in a sequence of letters. According to Gordon,
the first step is building a ‘parts list’
composed of different types of neuro s and
then mapping each of those parts in hysical
space. Currently, the parts list for mice is well
underway, whilst the human equivalent
could take another five to ten years. ut
understanding how these parts produce
behaviour is trickier still. “Each of those
parts also then has a constellation of
functions,” Gordon says. Eventually, there
should be enough detail in the map to explain
how neurons in certain brain circuit
function at a molecular level, to produce
specific behaviours.
The technologies being developed long the
way will have a wider impact on
neuroscience too, including research into a
broad spectrum of brain disorders from
epilepsy to Parkinson’s. Rapid single cell
sequencing now allows scientists to quickly
gather data from hundreds of thousands of
individual neurons, highlighting the DNA
that is switched on in each one. Meanwhile,
imaging tools for studying neurons in
exquisite detail and tracking their activities
in real-time are advancing.
- DEEPFAKE
WARFARE
An arms race will pit
AIs against each other
to discover what's real
and what’s notDeepfake videos have exploded
online over the past two years. It’s
where Artificial intelligence (AI) is
used to swap one person’s image in
a photo or video, for another’s.
Deeptrace, a company set up to
combat this, says in just the eight
months between April and
December 2019, deepfakes have
rocketed by 70% to 17,000.
Most deepfakes, about 96%, are
pornography. Here, a celebrity’s
face replaces the original. In its
2019 report, The State of
Deepfakes, Deeptrace says the top
four dedicated deepfake porn sites
generated 134,364,438 views.
As recently as five years ago,
realistic video manipulation
required expensive software and a
lot of skill, so it was primarily the
preserve of film studios. Now
freely-available AI algorithms, that
have learned to create highly-
realistic fakes, can do all the
technical work. All anyone needs
is a laptop with a graphics
processing unit (GPU).
The AI behind the fakes has been
getting more sophisticated too.
“The technology is really much
better than last year,” says
Associate Professor Luisa
Verdoliva, part of the Image
Processing Research Group at the
University of Naples in Italy. “If
you watch YouTube deepfake
videos from this year compared to
last year, they are much better.”
Now there are huge efforts
within universities and business
start-ups to combat deepfakes by
perfecting AI-based detection
systems and turning AI on itself. In
September 2019, Facebook,Microsoft, the University of Oxford
and several other universities
teamed up to launch the Deepfake
Detection Challenge with the aim
of supercharging research. They
pooled together a huge resource of
deepfake videos for researchers to
pit their detection systems against.
Facebook even stumped up $10
million for awards and prizes.
Verdoliva is part of the
challenge’s advisory panel and is
doing her own detection research.
Her approach is to use AI to spot
tell-tale signs – imperceptible to the
human eye – that images have been
meddled with. Every camera,
including smartphones, leaves
invisible patterns in the pixels
when it processes a photo. Different
models leave different patterns. “If
a photo is manipulated using deep
learning, the image doesn’t share
these characteristics,” says
Verdoliva. So, when these invisible
markings have vanished, chances
are it’s a deepfake.
Other researchers are using
different detection techniques and
while many of them can detect
deepfakes generated in a similar
way to the ones in their training
data, the real challenge is to
develop a stealthy detection system
that can spot deepfakes created
using entirely different techniques.
The extent to which deepfakes
will infiltrate our lives in the next
few years will depend on how this
AI arms race plays out. Right now,
the detectors are playing catch-up.White matter fibres
of the human brain