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Detecting Speech and Music in Audio Content

The Netflix TechBlog

Practical use cases for speech & music activity Audio dataset preparation Speech & music activity is an important preprocessing step to prepare corpora for training. Content, genre and languages Instead of augmenting or synthesizing training data, we sample the large scale data available in the Netflix catalog with noisy labels.

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What Is a Workload in Cloud Computing

Scalegrid

This is sometimes referred to as using an “over-cloud” model that involves a centrally managed resource pool that spans all parts of a connected global network with internal connections between regional borders, such as two instances in IAD-ORD for NYC-JS webpage DNS routing. Additionally. This also aids scalability down the line.

Cloud 130
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Lerner?—?using RL agents for test case scheduling

The Netflix TechBlog

Netflix engineers run a series of tests and benchmarks to validate the device across multiple dimensions including compatibility of the device with the Netflix SDK, device performance, audio-video playback quality, license handling, encryption and security. Likewise it has very low requirements on the initial amount of training data.

Testing 163
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What Adrian Did Next?—?Part 2?—?Sun Microsystems

Adrian Cockcroft

The early days at Sun Cambridge were special, I absorbed a lot about networking and the technical side of the role from my fellow systems engineer Martin Baines, and we were driving all over the region in cool company cars (I had a Citroen BX 16V) selling a really hot product.

Tuning 52
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Supercomputing Predictions: Custom CPUs, CXL3.0, and Petalith Architectures

Adrian Cockcroft

Here’s some predictions I’m making: Jack Dongarra’s efforts to highlight the low efficiency of the HPCG benchmark as an issue will influence the next generation of supercomputer architectures to optimize for sparse matrix computations. In early January a related paper was published by Satoshi Matsuoka et. petaflops, which is 0.8%

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Machine learning systems are stuck in a rut

The Morning Paper

Systems researchers are doing an excellent job improving the performance of 5-year old benchmarks, but gradually making it harder to explore innovative machine learning research ideas. Performance on accelerators matters because almost all current machine learning research, and most training of production models, uses them.

Systems 87
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Real-Real-World Programming with ChatGPT

O'Reilly

The inspiration (and title) for it comes from Mike Loukides’ Radar article on Real World Programming with ChatGPT , which shares a similar spirit of digging into the potential and limits of AI tools for more realistic end-to-end programming tasks. Setting the Stage: Who Am I and What Am I Trying to Build?