{"id":14093,"date":"2024-03-05T06:58:51","date_gmt":"2024-03-05T06:58:51","guid":{"rendered":"\/?p=14093"},"modified":"2024-03-05T07:01:07","modified_gmt":"2024-03-05T07:01:07","slug":"a-ripple-that-arrives-too-late-or-too-soon-can-have-an-impact-the-exact-opposite-of-what-is-hoped","status":"publish","type":"post","link":"\/?p=14093","title":{"rendered":"A “ripple” that arrives too late or too soon, can have an impact the exact opposite of what is hoped."},"content":{"rendered":"

Liset M de la Prida @LMPrida
\nOur latest paper with @acnavasolive & @adrubio1999 is out in @CommsBio A machine learning toolbox for the analysis of ripples across species https:\/\/nature.com\/articles\/s42003-024-05871-w In collaboration w @perpl_lab & @SAbbaspoor we found that AI algorithm trained in \ud83d\udc01 could be applied to https:\/\/pic.twitter.com\/MRVTgUOSkN
\nReplying to @LMPrida @acnavasolive and 4 others<\/p>\n

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Please look at all networks and processes in the real world on the Internet. They all can chain for many steps, affect billions. Statements or prompts out of context from a seemly safe source, can have large consequences. “Signals” or “messages” might be better than “ripples”.<\/span><\/div>\n<\/div>\n<\/div>\n
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Just being out of date, or without links and details for verification with reasonable time and effort – can change a safe source into a broken one. The AIs still are not open verifiable lossless or correctable by users — for all humans. Organizations, governments (organizations), industries (organizations), topic groups (organizations) all are making up and maintaining their own rules – mostly without feedback mechanisms of any sort. Let alone global open verifiable auditable ones with best in world, current methods.<\/span><\/p>\n

A “ripple” that arrives too late or too soon, can have an impact the exact opposite of what is intended.<\/p>\n<\/div>\n<\/div>\n<\/div>\n

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Richard Collins, The Internet Foundation<\/span><\/div>\n<\/div>\n<\/div>\n","protected":false},"excerpt":{"rendered":"

Liset M de la Prida @LMPrida Our latest paper with @acnavasolive & @adrubio1999 is out in @CommsBio A machine learning toolbox for the analysis of ripples across species https:\/\/nature.com\/articles\/s42003-024-05871-w In collaboration w @perpl_lab & @SAbbaspoor we found that AI algorithm trained in \ud83d\udc01 could be applied to https:\/\/pic.twitter.com\/MRVTgUOSkN Replying to @LMPrida @acnavasolive and 4 others
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