Online Journalism Blog: “Investigative journalists have been among the earliest adopters of artificial intelligence in the newsroom, and pioneered some of its most compelling — and award-winning — applications. In this first part of a draft book chapter, I look at the different branches of AI and how they’ve been used in a range of investigations. Investigative journalism’s close relationship with data journalism and open source intelligence (OSINT) provides fertile ground for experimentation with AI, and while the explosion of generative AI has opened up further territory for innovation, investigative journalism’s use of AI technology has so far been largely focused elsewhere. Although there is no widely accepted definition of the term (Wang, 2019; Russell, Norvig & Chang, 2022), within journalism “artificial intelligence” has been used to refer to a range of technologies whose functions range from classifying documents to the generation of video or images. But the technology has many branches, often with their own applications and challenges. Tools such as ChatGPT and Google’s Gemini, for example, use a form of AI known as large language models (LLMs). These are part of the wider field of generative AI which includes image generation tools such as DALL-E and Midjourney, video generation tools such as OpenAI’s Sora and audio tools including Meta’s AudioCraft. These models are trained on large datasets of images, video or audio, to build media by essentially predicting each word, pixel or sound as it writes, ‘draws’ or composes. That prediction is what gives an appearance of intelligence — but it doesn’t mean that the result is always going to be factually correct. Factual inaccuracies are such a recurring problem that a specific term has been coined to describe them: ‘hallucinations’.  Generative AI, in turn, is part of the branch of AI known as ‘deep learning’, which is itself a branch of the wider field of machine learning…”