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You Look Like a Thing and I Love You: How Artificial Intelligence Works and Why It's Making the World a Weirder Place

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AS HEARD ON NPR'S "SCIENCE FRIDAY" Discover the book that Malcolm Gladwell, Susan Cain, Daniel Pink, and Adam Grant want you to read this year, an "accessible, informative, and hilarious" introduction to the weird and wonderful world of artificial intelligence (Ryan North). "You look like a thing and I love you" is one of the best pickup lines ever... according to an AS HEARD ON NPR'S "SCIENCE FRIDAY" Discover the book that Malcolm Gladwell, Susan Cain, Daniel Pink, and Adam Grant want you to read this year, an "accessible, informative, and hilarious" introduction to the weird and wonderful world of artificial intelligence (Ryan North). "You look like a thing and I love you" is one of the best pickup lines ever... according to an artificial intelligence trained by scientist Janelle Shane, creator of the popular blog AI Weirdness. She creates silly AIs that learn how to name paint colors, create the best recipes, and even flirt (badly) with humans--all to understand the technology that governs so much of our daily lives. We rely on AI every day for recommendations, for translations, and to put cat ears on our selfie videos. We also trust AI with matters of life and death, on the road and in our hospitals. But how smart is AI really... and how does it solve problems, understand humans, and even drive self-driving cars? Shane delivers the answers to every AI question you've ever asked, and some you definitely haven't. Like, how can a computer design the perfect sandwich? What does robot-generated Harry Potter fan-fiction look like? And is the world's best Halloween costume really "Vampire Hog Bride"? In this smart, often hilarious introduction to the most interesting science of our time, Shane shows how these programs learn, fail, and adapt--and how they reflect the best and worst of humanity. You Look Like a Thing and I Love You is the perfect book for anyone curious about what the robots in our lives are thinking. "I can't think of a better way to learn about artificial intelligence, and I've never had so much fun along the way." - Adam Grant, New York Times bestselling author of Originals


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AS HEARD ON NPR'S "SCIENCE FRIDAY" Discover the book that Malcolm Gladwell, Susan Cain, Daniel Pink, and Adam Grant want you to read this year, an "accessible, informative, and hilarious" introduction to the weird and wonderful world of artificial intelligence (Ryan North). "You look like a thing and I love you" is one of the best pickup lines ever... according to an AS HEARD ON NPR'S "SCIENCE FRIDAY" Discover the book that Malcolm Gladwell, Susan Cain, Daniel Pink, and Adam Grant want you to read this year, an "accessible, informative, and hilarious" introduction to the weird and wonderful world of artificial intelligence (Ryan North). "You look like a thing and I love you" is one of the best pickup lines ever... according to an artificial intelligence trained by scientist Janelle Shane, creator of the popular blog AI Weirdness. She creates silly AIs that learn how to name paint colors, create the best recipes, and even flirt (badly) with humans--all to understand the technology that governs so much of our daily lives. We rely on AI every day for recommendations, for translations, and to put cat ears on our selfie videos. We also trust AI with matters of life and death, on the road and in our hospitals. But how smart is AI really... and how does it solve problems, understand humans, and even drive self-driving cars? Shane delivers the answers to every AI question you've ever asked, and some you definitely haven't. Like, how can a computer design the perfect sandwich? What does robot-generated Harry Potter fan-fiction look like? And is the world's best Halloween costume really "Vampire Hog Bride"? In this smart, often hilarious introduction to the most interesting science of our time, Shane shows how these programs learn, fail, and adapt--and how they reflect the best and worst of humanity. You Look Like a Thing and I Love You is the perfect book for anyone curious about what the robots in our lives are thinking. "I can't think of a better way to learn about artificial intelligence, and I've never had so much fun along the way." - Adam Grant, New York Times bestselling author of Originals

30 review for You Look Like a Thing and I Love You: How Artificial Intelligence Works and Why It's Making the World a Weirder Place

  1. 5 out of 5

    Blair

    A fun, irreverent guide to the world of artificial intelligence from the woman behind the fantastic AI Weirdness blog. The book's central premise can be summed up in a sentence: artificial intelligence is more widespread than we think... but it's also pretty stupid. Hence the many funny, charming and even cute examples of machine-generated oddness throughout: recipes that call for 'liquid toe water'; a list of Halloween costumes that includes 'Panda Clam' and 'Failed Steampunk Spider' (I A fun, irreverent guide to the world of artificial intelligence from the woman behind the fantastic AI Weirdness blog. The book's central premise can be summed up in a sentence: artificial intelligence is more widespread than we think... but it's also pretty stupid. Hence the many funny, charming and even cute examples of machine-generated oddness throughout: recipes that call for 'liquid toe water'; a list of Halloween costumes that includes 'Panda Clam' and 'Failed Steampunk Spider' (I actually want to see that one); and the book's title, which was the result of an AI being tasked with devising chat-up lines. Shane's light-hearted style is very accessible – there are loads of laugh-out-loud anecdotes, but you'll learn quite a bit too. I received an advance review copy of You Look Like a Thing and I Love You from the publisher through NetGalley. TinyLetter | Twitter | Instagram | Tumblr

  2. 4 out of 5

    Sleepless Dreamer

    Thanks to NetGalley and the publisher for providing me with a copy in return for my unbiased review! This book provides an excellent summary of AI and how it works. It's written in a funny and easy going style, with absolutely adorable sketches. Seriously, it's worth reading this for the AI doodles. They made me burst out laughing a few times. Moreover, as someone with almost no knowledge about AI, I can say confidently that this book manages to be clear and understandable, even if you don't Thanks to NetGalley and the publisher for providing me with a copy in return for my unbiased review! This book provides an excellent summary of AI and how it works. It's written in a funny and easy going style, with absolutely adorable sketches. Seriously, it's worth reading this for the AI doodles. They made me burst out laughing a few times. Moreover, as someone with almost no knowledge about AI, I can say confidently that this book manages to be clear and understandable, even if you don't know anything. It gets ideas across without being too technical or using too much professional jargon. The author uses hilarious metaphors and real life examples to highlight important points and it definitely makes it all clear and interesting! AI is such a buzzword nowadays so I enjoyed receiving more facts on the abilities of AI. Realizing that AI is best when solving narrow problems, that AI develops through mistakes, that AI struggles when seeing the unknown and also doesn't have a long term memory was all new to me. I found the parts that talked about creativity and AI absolutely fascinating. It's very cool to think about how AI isn't bound by our human thoughts and therefore can go to places and connections we usually don't. Like I'd struggle to think about original cat names but an AI with enough input can just list thousands (and yeah, most won't be relevant but still). I loved reading about AI shortcuts ("how do I gamble the best? Simply don't gamble"). Of course, it's concerning (like AI assuming there are no diseases because they are rare) but it's neat to think of how far this can go and how AI sees our world differently. All in all, if you're up for a short, funny and informative book about AI, this is a good read for you. What I'm taking With Me • The knowledge of AI very much depends on its data bank. Which makes me feel like we need philosophers and other humanists involved when creating AI for real life applications, you've got to have someone that's thinking about social repercussions, about the ethical implications of representation. • Companies often claim to use AI but in fact use people because it's cheaper. Combining AI with human help works well, such as advertising bots that redirect complicated questions to human workers. • Man, I'm just here waiting for AI to come up in a conversation so I can talk about this book. First Week Uni Adventures • My Peruvian roommate said that I'm so dramatic, I could be from Latin America. To be fair, she said this after she walked into the room and found me lying on my bed and saying, "math will be the death of me, I'm doomed". But really, math is freaking hard and I am scared. • People from my degree are so smart and so serious and all of them have so many life goals and I'm just here like, "idk man, I'll probably go back to being a graphic designer after this". • Econ is so confusing, what the heck • If one more person tells me I seem like I'm from Tel Aviv, I'm going to cry. • I need to stop signing up for things and I feel physically unable to because everything is so cool and interesting and I want to do it all. • Comparative Politics is the best thing ever and I am in love with our professor and really, it's just a wild class. • A guy in my PPE classes is convinced he saw me in a left wing propaganda video and like, I'd like to be confident enough to say that couldn't be me but I am scared it might be and that I don't know of it. • My dorm floor is about 50% international students. It's fun because I was considering studying abroad and well, I feel like I'm getting the dorm room experience of studying abroad.

  3. 4 out of 5

    Alex Sarll

    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 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)

  4. 5 out of 5

    E.M. Swift-Hook

    Secrets Snowmen Won't tell You In a time when we are all being told about the terrors of sentient AI taking over the world, AI's inventing their own languages and having to be turned off and other such terrifying prospects, discovering that an AI lists in it's top ten favourite animals 'razorbill with wings hanging about 4 inches from one's face and a heart tattoo on a frog' is the perfect antidote! This book is full of such hilarious AI misunderstandings, but it is also an excellent survey of Secrets Snowmen Won't tell You In a time when we are all being told about the terrors of sentient AI taking over the world, AI's inventing their own languages and having to be turned off and other such terrifying prospects, discovering that an AI lists in it's top ten favourite animals 'razorbill with wings hanging about 4 inches from one's face and a heart tattoo on a frog' is the perfect antidote! This book is full of such hilarious AI misunderstandings, but it is also an excellent survey of what AI can and can't do - and what it might and might not be expected to do in the future. For someone like me, who has only the vaguest of sci-fi show ideas about what AI really is, this is a great introduction to the topic. You will close the book feeling both reassured but also very aware of the real dangers of allowing AI to make decisions. Whilst its ability to spot anomalies in cells is already helping to make our lives better and safer assisting with medical diagnoses, there are many areas in which it is less helpful. When it can't tell the difference between a sheep and the field it is in, a puppy and the child who holds it, is it really a good idea to be thinking of allowing the military to use such tech to choose targets on a battlefield? When the AI is trained on a 'previous successful candidates' list, is it surprising that it throws out the resumes of women and those from ethnic minorities? When it is allowed to use postcode as a guide, is it really going to be an impartial aid to policing? The book explores such ethical issues as it looks at how AI learns what it learns and what can be done to make it learn better. It offers an ultimately optimistic view of what AI has to offer and an absolutely hilarious insight into how it does what it does. I loved this book. The title of this review is a quote from an AI in it, by the way. Since we all interact with it on a daily basis, everyone needs to understand the limits and strengths of AI. So I thoroughly recommended this demystifying book - especially to technophobes!

  5. 5 out of 5

    Shane

    This book was fun and informative enough to be worth reading, but it was also a little thin and repetitive at times. I do feel like I know more about how algorithms work and how they can go wrong, so there we go - mission accomplished.

  6. 5 out of 5

    Merc Rustad

    A delightful, hilarious, fascinating look at what AI can (and can't) do; the illustrations are like icing, so sweet and perfect. I loved every page of this book! :D A readable, cheerful voice and entertaining anecdotes about the weirdness of AI and machine learning makes this a fast-paced, completely absorbing read. It's wonderful and highly recommended!

  7. 5 out of 5

    Emily

    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 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.”

  8. 5 out of 5

    C. S.

    "For the foreseeable future, the danger will not be that AI is too smart but that it’s not smart enough." This was a really fun read. It's not the overly optimistic tech utopia book that I was afraid it would be, but also it has a lot of optimism in it. I also really liked how thoroughly the problem of bias in tech and how that translates to AI was covered. The material itself was fascinating and often hilarious, and if I have a complaint it's that a lot of the information is repeated in what "For the foreseeable future, the danger will not be that AI is too smart but that it’s not smart enough." This was a really fun read. It's not the overly optimistic tech utopia book that I was afraid it would be, but also it has a lot of optimism in it. I also really liked how thoroughly the problem of bias in tech and how that translates to AI was covered. The material itself was fascinating and often hilarious, and if I have a complaint it's that a lot of the information is repeated in what seemed like needless detail. Would definitely recommend.

  9. 5 out of 5

    Nicky Drayden

    I'm legit scared of murderbots now, so thanks? Great read. Fascinating insight into the best and worst AI has to offer.

  10. 5 out of 5

    Kam Yung Soh

    An excellent and hilarious book about the state of actual AI technology in the world (as opposed to the AIs you may see in popular media) and why they can do weird things. As it turns out, the weirdness can be due to the data used to train the AI, in how the AI processes the data and in how we tell the AI to solve a problem for us. You will get a good understanding of how AIs actually work and what they can (and can't) do and also how AIs can actually help humans do their jobs (or entertain us An excellent and hilarious book about the state of actual AI technology in the world (as opposed to the AIs you may see in popular media) and why they can do weird things. As it turns out, the weirdness can be due to the data used to train the AI, in how the AI processes the data and in how we tell the AI to solve a problem for us. You will get a good understanding of how AIs actually work and what they can (and can't) do and also how AIs can actually help humans do their jobs (or entertain us with hilarious failures). Chapter one looks at what kinds of AI are featured here. While the general public may have some ideas about AI from the popular media, the kinds of AIs looked at here are actual ones in use, which means machine based systems that accept data, apply machine learning algorithms to it, and produce an output. A brief look at how such AI are trained using data and what happens as it gradually learns what kind out output is 'acceptable' is shown. While humans may initially specify what to produce based on the provided input, such AIs may learn and process the data in unexpected ways, leading to weird and unexpected output. Chapter two looks at what AI systems are now doing. From running a cockroach farm, providing personalised product recommendation to writing news reports and searching scientific datasets, AIs has it uses. The flip side is AI being used to produce things like deepfakes (swapping people's heads or making people appear to do things they didn't). In general, AIs are currently better at very specific tasks (like handing initial customer support request) and not very good at more general tasks (like creating cooking recipes or riding a bike). One reason is that such general tasks usually require some kind of long term memory to remember what has been done (like when creating long-form essays) but current AIs systems lack this memory capacity. Also, some general tasks create situations that AIs may never encounter in their input training data (like driving safely upon seeing unexpected obstacles). Chapter three looks at how AIs actually learn by looking at the various types of AI systems: Neural Networks, Markov Chains, Random Forests, Evolutionary Algorithms and Generative Adversarial Networks. Examples of such AIs are given, like the autocorrect system used in smartphones, and their advantage and disadvantages in handling input data, processing it and producing the expected (or unexpected) output. Chapter four looks at why AIs don't appear to work despite them trying to produce acceptable output. There may several reasons for this: the problem the AI is being asked to solve may be too broad (like making cat pictures after being trained on pictures of people). Or the amount of data provided to the AI may be too little for the task required. The input data may also be too 'messy', full of information not actually required by the task or containing mistakes that confuse the AI. Chapter five shows how AI complete their task; only it's not the task the designers expected. There are several reasons for this. One is that the AI does the task, only not in the expected way. For example, moving a robot backwards because it was told not activate its bumper sensors (which are located at the front). AIs also usually learn in a simulated environment (to speed up learning), which may lead AIs to exploit 'glitches' in its simulated environment to solve a problem (like 'gaining' energy to jump high by making multiple tiny steps first). Other times, AI solve problems in unexpected ways because the expected learning behaviour is too hard. Like growing a long leg so as to fall from point A to point B instead of learning to walk (the expected behaviour) as walking is hard. Chapter six covers more examples of AIs completing tasks in unexpected ways. The main reason for this is that AIs work in a simulated environment and the solutions it comes up with may only work there. Examples include making optical lenses that are very thick, exploiting mathematical rounding errors or even bugs in the simulation. Such solutions will, of course, not work in the real world. Chapter seven looks at 'shortcuts' that AIs may take to get the solution. This is usually due to unexpected features found in the training data the AIs may fixate on. For example, an AI trained to recognise a certain type of fish based on images was found to be focusing on fingers in the images instead because in the training data, fingers were always present holding the fish to be recognised. Biases (sometimes hidden) in the input data can also cause the AI to provide biased solutions; for example recommending hiring only men because in the input data, men were the ones usually hired. Since how the AI comes to a decision is not usually examined, such biased decisions may instead become the norm based on the premise that 'the machine made the recommendation and the machine cannot be biased', not recognising that bias in the input data maybe the cause of the problem. Chapter eight considers whether the AI works like the human brain and in general, it is not. AIs have problems remembering things in the long term. For example, passages written by AI tend to meander from topic to topic, generating output that, taken as a whole, is inconsistent. AIs are also prone to 'adversarial attacks' due to it tending to put too much weigh on certain inputs. Examples including modifying an image to mislead an AI recognition program to think a submarine is a bonnet. Or gradually modifying an image of a dog into that of skiers, yet leaving the AI to think it is still looking at a picture of a dog. Chapter nine looks at the problem of distinguishing between an AI and a person doing a job. This is partially due to hyped up articles that proclaim that AI will be doing certain jobs instead of human (for example, driving). The author provides several ways to probe whether certain output has been produced by an AI or, possibly, a human pretending to be an AI. Chapter ten looks at the future and shows that current way forward is a world where both AIs and humans have decision making jobs to do. AIs can be trained on data but it is up to humans to determine whether the results are valid and to modify or update the input data so as the let the AI do a better job. For now, the future is one where both AIs and humans coexists.

  11. 4 out of 5

    Jerzy

    YES. THIS. I crack up every time I read Shane's ridiculous tumblr posts about neural-net-generated paint colors and recipes. So I asked for this book for Christmas, expecting merely a few more silly jokes. Instead, I got an incredibly well-written and thorough (but still funny!) overview of the realistic possibilities and limitations of what's currently being hyped as "Artificial Intelligence"*... It's not what I expected, but definitely wonderful. I was looking for a resource like this to give to YES. THIS. I crack up every time I read Shane's ridiculous tumblr posts about neural-net-generated paint colors and recipes. So I asked for this book for Christmas, expecting merely a few more silly jokes. Instead, I got an incredibly well-written and thorough (but still funny!) overview of the realistic possibilities and limitations of what's currently being hyped as "Artificial Intelligence"*... It's not what I expected, but definitely wonderful. I was looking for a resource like this to give to my students & colleagues who think the singularity is looming (it ain't!)... or who think that progress in Machine Learning and AI means that you don't need Statistics anymore (statistical thinking is exactly what you need to address AI's shortcomings, from biased data to inadequate testing/evaluation and beyond). Much of it was stuff I already know but phrased more effectively and humorously than I ever could---however, some of the particular foibles of neural nets were new to me, and I really enjoyed learning about them from Shane. My college would like to be a leader in AI education among small liberal arts colleges. I can't imagine a better way to start than by requiring *each* of our students to read this book. (I'd also like them to read O'Neil's Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy and Maciej Cegłowski's talks, but Shane somehow manages to be a gentler, friendlier intro while also going deeper into technical details. Genius.) *(One quibble: the term "Artificial Intelligence" has a long history, and it used to mean the kind of rules-based programming that Shane explicitly *excludes* from her definition of AI. This is fine---she's clear in the book that she uses "AI" as shorthand for the methods that are being hyped in today's AI revival---just be aware that not everyone who works on AI would use the same definition.) Notes to self / favorite parts: [TBD]

  12. 4 out of 5

    Aaron Mikulsky

    This book was a quick read with not a lot of meat. I’ve captured the nuggets below that highlight my findings from the book. I would not waste my time reading the book, but it’s ok if you know nothing about AI and ML. More and more of our lives are being governed by algorithms. Sometimes AI is only a small part of a program while the rest of it is rules-based scripting. Other programs start out as AI-powered but switch control over to humans (CSC from chat bot to humans or self-driving cars) if This book was a quick read with not a lot of meat. I’ve captured the nuggets below that highlight my findings from the book. I would not waste my time reading the book, but it’s ok if you know nothing about AI and ML. More and more of our lives are being governed by algorithms. Sometimes AI is only a small part of a program while the rest of it is rules-based scripting. Other programs start out as AI-powered but switch control over to humans (CSC from chat bot to humans or self-driving cars) if things get tough (pseudo-AI). “People often sell AI as more capable than it actually is.” Flawed data will throw an AI for a loop or send it off in the wrong direction. Since in many cases our example data is the problem we’re giving AI to solve, it’s no wonder that bad data leads to a bad solution. Machine Learning (ML) is a part of AI. It’s Deep Learning, Neural Networks, Markov Chains, Random Forests, etc. The difference between ML algorithms and traditional rules-based programs is ML figures out the rules for itself via trial and error. As AI tries to reach the goals its programmers specify, it can discover new rules and correlations. All it needs is a goal and data set to learn from. Algorithms are good at finding trends in huge data sets but not good with nuance. ML algorithms are just lines of computer code. Researchers are working on designing AIs that can master a topic with fewer examples (I.e. one-shot learning) but for now a ton of training data is required. While a human driver may only need to accumulate a few hundred hours of driving experience, Waymo’s cars have collected data from driving more than 6M road miles plus 5B more miles driven in simulation. “Many AIs learn by copying humans. The question they’re answering is not ‘What is the best solution?’ But ‘What would the humans have done?’” “It’s often not that easy to tell when AIs make mistakes. Since we don’t write their rules, they come up with their own...Instead, the AIs make complex interdependent adjustments to their own internal structures.” “A monkey writing randomly on a typewriter for an infinite amount of time will eventually produce the entire works of Shakespeare.” AI to generate new recipes - called for handfuls of broken glass. AI to generate pickup lines - the title of the book. AI to generate ice cream flavors - “Beet Bourbon” and “Praline Cheddar Swirl.” AI shapes our online experience and determines the ads we see. AI helps with hyperpersonalization for products, music and movie recommendations. Commercial algorithms write up hyperlocal articles about election results, sports scores, and recent home sales. The algorithm, Heliograf, developed by the Washington Post, turns sports stats into news articles. This journalism algorithm translates individual lines of a spreadsheet into sentences in a formulaic sports story; it works because it can write each sentence more or less independently. Google translate is a language-translating neural network. ANNs = Artificial Neural Networks, aka cybernetics or connectionism. They’re loosely modeled after the way the brain works. In the 1950s, the goal was to test theories about how the brain works. The power of the neural network lies in how these cells are connected. The human brain is a neural network made of 86B neural networks. Markov Chains, like Recurrent Neural Networks (RNN), look at what happened in the past and predicts what’s most likely to happen next. Markov Chains are used for the autocomplete function in smartphones. Android’s autocorrect app, called GBoard, would suggest “funeral” when you typed “I’m going to my grandma’s,” RANDOM FOREST ALGORITHM is a type of machine learning algorithm frequently used for prediction and classification. It’s made of individual decision trees, or flowcharts that leads to an outcome based on the information we have. It uses trial and error to configure itself. “If all the tiny trees in the forest pool their decisions and vote on the final outcome, they will be much more accurate than any individual tree.” Companies use AI-powered resume scanners to decide which candidates to interview and who should be approved for a loan, recognizing voice commands, applying video filters, auto tagging faces in photos, and powering self-driving cars. VW, while testing AI in Australia, discovered it was confused by kangaroos as it had never before encountered anything that hopped. AI is making decisions about who should get parole and powering surveillance. AI’s consistency does not mean it’s unbiased. An algorithm can be consistently unfair, especially if it learned by copying humans, as many of them do. Deepfakes allow people to swap one person’s head and/or body for another, even in video. They have the potential for creating fake but damaging videos - like realistic yet faked videos of a politician saying something inflammatory. AI is pointing people to more polarizing content on YouTube. Microsoft’s image recognition product tags sheep in pictures that do not contain sheep. It tended to see sheep in landscapes that had lush green fields - whether or not the sheep were actually there. The AI had been looking at the wrong thing. At Stanford, the team trained AI to tell the difference between pictures of healthy skin and skin cancer. They discovered they had inadvertently trained a ruler detector instead. AI found it easier to look for the presence of a ruler in the picture. AI is analyzing medical images, counting platelets or examining tissue samples for abnormal cells - each of these tasks are simple, consistent, and self-contained. The Turing test (as Alan Turing proposed in the 1950s) has been a famous benchmark for the intelligence level of a computer program. Chatbots will struggle if the topic is too broad. Facebook tried to create an AI-powered chatbot called M that was meant to make hotel reservations, book theater tickets, and recommend restaurants, August 2015. Years later, Facebook found that M still needed too much human help and shut down the service January 2018. ANI = Artificial Narrow Intelligence AGI = Artificial General Intelligence GAN’s = Generative Adversarial Networks = a sub-variety of neural networks (introduced by Ian Goodfellow in 2014). They’re 2 algorithms in one - 2 adversaries that learn by testing each other (1 the generator and the other the discriminator). Through trial and error, both the generator and discriminator get better. Researches have designed a GAN to produce abstract art - managing to straddle the line between conformity and innovation. GANs work by combining 2 algorithms - one that generates imagines and one that classifies images - to reach a goal. Microsoft’s Seeing AI app is designed for people with vision impairments. Artist Gregory Chatonsky used 3 ML algorithms to generate paintings for a project called It’s Not Really You. People have crowdsourced data sets - if you don’t have all the data you need on hand. Amazon Mechanical Turk - pays people to crowdsource data. ML algorithms don’t have context for the problems we’re trying to solve, they don’t know what’s important and what to ignore. Google trained an algorithm called BigGAN which had no way of distinguishing an object’s surrounding from the object itself. Security expert Melissa Elliott suggested the term giraffing for the phenomenon of AI overreporting relatively rare sights. Bias in the dataset can skew the AI’s responses. Humans asking questions about an image tend to ask questions to which the answer is yes. An algorithm trained on a bias dataset found that answering yes to any question that begins with “Do you see a...” would result in 87% accuracy. To maximize profit from betting on horse racing, a neural network determined the best strategy was to place zero bets. Trying to evolve a robot to not run into walls, the AI algorithm evolved to not move, and thus didn’t hit walls. It’s really tricky to come up with a goal that the AI isn’t going to accidentally misinterpret. The programmer still has to make sure that AI has actually solved the correct problem. Why are AIs so prone to solving the wrong problems? 1) They develop their own ways of solving problems, and 2) They lack the contextual knowledge. “It’s surprisingly common to develop a sophisticated ML algorithm that does absolutely nothing.” Dolphin trainers learned that to get dolphins to help keep their tanks clean, they’d train them to fetch trash and bring to their keepers in exchange for fish. Some dolphins learned the exchange rate - tearing off small pieces to bring to their keepers for a fish apiece. Navigation apps, during the 2017 CA wildfires, directed cars towards neighborhoods that were on fire since there was less traffic there. Google Flu algorithm in the early 2010s made headlines for its ability to anticipate flu outbreaks by tracking how often people searched for information on flu symptoms. It vastly overestimated the number of flu cases (overfitting). The algorithm COMPAS (sold by Northpointe) was widely used across the US to decide whether to recommend prisoners for parole, predicting whether released prisoners were likely to be arrested again. Unfortunately, the data the COMPAS algorithm learned from is the result of hundreds of years of systematic racial bias in the US justice system. In the US, black people are much more likely to be arrested for crimes than white people, even though they commit crimes at a similar rate. Amazon discontinued use of the AI tool for screening job candidates upon revealing it was discriminating against women. If the algorithm is trained in the way that human hiring managers have selected or ranked resumes in the past, it’s very likely to pick up bias. Since humans tend to be biased, the algorithms that learn from them will also tend to be biased. Predictive policing looks at police records and tries to predict where and when crimes will be recorded in the future. They send more police to those neighborhoods, and more crime will be detected there than a lightly policed but equally crime-ridden neighborhood, just because there are more police around. This can lead to over policing. Treating a decision as impartial just because it came from an AI is know as Math-washing or Bias Laundering. The bias is still there, because the AI copied if from its training data, but now it’s wrapped in a layer of hard-to-interpret AI behavior. There are companies that have begun to offer bias screening as a service. One bias-checking program is Themis. One way of removing bias from an algorithm is to edit the training data until the training data no longer shows the bias, or selectively leave some applications out of the training data altogether (preprocessing). Hackers may design adversarial attack’s that fool your AI if you don’t go to the time and expense of creating your own proprietary data set. People may poison publicly available data sets. People can contribute samples of malware to train anti-malware AI. For example, some advertisers have put fake specks of “dust” on their banner ads hoping people accidentally click on the ads while trying to brush them off their touch screen. The infamous Microsoft Tay chatbot, a ML-based Twitter bot that was designed to learn from the users who tweeted at it, in no time learned to spew hate speech. “In 2019, 40% of European start-ups classified in the AI category didn’t use any AI at all.” A lot of human engineering goes into the data set. A human has to choose the subalgorithms and set them up so they can learn together. “Practical ML ends up being a bit of a hybrid between rules-based programming, in which a human tells a computer step-by-step how to solve a problem, and open-ended ML, in which an algorithm has to figure everything out.” Sometimes the programmer researches the problem and discovers that they now understand it so well that they no longer need to use machine learning at all. We just sometimes don’t know what the best approach to a problem is. ML also needs humans for maintenance and oversight.

  13. 4 out of 5

    Samantha

    AI explained through a set of logical and entertaining examples. Sometimes the examples even stray towards the absurd, in the best way. Janelle Shane puts together a comprehensive look at what AI is, how it works, what it's capable of doing, and most importantly - what it's NOT capable of doing. A must read for anyone who is interested (or concerned) about how AI affects our world.

  14. 4 out of 5

    Emily

    So fun, with the same irreverent tone as her blog, but while also covering an insane amount of information about AI and its capabilities (and failures). I listened to the audiobook, which was really nicely narrated. Hearing the AI-generated knock-knock jokes read aloud was a huge highlight.

  15. 5 out of 5

    Bandit

    Well, first of all, You Look Like A Thing and I Love You is a pick up line AI came up with and as far as pick up lines go it’s actually pretty good. And hilarious. Pretty good and hilarious is an apt way to describe this entire book, actually. Especially if, like me, you’re interested in AI and find autocorrect hysterical. Because, as it turns out, advancements in robotics, specifically robotic intelligence are nowhere near as…well, as advanced as you might think. Or hope. Which, personally, I Well, first of all, You Look Like A Thing and I Love You is a pick up line AI came up with and as far as pick up lines go it’s actually pretty good. And hilarious. Pretty good and hilarious is an apt way to describe this entire book, actually. Especially if, like me, you’re interested in AI and find autocorrect hysterical. Because, as it turns out, advancements in robotics, specifically robotic intelligence are nowhere near as…well, as advanced as you might think. Or hope. Which, personally, I find very sad, I ‘m always hoping and wishing for some artificially intelligent company, since the alternative leaves so much to be desired. But no, feet are being dragged and there are still so many limitations. To be fair, we can get AI to do narrow limited tasks pretty well. But independence of thinking on the Turing Test passing level is still but a fantasy, mostly. This book started off as a blog and I’m so glad it was turned into a book, because I don’t read blog, but a book with this title, description and cover is certain to grab my attention. And so chapter by chapter the author subjects AI to test after test to produce recipes, pick up lines and dessert flavors. The results are laugh out loud funny, I don’t think I’ve ever laughed that much while reading a work of nonfiction. The robots are pretty adorable, much like the author’s accompanying drawings. And it isn’t just fun and games either, you do get a fair amount of information and science behind the AI development, which I found very interesting. Robots, much like us, can be quirky, random and have a penchant for shortcuts. They are just not quite ready yet for the complexity of tasks science fiction has them perform. That’s pretty much the gist of the book, it’s the sort of thing where you can read the final summarizing chapter and get it, but if you read the entire thing, you get the lovely drawings and the comedy, so it’s totally worth it. Plus it’s a very quick read. Thoroughly entertaining book, albeit sad on a personal level for someone who can’t wait for sci fi future with super intelligent robots. Even if they might take over the world. Recommended. Thanks Netgalley.

  16. 5 out of 5

    Wolfgang

    What a profound collection of accomplishments and caveats of AI algorithms and how they are being deployed. Running against the grain of today's hype of the field, Janelle Shane's book is deeply appreciated, especially since she is an accomplished practitioner in the field and not just a gadfly critic. I first became aware of her via her blog aiweirdness.com, which excells showcasing her irreverent approach to exploring what's today called AI. The blog serves as a stream of miniature snippets What a profound collection of accomplishments and caveats of AI algorithms and how they are being deployed. Running against the grain of today's hype of the field, Janelle Shane's book is deeply appreciated, especially since she is an accomplished practitioner in the field and not just a gadfly critic. I first became aware of her via her blog aiweirdness.com, which excells showcasing her irreverent approach to exploring what's today called AI. The blog serves as a stream of miniature snippets that both explain AI powers and pitfalls. Now this book is the a systematically organized overview that can serve as a good reference of reality vs. hype. I have seen some reviews that bemoan a lack of depth. This might be caused by the light-hearted tone of the text and the many cartoons spread throughout the book. I personally found these sketches and drawings unnecessary and sometimes too silly but to judge the core value of this work by that shows a lack of depth in itself. If you like them, enjoy, if you find them silly, stay with the text. Janelle Shane has written a very accessible and very balanced account of the current state of the art of AI.

  17. 4 out of 5

    John Deardurff

    A fun and witty introduction to Artificial Intelligence (AI). Janelle is the mastermind behind the AIWeirdness.com website and uses humor and cartoons to explain the complex concepts of machine learning and algorithms that are behind the world of AI. I also learned way too much about the cockroach industry. The title of the book comes from one of her experiments were she has a bot come up with romantic pick-up lines based on a collection of a thousand cheesy one-liners. If you are afraid of a A fun and witty introduction to Artificial Intelligence (AI). Janelle is the mastermind behind the AIWeirdness.com website and uses humor and cartoons to explain the complex concepts of machine learning and algorithms that are behind the world of AI. I also learned way too much about the cockroach industry. The title of the book comes from one of her experiments were she has a bot come up with romantic pick-up lines based on a collection of a thousand cheesy one-liners. If you are afraid of a future filled with Terminators destroying the planet, sit down with this book and a tasty, muddy, eggshell sandwich and all your fears will be erased. This book is a thing and I like it.

  18. 5 out of 5

    Sara Perkins

    This is a delightful and accessible introduction to the current capabilities and applications of AI. I learned a lot about the basic challenges of developing and training AI and the humorous anecdotes make the information more memorable. I have no background in computer science and I wanted to read this book at every work break I had. I know I will be following the author’s blog from now on.

  19. 4 out of 5

    Dave

    A fascinating overview of AI today. Easy to read and easy to understand, the book lays out just what is AI and perhaps importantly these days what isn't AI. Some of the examples and stories are fab and all torn from real-life and sometimes the headlines. Beware of giraffes... And also, what a fabulous title!

  20. 5 out of 5

    Andrew Breza

    You Look Like a Thing and I Love You is to deep learning what Nate Silver's The Signal and the Noise is for predictive modeling: a must read for everybody with even a passing interest in the topic. I run a data science department and spend much of my time in the weeds of building models, cleaning data, and attending meetings. It's easy to lose the big picture. This book offers an urgently needed high level view of the field of AI. Beginners and experts alike can benefit from Shane's insight and You Look Like a Thing and I Love You is to deep learning what Nate Silver's The Signal and the Noise is for predictive modeling: a must read for everybody with even a passing interest in the topic. I run a data science department and spend much of my time in the weeds of building models, cleaning data, and attending meetings. It's easy to lose the big picture. This book offers an urgently needed high level view of the field of AI. Beginners and experts alike can benefit from Shane's insight and humor.

  21. 4 out of 5

    Julia

    3.5 stars. This was funny and enjoyable, but it was more like a series of stories of how AI's have failed. It touched on some overarching topics, often very repetitively, but it didn't go into much depth. It's basically an entertaining listicle.

  22. 4 out of 5

    Teri Temme

    Everyone should read this book! Excellent!!!

  23. 4 out of 5

    Bowman Dickson

    Solid! Fun book really enjoyed it and learned a lot. I have been following true author on twitter for a while so recognized a lot of stuff and already appreciated her humor a lot, would be curious what those without the same background think

  24. 5 out of 5

    Pınar

    A short and sweet book about AI. Nothing too deep or complicated. Minus one star for repetitive bits despite the limited material.

  25. 4 out of 5

    Meagan Houle

    Fun, hilarious and accessible, you're sure to love this weird and wonderful explanation of how AI really works, what it can (and can't) do, and how it may shape our future. Even if you're not interested in the nuts and bolts of how neural networks are trained, the light-hearted laughs and dark cautionary tales should be plenty interesting on their own.

  26. 5 out of 5

    Pamela

    Interesting information about how AI works, it’s current uses, it’s possible futures. Also funny, because Shane has a sense of humor about her work and machine learning.

  27. 5 out of 5

    Brooke

    First, I’d like to thank Hachette Audio, Janelle Shane, and Libro.fm for allowing me to listen to this audiobook for free. This audiobook is under five hours long and gives a great explanation of AI (artificial intelligence) for those who don’t know much about it already. The narrator uses a very nice robot voice to represent the AI. Explanations use everyday language and gives meanings for computer/programming terms. Somewhere, I read that this book was almost like a “Astrophysics for People in First, I’d like to thank Hachette Audio, Janelle Shane, and Libro.fm for allowing me to listen to this audiobook for free. This audiobook is under five hours long and gives a great explanation of AI (artificial intelligence) for those who don’t know much about it already. The narrator uses a very nice robot voice to represent the AI. Explanations use everyday language and gives meanings for computer/programming terms. Somewhere, I read that this book was almost like a “Astrophysics for People in a Hurry” for AI; I agree. I feel like I learned a lot in a short amount of time, and will recommend this audiobooks to patrons at my library (I am a librarian at a public library).

  28. 4 out of 5

    Steve

    With You Look Like a Thing and I Love You, Janelle Shane has given us an amusing, engaging, in depth, and surprisingly approachable explanation of how AI works, what it’s good for (and not good for) and why. This is one of those unique books about technology that’s written for non-technologists, yet manages to be in-depth enough to be a resource for those whose knowledge ranges from “I heard about AI once” to “the concepts are familiar, but coding AI is not my day job.” While I build software With You Look Like a Thing and I Love You, Janelle Shane has given us an amusing, engaging, in depth, and surprisingly approachable explanation of how AI works, what it’s good for (and not good for) and why. This is one of those unique books about technology that’s written for non-technologists, yet manages to be in-depth enough to be a resource for those whose knowledge ranges from “I heard about AI once” to “the concepts are familiar, but coding AI is not my day job.” While I build software systems, and have worked around AI and Machine Learning systems, I’ve not built or worked on their internals. This book inspired me to want to learn more. As AI (and Machine Learning) in its various forms touches many aspects of our lives, this book is a must read for anyone who wants to know more what AI does well, what it does poorly, and why . You’ll learn about the difference between narrow and general AI, basic concepts like Neural Nets and Markov chains, and how AI’s learn, including the impact of training data on how well they do. You’ll also learn about how AI systems can go astray, either through incidental issues (poor training data, for example) or malicious actions. While the concepts sound technical. Shane makes them very approachable though clear language, and memorable through humor. Since it’s not a text book on the subject, there may be a few places where more technical minded readers may see a few conceptual details skipped over, but between the notes and references, and the context the book gives you to do a good web search, this is not a major problem. The style of this book reminds me of some of Mary Roach’s books on science, such as Stiff: The Curious Lives of Human Cadavers and Gulp: Adventures on the Alimentary Canal, in how it easily mixes humor in with important factual information. At various times in the book Shane provides examples of some AI-Generated “recipes” to show how the wrong training data can lead to bizarre results. My family read some of these aloud and could we on the floor laughing. In addition to being an excellent primer on AI concepts, I also started thinking about how many of things that set AIs astray are also things that lead humans to the wrong solutions too, even as we are better equipped to compensate. Some recurring themes are being given the wrong definition of the problem (consider incentives at work and how they often lead to the wrong global results) and introducing biases through the examples we learn from, which lead to the wrong solutions. While Shane doesn’t seem to set out to make people understand how to learn better, I can’t help but think that there are lessons in the book for how we can be better at problem solving. This amusing, thought provoking and educational book got be excited to learn more about the subject. As I read the book, I wanted to slot time into my schedule to experiment with some machine learning code to better understand the ideas. But even if that isn’t something you are likely to do, the book can inspire you to think more critically about your experiences with Chatbots, recommendation engines, and other places where AI and ML technologies touch your life.

  29. 5 out of 5

    Kitty

    I've been following this author's blog for awhile now and I was so excited when I saw this book was going to be released that I requested it from my library ahead of time. It was exactly the kind of humorous and informative writing I've come to appreciate from the author's blog. I'm good with computers, but I'm no programmer (though whenever I read about neural networks I wish I'd had more time to take some computer science classes in college). The author is good at boiling things down in a way I've been following this author's blog for awhile now and I was so excited when I saw this book was going to be released that I requested it from my library ahead of time. It was exactly the kind of humorous and informative writing I've come to appreciate from the author's blog. I'm good with computers, but I'm no programmer (though whenever I read about neural networks I wish I'd had more time to take some computer science classes in college). The author is good at boiling things down in a way that even a layperson with only a basic understanding of computers and machine learning can grasp the concepts being presented. I find AI to be an endlessly fascinating topic, both with regards to its current real-world development and practical applications and its ultimate potential: What is theoretically possible for AI and what's just wishful thinking and/or nightmarish speculative sci-fi? A standard sci-fi trope is the AI Apocalypse/Robot Overlord scenario, but the author presents a more realistic picture: AI isn't too smart, it's not smart enough, at least not with our current technology. A single AI simply does not do well with complex tasks, and the author does a good job of explaining why that is. I especially love the examples of how AI often finds quirky solutions to problems in ways that were unforeseen and unintended by programmers. An AI given a task of sorting a list of numbers deletes the list so that technically it's not unsorted. An AI getting a perfect score by simply finding where the answers are stored and deleting them. Two computers given the task of playing tic-tac-toe against one another, and one AI learning to infinitely expand the game board until its opponent runs out of memory and crashes. AI given a task of evolving a method of locomotion simulating a creature that is very tall and just falling over, so that technically it went a far distance in a short amount of time. AI exploiting collision physics within a simulation or discovering glitches in a video game in order to speed run a level or maximize a score. These solutions work, technically speaking -- they do complete the task or solve the problem that the AI was told to solve, but in a destructive way or in a way that a human would look at and say, "Hey -- that's cheating!" Programmers often have to introduce new rules to prevent AI from taking shortcuts like these, and even then, sometimes the AI finds new shortcuts or loopholes to exploit. The good thing, though, is that in this way, AI can inspire out-of-the-box solutions that humans haven't considered, or at the very least help programmers continue to fine-tune programs and algorithms to solve the task that we actually intend for them to solve in a certain way. I also appreciate the author going into the social repercussions of trusting AI to do certain tasks. Neural networks (much like the human brain) are pattern-seeking, and their output depends on the data set they're trained on. Sometimes we see AI as an objective actor that might be better at making fair, logical decisions than a human would (given the fact that every human has both conscious and unconscious prejudice, even when we try to avoid acting on it). But if the data set that an AI is trained on is biased, then AI will reproduce that bias -- which is how we get algorithms that reproduce oppressive dynamics that exist in human societies. It's why we get YouTube algorithms that recommend white supremacist videos to people (sometimes young and impressionable kids and teens) who are initially just watching video game playthroughs, and why it's so easy these days to fall into a conspiracy theorist rabbit hole on YouTube. It's why Twitter managed to teach Microsoft's AI chatbot to reproduce racist Tweets in less than a day. And it's why there's danger in trusting AI to do jobs that require human judgment. The author presents a really good example illustrating this concern: using AI to determine which neighborhoods should have greater police presence. We have for decades had a well-known problem (at least in the U.S.) where neighborhoods with a higher concentration of low-income people and people of color are overpoliced, contributing to (1) a misconception that these neighborhoods intrinsically have a higher incidence of crime than, say, predominantly white middle- or upper-class suburbs and (2) a higher arrest (and, ultimately, conviction and incarceration) rate for people living in those overpoliced neighborhoods, which paves the way for the school-to-prison pipeline, mass incarceration disproportionately affecting certain demographics (e.g. Black and Latino people), voter suppression (disenfranchisement of people with felony records), unemployment and poverty even after being released from prison (many employers will avoid someone with a criminal record), which increases recidivism rates... it goes on and on. You might have a similar crime rate in a neighborhood with a large police presence and a neighborhood not crawling with cops, but it artificially looks like the overpoliced neighborhood has more crime just because there are more cops around to scrutinize the neighborhood. (This isn't even getting into the history of, say, crack/cocaine sentencing disparity.) SO, if we train an AI on arrest and incarceration records, and then give it the task of extrapolating crime rates based on that data and outputting suggestions for where police presence should be increased... it's just a modern twist on the existing and centuries-old Ouroboros-eating-its-own-tail state of racial inequality and the U.S. law enforcement system. It's just one example of using AI to do a job that it just isn't equipped to perform in an ethical or unbiased manner, but it's a powerful one. In the end, I'd recommend this book to anyone who wants to understand how machine learning and AI work in their current iterations, the potential future of AI (what we can realistically expect and what might be far more improbable), and the sociological aspects of AI.

  30. 4 out of 5

    Laura

    I received an ARC of this book thanks to Net Galley and publisher Voracious/Little, Brown and Company in exchange for an honest review. So fun fact: I am on a PhD program currently with 9 other students and about 5 of those students are doing hardcore AI projects. Personally I know nothing about AI and whenever they would talk about their research, I would automatically switch off. However, this doesn't build the best working relationship so I thought I would try reading around the area so I I received an ARC of this book thanks to Net Galley and publisher Voracious/Little, Brown and Company in exchange for an honest review. So fun fact: I am on a PhD program currently with 9 other students and about 5 of those students are doing hardcore AI projects. Personally I know nothing about AI and whenever they would talk about their research, I would automatically switch off. However, this doesn't build the best working relationship so I thought I would try reading around the area so I could at least carry a conversation with them. This book is AMAZING. Honestly, it is written in such an engaging and accessible way that me, the anti AI queen, could not put it down. Firstly I should clarify what this book is not. It is not a deep guide to how to create or work with AI systems. Instead what it is is essentially an overview of what AI means, how it functions (on a very accessible level) and the wonderful mistakes the systems make. One of my favourite parts of this book is all the examples it gives of things generated by real life AI systems and they are hilarious! I was laughing so loud on the bus that people were giving me weird looks. In terms of the layout, each chapter flows very naturally into the next one. I've already mentioned that I think it's accessible but things really are broken down very clearly. Shane has a fantastic style of prose which is informative but engaging, and I didn't even zone out once which is impressive for even the most interesting of nonfiction books. The text is also peppered with frankly adorable drawings (see the one on the cover) which really helped bring the information to life and added to the overall feel. Overall, I am blown away by this book. I thought it would help with my PhD but I had no idea how much I would enjoy reading it. I have already bought a copy for a friend as a birthday present and I would recommend this to anyone with even a passing interest in the topic. It really does teach you more about how the world works and I feel like I will definitely read more about AI in the future because of it. Overall Rating: 5/5

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