How does it work?

 Kouzou Sakai for Quanta Magazine

AI is everywhere, neural nets pull in data from the analogue perspective to serve it up to computers able to crunch the input digitally to come up with a right answer to question "A" with the caveat we don't know how the AI in question works. With a machine scientist, the purpose of the system is to find relationships in the data presented and come up with equations to explain how a particular event functions. In the case of the QM article, the question asked was which factors might trigger cell division?

A revolution is nigh ... with neural nets used in conjunction with machine scientists.

Researchers say we’re on the cusp of “GoPro physics,” where a camera can point at an event and an algorithm can identify the underlying physics equation.

Channeling the past ...

Occasionally physicists arrive at grand truths through pure reasoning, as when Albert Einstein intuited the pliability of space and time by imagining a light beam from another light beam’s perspective. More often, though, theories are born from marathon data-crunching sessions. After the 16th-century astronomer Tycho Brahe passed away, Johannes Kepler got his hands on the celestial observations in Brahe’s notebooks. It took Kepler four years to determine that Mars traces an ellipse through the sky rather than the dozens of other egglike shapes he considered. He followed up this “first law” with two more relationships uncovered through brute-force calculations. These regularities would later point Isaac Newton toward his law of universal gravitation.

Into the present ...

The goal of symbolic regression is to speed up such Keplerian trial and error, scanning the countless ways of linking variables with basic mathematical operations to find the equation that most accurately predicts a system’s behavior.

In essence ...

Deep knowledge goes real time and becomes ubiquitous and embedded.

Merrill Sherman/Quanta Magazine