intelligent functions on ultra-low power devices. Such devices are typically bound by computation and memory; however, with the help of sensors, they can physically monitor the surroundi...
A new computational approach reveals how cells communicate to assemble into tissues. This could accelerate stem cell engineering, improve disease models, and inform therapies.
Zero-knowledge proofs can verify computing results without rerunning workloads or exposing sensitive data. Magnetar helps bring them into the mainstream with support for standard programming languages, major speed gains, and quantum-resistant security. The post Proof-based general-purpose computing with Magnetar appeared first on Microsoft Research.
By rethinking how large cloud computing systems operate, Associate Professor Christina Delimitrou seeks to make data centers more energy efficient.
A Blog post by Liquid AI on Hugging Face
An ongoing survey tracks the latest AI accelerator systems to keep hardware relevant for Lincoln Laboratory staff and sponsors.
Jennifer Neville did not want to go into computer science—but that’s exactly where she landed. Neville discusses the starts and stops that led to her professional sweet spot and her work identifying “surprising failures” making it hard for AI to handle complexity. The post What AI gets wrong and what failure teaches us appeared first on Microsoft Research.
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