R0, mortality rate, and all that: the science of how disease spreads

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The science of how diseases spread

How epidemiology puts the COVID-19 virus in perspective.

For Popular Science:

Scientists, medical professionals, and governments around the world are working to understand how the new respiratory disease ravaging Hubei province spreads—and how bad it could be for the rest of the world. Part of this effort is epidemiology: the study of how infections move through populations and how to control them.

Epidemiology incorporates everything from geography to complex mathematics in its effort to understand the spread of disease. Here are some basic epidemiological concepts that can help you get past the panic, misinformation, and xenophobia that tend to drive conversations around a newly emerging illness.

[read the rest at Popular Science]

Weird discrepancy in cosmic measurements has cosmologists puzzled

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The growing crisis in cosmology

For The Week:

How rapidly is the universe expanding?

Since Edwin Hubble first discovered in 1929 that galaxies are getting farther apart over time, allowing scientists to trace the evolution of the universe back to an initial Big Bang, astronomers have struggled to measure the exact rate of this expansion. In particular, astronomers want to determine a number called the Hubble parameter, a measurement of how fast the cosmos is expanding as we speak. The Hubble parameter tells us the age of the universe, so measuring it was a major goal for many astronomers in the latter half of the 20th century.

The problem, however, is that measuring the Hubble parameter is, perhaps unsurprisingly, quite difficult. There are multiple methods for doing so, and modern observatories are coming up with different numbers depending on which method they use. It seems the number obtained based on the appearance of the universe shortly after the Big Bang is significantly smaller than the number obtained when looking at measurements involving objects closer by.

[Read the rest at The Week]

The threat of AI comes from inside the house

My other SIAM News contributions are necessarily math-focused. This one is a bit different: a review of a very good and  funny popular-science book about machine learning and its failures.

[ This blog is dedicated to tracking my most recent publications. Subscribe to the feed to keep up with all the science stories I write! ]

The Threat of AI Comes from Inside the House

For SIAM News:

Artificial intelligence (AI) will either destroy us or save us, depending on who you ask. Self-driving cars might soon be everywhere, if we can prevent them from running over pedestrians. Public cameras with automated face recognition technology will either avert crime or create inescapable police states. Some tech billionaires are even investing in projects that aim to determine if we are enslaved by computers in some type of Matrix-style simulation.

In reality, the truest dangers of AI arise from the people creating it. In her new book, You Look Like a Thing and I Love You, Janelle Shane describes how machine learning is often good at narrowly-defined tasks but usually fails for open-ended problems.

Shane—who holds degrees in physics and electrical engineering—observes that we expect computers to be better than humans in areas where the latter often fail. This seems unreasonable, considering that we are the ones teaching the machines how to do their jobs. Problems in AI often stem from these very human failings.

[Read the rest at SIAM News…]