Digital Transformation: Survive and Thrive in an Era of Mass Extinction

(or millions) of historic cases while adjusting the relative importance (weights) of each of the features until it can infer the output (i.e., engine failure) as accurately as possible.8 The result of a trained machine learning algorithm is a set of weights that can be used to infer the proper output for any input.

Robotics and artificial intelligence systems will not only be used to replace human tasks, but to augment their skills (for example, surgeons working with advanced robotics systems to perform operations). This, too, will provide challenges for businesses, which will need to reskill employees so they can work effectively with new technology. Reskill

And what is more valuable: Your IP around powertrain design, or your AI-based self-driving algorithms fed by real-time telemetry and usage data generated by the vehicles you make?

Feature engineering relies on experienced data scientists working collaboratively with subject matter experts to identify the significant data and data representations or features (e.g., engine temperature differential, flight hours) that influence an outcome (in this case, engine failure). The complexity comes from choosing among the hundreds or t

The machine learning algorithm is trained by iterating over thousands

For example, an application may use Microsoft Azure for storage, AWS for compute, IBM Watson for deep learning, and Google Cloud for image recognition.

This means training people not only in technical skills, like coding, but also in skills that will increasingly be needed in the digital age to complement the work of machines—creativity, teamwork, and problem-solving.

The implications are significant. An experienced mechanical engineer is no longer required to predict engine failures. An experienced physician is no longer required to predict the onset of diabetes in a patient. A geological engineer is no longer required to predict oil well placement for optimal production. These can all be learned from data by t