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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. 5 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)

  3. 5 out of 5

    Sleepless

    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.

  4. 4 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

    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.

  6. 4 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. 4 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.

  8. 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.

  9. 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.

  10. 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.

  11. 5 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.

  12. 5 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

  13. 4 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).

  14. 5 out of 5

    Lucas Wiman

    This book is really a pretty impressive achievement: it's very funny, informative, topical and the cartoons are cute too. The writing style is somewhat reminiscent of Randall Monroe (author of xkcd and "What If?"), though with fewer digressions into unrelated subjects. The audience for this book is really quite broad: I think even top experts will get something out of reading it, but it's also appropriate for anyone who wants to learn more about AI or just read a funny book. I know a little about This book is really a pretty impressive achievement: it's very funny, informative, topical and the cartoons are cute too. The writing style is somewhat reminiscent of Randall Monroe (author of xkcd and "What If?"), though with fewer digressions into unrelated subjects. The audience for this book is really quite broad: I think even top experts will get something out of reading it, but it's also appropriate for anyone who wants to learn more about AI or just read a funny book. I know a little about AI from my work as a software developer and my background in mathematics, though it's definitely a topic I'm keen to learn more about. This book didn't say anything I know to be incorrect, which is encouraging for the material in the book I'm less familiar with. Unlike many popular books/articles on AI, this book didn't dumb things down, though it did omit a lot of more mathematical topics. Of the mathematical topics Shane did discuss (like linear models and overfitting), she did an impressive job of explaining them accurately without jargon.

  15. 4 out of 5

    Scott

    I'm a longtime follower of AI Weirdness, and this is a great book. If you are new to AI, it's easy to understand the key concepts, as Janelle makes complex topics accessible through wonderful examples. Reading this book is a fun way to get yourself familiar with the language of AI as you will inevitably be faced with it. There are killer (literally in some cases) examples, funny (if the AI isn't making the jokes) cartoons and great (if you are a human reading the book) takeaways. I loved it so I'm a longtime follower of AI Weirdness, and this is a great book. If you are new to AI, it's easy to understand the key concepts, as Janelle makes complex topics accessible through wonderful examples. Reading this book is a fun way to get yourself familiar with the language of AI as you will inevitably be faced with it. There are killer (literally in some cases) examples, funny (if the AI isn't making the jokes) cartoons and great (if you are a human reading the book) takeaways. I loved it so much that I bought three extra copies to give away to people who answered questions correctly in a session I delivered. This book pairs well with "Invisible Robots in the Quiet of the Night" by Craig Le Clair, which covers some of the impacts of AI-driven automation on businesses and people using some extensive research.

  16. 4 out of 5

    Todd N

    I’ve always been a fan of Ms. Shane’s blog aiweirdness.com because it is informative, weird, and funny. In this book she turned up the informative and turned down the other two just a bit. It’s a great overview of what machine learning/AI (mostly neural nets) can and cannot do. These neural nets can start working in unintended ways, which brings up really interesting ethical issues (in particular issues with bias) and either surprisingly funny or dire solutions to the problems they are supposed I’ve always been a fan of Ms. Shane’s blog aiweirdness.com because it is informative, weird, and funny. In this book she turned up the informative and turned down the other two just a bit. It’s a great overview of what machine learning/AI (mostly neural nets) can and cannot do. These neural nets can start working in unintended ways, which brings up really interesting ethical issues (in particular issues with bias) and either surprisingly funny or dire solutions to the problems they are supposed to solve. Recommended. There is not much technical content, so don’t be intimidated by it at all. Instead there are a lot of great and surprisingly funny examples that point out particular aspects of AI.

  17. 4 out of 5

    Shannon Clark

    Everyone should read this funny and important book about the limits of AI. Not just technologists and company founders like myself who are building companies that may use AI but everyone who lives and works in our modern society where the impact of AI is increasingly all around us. For good and for ill. (My autocorrect on my phone for example kept trying to make ill into I’ll in that last sentence. AI is simultaneously under and over hyped and poorly understood by nearly everyone. This is a Everyone should read this funny and important book about the limits of AI. Not just technologists and company founders like myself who are building companies that may use AI but everyone who lives and works in our modern society where the impact of AI is increasingly all around us. For good and for ill. (My autocorrect on my phone for example kept trying to make ill into I’ll in that last sentence. AI is simultaneously under and over hyped and poorly understood by nearly everyone. This is a fantastic book that illustrates highly complex technology in ways that even non programmers can appreciate and understand and with humor and memorable examples that will stick with readers. Giraffe? (Read to understand that)

  18. 5 out of 5

    Grace T

    Super accessible, lots of laugh-out-loud moments (not least the list of AI-generated appellations for Benedict Cumberbatch, included for no particular reason, and the various ways AIs cheat as they try to learn new things), and all the adorable little cartoons. I got this book from the library mainly because the title and cover grabbed me immediately, and it did not disappoint. I probably haven't absorbed all of the terms that were introduced, but I definitely have a better sense of what AI can Super accessible, lots of laugh-out-loud moments (not least the list of AI-generated appellations for Benedict Cumberbatch, included for no particular reason, and the various ways AIs cheat as they try to learn new things), and all the adorable little cartoons. I got this book from the library mainly because the title and cover grabbed me immediately, and it did not disappoint. I probably haven't absorbed all of the terms that were introduced, but I definitely have a better sense of what AI can and cannot do in the real world, and it was a fun read all the way. I'd definitely recommend this book if you want to 1) laugh a lot and 2) feel smarter while you're doing it.

  19. 4 out of 5

    Michelle

    If you know anything about ML/AI, this is a great book to read, packed with stories about how various "AIs" (I hate that term) hack their datasets/environments and their own reward functions to solve problems. The stories are laugh-out-loud funny and insightful, and illustrated with the cutest comics. (Just don't expect to actually learn how to program anything in ML/AI from this book. There is a chapter on algorithms and how they work, but it's kind of weirdly-organised and at best will only If you know anything about ML/AI, this is a great book to read, packed with stories about how various "AIs" (I hate that term) hack their datasets/environments and their own reward functions to solve problems. The stories are laugh-out-loud funny and insightful, and illustrated with the cutest comics. (Just don't expect to actually learn how to program anything in ML/AI from this book. There is a chapter on algorithms and how they work, but it's kind of weirdly-organised and at best will only give you a list of things to google.)

  20. 5 out of 5

    Paul Miller

    Approachable introduction to the limitations of AI/Machine Learning. Makes a convincing case for AI's limitations (think the brain of a worm), but doesn't paint any of the future (beyond it's very far away). Given the level of investment (and theft) by the Chinese in AI technology, plus the rate of technology advancement, in the end it's hard to buy that the current limitations will persist for decades.

  21. 5 out of 5

    Anne Janzer

    Our AI overlords are coming and they're ... somewhat absurd. Whether you believe the hand-wringing or the hype about artificial intelligence, this book is both reassuring. Janelle Shane manages to make topics like Markov Chains and unintentional memorization fascinating and funny. That's an exceptional feat of nonfiction writing. I'd recommend this book to anyone even mildly interested in better understanding all of the buzz around Artificial Intelligence and Machine Learning.

  22. 4 out of 5

    Kristine

    You Look Like a Thing and I Love You by Janelle Shane is a free NetGalley ebook that I read in late October. Ahh, this is the source for those weird-ass list of cat names and recipes. With the help of programmers, AI draws from a huge catalog of pickup lines to create their own. There's a minor hit to major miss ratio when you ask AI for its own original ideas. With its cute comic sketches, oof, people who love punnynerdy jokes and memes would adore this book.

  23. 4 out of 5

    Bakertyl

    This book isn't for everyone, but it is for people looking at our own future with AI. So, I guess it is for everyone. This book reads easily, complex computer science is presented at a layman's level without sounding condescending... all-around a well written book. Full on anecdotes to make (what could be) a dry subject funny at times, there are plenty of real-world examples of AI effecting our lives today. **I received this book from NetGalley in exchange for an honest review.

  24. 5 out of 5

    Jesse JP

    I usually don’t enjoy non-fiction, but this book was so much fun and so easy to read. Excellent anecdotes, really great level of information, funny and cute. If ever you wanted to know anything about AI, this book is a really great place to start. I hovered between rating 4 and 5 stars but am giving 5 stars because I enjoyed it so much even though it’s a genre I struggle to read, although I will say I thought the pictures in it were cute but didn’t really add much.

  25. 4 out of 5

    Stephanie

    It's a cute and fun book about AI and ML that still does a good job touching on its major issues. A lot of the books you read on the topic are (understandably) dark af and I appreciated the levity. This is a good example of computer science communication. You don't need to be an expert or even a beginner to get something real out of this.

  26. 5 out of 5

    Michael de Plater

    Great example of what humans and AI can achieve together I loved this book. It was funny, fascinating and more inspiring for thinking about our sci-fi future than Black Mirror and Westworld. It cuts through a lot of science fiction cliches and technology hype to give an excellent overview of current and near future AI potential and limitation.

  27. 5 out of 5

    Annarella

    This is one the best book I've ever read about AI. it's well researched and humorous at the same time. I loved the humour, the style of writing and the clarity of the explanations. I will surely visit the author's blog and look forward to reading her further books. Highly recommended Many thanks to the publisher and Netgalley for this ARC, all opinions are mine.

  28. 5 out of 5

    Burhan Syed

    The book is well written and balances being highly entraining with educational. I do wish they're was a glossary at the end of the book so I didn't have to keep going back and forth whenever a technical term was used. Highly recommend if you're interested in learning about AI

  29. 5 out of 5

    Helena

    Fun and informative I have long followed Janelle on Twitter and like to see the things her bots try to create so I immensely enjoyed getting to learn, in a very accessible way, the steps they took to get there.

  30. 5 out of 5

    whitney

    So great! I’ve loved Janelle Shane’s AI Weirdness site for a long time, and she does a wonderful job of explaining how current machine-learning based AI works with humor as well as a great deal of sensitivity towards the social and ethical issues at play.

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