Have you heard of webmentions? They’re similar to pingbacks—but modern—and allow websites to notify each other about different types of activity (like replies on social media). As of 2017, the protocol is a W3C recommendation.
Pushing The Limits Of HPC And AI Is Becoming A Sustainability Headache - The Next Platform
As Moore’s law continues to slow, delivering more powerful HPC and AI clusters means building larger, more power hungry facilities. “If you want more performance, you need to buy more hardware, and that means a bigger system; that means more energy dissipation and more cooling demand,” University of Utah professor Daniel Reed explained as a
Last week in our Error 402 series on the history of web monetization, we wrote about the earliest forms of web advertising: banner ads. As we noted, this “simple” way of making money seemed to dera…
Building new kernels and booting into them is an unavoidable—and
time-consuming—part of kernel development. Andrea Righi works for
Canonical on the Ubuntu kernel team, so he does a lot of that and wanted to
find a way to speed up the task. To that end, he has been working
on virtme-ng, which is a
way to boot a new kernel in a virtual machine, and it does
so quickly. He came to the 2023
Linux Plumbers Conference (LPC) in Richmond, Virginia to introduce the
project to a wider audience.
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Knowing the right data enrichment techniques is crucial | TechTarget
In an excerpt from 'The Enrichment Game,' expert Doug Needham explains why knowing the right data enrichment techniques can bring big business advantages.
Automating Data Augmentation: Practice, Theory and New Direction
Data augmentation is a de facto technique used in nearly every state-of-the-art machine learning model in applications such as image and text classification. Heuristic data augmentation schemes are often tuned manually by human experts with extensive domain knowledge, and may result in suboptimal augmentation policies. In this blog post, we provide a broad overview of recent efforts in this exciting research area, which resulted in new algorithms for automating the search process of transformation functions, new theoretical insights that improve the understanding of various augmentation techniques commonly used in practice, and a new framework for exploiting data augmentation to patch a flawed model and improve performance on crucial subpopulation of data.