I absolutely LOVED this book. I have been following Janelle Shane's blog, AI Weirdness, for a few years now and was so excited when she announced a book's coming out, and it definitely did not disappoint. It is simultaneously laugh-out-loud hilarious, profound, critical, and comprehensive in its analysis of what AIs can and cannot do.

Here are just some of the things you'll find in this book:
- a thorough description of the entire process of AI training, and them consequently forgetting their training (if need to. Or not)
- humans pretending to be AI
- commentary on the built-in bias of AIs and real-life examples on what is being done to fix it (or to deny it)
- hilarious illustrations
- examples of AI-generated flavors of ice cream and BuzzFeed article titles that will make you hiccup from laughter (seriously, who doesn't want to read about "25 unfortunate cookie perfomances from around the world" and learn "43 quotes guaranteed to make you a mermaid immediately")
- comprehendible but not patronizing language, no jargon that could intimidate an amateur
- and so much more!

This is the best non-fiction book I've read this year and I cannot wait to recommend it to everyone!

I have to be very careful when I check Janelle Shane's AI Weirdness blog, because it has more than once left me laughing so much I couldn't breathe with its lists of an artificial intelligence's efforts to generate new entries in a given category – if you've somehow not seen any, I'd particularly recommend the paint colours and the names for guinea pigs. This book does draw from those lists, not least in the title – an AI-suggested chat-up line, and TBH one which would probably work on me. But more than the blog it tries to restrain itself to using them as examples, while educating the general reader in how AI works in the real world, as against the bolder projections of science fiction (a category which includes much mainstream media coverage of AI). As Shane is at pains to remind us, for the moment most AI has approximately the cognitive capability of a worm, rather than Skynet, and when it goes wrong even the dangers are more likely to stem from stupidity than omniscience. That can be human stupidity too, though, whether in terms of machines replicating the biases of the lamentable species which created them, or being given a bad initial dataset from which to learn, or simply not having the nature of the question properly spelled out for them. Google researcher Alex Irpan* says he's found it helpful to picture AI as a a demon deliberately trying to misinterpret any instructions it's given, which while amusing is also one of the more alarming moments in the book – see also the bit where the NPCs in Oblivion had to be toned down a bit because they were getting up to the sort of mischief only players were supposed to be able to do. More often, though, this results in robots which fall over because it's easier than walking, or conclude that the best way to stop a car crashing is to immobilise it, or just start claiming there are giraffes everywhere (a more common failure mode than you might have expected). Although I didn't find the algorithmically created recipes significantly more nonsensical than the ones humans perpetrate, and given my feelings on sport, I love that one task simple enough for them to reliably handle is match reports. I may or may not remember the difference between a Markov chain and a GAN by the end of next month (assuming, of course, that technological civilisation in Britain lasts beyond the end of next month anyway), but the general understanding of how to spot ludicrous overclaiming for the powers of AI, and why some tasks really don't suit it, will definitely remain. I also have a newfound respect for their determination to solve many problems either by strategic laziness, or rewriting the laws of the universe.

*Does nominative determinism include initials too? There's also a Karl Sims working on simulations.

(Netgalley ARC)