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“There comes a time in the life of a subject when someone steps up and writes the book about it. AIQ explores the fascinating history of the ideas that drive this technology of the future and demystifies the core concepts behind it; the result is a positive and entertaining look at the great potential unlocked by marrying human creativity with powerful machines.” —Steve “There comes a time in the life of a subject when someone steps up and writes the book about it. AIQ explores the fascinating history of the ideas that drive this technology of the future and demystifies the core concepts behind it; the result is a positive and entertaining look at the great potential unlocked by marrying human creativity with powerful machines.” —Steven D. Levitt, bestselling co-author of Freakonomics From leading data scientists Nick Polson and James Scott, what everyone needs to know to understand how artificial intelligence is changing the world and how we can use this knowledge to make better decisions in our own lives. Dozens of times per day, we all interact with intelligent machines that are constantly learning from the wealth of data now available to them. These machines, from smart phones to talking robots to self-driving cars, are remaking the world in the 21st century in the same way that the Industrial Revolution remade the world in the 19th century. AIQ is based on a simple premise: if you want to understand the modern world, then you have to know a little bit of the mathematical language spoken by intelligent machines. AIQ will teach you that language—but in an unconventional way, anchored in stories rather than equations. You will meet a fascinating cast of historical characters who have a lot to teach you about data, probability, and better thinking. Along the way, you'll see how these same ideas are playing out in the modern age of big data and intelligent machines—and how these technologies will soon help you to overcome some of your built-in cognitive weaknesses, giving you a chance to lead a happier, healthier, more fulfilled life.


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“There comes a time in the life of a subject when someone steps up and writes the book about it. AIQ explores the fascinating history of the ideas that drive this technology of the future and demystifies the core concepts behind it; the result is a positive and entertaining look at the great potential unlocked by marrying human creativity with powerful machines.” —Steve “There comes a time in the life of a subject when someone steps up and writes the book about it. AIQ explores the fascinating history of the ideas that drive this technology of the future and demystifies the core concepts behind it; the result is a positive and entertaining look at the great potential unlocked by marrying human creativity with powerful machines.” —Steven D. Levitt, bestselling co-author of Freakonomics From leading data scientists Nick Polson and James Scott, what everyone needs to know to understand how artificial intelligence is changing the world and how we can use this knowledge to make better decisions in our own lives. Dozens of times per day, we all interact with intelligent machines that are constantly learning from the wealth of data now available to them. These machines, from smart phones to talking robots to self-driving cars, are remaking the world in the 21st century in the same way that the Industrial Revolution remade the world in the 19th century. AIQ is based on a simple premise: if you want to understand the modern world, then you have to know a little bit of the mathematical language spoken by intelligent machines. AIQ will teach you that language—but in an unconventional way, anchored in stories rather than equations. You will meet a fascinating cast of historical characters who have a lot to teach you about data, probability, and better thinking. Along the way, you'll see how these same ideas are playing out in the modern age of big data and intelligent machines—and how these technologies will soon help you to overcome some of your built-in cognitive weaknesses, giving you a chance to lead a happier, healthier, more fulfilled life.

30 review for AIQ: How People and Machines Are Smarter Together

  1. 5 out of 5

    Monica

    I bought this one during an Amazon sale on a whim. Generally with technology books, you must read them sooner rather than later, else you run the risk of them being outdated. Technology advances at breakneck speed. This book mixes history with technology to instruct in how AI works and how and why humans are still required...at least so far. I loved the chapter showcasing Grace Hopper as the inventor of the subroutine for programming (among many other things). A mixture of AI learning and comment I bought this one during an Amazon sale on a whim. Generally with technology books, you must read them sooner rather than later, else you run the risk of them being outdated. Technology advances at breakneck speed. This book mixes history with technology to instruct in how AI works and how and why humans are still required...at least so far. I loved the chapter showcasing Grace Hopper as the inventor of the subroutine for programming (among many other things). A mixture of AI learning and commentary on the vast accumulations of data how we use it and what/why we need it and AIs. Also a primer on statistics and an intro to "deep learning" (how AIs think). Very well done and a fascinating blend of history that lead to the foundations of the technology and the mathematical connections to the technology. Ever hear of Isaac Newton's "Trial of Pyx" during his role as the warden of the Royal Mint? This was the history used to instruct on the importance of understanding variance. How about an AI prediction for trends among contraception that said that in 10 yrs on the pill a person would have a 60% chance of getting pregnant. That chapter was an instruction on the dangers of a model based upon the wrong data. An example of why AI currently needs humans to interpret findings. An AI can spot trends mining mountains of data, but humans have to create the model, evaluate and refine it based upon findings. "That’s because there’s no algorithm yet invented that can propose, test, and justify its own assumptions. Algorithms just do exactly what they’re told". The book also went into detail about the medical applications using Florence Nightingale's historic attention to detail to describe how information can be used to help diagnose and cure medical issues. Also gives breakdown of the logic that goes into creating a driverless car. If you drive for Lyft, this technology is coming fast. More voice/speech recognition technology and the logic behind it. The authors (both data scientists with a background in statistics) respected their readers enough not to dumb it down too much, but also acknowledged that not every one is a data scientist nor do we want to be (though maybe). Don't let the uninspiring cover and/or title fool you. This was another random purchase during the kindle sale that turned out to be a winner (though I am a technology geek). I really enjoyed this!! 4+ Stars Read on kindle

  2. 4 out of 5

    Daniel

    This is by far the clearest and realistic book about Artificial Intelligence that I have ever read to date. Rather than testing AI like a black box and readers like kindergarten kids (you won’t understand anyway), Polson explains in clear English how AI works. Basically AI uses Bayes’ theorem. Start with base rates, and adjust when new data comes so you get better and better. Even though a lot of the AI has proprietary algorithms (trade secrets), the principles are the same. Each AI algorithm on This is by far the clearest and realistic book about Artificial Intelligence that I have ever read to date. Rather than testing AI like a black box and readers like kindergarten kids (you won’t understand anyway), Polson explains in clear English how AI works. Basically AI uses Bayes’ theorem. Start with base rates, and adjust when new data comes so you get better and better. Even though a lot of the AI has proprietary algorithms (trade secrets), the principles are the same. Each AI algorithm only does 1 thing well pretty much like an app. Polson thinks that worrying about AI getting sentience is like worrying about our space vehicles approaching a black hole when we have just invented the first aeroplane. He categorically states that no matter how smart AI looks, it does not really understand anything and is just crunching numbers. So what makes good AI? 1. A large data set and lots of computing power. 2. Smart AI researchers who ask the right questions and tries to eliminate biases. If we just take raw data which already incorporate biases (e.g. women are paid less for the same job), then AI will just propagate the same bias. 3. Continual improvement because AI algorithm grows old pretty quickly. He gives the cautionary tale of Google’s (now stopped) Flu Epidemic detection app. It overestimated flu severity and has been scrapped. This is because by prompting searches with search suggestions, it changes the search itself. I especially like the clear explanation on speech recognition by ‘word vector’. Reading this book makes you realise that Skynet is still very far away.

  3. 4 out of 5

    Marta

    This book is not really about how artificial intelligence works, but more a collection of interesting historical tidbits and examples of current AI use in the real world. As such, it is quite entertaining, but not particularly illuminating. AI in its current form performs statistical analysis on vast amounts of data to discover patterns. With the explosion of the internet and mobile devices, the data collected about anything has grown huge. This enabled the creation of self-training algorithms. This book is not really about how artificial intelligence works, but more a collection of interesting historical tidbits and examples of current AI use in the real world. As such, it is quite entertaining, but not particularly illuminating. AI in its current form performs statistical analysis on vast amounts of data to discover patterns. With the explosion of the internet and mobile devices, the data collected about anything has grown huge. This enabled the creation of self-training algorithms. They rely on some basic assumptions, then gather data, then adjust the previous assumptions with new predictions. As the dataset grows, predictions get better. This is how they managed to get speech recognition and translation much better - not by encoding rules, but discovering patterns in the growing set of recorded speech of many people across networks. This is not the glamorous android assistant of sci-fi novels, but a bunch of number-crunching algorithms chained together, built on top of massive cloud computing power. But, it is way more pervasive in changing our lives. Polson scans the fields of usage from Netflix recommendations through self-driving cars to healthcare. He also talks of the pitfalls of wrong assumptions, letting models go out of date, fears from AI taking over (sorry, Elon Musk, you are a bit of a nutjob), cultural and moral implications. If you want to learn about AI, this is not the book. It is however an entertaining collection of stories related to AI technology. It was not what I expected, but I liked it.

  4. 4 out of 5

    Quintin Zimmermann

    AI pervades the fabric of our society and its influence upon our daily lives is expanding exponentially, literally "changing the world one smartphone at a time." In AIQ, Nicholas Polson and James Scott who are bona fide data scientists, urge us to not only embrace this technological revolution, but also to give remembrance to mostly unheralded historical figures from a much earlier time, whose mathematical theories and data-analysis forms the very bedrock of AI technology and development in its AI pervades the fabric of our society and its influence upon our daily lives is expanding exponentially, literally "changing the world one smartphone at a time." In AIQ, Nicholas Polson and James Scott who are bona fide data scientists, urge us to not only embrace this technological revolution, but also to give remembrance to mostly unheralded historical figures from a much earlier time, whose mathematical theories and data-analysis forms the very bedrock of AI technology and development in its current sophisticated state. From Facebook to self-navigation cars to seeking nuclear warheads, you will be amazed at how centuries old math is still informing the math behind modern technology. The surge in AI development in the last decade is owed to three enabling technological forces that have supercharged AI into a new age. Namely, the exponential growth in the speed of computers called Moore's law, the explosive growth in the amount of data generally available and cloud computing, all resulting in the minimising of cost and radically upscaling the computing function of powerful algorithms. What truly enhances the reading and intellectual pleasure of AIQ, is the fact that the authors didn't seek to dumb down the subject matter, but rather took their readers by the hand and guided them in a very accessible way through the myriad of math and theory, so that we can truly appreciate and understand this complex topic in a meaningful way, with illuminating real life examples to illustrate their technical points and ultimately help readers close out AIQ with a better understanding of the digital world around us.

  5. 4 out of 5

    Fraser Kinnear

    Pretty interesting, but really mostly enjoyable as a launch pad for other interesting stuff. It's really hard to cover this subject matter in a mass-market book. For example, Three Blue One Brown has a far superior explanation for how neural networks function, through his incredible YouTube series Theres some nice stories to explain statistical concepts like survivorship bias, Bayes' theorem, factor analysis, the base rate fallacy, and SLAM, but you're better off just with the wikipedia pages. One Pretty interesting, but really mostly enjoyable as a launch pad for other interesting stuff. It's really hard to cover this subject matter in a mass-market book. For example, Three Blue One Brown has a far superior explanation for how neural networks function, through his incredible YouTube series Theres some nice stories to explain statistical concepts like survivorship bias, Bayes' theorem, factor analysis, the base rate fallacy, and SLAM, but you're better off just with the wikipedia pages. One particular story stood out for me, which involved Isaac Newton not understanding the implications of the Standard Error of the Mean (discovered by De Moivre), and subsequently losing the British government enormous amounts of money to coin clipping. De Moivre's idea was that averages of samples should vary less than the entire population, because the highs and lows in a sample will average out. The variability between averages should equal the variability of the population divided by the square root of the sample size. Newton didn't understand this, and thought when he pulled samples of coins to average, he thought he could allow for the accepted variability for the entire population. I found this pdf which has other cool examples of this statistical fallacy.

  6. 5 out of 5

    Karel Baloun

    This is one of the most approachable math or computer science books I’ve ever read, and on a super important topic. Everyone who wants to be smarter should put this at the top of their list. Starts as presenting itself as a very approachable general reader introduction to the broadly applicable value of machine learning. Entertaining writing, even some hilarious phrasing. e.g. “you can see parallax for yourself […] raise your index finger, or perhaps a different finger if you’re cursing us for al This is one of the most approachable math or computer science books I’ve ever read, and on a super important topic. Everyone who wants to be smarter should put this at the top of their list. Starts as presenting itself as a very approachable general reader introduction to the broadly applicable value of machine learning. Entertaining writing, even some hilarious phrasing. e.g. “you can see parallax for yourself […] raise your index finger, or perhaps a different finger if you’re cursing us for all the math..” (p52). “ but this is a textbook case of economists naming obvious things after other economists.” (p152) Chapter one starts very strong, explaining conditional probably vividly and super memorably with Abraham Wald and his WWII airplane survival analyses. “Smart people who care about the world simply must know more about AI. That’s one reason we wrote this book.” (p39) And it is a great reason why you should read it!! Each story herein was new to me, and they were, with just one trivial exception, all interesting and memorable. Excellent conclusion (p238): “Now imagine a world where were actually smart about our efforts – a world where we put the right experts and the right legal protections in the right places, and where we’re eternally vigilant about the biases and assumptions of our algorithms. In that world our decision-making protocols could become radically better than the bias-riddled ones we have now. [...] our collective vision and technology have reached a point where we can successfully teach machines to drive a car, predict kidney disease, and carry on a conversation. We can certainly teach those machines to play fair. They might even teach us. [...] when it comes to the important decisions in life, we can and should combine artificial intelligence with human insight and human values. All it takes is people machines working together.”

  7. 4 out of 5

    Brad Revell

    See my review here: https://www.bradrevell.com/2018/10/ai... Three key takeaways from the book: 1. A domain specific illusion of intelligent behaviour is what happens when you chain a lot of algorithms together in a clever way. This is essentially what AI, Machine Learning, Deep Learning is. There is a lot of sales speak on these terms however “domain” and “specific” is absolutely key aspects to the AI definition; now and for the foreseeable future. 2. In AI, the role of the programmer isn’t to tel See my review here: https://www.bradrevell.com/2018/10/ai... Three key takeaways from the book: 1. A domain specific illusion of intelligent behaviour is what happens when you chain a lot of algorithms together in a clever way. This is essentially what AI, Machine Learning, Deep Learning is. There is a lot of sales speak on these terms however “domain” and “specific” is absolutely key aspects to the AI definition; now and for the foreseeable future. 2. In AI, the role of the programmer isn’t to tell the algorithm what to do. It’s to tell the algorithm how to train itself what to do, using data and the rules of probability. 3. To a learning machine, “personalisation” means “conditional probability”.

  8. 5 out of 5

    Nicholas Broussard

    I love this book. The authors literally describe AI without using math! It’s incredible. There’s a nice bit of quantitative expounding, but each chapter mostly focuses on the actuality, history, and practical application of big AI concepts, such as Bayesian stats, deep learning, and neural networks. I really loved the chapters on Grace Hopper and Florence Nightingale :)

  9. 4 out of 5

    Amit Udata

    Interesting examples and powerful storytelling! Particularly loved the chapter on Bayes' theorem (search operation for USS Scorpion) and the chapter on Sampling (trial of the Pyx) in which the authors mercilessly go after Isaac Newton. Overall, a great read (despite some dramatization here and there) Interesting examples and powerful storytelling! Particularly loved the chapter on Bayes' theorem (search operation for USS Scorpion) and the chapter on Sampling (trial of the Pyx) in which the authors mercilessly go after Isaac Newton. Overall, a great read (despite some dramatization here and there)

  10. 4 out of 5

    Jigar

    Context is the biggest motivator there is to learn something. This book is on point in that regard.First let us learn about how AI came to be, what it really does and how it does it. Then jump into writing code and working fancy math.

  11. 5 out of 5

    Jen Watkins

    I give these authors great credit for actually trying to explain how it all works.

  12. 4 out of 5

    Mohammed Alali

    It explains in details the methods of Artificial Intelligence and describes the statistics behind them. Also it presents real life applications and the future of this promising field.

  13. 4 out of 5

    Evan

    I read this book after seeing a review about it in the Wall Street Journal. Then, after I started it, Wes Gray (Alpha Architect) recommended AIQ as the best book to get an understanding of artificial intelligence in a book. Having finished it, I agree. Obviously, one 250 page book can't cover everything, but they build a solid foundation. However, I don't have a background in computer science or coding, so I could be wrong. To summarize what I took away from the book: 1) Conditional probability ha I read this book after seeing a review about it in the Wall Street Journal. Then, after I started it, Wes Gray (Alpha Architect) recommended AIQ as the best book to get an understanding of artificial intelligence in a book. Having finished it, I agree. Obviously, one 250 page book can't cover everything, but they build a solid foundation. However, I don't have a background in computer science or coding, so I could be wrong. To summarize what I took away from the book: 1) Conditional probability has been the key to personalizing and advancing AI over the past two decades. The computing power needed to determine such probabilities were not available until the 1990's and the massive datasets needed to analyze weren't available until the internet exploded. 2) Modeling, in AI, is based on prediction rules. The prediction rule for a linear equation is y = mx +b, while the prediction rule for a photo, can have up to 400,000 variables (which is why massive datasets are required to be able to derive such equations without having overfitting). 3) the prediction equations, using massive datasets and massive variables, are referred to as neural networks 4) For autonomous vehicles, a major challenge is simultaneous localization and mapping (SLAM). Apparently, humans ability to walk into a room and orient themselves is a skill that is not fully understood and is incredibly hard to model for vehicles. 5) Natural language processing has improved exponentially since 2010 because a rules-based approach to language has been abandoned. Instead, words are converted to numbers and assigned probabilities (word2vec). Assigning numerical values to words has resulted in applications like Alexa and Google Home. There is a lot of interesting history in this book as well as a lot of great examples. They also cover specific examples of AI application (like in healthcare), but the most interesting part of the book (to me) was the explanation of prediction rules and challenges presented when implementing AI.

  14. 4 out of 5

    Tim Dugan

    This book is 90% fluff/backstory and 10% AI details. Yeah sure some of the fluff was interesting, but it’s not AI. And for that matter, the AI info was mostly Bayesian statistics. Not a very broad approach. Not a very deep approach. I know they were avoiding too much math, but they went overboard

  15. 4 out of 5

    Peter

    Long winded explanation of a myopic view of AI, obsessed by analytics while ignoring pretty much everything else. Needs a good edit and new title. Not terrible though, for what it is.

  16. 5 out of 5

    Angshuman Bhattacharya

    I don't remember I have ever bought a book just because of the look of it. But this one, in Delhi airport few months ago, attracted me by the sheer fluorescent yellow cover and shining metal blue lettering; and I just bought it. The subject of AI is pretty close to my personal and professional interest and thought it wouldn't be a misfit in my library! Little did I know that it will e suchhh and intersting reading pleasure. The authors narrative style, always picking up a story from the human his I don't remember I have ever bought a book just because of the look of it. But this one, in Delhi airport few months ago, attracted me by the sheer fluorescent yellow cover and shining metal blue lettering; and I just bought it. The subject of AI is pretty close to my personal and professional interest and thought it wouldn't be a misfit in my library! Little did I know that it will e suchhh and intersting reading pleasure. The authors narrative style, always picking up a story from the human history of different ages, and then linking the same with one modern marvel of artificial intelligence solution; is a delight to any reader. For example, how he linked the great Florence Nightingale, the pioneer in modern scientific nursing, how she overcome so many social and personal obstacles to take up the cause of serving the ailing, and her fearless attitude of serving the war victims. But little did we know that she was also pioneer in enumerating the basic health parameters regularly for a patient; that help monitor the condition of the patient over time and how it helped doctors to make more wise, data driven decision. Then he links this story to the modern medicine and clinical methodologies; and extends this further to the health apps of today and tomorrow - which will monitor the vitals of a health or ailing person and predict the need for medical attention or lifestyle balancing. Each chapter is full with such wonders. One must read through all to get the big picture of AI - how it is impacting all the aspects of our lives. One small disappointment was that not all the examples he has mentioned are great AI examples or use cases, some are pretty simplistic and old by now. And there are much more advancement already taken place in many use cases which the author have missed. but this could be the case that we the industry insiders are privy to such cases and these are not common knowledge yet.

  17. 5 out of 5

    Tannistha Ganguly

    I thoroughly enjoyed reading this book. Nick & James have very successfully explained AI in a simple fashion and in the process have busted many myths and lies surrounding the concept. Even better, they have done so with humour. In doing so they have brought the subject down to the levels where non-technical people will be able to relate to it. I will highly recommend this book to everyone.

  18. 5 out of 5

    James La Vela

    I recommend this book to anyone who is even remotely interested in understanding how artificial intelligence is starting to transform business and society. The story-based writing style makes this rather intimidating topic much easier to comprehend too.

  19. 5 out of 5

    Daniel Olshansky

    AIQ Review A short and educational read explaining how Artificial Intelligence is nothing more than statistics with a ton of data on steroids. The book does a great job at raising the curtains that make AI seem so magical and mysterious. It also grounds all the misconceptions and hype around a Skynet type general AI. Computers are much better than humans at remembering things, processing large amounts of data, and doing repetitive tasks very quickly. Similarly, a hammer is much better than humans AIQ Review A short and educational read explaining how Artificial Intelligence is nothing more than statistics with a ton of data on steroids. The book does a great job at raising the curtains that make AI seem so magical and mysterious. It also grounds all the misconceptions and hype around a Skynet type general AI. Computers are much better than humans at remembering things, processing large amounts of data, and doing repetitive tasks very quickly. Similarly, a hammer is much better than humans at hitting things without being hurt or damaged. Both of these are tools that benefit humans, and the fear of being taken over by the tools we built is overhyped science fiction. As a software engineer, who works closely with machine learning scientists, and has gone through Andrew Ng’s Coursera ML course, I still got a ton of value out of this book. I would recommend it to anyone who wants to freshen up on their stats knowledge, or expand their knowledge of history. In particular, the book does a great job at correlating modern advancements and uses of ML/AI to scientific developments of the past 100 years. One of the things I hadn’t realized is that the big data, machines learning and cloud computing boom started around 2010. I was already in college and literally saw this revolution happen before my eyes! Over the past few decades, engineers/scientists in this field really made a lot of mathematical and scientific breakthroughs. However, after reading this book, my previous assumption were validated. Almost nobody does “real machine learning” nowadays. Since various frameworks (pytorch, tensorflow, keras, caffe, etc…) have commoditized the ability to build and train neural networks, feature extraction has become the most difficult part of machine learning. The process of data collection, data cleansing, data analysis, data pipeline, and deciding how to represent that data is all machine learning has really come to. Everything else is just trial and error along with throwing a ton of data (i.e. steroids) at the problem. The book does a great job at exemplifying this by referring to two letters: Big N (big number of independent data points) and Big D (a lot of detail in every data point). "It may seem like we rely on depend smart machines for everything these days, but in reality, they depend on us a lot more." ==== Some cool facts from the book that stood out ==== Moore’s Law One of the AI/ML advancement enablers in the past decade is Moore’s Law. However, it’s not just Moore’s Law in the context of processors, but also in the context of data quantity and the availability cloud compute. All of this, along with many other enables, all played a key role in AI’s hyper growth. UBI A common concern amongst many who are afraid of AI is the number of jobs (i.e. truck driving) it’ll make disappear. As a big Andrew Yang fan myself, and a big proponent of Universal Basic Income, I really connected with the authors’ similar viewpoin. I do not want technological advancement to be hindered by a fear of job loss, but am aware that it could lead to an increase in unemployment. For example, truck driving, which is the most popular job in the will most certainly be impact by the ubiquity of autonomous vehicles. I believe that as long as we put UBI policies in place, and provide the infrastructure for individuals to be retrained in a different career path, if they choose to, we can move forward without looking back. Netflix In 2007, Netflix offered $1MM to anyone who could beat their recommendation algorithm by more than 10%. A lot of teams came really close, but it took 2 years for a team to beat Netflix’s recommendation algorithm by 0.06%. Ironically, another team also managed to submit a winning solution exactly 19 minutes after the first. House of Cards was the first Netflix original, and as we all know, a great success. The reason this is the case is because Netflix had gathered a lot of data on what kind of shows that appeal to the general public, and therefore knew it was going to be a success. The authors discuss how traditional television networks waste hundreds of millions to pilot many different shows to see what sticks. Netflix’s data allows them to create successful shows with a very low probability of failure. World War 2 Abraham Wald played a key role in mathematic and statistical developments during WWII. The planes that were returning from battle had a lot of holes in the fuselage, so the generals thought of adding more armor to that section of the plane. However, Abraham correctly identified that we should make note of the planes that do not return, which were likely shot in the engine, and that is where more armor should be added. The key point of comparison between the bombers returning and Netflix’s recommendation system is that both of these systems use conditional probability with large data sets and latent features. Abraham Wald didn’t know where the non-returning planes were getting hit, and Netflix doesn’t know viewer’s opinions on most movies because most users do not watch most movies. After WWII, the US spent $17B in the early 60s to install microphones across the whole Atlantic. How cool is that! Asia - Toilet Paper Theft Individuals in Japan and China used to steal toilet paper from public spaces. Authorities tried to limit the toilet paper “per visit” to 6 sheets. While this slowed things down, the thieves were just walking around and returning to the same booths periodically. The final solution was to use facial recognition and only dispense toilet paper per person every X hours. Physicists Hubble used the pulsating star theory, and collected a lot of data, to prove that Andromeda was located in a different Galaxy, thereby disproving the “single Galaxy theory”. Elina Berglund, who helped discover the Higgs boson, decided to use big data to kill the birth control pill. A smartphone app, along with input from the user concerning body temperature and menstrual cycle became highly accurate at determining when a woman is ovulating. Moravec's paradox From wikipedia: “It is comparatively easy to make computers exhibit adult level performance on intelligence tests or playing checkers, and difficult or impossible to give them the skills of a one-year-old when it comes to perception and mobility” Bayes' theorem At the basis of all probability analyses is Baye’s theorem, which is actually quite straight forward:  the probability that an event A occurs given that another event B has already occurred is equal to the probability that the event B occurs given that A has already occurred multiplied by the probability of occurrence of event A and divided by the probability of occurrence of event B. SLAM At the time of writing the book, GPS is only accurate up to 5m. This is good for most uses cases but not for autonomous vehicles, drones, etc… This is where SLAM (simultaneous localization and mapping) prevails. It does introspection by collecting data from various censors (color, grayscale, IMUs, depth, etc..) and exterminates (i.e. predicts) the device’s location/pose. Grace Hopper I had always heard of the Grace Hopper conference for women, but never realized how influential she was in the world of computer science. Hopper grew up in a military family and managed to become one of the first female naval officers. She wars the first American computer expert after she was assigned to work on the Mark I at Harvard. After that, she started working on the UNIVAC, which was the first large scale electronic computer adopted by various corporations such as ADP or Dupont for database operations. She was a pioneer in NLP, computer compilers, and played a key role in making sure that the American government adapt to using modern computer systems. Coin Clipping The book went into great depth about coin clipping in the 16th century. Due to the variability in coin weight, it was easy to clip “excess” coin on heavy coins and make a little bit of extra money. It took a while for Isaac Newton to develop the method of least squares, which was followed by the great recoinage, to avoid this sort of theft in the future. Cool Applications of AI - There is a smart knife that tells the surgeons whether they’re cutting/burning healthy or cancerous tissues. This is done by sending the burning smoke to a mass spectrometer, analyzing the data and feeding it into a prediction rule This takes 3 seconds and has a 100% accuracy rate. - Ali baba ships products to nearby warhorses before items are purchased to guarantee faster delivery, since they can predict which products are going to be bought when and where! Florence Nightingale Florence nightingale was a nurse and data scientist. In the 1800s, she was driven by both maths and helping people. She used her skills and interests to built an efficient data driven hospitals/camps to help as many injured soldiers as possible. She was the first woman elected to the UK data analytics organization, and was truly a remarkable and influential individual. Compounding If something has a 0.9 chance of happening, the compounding rule states that the chance of it happen 10 times is 0.9^10. However, the compunding rule does not apply to population averages where there are a lot of lurking variables that need to be taken into consideration, and often leads to conclusion bias. For example, if 90% of the population has a tails only coin, then 90% of all flips will always result in tails. However, the compounding rule will apply ONLY IF all the coins are fair.

  20. 5 out of 5

    Chris

    They have changed the title from the previous "How Artificial Intelligence Works and How We Can Harness Its Power for Good." The new title is better because the old subtitle claims too much--this book won't tell you how AI works. But it does give you a sense of how huge data sets and blazing fast computers are starting to come close to doing things that seemed beyond the reach of computers in the not at all distant past. I found the discussion of speech recognition to be particularly interesting They have changed the title from the previous "How Artificial Intelligence Works and How We Can Harness Its Power for Good." The new title is better because the old subtitle claims too much--this book won't tell you how AI works. But it does give you a sense of how huge data sets and blazing fast computers are starting to come close to doing things that seemed beyond the reach of computers in the not at all distant past. I found the discussion of speech recognition to be particularly interesting. In the past scientists had tried to teach computers to "understand" or at least transcribe the language they hear based on a huge dictionary of rules for analyzing human speech. What we might call a grammar. Even with advances in computing power, this approach never really worked. Recently, though, a breakthrough has led to great advances. By the use of Bayesian probabilities and the analysis of huge oceans of language data, computers have been taught to recognize speech based on the probabilities of particular words occurring together in any given utterance. Once the words are converted to a set of probabilities based on collocations found in the emerging language data available now thanks to the WWW, the computers have become increasingly good at taking dictation. This is just one of the AI anecdotes told in the book, giving an interesting glimpse into the world of AI. If you have an interest in AI and you are not looking for a technical discussion of how it works, this book won't disappoint.

  21. 5 out of 5

    Jason Nash

    A good book lots of good side stories like Florence Nightingale being a statistician. It’s also the first time I’ve been able to understand speech vectors. I would’ve given it more stars except there were a load of maths examples which although we are very well explained would’ve benefited from a clearer use of diagrams describing the equations.

  22. 4 out of 5

    Jim Connelly

    Great introduction to the understanding of what artificial intelligence is and the enormous potential it has for making our lives better and smarter. Written for the non-scientist or mathematician this book explains what AI is, its evolution, where it is now and what it offers us going forward. For those with vague fears of computers taking over the world AIQ frames the reality of human control and benefits vs. pitfalls. Recommended.

  23. 4 out of 5

    Curt

    Easy-to-read overview of algorithms and data science concepts, enlivened with stories of the historic roots of various techniques. I especially liked the chapter on natural language processing. A few equations, just enough to get the idea across.

  24. 4 out of 5

    James Mundy

    A nice introduction to some of the concepts behind natural language processing, recommendation algorithms, image recognition and other places where machine learning and data are changing the world.

  25. 5 out of 5

    Bari Dzomba

    Dumber down but good book. Healthcare examples right on the mark.

  26. 4 out of 5

    Mal Warwick

    Most of the books about artificial intelligence highlight such things as self-driving cars and facial recognition—the brilliant innovations in hardware and software by the engineers and coders who build the stuff and make it work. They dwell variously on the field’s potential and its impact on our lives today. This book, by two academic data scientists, instead drills down into the underlying logic of AI, the fuel that powers it, and the statistical rules that govern its operations. Because arti Most of the books about artificial intelligence highlight such things as self-driving cars and facial recognition—the brilliant innovations in hardware and software by the engineers and coders who build the stuff and make it work. They dwell variously on the field’s potential and its impact on our lives today. This book, by two academic data scientists, instead drills down into the underlying logic of AI, the fuel that powers it, and the statistical rules that govern its operations. Because artificial intelligence is all about statistical probabilities. And AIQ by Nick Polson and James Scott truly fulfills the promise of its subtitle: How Artificial Intelligence Works and How We Can Harness Its Power for a Better World. The statistical rules behind how artificial intelligence works Surprised that statistics should be central to artificial intelligence? Polson and Scott explain. “Open up a blank email on your phone and try dictating a test phrase: ‘The weather report calls for rain, whether or not the reigning queen has an umbrella.’ If you’re a native English speaker and your phone runs iOS or Android, it will almost surely get the sentence right, without confusing weather/whether or rain/reign . . . The software knows that ‘whether’ and ‘reign’ are statistically more likely in some contexts, while ‘weather’ and ‘rain’ are more likely in others. This isn’t because your phone somehow understands the meaning of words. There’s no meaning involved, just a rich set of context-specific probabilities for basically every English word and phrase ever uttered on the internet.” And how does it know all that? Because the superfast computers on which the software runs have surveyed “basically every English word and phrase ever uttered on the internet.” (Yes, they can do that.) The people who laid the foundation for artificial intelligence One of the reasons this book reads so well is that the authors infuse each chapter with the story of an individual whose work—sometimes centuries ago—laid the foundation for what we now call artificial intelligence. And what people they are! The group includes some who are truly famous (Sir Isaac Newton and nurse-administrator Florence Nightingale) or near-famous (computer scientist Grace Hopper and baseball great Joe Dimaggio) as well as those you’ve probably never heard of (a Hungarian-American statistician named Abraham Wald and astronomer Henrietta Leavitt). The surprising work of Florence Nightingale Every one of these stories is fascinating, but the most in-depth of them features Florence Nightingale. “It turns out that when she wasn’t caring for soldiers, Nightingale was also a skilled data scientist who successfully convinced hospitals that they could improve health care using statistics. In fact, no other data scientist in history can claim to have saved so many lives as Florence Nightingale. In 1859, in honor of these achievements, she became the first woman ever elected to the U.K.’s Royal Statistical Society.” Surprising, isn’t it? (If not, go to the head of the class.) It’s happening now, and it’s only the beginning For the most part, in their effort to explain how artificial intelligence works, Polson and Scott dwell on its history and underlying logic. But they venture into the future as well. “Today?” they write. “Self-driving cars are just the start. Don’t forget about autonomous flying taxis, like the ones the government of Dubai has been testing since September of 2017. Or the autonomous iron mine run by Rio Tinto, in the middle of the Australian outback. Or the autonomous shipping terminal at the Port of Qingdao, in China—six enormous berths spanning two kilometers of coastline, 5.2 million shipping containers a year, hundreds of robot trucks and cranes, and nobody at the wheel.” So, is this a good thing, or bad? How many jobs will be lost through such projects? Polson and Scott don’t spend time on the question. You’ll need to read another book for perspective on the matter. This book will just help you understand how very logical it all is.

  27. 4 out of 5

    William

    If you pick up a book on technology, it is likely to be outdated if it is more than a year or two old. AIQ seems to be written in a way that will keep the content relevant for future readers. The authors market the book to everyone, regardless of prior knowledge of math and statistics. Largely, I agree. At a bare minimum you have to understand the basics of probability. If you don't understand how the probability of flipping a two-sided coin and getting the same result twice in a row is 25%, upo If you pick up a book on technology, it is likely to be outdated if it is more than a year or two old. AIQ seems to be written in a way that will keep the content relevant for future readers. The authors market the book to everyone, regardless of prior knowledge of math and statistics. Largely, I agree. At a bare minimum you have to understand the basics of probability. If you don't understand how the probability of flipping a two-sided coin and getting the same result twice in a row is 25%, upon being told that flipping a two-sided coin and getting any particular result is 50%, you may have trouble enjoying some of the book. The major issue that might turn people off in the early part of the book is the use of probability notation, i.e., P(B|A) = The probability of B given A. They don't use the "phrasing" enough before going purely notation-based explanations. I can see how someone would struggle if they didn't fully grasp what the notation meant. I would have given this book only three stars, because while it explained AI at a high level, it glossed over the technical side of it too much. It earned the fourth star by the inclusion of historical vignettes in each chapter. The extra historical context made it a fun and easy read. Bottom line: you'll learn enough about AI to have a conversation about its societal impact with friends, have a basic understanding of why a new startup is important, or to have a starting point for public policy concerns. It is not a text book.

  28. 5 out of 5

    Klaus-Michael Lux

    Apparently, this book was released in some markets with a different subtitle, something I only became aware of after I had started reading. In my opinion, the "how AI works and how we can harness its power" heading fits the contents of this book much better than the "how people and machines are smarter together", which had misled me to believe hybrid AI and human-computer interaction would get some coverage. They don't at all. Rather, this is a decently written introductory text to a number of A Apparently, this book was released in some markets with a different subtitle, something I only became aware of after I had started reading. In my opinion, the "how AI works and how we can harness its power" heading fits the contents of this book much better than the "how people and machines are smarter together", which had misled me to believe hybrid AI and human-computer interaction would get some coverage. They don't at all. Rather, this is a decently written introductory text to a number of AI applications that have become mainstream in recent years, with the unique distinguishing factor being its use of historical figures to illustrate particular points. Consequently, the reader for example gets to know how Isaac Newton worked for the royal mint and did not notice a rather essential law about sampling larger sets of coins inducing the boundaries for the observed error in their weight to go down. While entertaining and filled with interesting historical anecdotes, this book has little to offer to people who already have some familiarity with AI. I'm aware of the whole trope of editors being unfaithful to the meaning and intention of the texts they write headlines for, but I still feel that book-length treatments should get a little more love in this area. Pick this up for a relative or friend who's new to AI or as a light read for an airplane journey, but don't expect major new insights.

  29. 5 out of 5

    Yuriy Stasyuk

    This was a quick and easy introduction to the state of artificial intelligence and data science today. Unlike modern media sensationalism and allusion to skynet, this shows a that most AI is mathematical modeling and predictions based on large data sets, its not about sentience or consciousness. There is math in this book, but its lucidly explained without resorting to pedantic mathematical explanations. There is a great explanation of Bayes theorem using searches for lost submarines. The key ta This was a quick and easy introduction to the state of artificial intelligence and data science today. Unlike modern media sensationalism and allusion to skynet, this shows a that most AI is mathematical modeling and predictions based on large data sets, its not about sentience or consciousness. There is math in this book, but its lucidly explained without resorting to pedantic mathematical explanations. There is a great explanation of Bayes theorem using searches for lost submarines. The key takeaway is that consulting prior probability and adjusting this in light of new evidence, helps us come up with more accurate conclusions. This idea is repeated throughout the book, from stories about Issac Newton missing simple coin size averaging issie during his stint as treasurer to stories of missing kidney decline by failing to draw a trend line. The assumptions you bring into artificial intelligence are pivotal to it's product. Another memorable anecdote is about an army tank identification algorithm. Thousands of images of an empty forest were compared to images containing hidden tanks. The algorithm performed perfectly on the sample dataset but failed when deployed in the real world. Why? Because all the forest images were taken on a cloudy day, while tank pictures were taken on a sunny day. Three are biases everywhere and our assumptions are key.

  30. 5 out of 5

    ML O'Brien

    Whilst a tad heavy going in parts on the maths for a holiday read there was no doubt that the historical characters of Florence Nightingale, Abraham Wald, Isaac Newton, Henrietta Leavitt & Grace Hopper and their stories, gave this book some great historical reference and proof points. The books case studies show us how “thinking a bit more like a machine- that is, making decisions using data and the riles of probability” can empower us all. As a enthusiast for autonomous vehicles I loved the opp Whilst a tad heavy going in parts on the maths for a holiday read there was no doubt that the historical characters of Florence Nightingale, Abraham Wald, Isaac Newton, Henrietta Leavitt & Grace Hopper and their stories, gave this book some great historical reference and proof points. The books case studies show us how “thinking a bit more like a machine- that is, making decisions using data and the riles of probability” can empower us all. As a enthusiast for autonomous vehicles I loved the opportunity to reflect on the current issues the engineers are trying to overcome (bicycles, snow & kangaroos) and the question was posed: “If you had to put a loved one in a taxi, would you rather have it driven by a randomly sampled 16 year old with a drivers licence, or by one of Waymo’s (Googles autonomous) cars?” A large chapter of the book was dedicated to AI in the health sector and the potential it has, if only we had global standardised data sets that would allow predictive AI to do its magic and a way to overcome the fear, the privacy, the cultural and the regulatory issues. When there is so much potential and the technology already exists we can ask why it isn’t already and the authors response is, “ Cancer and kidney disease have no nationality, but there’s a word for bureaucracy in every language”.

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