📚 Josh Beckman's Highlights

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What this work shows is that if you have the right input basic material (data) with the right distribution (here, a heterogeneous one across a bunch of robots), and then you train a high-capacity neural net on it, you get out something greater than the sum of its parts - a model with surprisingly good out-of-distribution generalization as a consequence of some critical reaction that occurs due to your combo of data + architecture + complexity.

Import AI 343: Humanlike AI; LLaMa 2 Protests; The NSA's New AI Center

Jack Clark

When adding jitter to scheduled work, we do not select the jitter on each host randomly. Instead, we use a consistent method that produces the same number every time on the same host. This way, if there is a service being overloaded, or a race condition, it happens the same way in a pattern. We humans are good at identifying patterns, and we're more likely to determine the root cause. Using a random method ensures that if a resource is being overwhelmed, it only happens - well, at random. This makes troubleshooting much more difficult.

Timeouts, Retries, and Backoff With Jitter

Amazon Web Services, Inc.

Sidechain compression listens to a second track to decide when to bring the input track volume down. The most common use is to use the kick drum track as the second trigger track. This means when the kick hits, your sidechained track momentarily comes down in volume. This means the kick has fewer sounds to compete with, which makes it sound clearer in the mix.

How Kaytranada Produced Glowed Up

Reverb Machine

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