The Guardian Weekly 14 January 202230 Spotlight
The Guardian Weekly y14 January 202230 Spotlight
ScienceI
saac Newton apocryphally
discovered his second law – the
one about gravity – after an apple
fell on his head. Much experi-
mentation and data analysis later,
he realised there was a fundamental
relationship between force, mass and
acceleration. He formulated a theory
to describe that relationship – one that
could be expressed as an equation,
F=ma – and used it to predict the
behaviour of objects other than apples.
Contrast how science is increasingly
done today. Facebook’s machine
learning tools predict your preferences
better than any psychologist. Alpha-
Fold, a program built by DeepMind,
has produced the most accurate pre-
dictions yet of protein structures based
on the amino acids they contain. Both
are silent on why they work: why you
prefer this or that information; why
this sequence generates that structure.
You can’t lift a curtain and peer into
the mechanism. They off er no explan-
ation, no theory. They just work. We
witness the social eff ects of Facebook’s
predictions daily. AlphaFold has yet
to make its impact felt, but many are
convinced it will change medicine.
Somewhere between Newton and
Mark Zuckerberg, theory took a back
seat. In 2008, Chris Anderson, the
then editor -in -chief of Wired maga-
zine , predicted its demise. So much
data had accumulated, he argued,and computers were already so much
better than us at fi nding relationships
within data, that our theories were
being exposed for what they were –
over simplifi cations of reality. Soon,
the old scientifi c method – hypo-
thesise, predict, test – would be rele-
gated to the dustbin of history. We’d
stop looking for the causes of things
and be satisfi ed with correlations.
With the benefi t of hindsight, we
can say that what Anderson saw is
true (he wasn’t alone). The complexity
that this wealth of data has revealed
to us cannot be captured by theory as
traditionally understood. “We have
leapfrogged over our ability to even
write the theories that are going to be
useful for description,” said compu-
tational neuroscientist Peter Dayan,
director of the Max Planck Institute
for Biological Cybernetics in Tübin-
gen, Germany. “We don’t even know
what they would look like.”
There are several reasons why
theory refuses to die, despite the suc-
cesses of such theory-free prediction
engines as Facebook and AlphaFold.
They force us to ask: what’s the best
way to acquire knowledge and where
does science go from here?
The first reason is that we’ve
realised that artifi cial intelligences
(AIs), particularly a form of machine
learning called neural networks, which
learn from data without having to be
fed explicit instructions, are them-
selves fallible. Think of the prejudice
surrounding Google’s search engines
and Amazon’s hiring tools.
The second is that humans turn out
to be uncomfortable with theory-free
science. We don’t like dealing with a
black box – we want to know why.
And third, there may still be plenty
of theory of the traditional kind – that
is, graspable by humans – that explains
much but has yet to be uncovered.
So theory isn’t quite dead but
it is changing – perhaps beyond‘Theories
with a huge
amount of
data look
diff erent
from those
with small
amounts’Tom Grif fi ths
PsychologistARTIFICIAL INTELLIGENCEThe dawn
of post-
theory
science
Does the advent of AI and
machine learning mean
that the classic scientifi c
methodology – hypothesise,
predict , test – has had its day?
By Laura Spinneyrecognition. “The theories that make
sense when you have huge amounts
of data look quite diff erent from those
that make sense when you have small
amounts,” said Tom Griffi ths, a psy-
chologist at Princeton University.
Griffiths has been using neural
nets to help him improve on existing
theories in human decision-making.
A popular theory of how people
make decisions when economic risk
is involved is prospect theory, which
was formulated by the behavioural
economists Daniel Kahneman and
Amos Tversky in the 1970s (it later won
Kahneman a Nobel prize ). The idea at
its core is that people are sometimes,
but not always, rational.
These counter-examples were
highly informative, Griffiths said,
because they revealed more of the
complexity that exists in real life. For
example, humans weigh up probabili-
ties based on incoming information, as
prospect theory describes. But when
there are too many competing proba-
bilities for the brain to compute, they
might switch to a diff erent strategyE n g l i s h m a t h e m a t i c i a n
and physicist Isaac
Newton, seen in this 19th
century wood engraving,
is said to have come up
with gravitational theory
when he saw an apple fall
to ear th, around 1666.