Tracker Beeper - Bert Hubert's writings

A week ago, I finally got round to implementing an idea I’d been toying with for years: what if your computer made a little bit of noise every time it sent data to Google? From studying logs, I’d long known just how many sites send all your visits and clicks to (at least) Google, but a log that you have to manually create first and then analyze is not very dramatic.

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After a full year of not thinking about printers, the best printer is still whatever random Brother laser printer that’s on sale.

The Rise of Particulars: AI and the Ethics of Care

Machine learning (ML) trains itself by discovering patterns of correlations that can be applied to new inputs. That is a very powerful form of generalization, but it is also very different from the sort of generalization that the west has valorized as the highest form of truth, such as universal laws in some of the sciences, or ethical principles and frameworks in moral reasoning. Machine learning’s generalizations synthesize the general and the particular in a new way, creating a multidimensional model that often retains more of the complex differentiating patterns it has uncovered in the training process than the human mind can grasp. Particulars speak louder in these models than they do in traditional generalizing frameworks. This creates an odd analogy with recent movements in moral philosophy, particularly the feminist ethics of care which rejects the application of general moral frameworks in favor of caring responses to the particular needs and interests of those affected by a moral decision. This paper suggests that our current wide-spread and justified worries about ML’s inexplicability—primarily arising from its reliance on staggeringly complex patterns of particulars—may be preparing our culture more broadly for a valorizing of particulars as at least as determinative as generalizations, and that this might help further advance the importance of particulars in ideas such as those put forward by the ethics of care.

Wikimedia Foundation (@[email protected])

Attached: 4 images Wikipedia's vision is of a world in which everyone can freely share in the sum of all human knowledge. But we cannot achieve that when we are missing so much knowledge about women. This #WomensHistoryMonth, join us in closing the gender knowledge gap on Wikipedia and across the Wikimedia projects. #ChangeTheStats #OpenTheKnowledge Get started: https://wikimediafoundation.org/wikipedia-needs-more-women

Mondo 2000 - Anarchivism