Just as with the internet and the Interstate Highway System, military technology will lead the way in how IoT sensor data is collected and analyzed.

The 21st-century battlefield does not suffer from a shortage of sensors spread across soldier wearables, vehicles, drones, video cameras, spectrum, signal and radio sensors, cyber sensors and scores of other devices that comprise the Internet of Battlefield Things.

More sensors mean more data – too much data – which limits the DoD’s ability to turn that information into actionable intelligence in a timely fashion. But AI is poised to change that equation by shifting the burden from human to machine so that only the most relevant and timely data reaches those who need it.

Swift is headed towards a decidedly heavy AI future.

The core development team behind Apple’s Swift programming language has set priorities including refining the language for use in machine learning.

Ambitions in the machine learning space are part of plans to invest in “user-empowering directions” for the language. Apple is not the only company with machine learning ambitions for Swift; Google has integrated Swift with the TensorFlow machine learning library in a project called Swift for TensorFlow. And the Swift community has created Swift Numerics, a library that can be used for machine learning.