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Internet chat rooms, for example, are easy to start up, join, and participate in.
Early in the history of artificial intelligence, researchers came up against what is referred to as Moravec's paradox: tasks that seem laborious to us (arithmetic, for example) are easy for a computer, whereas those that seem easy to us (like picking out a friend's voice in a noisy bar) have been the hardest for A.I. to master.
Quarterly statements, for example, are easy to find online.
QR Codes, for example, are easy to integrate into text-and-picture-based services.
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Macrophages, for example, are easier to target since they naturally phagocytose particles.
Delinking the minimum wage from pensions, for example, "is easy to say and very difficult to do".
Providing slums with drinking water and sanitation, for example, is easy to do and a cost-effective way to prevent deaths and disease.
Making your own granola bars, for example, is easy, and it's healthier than buying them in the store because people tend to use much healthier ingredients in their own cooking.
But we can agree, the professor example is easy.
Kids, for example, were easy to fundraise for.
Although the example is easy to understand, in a general graph G, it requires exponential time to enumerate all the simple paths.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.
Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com