| You anchor on what "AI" actually is. "Artificial Intelligence" is, IMO, useless phrase. Make them clearly define what they're talking about. AI is a bunch of different technologies that have many uses—neural networks, natural language processing, OCR, speech recognition, machine learning, computer vision, image classification, upscaling models, and our favorite new friends "generative pre-trained transformers" (GPT) and "large language models" (LLM) that make up key parts of "generative AI." Once you make them specify what they're talking about, you talk about the nature and inherent limitations in the technology. I like to call GPT and LLM "statistical binary string predictors." IE: given a string of binary, predict the expected binary string based on the inputs. It's an amazing technology, don't get me wrong. We're starting to see the limits already though. Limited context windows. Larger context/training == lower quality results. More input tokens = lower quality. In some respects, newer models are now regressing from earlier models because they're chasing benchmarks and not the real world use cases. Start to dive into the details. Ask them to admit the problems with LLM and GPT. Ask them how they see these problems getting resolved. Most AI fanboys don't understand the technologies involved. Expose it. |