AI Transforms the Way We Do Science

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AI Transforms the Way We Do Science

As we explained in Ride the Wave, the true magic of the coming phase of the Fifth Techno-Economic Revolution lies in mankind’s growing ability to qual- itatively and quantitatively enhance almost every aspect of life and business using digital technology. Nowhere is this “more true” than in scientific re-search, where better, faster, and cheaper ways to conduct every aspect of research are already creat- ing a self-re-enforcing virtuous cycle. More productive research leads quickly and cheaply to important new discoveries, which increase wealth, enabling society to devote more resources to research.

Even better, digital research is enabling us to do things that could never have been done in the past, regardless of the amount of traditional resources al- located. And, at the same time, it’s enabling us to in- vent solutions that benefit the wealthiest among us today, and even the poorest within just a few years.

Never before, has discovery been moving so rapidly and been so accessible on a global basis. Because of its pace, scientists themselves are frequently only aware of the revolution within their own scientific research specialty. And very few managers or policy-makers appreciate the enormous scope of the digital revolution in science which is still in its infancy.


To understand this revolution, it’s necessary to understand the five steps of the scientific discovery cycle and consider how digitization can enhance the process at each step.

Step One: Explore the scientific literature. Here the never-ending task is to identify the relevant scientific papers in a sea of millions, while tracking new topics as they emerge.

Step Two: Design experiments. Here the challenge is to formulate hypotheses and determine how they can be tested. Like business strategy, experimental design determines the execution, investment, and metrics guiding the rest of the study. The key is to find the right trade-off between exploration of new ground and exploitation of well-understood phenomena.

Step Three: Run experiments. Keep track of mil- lions of data points and their relationships. In the case of the life sciences, for instance, thousands of tiny tubes containing experiments on various molecules and cells must be meticulously monitored over precisely determined time periods, while avoiding contamination. Errors at this stage, can lead to career-ending consequences.

Step Four: Interpret the Data...

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