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The Age of Prediction

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The Age of Prediction

Algorithms, AI, and the Shifting Shadows of Risk

MIT Press,

15 min read
8 take-aways
Audio & text

What's inside?

The emergence of big data and increasingly accurate machine learning prediction models are disrupting industries and life as you know it, write Igor Tulchinsky and Christopher Mason – learn about what to expect in the “Age of Prediction.”

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Editorial Rating

9

Qualities

  • Analytical
  • Eye Opening
  • Visionary

Recommendation

Humanity is on the cusp of a new age, write Igor Tulchinsky and Christopher Mason – “The Age of Prediction.” AI algorithms and the exponential explosion of big data are giving scientists across myriad industries the means to harness the power of increasingly accurate predictions, reducing uncertainty and many of the associated risks. However, new risks are emerging, such as the existential threat of autonomous AI-enabled weapons systems and threats to democracy. Drawing on their backgrounds in quantitative predictions in finance and genomics, Tulchinsky and Mason share both disquieting and hopeful insights on how machine learning and predictive algorithms could radically transform humanity’s future.

Summary

In “The Age of Prediction,” scientists are reducing uncertainty across industries.

Society is moving into a new era, “the Age of Prediction,” in which billions of algorithms enable people to identify future events before they happen, reducing uncertainty and risk. Whether mapping the human genome or analyzing financial market data, an exponential increase in data and the emergence of technologies such as AI are giving scientists new tools to identify patterns and make increasingly accurate predictions. While the COVID-19 pandemic was by no means a “triumph of prediction,” humanity responded by broadening predictive possibilities and perspectives in many ways, harnessing the potential of big data. For example, partnering with Pfizer, BioNTech used machine-learning trained algorithms to create predictions, enabling it to rapidly create a COVID-19 vaccine, rolling out a US Food and Drug Administration-approved vaccine in just nine months.

You can visualize the predictive modeling approaches taken to anticipate COVID-19’s effects much like those of a “layer cake”: The top layer of the “cake” is comprised of epidemic...

About the Authors

Igor Tulchinsky is the founder, CEO and chairman of a global quantitative asset management firm, WorldQuant, as well as an investor, venture capitalist, philanthropist, entrepreneur and author. Christopher E. Mason is a genomics, physiology and biophysics professor at Weill Cornell Medicine, and founding director of the WorldQuant Initiative for Quantitative Prediction.

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