Still relevant despite recent advances in AI-generated imagery and text, because the new systems still work on the same principles as the ones that were around three years ago. They just have a lot more data and processing power. This also means they have the same limitations and blind spots. What was it trained on? How was it trained? (This is the most obvious way human bias can leak into an AI model.) How well is the goal specified? And of course, did the AI actually latch onto relevant details, or did it notice that all the training pictures labeled sheep had green fields and blue skies, and completely ignore the actual sheep?
These are things to keep in mind as we enter the landscape of generative AI tools like ChatGPT: You can train an LLM to write a book review, and it'll give you a great piece of text that reads like a book review -- but it's not going to have actually evaluated the book. For that, you'd have to train another AI to categorize books as good, bad, interesting, dull, and so on. But even that can only be as good as its training data. (I don't remember whether the classic phrase "garbage in, garbage out" is used anywhere in the book, but it still applies today!)
The author has a blog/newsletter, AI Weirdness www.aiweirdness.com/, where she pushes AI over the edge to sometimes hilarious results.