This came out in 2019, after OpenAI released GPT-2 but well before ChatGPT's release. While I'd love to read an updated work by Shane (no amount of checking has made it poof into existence, alas), as far as I could tell this was still a really useful introduction to how artificial intelligence works and what its strengths and weakness are. Shane lays out what AI is and isn't, how it learns, the various ways it can run into trouble, the instances of disconnect between what humans ask AI to do and what it actually does, and more.

I first became aware of this work after stumbling on some of Shane's hilarious machine learning blog posts on Twitter (way back when Twitter was Twitter). In fact, the title of this book comes from one such post on AI-generated pickup lines. Still, it sat on my TBR pile for years until ChatGPT came out and became a hot enough topic in academia to be mentioned several times during a Q&A session with a library job candidate.

While I appreciated Shane's humor and adorable little AI illustrations throughout, this also contained plenty of useful information written in a way that was relatively easy for someone without much of a technical background to understand. I'd have liked to see slightly more technical information than Shane provided (for example, I feel like I got a good general understanding of how AI training works, but I still can't picture what actually doing it looks like), but overall Shane's explanations were really clear and made good use of examples. One real-world example that stuck with me that illustrated AI's reliance on its training data and difficulties when asked to do a broader task than it was trained for (because AI does better with narrower tasks) was a self-driving car that had only been trained for highway driving. Its human driver had it take over while it was still in the city and it ended up hitting the side of a semi - it had only ever been trained to recognize semis from the back, so when it saw one from the side it interpreted it as best it could, decided it was an overhead sign, and didn't slow down for it.

I've already recommended this book to several of my fellow librarians as an accessible way to learn about AI and maybe get some ideas for how to talk about it to faculty and students.

(Original review posted on A Library Girl's Familiar Diversions.) ( )

I cannot recommend "You Look Like a Thing and I Love You" highly enough. If you've ever had the slightest interest in AI, this is your cup of tea ;-) Very informative, very accessible, easy to read, and very very very funny. It is also a great book for those who are sure that AI will take their jobs/take over the world tomorrow. (The answer is: not really.) I also feel like reading more about AI now... ( )

I loved this so much. I know that's what all my reviews say but this one is *chef kiss* ( )

I've enjoyed the author's blog, AI Weirdness, for quite some time, so I was looking forward to reading this. I was not disappointed - not only is this one of the clearest explanations of how artificial intelligence works I've ever come across, it's also often laugh-out-loud funny. I flew through it in record time, enjoying every moment. If you are at all curious about AI and enjoy bizarre non-sequiturs, you will enjoy this. Highly recommended. ( )

I would have loved to have this on my kindle, because there was plenty of highlight-worthy material: lots of interesting facts to remember and lots of hilarious AI-generated lists.
I’m not sure why I developed such a fascination with AI, but it’s probably Hannah Fry’s fault. Her delightful Hello, World certainly encouraged it. People who enjoyed that book would enjoy this one too.
Janelle Shane based it on her blog aiweirdness.com, and sections of it made me laugh so hard a coworker threatened to ban it from the break room.
It’s an informative book too, and I learned a lot about how neural networks process datasets to generate their own original—and often super weird—output. The title of the book is from a list of pick-up lines an AI generated after the author trained it on a large dataset of actual pick-up lines.
It was surprising to see what AI came up with in the early stages of learning, such as the lines of k’s that it thought were knock-knock jokes in a different training scenario.
The author’s explanations got a little mathy at times, but for the most part, I understood what she was saying, and I understood more than I ever have the limits to what AI can do. As the author said in her last chapter, “Will it get smart enough to understand us and our world as another human does—or even to surpass us? Probably not in our lifetimes. For the foreseeable future, the danger will not be that AI is too smart but that it’s not smart enough...it’s all pattern matching. It only knows what it has seen and seen enough times to make sense of.”

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