Janelle Shane

I've been teaching masters students about data mining and machine learning for a couple of years now, so the main points of 'You Look Like a Thing and I Love You' were familiar. I was really reading it for the entertaining examples, which were much more fun than my own. I liked the repeated cockroach factory motif and laughed several times at neural net-generated recipes, names, and general nonsense. Moreover, I learned much more about Markov chains and Generative Adversarial Networks than I knew before. Shane is a really engaging and fun writer, who makes complex concepts easy to understand. Most importantly, and I also tried to do this in my teaching, she demystifies narrow AI and deflates the hype around it. As neatly summarised at the end:

On the surface, AI will seem to understand more. It will be able to generate photorealistic scenes, maybe paint entire movie scenes with lush textures, maybe beat every computer game we can throw at it. But underneath that, it's all pattern matching. It only knows what it has seen and seen enough times to make sense of.

Thus the book spends many chapters explaining the mistakes that machine learning makes, which can be very different to the mistakes humans make. It often replicates and amplifys human biases as well, a very important point. I appreciated Shane's scepticism about fully automating cars, as driving involves responding to an incredibly wide range of different situations. It's hard to see how training data could ever cover them all adequately.

Personally, I think using the term Artificial Intelligence for machine learning is highly misleading. A so-called narrow AI may be able to optimise a very specific task, but it is not intelligent in any useful or meaningful sense. I grew up reading cyberpunk, in which AIs are godlike incomprehensible beings, not irritating bits of glitchy code that keep showing you ads for life insurance. AI has become an empty buzzword, as this book makes clear. Shane notes that many so-called AI startups never get machine learning to do the intended tasks, so humans end up doing it instead. There's even the phenomenon of bot farms, in which humans pretend to be automated algorithms on social media. We certainly live in a cyberpunk reality, just not quite the one that 80s and 90s sci-fi led me to expect. For one thing, I anticipated wearing sunglasses a lot more often.

Anyhow, the fact that I read this book in one sitting without intending to demonstrates that it's an accessible, amusing treatment of an important and interesting topic. If you enjoyed it and fancy a much more worrying book about the economic implications of machine learning, may I recommend [b:The Age of Surveillance Capitalism: The Fight for a Human Future at the New Frontier of Power|26195941|The Age of Surveillance Capitalism The Fight for a Human Future at the New Frontier of Power|Shoshana Zuboff|https://i.gr-assets.com/images/S/compressed.photo.goodreads.com/books/1521733914l/26195941._SY75_.jpg|46170685].… (more)

The author is a scientist and blogger. The book takes a look at Artificial Intelligence (AI), how it works, and some of the humourous outcomes (the title is a unique AI pick-up line).

This was quite enjoyable. There is plenty of humour (from pick up lines to cat names to recipes to ice cream flavours). Also some very cute illustrations of AI (AI itself is illustrated as a box with eyes and stick arms). And of course, interesting information on how it works. A couple of things I will remember: it works better if the focus is quite narrow; it also has very little in the way of memory. Now, I should add that the book was published 5 years ago, so pre Chat-GPT and other more current versions of AI that have come out for widespread use, so I don’t know how much improvement there has been since the author wrote the book.… (more)

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.)… (more)