Four short links: 16 Sep 2020
Concurrency, Quadruped Robot, Ethics Groups, and Threat Models for Differential Privacy
- A Concurrency Cost Hierarchy — a higher level taxonomy that I use to think about concurrent performance. We’ll group the performance of concurrent operations into six broad levels running from fast to slow, with each level differing from its neighbors by roughly an order of magnitude in performance. They are: Contended Atomics, System Calls, Implied Context Switch, Catastrophe, Uncontended Atomics, Vanilla Instructions.
- Open Source Quadruped Robot — Now with a robotic arm.
- AI Ethics Groups — without more geographic representation, they’ll produce a global vision for AI ethics that reflects the perspectives of people in only a few regions of the world, particularly North America and northwestern Europe. […] This lack of regional diversity reflects the current concentration of AI research (pdf): 86% of papers published at AI conferences in 2018 were attributed to authors in East Asia, North America, or Europe. And fewer than 10% of references listed in AI papers published in these regions are to papers from another region. Patents are also highly concentrated: 51% of AI patents published in 2018 were attributed to North America.
- Threat Models for Differential Privacy — Looks at risks around central, local, and hybrid models of differential privacy. Good insight and useful conclusions, e.g. As a result, the local model is only useful for queries with a very strong “signal.” Apple’s system, for example, uses the local model to estimate the popularity of emojis, but the results are only useful for the most popular emojis (i.e. where the “signal” is strongest). The local model is typically not used for more complex queries, like those used in the U.S. Census  or applications like machine learning.