These Robots Are Learning to Conduct Their Own Science Experiments
These Robots Are Learning to Conduct Their Own Science
Experiments
Carnegie Mellon professors plan to gradually outsource
their chemical work to AI.
By April 11, 2018, 3:01 AM PDT
Inside a lab at Carnegie Mellon University in Pittsburgh,
a robot arm lifts a bottle filled with chemical reagents and carries it over a
bank of test tubes, where it dispenses a precise number of drops into each one.
The arm swivels, replaces the bottle, swivels again, and picks up another
container. Gracelessly, tirelessly, the machine thrums on, carrying out test
after test. The experiments are part of an ongoing project to determine the
ideal chemical makeup for high-capacity electric car batteries. Soon, machines
won’t just run the experiments—they’ll devise them, too.
Over the next few months, an artificial intelligence
algorithm will gradually take over the planning of experiments based on the
battery test runs. Once fully functioning, this robot graduate student will
decide how to modify the concentrations of the ingredients it’s testing. “It’s
automating not only the manual part of doing the experiment but also the
planning part,” says Brian Storey, the Toyota Research Institute scientist
leading the project.
Science has long been considered one of the human
activities least likely to be farmed out to robots. That’s changing as sensors,
sequencers, and satellites churn out digital information by the terabyte. “We
just cannot handle the amount of data anymore,” says Manuela Veloso, who heads
Carnegie Mellon’s machine learning department. It’s a daily concern for biotech
companies and a wide range of other businesses struggling to make sense of the
unprecedented swell of raw information.
AI software designed to identify and sort patterns has
been deployed across a wide swath of science, from marine biology (identifying
wild dolphin vocalizations from hydrophone recordings) to astronomy (detecting
the presence of planets from subtle fluctuations in the brightness of thousands
of stars). To discover the Higgs boson, the so-called God particle, an
algorithm sifted billions of particle tracks generated within the Large Hadron
Collider in Switzerland. AI is fast becoming an essential part of university
science curricula.
Automating the process of discovery doesn’t just free up
researchers’ time. It could potentially change what sorts of discoveries are
made. “I can easily imagine cases in which AI would recommend experiments to
try to synthesize a chemical molecule that you wouldn’t think possible, but the
AI will be able to do it,” says Barnabás Póczos, a Carnegie Mellon machine
learning professor collaborating on the Toyota project.
Unfortunately, generating novel predictions isn’t all
that useful by itself. What scientists are after is less what than why—the
elegant theoretical formulations that let them understand how the universe
works, such as Newton’s first law or E=mc². So far, the neural networks
underlying AI software can’t really explain how they arrive at their answers.
Humans, in contrast, are pretty good at that. So in the
near term, the most promising approach will be for humans and AI to work
together. In February, Dutch publisher Elsevier announced a trial collaboration
with software maker Euretos, using AI to assess millions of peer-reviewed
scientific articles to suggest hypotheses in the field of biochemistry.
Academics will cull these hypotheses online, basing experiments on the most
encouraging ones. “The vision is that the discussion becomes a much more
automated process,” says Euretos co-founder Arie Baak.
And after that? “People have wondered if you could have
the computer automatically figure out the principles underlying physics,” says
Toyota’s Storey. “I don’t think we’re going that far out now.”
BOTTOM LINE - A new world of sensors and satellites has
overwhelmed researchers with more data than they can meaningfully appreciate,
so they’re training software to do higher-order analysis.
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