AI and machine learning technology have spread rapidly as a scientific tool, enabling discoveries in fields as diverse as animal behaviour, nuclear physics, and exoplanet hunting. As its capabilities expand, artificial intelligence may soon change not just how scientists work, but how they think.
The impact of machine learning on scientific inquiry has been magnified by the changing nature of data collection. Long gone are the days when experimenters would collect individual observations and log them by hand. Modern instruments, whether aboard satellites or lurking at the bottom of the ocean, are constantly generating vast amounts of information — so much that human beings are incapable of processing it.
Machine learning algorithms, in contrast, have no trouble sifting through reams of data. They are designed to identify patterns and sort them into categories.
In the wake of the 2010 Deepwater Horizon disaster in the Gulf of Mexico, oceanographer Kaitlin Frasier of the University of California, San Diego, set out to assess the damage that the massive oil spill caused. “We needed to know what happened to marine mammals,” she says.
Specifically, Frasier was concerned with the spill’s impact on dolphin populations. Trying to track the animals from the surface is expensive and time consuming, so Frasier used a different approach: deploying hydrophones to the seabed to passively record every sound in the ocean. By separating out dolphin vocalisations from the general ocean noise, Frasier hoped to detect trends in the animals’ population density.
The first part of the experiment was successful: Frasier’s hydrophones captured thousands of hours of sea noise that included hundreds of dolphin vocalisations. “We collected terabytes of data,” Frasier says. But that abundance posed a problem. Listening to all those recordings and sorting out the different kinds of vocalisations into categories would leave her no time to do anything else.
Frasier was familiar with advances in artificial intelligence technology, so set up an algorithm to sift through the masses of data and sort the dolphin sounds into categories. “The algorithm is unsupervised, meaning that you’re turning it loose on the dataset,” Frasier says. By the time it finished running, the software had identified seven types of distinct clicks, only one of which had previously been identified as coming from a known species of dolphin.
Humans think analytically. A computer doesn’t have to go through the reasoning process, so it can generate many more hypotheses than a human would.