AI

Hype and skepticism aside, there are several tasks and journalism-related use cases that I would actually consider to be useful or viable applications of this technology. I’ve listed several below: Drafting meeting outlines, project budgets, and grant proposals Analyzing and summarizing notes produced by Documenters Generating lists of story ideas Creating event agendas and materials Translating stories into different languages Drafting scripts for news broadcasts or podcasts Suggesting different headline variations for articles Review articles and other news content for copywriting errors and adherence to your newsroom’s style guide Automating routine administrative tasks like scheduling, invoicing, and record-keeping Generating templates for different types of news stories, like obituaries, crime reports, and human-interest pieces Generating customized newsletters based on user preferences and interests Analyzing social media and other online sources for story leads and trends Generating news quizzes and other information-based games and activities Converting plain text lists into machine-readable languages to instantly generate flowcharts and visual diagrams Generating interactive dashboards from user activity or community survey data Create a dynamic tagging system that evolves as new topics emerge in your community Allow journalists and residents to ask natural language questions and get relevant snippets from Documenter notes Summarizing large amounts of text Quickly creating lists of FAQs Responding to FAQs for a project or story Generating summaries of public meetings and reports Creating social media posts from news stories Creating transcripts of audio and video recordings Categorizing and labeling community feedback for later analysis and response Suggesting alternative angles or perspectives for a story Developing custom AI chatbots for engaging with readers or specific topics Extracting unstructured data from stories and reports Sort issues by topic, urgency, and sentiment to get a bird's-eye view of community concerns Offering personalized recommendations based on user interests and questions Drafting social media calendars and publishing schedules Acting as a learning guide for personal and professional development Creating and executing onboarding and employee training protocols and timelines Generating structured journal entries from stream-of-consciousness notes Gauge the overall tone and sentiment of community meetings or feedback Track issues over time, identifying emerging trends or recurring problems in the community Create interactive dashboards to help parse large amounts of info or data from documents and files

This is an ‘unfair’ test. It’s a good example of a ‘bad’ way to use an LLM. These are not databases. They do not produce precise factual answers to questions, and they are probabilistic systems, not deterministic. LLMs today cannot give me a completely and precisely accurate answer to this question. The answer might be right, but you can’t guarantee that.

There are 1 billion professionals in the world, and on average we each receive 3 million words of email every single year. That's more than 60 average length novels sent to each of us, every single year. Multiplied by 1 billion professionals, this rapidly becomes an unimaginable amount of information.