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A review by tachyondecay
You Look Like a Thing and I Love You: How Artificial Intelligence Works and Why It's Making the World a Weirder Place by Janelle Shane
funny
informative
lighthearted
fast-paced
3.0
Machine learning is a hot topic. You have probably seen those social media posts that start with, “I made an AI watch …” and then proceeded to share a script “written” by the AI? Those are almost entirely fake, of course—as Janelle Shane explains in You Look Like a Thing and I Love You: How Artificial Intelligence Works and Why It's Making the World a Weirder Place, artificial intelligence is just not there yet. Not only are we nowhere close to Skynet … our AIs tend to have about as much brainpower as a worm.
Shane’s book is a short but comprehensive dive into what machine learning actually is, how it works, and why it so often goes wrong—by which she means, it doesn’t actually solve the problem it was meant to solve, or it solves the problem but in a really stupid way. Indeed, this is probably the main takeaway of the book: AIs are not smart. Not at all. And in the rare occasion when we do manage to make a smart AI, like Google’s Alpha Go or IBM’s Watson, it is incredibly specialized (and probably more expensive than it is worth).
Each chapter examines machine learning from a different angle. Some chapters cover problems—such as how difficult it is to assemble a good data set, how bias can creep into the training data, etc. Other chapters spend more time exploring the different approaches researchers and programmers take to machine learning, such as evolutionary algorithms. While there was plenty in this book I was aware of (particularly along the bias dimension), there was still a lot I either didn’t know or learned more about from the book.
Also, Shane is funny. Her writing is laced with a graceful wit, and it pairs perfectly with the examples of text she has had various AIs produce, from ersatz and confused recipes to names for ice cream flavours and verses of songs. I literally laughed out loud reading this book—did not expect that from a book on this subject, but I couldn’t resist how truly hilarious some of Shane’s results sound. This drives home the fact that AI is just not there yet, for most of our purposes, more so than any dry and technical explanation ever might.
Also also, shout out to Shane’s wonderful sketches throughout the book.
Despite all of the above, I would hesitate to recommend this to just anyone. While reading this book, I thought about my bestie, who has pivoted into freelance copywriting. She does a lot of copywriting for tech or tech-adjacent companies, so it behoves her to read more about the field, including AI. Nevertheless, I stopped short at recommending You Look Like a Thing and I Love You to her. Why? Simply put, the book lacks a human-interest through-line.
To be fair to Shane, it doesn’t need one. It is structured fine as it is (though I found she repeats some of her explanations of concepts at times). But my bestie prefers non-fiction that tells its story around human characters and experiences. While Shane sprinkles her explanations of concepts with anecdotes, the book lacks a true central protagonist or story.
Consequently, while the book is not too technical for laypeople, I recommend it more towards people who are interested in learning about AI for AI’s sake.
Still, don’t let me damn the book with faint praise: I enjoyed it. Muchly. If anything, it gives me plenty of ammunition to remain skeptical and challenge the next person or company that claims their AI-powered product is going to change my life.
Pair this with Hello World, by Hannah Fry; Weapons of Math Destruction, by Cathy O’Neil; and [Algorithms of Oppression], by Safiya Noble.
Originally posted at Kara.Reviews.
Shane’s book is a short but comprehensive dive into what machine learning actually is, how it works, and why it so often goes wrong—by which she means, it doesn’t actually solve the problem it was meant to solve, or it solves the problem but in a really stupid way. Indeed, this is probably the main takeaway of the book: AIs are not smart. Not at all. And in the rare occasion when we do manage to make a smart AI, like Google’s Alpha Go or IBM’s Watson, it is incredibly specialized (and probably more expensive than it is worth).
Each chapter examines machine learning from a different angle. Some chapters cover problems—such as how difficult it is to assemble a good data set, how bias can creep into the training data, etc. Other chapters spend more time exploring the different approaches researchers and programmers take to machine learning, such as evolutionary algorithms. While there was plenty in this book I was aware of (particularly along the bias dimension), there was still a lot I either didn’t know or learned more about from the book.
Also, Shane is funny. Her writing is laced with a graceful wit, and it pairs perfectly with the examples of text she has had various AIs produce, from ersatz and confused recipes to names for ice cream flavours and verses of songs. I literally laughed out loud reading this book—did not expect that from a book on this subject, but I couldn’t resist how truly hilarious some of Shane’s results sound. This drives home the fact that AI is just not there yet, for most of our purposes, more so than any dry and technical explanation ever might.
Also also, shout out to Shane’s wonderful sketches throughout the book.
Despite all of the above, I would hesitate to recommend this to just anyone. While reading this book, I thought about my bestie, who has pivoted into freelance copywriting. She does a lot of copywriting for tech or tech-adjacent companies, so it behoves her to read more about the field, including AI. Nevertheless, I stopped short at recommending You Look Like a Thing and I Love You to her. Why? Simply put, the book lacks a human-interest through-line.
To be fair to Shane, it doesn’t need one. It is structured fine as it is (though I found she repeats some of her explanations of concepts at times). But my bestie prefers non-fiction that tells its story around human characters and experiences. While Shane sprinkles her explanations of concepts with anecdotes, the book lacks a true central protagonist or story.
Consequently, while the book is not too technical for laypeople, I recommend it more towards people who are interested in learning about AI for AI’s sake.
Still, don’t let me damn the book with faint praise: I enjoyed it. Muchly. If anything, it gives me plenty of ammunition to remain skeptical and challenge the next person or company that claims their AI-powered product is going to change my life.
Pair this with Hello World, by Hannah Fry; Weapons of Math Destruction, by Cathy O’Neil; and [Algorithms of Oppression], by Safiya Noble.
Originally posted at Kara.Reviews.