Download PDF Outnumbered: Exploring the Algorithms That Control Our Lives

Download PDF Outnumbered: Exploring the Algorithms That Control Our Lives

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Outnumbered: Exploring the Algorithms That Control Our Lives

Download PDF Outnumbered: Exploring the Algorithms That Control Our Lives

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Audible Audiobook

Listening Length: 8 hours and 1 minute

Program Type: Audiobook

Version: Unabridged

Publisher: Audible Studios for Bloomsbury

Audible.com Release Date: May 17, 2018

Whispersync for Voice: Ready

Language: English, English

ASIN: B07CK9WL67

Amazon Best Sellers Rank:

The book reviews mathematical algorithms that are implemented in various applications, such as Facebook, and criticizes the common fears regarding Artificial Intelligence. The arguments are clear and interesting.

Great book and great service!!

Modeling human behavior is hard - but it is increasingly important for the layperson to understand how and why algorithms impact our lives. This book is a very readable analysis of how data science is used to influence our behavior - for better or worse. It describes the statistical techniques that are used and provides real-life examples of how those techniques work.The sub-title of this book really tells the story - “From Facebook and Google to fake news and filter-bubbles - the algorithms that control our lives." Most people understand that today’s technology behemoths use data science techniques to better understand their users and customize their offerings but it generally stops there. This book attempts to explain to the layman more specifically how companies use mathematics and statistics to better understand us and make predictions as to our behavior.At a more detailed level, the book is divided into 3 parts.* Analyzing Us * This section is mainly about Facebook and Google and how they use different data science techniques to better understand their users and deliver meaningful content. For example: * Facebook - the importance of understanding how social media companies collect information about our personalities and use this data to predict our future behavior. It starts by explaining principal component analysis - a technique used by Facebook to categorize our personalities, values and socio-economic status. While most people tend to think of themselves as multi-dimensional; psychologists have boiled down human traits to a mere 5 elements - openness, conscientiousness, extroversion, agreeableness and neuroticism. Facebook can then analyze our posts, likes and pictures to better understand us across these 5 dimensions and then pitch products to us that are more likely to appeal. * Cambridge Analytica - the importance of understanding probabilities and the inherent limitations of algorithmic predictions. This chapter is fascinating and sets the stage by pointing out that the algorithms underpinning data science are probabilistic - they calculate a number that is proportional to the probability of a specific fact being true about a person. He goes on to explain how regression models use data fitted to one group of people to infer the preferences of others. In the old days (1990's) these regression models could use inputs such as age, gender, class etc to determine the probability that a person would vote for a particular political party. However nowadays, modern data analytics companies have access to an almost limitless variety of data. * Northpointe - the importance of data bias and false positives/negatives as it relates to criminal recidivism * Spotify -- the importance of "data alchemy”, or the need for intuition and expertise by the human data modeler to more fully understand our needs.Sumpter concludes this section with a discussion of Julia Dressel, a student who conducted an experiment that determined that, when all is said and done, human judgment is just as sound as algorithmic predictions, although computers can operate at scale and are faster and cheaper than people. While this may be true when looking at the big picture, I suspect that algorithmic predictions will outperform humans at the margin where a more granular analysis of variables is important.* Influencing Us * This section moves the discussion forward by outlining how data science has become more sophisticated in attempting to better understand us as humans and thereby influence our decisions and choices, such as: * FiveThirtyEight and the booming election forecasting business. There is an inherent challenge in creating probabilistic models to forecast binary events - such as elections or sporting events, although multiple and/or braver predictions (>95% probability) are more meaningful. Further, such predictions must attempt to adjust for the inherent shortcomings in poll data - the primary input in election forecasting, unfortunately, it is very difficult to incorporate the wisdom of the crowd into such models. Statistical models need to understand our sometimes subjective and erratic human behavior otherwise, these models typically do not perform much better than human judgment. * One way of achieving this understanding is to move beyond simply categorizing us to trying to understand and influence us. Amazon recognized this early on, and using a form of collaborative filtering is able to make recommendations as to what we may like. Knowing that people similar to us “also liked” some other product provides the consumer with a level of confidence in a prospective purchase. * Social media has also become a prevalent way to influence our behavior but it does so by amplifying our pre-existing biases. People tend to click on links that take them to new sites containing similar thoughts and ideas - in effect an Echo chamber forms where our beliefs are reinforced. Likewise, filter bubbles use an algorithmic approach to steer us towards material it believes we will appreciate. That said, Sumpter notes that the media tends to exaggerate and sensationalize the influence these algorithms have on our behavior - our real-life interactions with friends and family are more important determinants of our behavior.* Becoming UsSumpter notes that the biggest challenge for the current state of algorithms used by the Facebook's and Google’s of the world is that they do not properly understand the meaning of the information we are sharing with each other. Consequently, these very same companies are attempting to solve this problem by chasing the holy grail - artificial intelligence. * To achieve this, the algorithms must understand context and analogies and to do this they must be able to analyze text, such as the GloVe (Global Vector) algorithm discussed by Sumpter that maps words used together near each other to find meaning and is thereby able to auto-complete our text searches on Google. * Neural networks, which are designed to mimic the way our brains function, have long been used to recognize patterns. However, more recently developed convolutional neural networks can solve problems without being told which problem they are solving. Computers have been trained to use brute force to play and win games like chess and Jeopardy, but more recently, the folks at Google have been able to learn games from the ground up, through trial and error and understanding the objective function of the game * The human body contains 37 trillion cells and the brain 86 billion neurons - that represents a lot of catching up for the AI scientists. * Smart people disagree on the potential perils of AI - for every foreboding Elon Musk or Stephen Hawking there is a far more ambivalent Bill Gates. So where should the average Joe come down on this issue? Maybe they should do as Sumpter suggests and check out Mark Zuckerberg's AI robot (Jarvis) video to see what he can do. IMHO, it's not much more than a slightly smarter home than we have available to us today. * So, is AI’s role in our lives to be relegated to a smart home assistant and a champion game player or will it evolve to a form of general AI? Many smart minds are working on important problems but there is still more hype and hope than results. Sumpter takes a somewhat more skeptical view and I suspect he may be right. Computers and algorithms may be able to do more and more but at what point will the market rebel - I for one am all for AI but I wouldn’t be caught dead in a self-driving car (at least, not yet!)

Where most writers rely on hyperbole and fear around issues like big data and algorithmic decision-making, Sumpter writes clearly, crisply, and with a level head. He does an amazing job of walking you through just enough of the math so that you can understand how data is used -- which then helps you understand how algorithms are good at doing certain things and very limited at doing others. I follow these issues pretty closely for work and was happy to find someone that could address the details and, more importantly, why they matter.

So good. I borrowed it from the library and now just bought my own copy...it’s the kind of book that one can’t help but mark up with notes and highlighting. Not only is it fascinating, but it also gives a lay person a good peek at how these algorithms work. The author is masterful at describing these sorts of complexities in a very easy-to-digest way, without boring the reader with technical jargon.

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Outnumbered: Exploring the Algorithms That Control Our Lives PDF

Outnumbered: Exploring the Algorithms That Control Our Lives PDF