The Naked Future | Patrick Tucker

Summary of: The Naked Future: What Happens in a World That Anticipates Your Every Move?
By: Patrick Tucker

Introduction

Embark on a captivating journey into ‘The Naked Future,’ where big data makes everything about you known and predicts your every move. In this book summary, we will explore the ever-growing world of telemetry enabling data sharing, the beneficial and controversial aspects of big data prediction, and the increasing applications of RFID tags in everyday objects. Encompassing diverse topics such as health, climate change, education, and even love, author Patrick Tucker sheds light on a future that intricately intertwines data and our lives. Discover how the power of prediction can transform industries, enhance public health, and change the way we understand ourselves and the world.

The Power of Big Data

Big data has transformed from being too vast or disparate to being utilized by corporations to predict consumer behavior and preferences. However, this has led to a general suspicion of big data, with concerns of overreach and potential misuse. Despite this, changes in big data have the power to empower consumers to push back against data users and balance the scales. It is important to recognize the potential for both negative and positive applications of big data and work towards ensuring that this technology is used for the betterment of society.

Big Data and Personal Predictions

Telemetry and RFID tags are allowing data sharing between devices and generating more accurate predictions about various aspects of our lives. As RFID tags become more commonplace, the predictions and modifications by analysts will increase. The benefits of this technology range from broad social advantages such as earthquake warnings to specific situations where it can aid first responders. Ultimately, the amount of data generated by this technology is becoming smaller and more personal, fitting inside a single push-notification alerting individuals in real-time.

The Power of Self-Tracking Technology

The “Quantified Self” or “QS” movement encourages people to track their patterns and preferences by monitoring their actions using technology. Self-monitoring has been around for centuries, with Benjamin Franklin being an early adopter. Self-tracking used to be a lot of work, but technology makes it possible for everyone. Big data is also used in the medical field to predict diseases and improve public health. Doctors apply big data to make predictions about group and individual health, and the use of “field data broadcast in real-time” fights threats like pandemics. The “Google Flu Trends” tracker even predicts flu trends before the CDC reports its findings. As computing power grows, professionals get better at modeling the paths viruses will take. However, there are also risks associated with sharing personal data.

The Evolution and Future of Weather Predictions

The book discusses the journey from John von Neumann’s ambition to predict weather with an automatic computer to the present-day methods of weather predictions. Now, various instruments such as stopwatches, calendars, and temperature recordings are used. The debate on “What can and cannot be modeled?” remains a fundamental question in climate science. Despite a consensus among scientists that man-made climate change is driving temperatures up by 4 to 6 degrees Fahrenheit, less certain information is available to the general public. While the US government has cut funding for gathering environmental data, private companies like The Climate Corporation offer climate insurance to vulnerable businesses and pay claims faster than government disaster insurance. The future of weather forecasting seems bright as businesses and organizations improve collaborative dynamics by measuring weather patterns accurately.

Improving Movie Success

By using big data, a marketing professor was able to create an algorithm, BART-QL, that predicts a movie’s performance. This algorithm helps predict certain changes that film makers can make to improve their films’ commercial prospects.

The film industry constantly seeks to make movies more commercially successful. Netflix, specifically, offered a $1 million prize to those who could improve the algorithm by 10% that recommends movies to its customers. Marketing professor Jehoshua Eliashberg decided to tackle the question of quality directly. He built a model that integrates critical perspectives with technical and specific issues, such as determining how long flashbacks should last or how many interior scenes people prefer. Eliashberg analyzed more than 200 scripts from a decade of movies to create an algorithm called the “BART-QL” approach – or Bayesian Additive Regression Tree for Quasi-Linear. This algorithm is now used to predict how well a film will do as it is and allows filmmakers to forecast how certain changes will improve their films’ commercial prospects.

The BART-QL approach uses big data to predict a film’s success, giving movie makers more control over the commercial success of their movies. This change not only revolutionized the way we think about the success of movies but it also demonstrates how big data can be used to solve complex problems in the entertainment industry.

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