Shulou(Shulou.com)11/24 Report--

CTOnews.com Gannett, the largest newspaper group in the United States, plans to introduce generative artificial intelligence into its news distribution system to save costs and improve efficiency. But the group said it would involve human editors to prevent AI from automatically publishing without oversight.

Renn Turiano, Gannett's senior vice president of products, said generative AI could create efficiencies for journalists and eliminate some tedious tasks, but added,"It's a mistake for some other news organizations to want to roll out this technology quickly, and we won't make that mistake." "He did not specify which agencies.

Gannett isn't the only news organization balancing the pros and cons of this technology; many U.S. news organizations are exploring how best to leverage generative AI tools that can generate new content or data based on user prompts or questions. But some experts say the limitations of generative AI, including "hallucinations," are especially problematic for an industry that demands accuracy.

Gannett will launch a real-time pilot project using AI to identify the most important points in articles and create bulleted summaries at the top next quarter, and will launch this feature in USA Today in the fourth quarter. Journalists will have the final say on whether to use AI-provided content, and Gannett will eventually incorporate this summary technology into its distribution system.

Gannett's journalists are working to ensure they are not displaced by the technology. On June 5, hundreds of journalists from the group went on strike over layoffs and non-pay increases, and generative artificial intelligence was a focal point in their negotiations with the company.

In addition, Gannett is developing a generative AI tool that can break down long stories into different lengths and formats, such as creating slideshows with bullets or captions on photos.

Gannett used Cohere's AI technology to train Cohere's large language model in two weeks using 1000 previously published news articles with summaries written by journalists. To train the model further, journalists from USA Today's political team reviewed and edited the automatically generated summaries and highlights.

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