In counting craters to determine age on other planets, AI does not do a good job
Researchers from the Southwest Research Institute (SwRI) have found that all eight AI-generated lunar crater catalogs failed to meet the standards achieved by human catalogs.
Astronomers use crater counts on other planets to roughly determine the age of the surface.The more craters the older the surface. The work is however tedious and time-consuming, and it was hoped that artificial intelligence (AI) could speed up the process and make it more efficient. Unfortunately, this new study [pdf] shows that AI is not ready for prime time. From the abstract:
In this work, we compare eight crater databases using several standardized metric sets, including a tight tolerance based on manual researcher repeatability. Of the six databases that quote metrics, we find that under these strictest criteria, practically all databases return worse metrics—some dropping by factors of >10. When we resolve these metrics in diameter space, we find that most databases do well in some diameter ranges and poorly in others, meaning that single-value overall metrics can be misleading.
Our work shows that the current state of machine learning–based AI still has a long way to go before it can be relied upon to create a database comparable in quality with manual efforts. We recommend researchers examine any database they plan to use and independently determine if it is of sufficient quality for their application.
The researchers are not opposed to using AI for this purpose. They are simply warning others that it is not yet capable of doing what many promise.
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