You Look Like a Thing and I Love You (2019, Wildfire)
Published Nov. 7, 2019 by Wildfire.
Its important for me to understand AI models and capabilities to a certain extent for my job. The author did a good job of writing a book that explains these concepts in an entertaining way. This is one you should absolutely read if you are interested in AI but dont want to get caught up in a "web 3" grift.
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 …
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.
i read this, like many people did i suspect, because i like Janelle Shane's AI Weirdness blog. This book does rehash some of the material from the blog as you'd expect, but the focus is more on explaining AI in a non-technical, non-sensational, & friendly manner. Probably the people who would get the most out of it are those whose knowledge of AI begins & ends with how they're portrayed in the news & in fiction.
