The Globe and Mail reports in its Monday edition that OpenAI released ChatGPT one year ago, kicking off a boom in generative artificial intelligence. Since then, the advances have come at a furious pace. The Globe's Joe Castaldo writes that large language models, which underlie chatbots, can now be used to analyze data, create charts and graphs, interpret photographs, and converse in real time with audio features. At least one fundamental flaw remains: Large language models make stuff up. These errors have become known as "hallucinations," although confabulations is a more appropriate word. The possibility of mistakes is an obstacle for deploying LLMs, especially in settings where accuracy is crucial, such as health care, finance, law and education. LLMs get things wrong partly because the data on which they are trained, much of it pulled from the Internet, may contain errors, biases and falsehoods that inevitably creep out. LLMs have no concept of whether the output is factual or even logical, which is where things can go off the rails. "They're just not reasoning about what they're actually saying. They're just essentially parroting from all the data that they've observed before," said Meta's Joelle Pineau in Montreal.