Summary of “How Artificial Intelligence Is Changing Science”

“The approach is to say, ‘I think I know what the underlying physical laws are that give rise to everything that I see in the system.’ So I have a recipe for star formation, I have a recipe for how dark matter behaves, and so on. I put all of my hypotheses in there, and I let the simulation run. And then I ask: Does that look like reality?” What he’s done with generative modeling, he said, is “In some sense, exactly the opposite of a simulation. We don’t know anything; we don’t want to assume anything. We want the data itself to tell us what might be going on.”
The apparent success of generative modeling in a study like this obviously doesn’t mean that astronomers and graduate students have been made redundant – but it appears to represent a shift in the degree to which learning about astrophysical objects and processes can be achieved by an artificial system that has little more at its electronic fingertips than a vast pool of data.
“I just think we as a community are becoming far more sophisticated about how we use the data. In particular, we are getting much better at comparing data to data. But in my view, my work is still squarely in the observational mode.”
These systems can do all the tedious grunt work, he said, leaving you “To do the cool, interesting science on your own.”
Whether Schawinski is right in claiming that he’s found a “Third way” of doing science, or whether, as Hogg says, it’s merely traditional observation and data analysis “On steroids,” it’s clear AI is changing the flavor of scientific discovery, and it’s certainly accelerating it.
Perhaps most controversial is the question of how much information can be gleaned from data alone – a pressing question in the age of stupendously large piles of it.
In The Book of Why, the computer scientist Judea Pearl and the science writer Dana Mackenzie assert that data are “Profoundly dumb.” Questions about causality “Can never be answered from data alone,” they write.
“Anytime you see a paper or a study that analyzes the data in a model-free way, you can be certain that the output of the study will merely summarize, and perhaps transform, but not interpret the data.” Schawinski sympathizes with Pearl’s position, but he described the idea of working with “Data alone” as “a bit of a straw man.” He’s never claimed to deduce cause and effect that way, he said.

The orginal article.