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Science

A collection of 31 issues

A Benchmark For Benchmarks

Benchmark datasets like MNIST, ImageNet, etc., abound in machine learning. Such datasets stimulate work on a problem by providing an agreed-upon mark to beat. Many of the benchmark datasets, however, are constructed in an ad hoc manner. As a result, it is hard to understand why the best-performing models vary

Missing Market for Academics

There are a few different options for buying time with industry experts, e.g., https://officehours.com/, https://intro.co/, etc. However, there is no marketplace for buying academics’ time. Some surplus is likely lost as a result. For one, some academics want advice on what they write. To get

Cracking the Code: Addressing Some of the Challenges in Research Software

Macro Concerns 1. Lack of Incentives for Producing High-Quality Software. Software’s role in enabling and accelerating research cannot be overstated. But the incentives for producing software in academia are still very thin. One reason is that people do not cite the software they use; the academic currency is still

The Story of Science: Storytelling Bias in Science

Often enough, scientists are left with the unenviable task of conducting an orchestra with out-of-tune instruments. They are charged with telling a coherent story about noisy results. Scientists defer to the demand partly because there is a widespread belief that a journal article is the appropriate grouping variable at which

94.5% Certain That Covid Vaccine Will Be Less Than 94.5% Effective

“On Sunday, an independent monitoring board broke the code to examine 95 infections that were recorded starting two weeks after volunteers’ second dose — and discovered all but five illnesses occurred in participants who got the placebo.” Moderna Says Its COVID-19 Vaccine Is 94.5% Effective In Early Tests The data

What Academics Can Learn From Industry

At its best, industry focuses people. It demands that people use everything at their disposal to solve a problem. It puts a premium on being lean, humble, agnostic, creative, and rigorous. Industry data scientists use qualitative methods, e.g., directly observe processes and people, do lean experimentation, build novel instrumentation,