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Leaders | When robots do research

How artificial intelligence can revolutionise science

Consider the historical precedents

[20230916_LDD002]image: Pete Ryan
Sep 14th 2023
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DEBATE about artificial intelligence (AI) tends to focus on its
potential dangers: algorithmic bias and discrimination, the mass
destruction of jobs and even, some say, the extinction of humanity.
As some observers fret about these dystopian scenarios, however,
others are focusing on the potential rewards. ai could, they claim,
help humanity solve some of its biggest and thorniest problems. And,
they say, ai will do this in a very specific way: by radically
accelerating the pace of scientific discovery, especially in areas
such as medicine, climate science and green technology. Luminaries in
the field such as Demis Hassabis and Yann LeCun believe that AI can
turbocharge scientific progress and lead to a golden age of
discovery. Could they be right?

Such claims are worth examining, and may provide a useful
counterbalance to fears about large-scale unemployment and killer
robots. Many previous technologies have, of course, been falsely
hailed as panaceas. The electric telegraph was lauded in the 1850s as
a herald of world peace, as were aircraft in the 1900s; pundits in
the 1990s said the internet would reduce inequality and eradicate
nationalism. But the mechanism by which AI will supposedly solve the
world's problems has a stronger historical basis, because there have
been several periods in history when new approaches and new tools did
indeed help bring about bursts of world-changing scientific discovery
and innovation.

In the 17th century microscopes and telescopes opened up new vistas
of discovery and encouraged researchers to favour their own
observations over the received wisdom of antiquity, while the
introduction of scientific journals gave them new ways to share and
publicise their findings. The result was rapid progress in astronomy,
physics and other fields, and new inventions from the pendulum clock
to the steam engine--the prime mover of the Industrial Revolution.

Then, starting in the late 19th century, the establishment of
research laboratories, which brought together ideas, people and
materials on an industrial scale, gave rise to further innovations
such as artificial fertiliser, pharmaceuticals and the transistor,
the building block of the computer. From the mid-20th century,
computers in turn enabled new forms of science based on simulation
and modelling, from the design of weapons and aircraft to more
accurate weather forecasting.

And the computer revolution may not be finished yet. As we report in
a special Science section, AI tools and techniques are now being
applied in almost every field of science, though the degree of
adoption varies widely: 7.2% of physics and astronomy papers
published in 2022 involved AI, for example, compared with 1.4% in
veterinary science. AI is being employed in many ways. It can
identify promising candidates for analysis, such as molecules with
particular properties in drug discovery, or materials with the
characteristics needed in batteries or solar cells. It can sift
through piles of data such as those produced by particle colliders or
robotic telescopes, looking for patterns. And AI can model and
analyse even more complex systems, such as the folding of proteins
and the formation of galaxies. AI tools have been used to identify
new antibiotics, reveal the Higgs boson and spot regional accents in
wolves, among other things.

All this is to be welcomed. But the journal and the laboratory went
further still: they altered scientific practice itself and unlocked
more powerful means of making discoveries, by allowing people and
ideas to mingle in new ways and on a larger scale. AI, too, has the
potential to set off such a transformation.

Two areas in particular look promising. The first is
"literature-based discovery" (LBD), which involves analysing existing
scientific literature, using ChatGPT-style language analysis, to look
for new hypotheses, connections or ideas that humans may have missed.
LBD is showing promise in identifying new experiments to try--and even
suggesting potential research collaborators. This could stimulate
interdisciplinary work and foster innovation at the boundaries
between fields. LBD systems can also identify "blind spots" in a
given field, and even predict future discoveries and who will make
them.

The second area is "robot scientists", also known as "self-driving
labs". These are robotic systems that use AI to form new hypotheses,
based on analysis of existing data and literature, and then test
those hypotheses by performing hundreds or thousands of experiments,
in fields including systems biology and materials science. Unlike
human scientists, robots are less attached to previous results, less
driven by bias--and, crucially, easy to replicate. They could scale up
experimental research, develop unexpected theories and explore
avenues that human investigators might not have considered.

The idea that AI might transform scientific practice is therefore
feasible. But the main barrier is sociological: it can happen only if
human scientists are willing and able to use such tools. Many lack
skills and training; some worry about being put out of a job.
Fortunately, there are hopeful signs. AI tools are now moving from
being pushed by AI researchers to being embraced by specialists in
other fields.

Governments and funding bodies could help by pressing for greater use
of common standards to allow AI systems to exchange and interpret
laboratory results and other data. They could also fund more research
into the integration of AI smarts with laboratory robotics, and into
forms of AI beyond those being pursued in the private sector, which
has bet nearly all its chips on language-based systems like ChatGPT.
Less fashionable forms of AI, such as model-based machine learning,
may be better suited to scientific tasks such as forming hypotheses.

The adding of the artificial

In 1665, during a period of rapid scientific progress, Robert Hooke,
an English polymath, described the advent of new scientific
instruments such as the microscope and telescope as "the adding of
artificial organs to the natural". They let researchers explore
previously inaccessible realms and discover things in new ways, "with
prodigious benefit to all sorts of useful knowledge". For Hooke's
modern-day successors, the adding of artificial intelligence to the
scientific toolkit is poised to do the same in the coming years--with
similarly world-changing results. #

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This article appeared in the Leaders section of the print edition
under the headline "How AI can revolutionise science"

Leaders September 16th 2023

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How AI can revolutionise science

From the September 16th 2023 edition

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