When using generative AI for marketing, advertising or entertainment, it might be acceptable to have the occasional response that is professionally written but factually inaccurate.
Suspected Trump Shooter Remembered By Rifle Team Member As ‘Comically Bad Shot’: What We Know About Thomas Matthew CrooksLLMs are trained on vast datasets consisting of text from the internet, books and more. These datasets might contain inaccuracies, biases or outdated information. The LLM can learn and replicate these flaws in its outputs.LLMs generate responses based on patterns and associations in the data they have been trained on.
As with all new tech, the focus and excitement is on building new GenAI applications. Still, the real value of GenAI will only be captured once CIOs and AI leaders can feel confident in the outputs. AI, particularly in its use of neural networks, mimics this process by drawing on vast datasets to produce new patterns or ideas not present in its training data. The resulting AI hallucinations are like the brain's creative leaps in connecting disparate ideas. These leaps hint at early-stage creativity within the GenAI, offering exploration, learning and problem-solving reminiscent of human imagination.