AI's Impact on Local News Production

LLMs, when used alone, cannot reliably be deployed in journalism, especially in real-time information generation. Here are the key issues and the ways to address them: 1. Inability to Adapt to New Information: LLMs excel at processing existing language data but struggle with “innovative thinking” and real-time adaptation, which are crucial in news reporting. Since they are trained on pre-existing datasets, they can’t dynamically update their knowledge post-training. For instance, when mining local government data, LLMs might overlook recent policy changes or budget updates. The solution involves developing real-time event detection systems that can monitor and analyze local government records, such as council meeting minutes or budget reports. Such systems use what is called an ‘editorial algorithm’ to identify noteworthy changes in the data based on criteria defined by journalists. 2. Lack of Guaranteed Accuracy: LLMs cannot ensure the accuracy of their output, as their responses are based on patterns from training data and lack a mechanism for verifying factual correctness. Continuing with the example above, an LLM might inaccurately write an analysis of a significant policy change detected by an editorial algorithm. To address this issue, we can develop domain-specific models trained to understand a particukar coverage area (like a beat reporter). Any analysis produced by an LLM should be subjected to automated fact-checking against quantifiable editorial benchmarks using reinforcement learning with AI feedback (RLAIF). These benchmarks involve cross-referencing with official records, verifying historical accuracy, and ensuring alignment with journalistic standards. This method, known as ‘editorial AI,’ makes the AI follow journalistic guidelines to maintain the integrity and accuracy of news content derived from complex data.

It's really early days of #GenAI and as an AI researcher I'm fascinated by its potential. However, as with any other technology, it is important to be mindful of the societal implications especially given the disproportionate emphasis on its "long term benefits" which belies the costs of short term harms including the accelerated erosion of trust on the internet. Partnering with newsrooms in the past two years has been an eye-opener about the daily struggles publishers had to face even before #LLMs and diffusion models changed the dynamics of content generation completely. GenAI presents a wonderful opportunity to reduce the time-to-publication and information gathering bottleneck, among other operational improvements to the media machinery. However, there are also unforeseen failures emerging as a result of trying to take "AI short-cuts", so to speak. This might be a cynical take but I think we could do with some more 'real talk' about the challenges associated with GenAI for media orgs. especially centered around #trust in #journalism and news over a longer horizon. So I am very grateful to Julius Endert and the DW Akademie team for allowing me to share my thoughts and some critical opinions about the state of online information and how GenAI affected it. https://lnkd.in/eBfCTWUN

Channel 1 isn't just another news broadcaster. They’re creating live news programs that are hosted by AI-generated avatars. There’s tons of unknowns, and plenty worth debating. But here's why this has the potential to be a game-changer: 𝟭. 𝗧𝗿𝘂𝗹𝘆 𝗥𝗲𝗮𝗹-𝗧𝗶𝗺𝗲 𝗨𝗽𝗱𝗮𝘁𝗲𝘀 Channel 1 could deliver news as it unfolds. No delays, no waiting. 24/7/365. 𝟮. 𝗚𝗹𝗼𝗯𝗮𝗹 𝗥𝗲𝗮𝗰𝗵 Lower costs of production means they could cover way more – from local stories to global events. 𝟯. 𝗣𝗲𝗿𝘀𝗼𝗻𝗮𝗹𝗶𝘇𝗲𝗱 𝗘𝘅𝗽𝗲𝗿𝗶𝗲𝗻𝗰𝗲 Imagine a news channel that knows what you're interested in, and presents stories in your preferred language and style. The implications could be massive: • AI is going to dramatically reduce the costs of content production, kind of like how YouTube & social media brought the cost of distribution to zero. • While the idea of tuning into a show to have an AI avatar report your news might seem crazy today, they’re only going to get more realistic as time goes on. What do you think? Could this approach provide a much more sustainable path forward for things like local news? Can you envision yourself consuming content with AIs, not people, anchoring the desk? What do you think of Channel 1? Share your thoughts with me Nick M..