Hitting the Books: Why AI won't be taking our cosmology jobs
Consider the Vera Rubin Observatory, a giant telescope built atop a Chilean mountain and designed to repeatedly photograph the sky over the coming decade. It will not just build a static picture: it will particularly be searching for objects that move , or change brightness , as part of our ongoing campaign to understand the ever-changing cosmos. Machine learning can be trained to spot these objects, allowing them to be studied with other, more specialized telescopes.
To understand the importance of context and understanding in science, consider the case of the OPERA experiment which in 2011 seemingly determined that neutrinos travel faster than the speed of light. The claim is close to a physics blasphemy, because relativity would have to be rewritten; the speed limit is integral to its formulation. Given the enormous weight of experimental evidence that supports relativity, casting doubt on its foundations is not a step to be taken lightly.
The human elements of science will not be replicated by machines unless they can integrate their flexible data processing with a broader corpus of knowledge. There is an explosion of different approaches toward this goal, driven in part by the commercial need for computer intelligences to explain their decisions.
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