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Online translators are sexist – here s how we gave them a little gender sensitivity training

Online translators are sexist – here s how we gave them a little gender sensitivity training
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How to Make Artificial Intelligence More Democratic

Scientific American How to Make Artificial Intelligence More Democratic A new type of learning model uses far less data than conventional AIs, allowing researchers with limited resources to contribute Advertisement This year, GPT-3, a large language model capable of understanding text, responding to questions and generating new writing examples, has drawn international media attention. The model, released by OpenAI, a California-based nonprofit that builds general-purpose artificial intelligence systems, has an impressive ability to mimic human writing, but just as notable is its massive size. To build it, researchers collected 175 billion parameters (a type of computational unit) and more than 45 terabytes of text from Common Crawl, Reddit, Wikipedia and other sources, then trained it in a process that occupied hundreds of processing units for thousands of hours.

Can we measure fairness? A fresh look at a critical AI debate

Read later Summary: By now, most AI practitioners acknowledge the universal prevalence of bias, and the problem of bias in AI modeling. But what about fairness? Can fairness be measured via quantifiable metrics? Some say no - but this is where the debate gets interesting. Bias is a universal trait among humans. It can be meaningless, or it can be dangerous. For example, I am biased about the Philadelphia Phillies baseball team. If a Phillies pitcher competes for the Cy Young Award at the end of the season, I am likely to extol the former s virtues and denigrate those of the latter. Unless I m a member of the Baseball Writers Associations who vote on such things, it makes no difference. It affects no one. It s harmless, and we all have them.

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