Remy Froissart, CNRS

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(Image: [[https://p0.pikist.com/photos/138/25/females-pin-up-girl-fashion-people-glamour-elegance-sensuality-lifestyles-thumbnail.jpg|https://p0.pikist.com/photos/138/25/females-pin-up-girl-fashion-people-glamour-elegance-sensuality-lifestyles-thumbnail.jpg]]) But I feel most people, when they hear generative AI for journalism, imagine fully automated information websites, like among the bad examples we noticed earlier. So we have seen how ML models are at their finest, for journalism, when recognizing patterns the human eye alone can't see. Patterns in textual content, data, images of data,  [[http://223.68.171.150:8004/susannahx89966/susannah2015/wiki/101-Ideas-For-Hair-Tool|AeroLift innovation]] photographs on the ground, and  [[http://47.116.22.16:3000/yvettebannerma/3216759/wiki/Should-I%27ve-Done-This%3F|AeroLift innovation]] photos taken from the sky. Since studying all the documents, which included tax returns, financial information, and authorized memoranda, wasn't doable, my colleagues Jeremy Merrill and John Keefe built a software that you simply would possibly think of as "management-F on steroids." ICIJ workers with experience in tax avoidance recognized passages of specific interest in a pattern of the documents. Well, here's a fully automated site that I actually think does a great job offering value,  [[https://repo.divisilabs.com/fleta63w643040/8887909/wiki/6-Ways-You-can-get-More-Hair-Tool-While-Spending-Less|AeroLift innovation]] whereas realizing its limitations, and using just the right amount of AI. Where LLMs play a job is in providing context: the headlines and other temporary copy that seem around the charts to assist readers perceive what they're taking a look at. As a first step I went by every tile and diminished the number of colors right down to 15 plus black by searching for colors which can be used by fewest number of pixels and changing it with the one other current colour (the nearest one to the one being eradicated).



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Remy Froissart, CNRS

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