Month: July 2021

Enlarge (credit: KJ Parish)

For years, misinformation has flourished on Facebook. Falsehoods, misrepresentations, and outright lies posted on the site have shaped the discourse on everything from national politics to public health.

But despite their role in facilitating communications for billions of people, Facebook executives refused to commit resources to understand the extent to which COVID-19-related misinformation pervaded its platform, according to a report in The New York Times.

Early in the pandemic, a group of data scientists at Facebook met with executives to propose a project that would determine how many users saw misleading or false information about COVID. It wasn’t a small task—they estimated that the process could take up to a year or more to complete—but it would give the company a solid understanding of the extent to which misinformation spread on its platform.

Enlarge (credit: Aurich Lawson | Getty Images)

We're in phase three of our machine-learning project now—that is, we've gotten past denial and anger, and we're now sliding into bargaining and depression. I've been tasked with using Ars Technica's trove of data from five years of headline tests, which pair two ideas against each other in an "A/B" test to let readers determine which one to use for an article. The goal is to try to build a machine-learning algorithm that can predict the success of any given headline. And as of my last check-in, it was… not going according to plan.

I had also spent a few dollars on Amazon Web Services compute time to discover this. Experimentation can be a little pricey. (Hint: If you're on a budget, don't use the "AutoPilot" mode.)

We'd tried a few approaches to parsing our collection of 11,000 headlines from 5,500 headline tests—half winners, half losers. First, we had taken the whole corpus in comma-separated value form and tried a "Hail Mary" (or, as I see it in retrospect, a "Leeroy Jenkins") with the Autopilot tool in AWS' SageMaker Studio. This came back with an accuracy result in validation of 53 percent. This turns out to be not that bad, in retrospect, because when I used a model specifically built for natural-language processing—AWS' BlazingText—the result was 49 percent accuracy, or even worse than a coin-toss. (If much of this sounds like nonsense, by the way, I recommend revisiting Part 2, where I go over these tools in much more detail.)

Enlarge / The biggest impediment to EV adoption appears to be cost of ownership, according to EY's 2021 Mobility Consumer Index. (credit: Carlos Sanchez Pereyra/Getty Images)

Electric vehicles are increasingly breaking into the mainstream. According to a new survey conducted by EY, 41 percent of consumers planning to buy a car say their next vehicle will be a plug-in. And they're mainly making that decision because of the environmental impact.

EY surveyed 9,000 consumers across 13 countries (Australia, Canada, China, Germany, India, Italy, Japan, New Zealand, Singapore, South Korea, Sweden, the UK, and the US) in June of this year as part of its Mobility Consumer Index. The last time the firm conducted this survey, in September 2020, just 30 percent said their next car would be either a battery EV or plug-in hybrid EV.

Where do BEVs beat ICE?

EV adoption is moving faster in some places than others. In China, for example, 48 percent say their next car will be an EV, and only 43 percent say it will have an internal combustion engine (with 3 percent looking for a hydrogen fuel cell EV and the remaining 5 percent saying they are unsure). Sweden's numbers are near-identical, with a matching 48 percent wanting an EV.