Learning Engineering [...]

One of the signs that your field/area might be rushing too fast to make something happen is when people fail to think critically about what they share before they share it. (Source)

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Machine Learning [...]

machine learning is effective, has an enormous appetite for data, requires large computational resources, makes decisions that resist analysis, excels at finding latent structure in data, obscures the link between source data and outcomes, defies many human intuitions, and is readily fooled by a knowledgeable adversary. (Source)

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Agnotology [...]

What’s at stake right now is not simply about hate speech vs. free speech or the role of state-sponsored bots in political activity. It’s much more basic. It’s about purposefully and intentionally seeding doubt to fragment society. (Source)

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Utility Fallacy [...]

The point too often missed in a cooly instrumentalist understanding of technology is that we don’t use these tools in a vacuum; we instead participate in complicated social systems that can careen in unforeseen directions when powerful new technological forces are introduced. Features are important, but they’re not the whole story. (Source)

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