By Javier Surasky-
Before
beginning, it's essential to make two clarifications: firstly, I am not a
scientist in the hard sciences, but a social scientist passionate about hard
sciences; secondly, I do not respect the Nobel Prizes, which I understand have
been awarded for years prioritizing political reasons over scientific or
artistic ones.
With these
clarifications, I intend to delve into a trilogy of these prizes for the year
2024 for what they are telling us, precisely in political terms above
scientific ones, about the world we live in and are entering. As I see it,
three Nobel Prizes have an intimate connection in this sense:
The Nobel Prize in Physics: awarded to John Hopfield
(Princeton University, United States) and Geoffrey Hinton (University of
Toronto, Canada) for making "fundamental discoveries and inventions that
enable machine learning with artificial neural networks."
The Nobel Prize in Chemistry: awarded, for half of its value, to
David Baker (University of Washington and Howard Hughes Medical Institute,
United States) for "computational protein design," and a quarter of
its value each to Demis Hassabis and John M. Jumper (both from Google DeepMind,
United Kingdom), for "protein structure prediction."
The Nobel Prize in Literature: awarded to Han Kang (South Korea)
"for her intense poetic prose, which confronts historical traumas and
exposes the fragility of human life."
The Nobel Peace Prize:
awarded to the organization Nihon Hidankyo (Japan), composed of Hiroshima and Nagasaki
atomic bombings survivors, in recognition of “their efforts to achieve a world
free of nuclear weapons and for demonstrating through witness testimony that
nuclear weapons must never be used again.” In the prize statement, the
Norwegian Nobel Committee states: “Nuclear powers are modernizing and upgrading
their arsenals,” a reference that resonates with the use of lethal autonomous weapons
systems and the dangers of the unchecked use of technologies when applied to the
military field.
The Nobel
Prize in Economics: jointly awarded to Daron Acemoglu, Simon Johnson (both from
the MIT, United States), and James Robinson (University of Chicago, United States)
for “their studies on how institutions are formed and how they affect prosperity,”
a critical issue in the AI Governance building.
The links
between the first two are pretty evident: while Hopfield proposed, in 1982, a
model of an artificial neuron network with associative memory
characteristics (let's say it remembered and combined memories), and four years
later, Hinton applied principles of statistical physics to Hopfield's network
so that it could "learn" visual patterns (remember from images and
combine its memories of images to create classifications of these). Eureka!
Artificial intelligence neural networks as we know them today began to operate
and became the cement of machine learning that would allow a qualitative leap
in AI. Since then, Hinton has been known as the "godfather of AI."
Hinton had
already won the "Nobel of computing," the Alan Turing Award, in 2018
and worked at Google until 2023, leaving the company to "be able to speak
about the dangers of AI without considering how this affects Google" (see here). Yes, Hinton is one of the foremost voices
announcing the danger of AI developing without controls (or even with them).
The essential data here, however, is something else. The Turing Award that
Hinton received, to be fair, along with two students, was for the development
of neural networks that they carried out in 2012 and which was the basis of the
company he founded, DNN Research Inc., later bought by Google. This means the
awarded work was carried out partially in academic research and the private sector.
The
application of AI tools, particularly neural networks, earned the Nobel Prize
in Chemistry for its three winners, who work on proteins and their modeling.
Let's remember that proteins are responsible for the control and production of
chemical reactions that form the basis of life. The word protein comes from
"Proteus," a Greek sea deity (let's remember that science tells us
that life began in water) whose name can be translated as "the
primordial." Proteus could predict the future and change his form as a
strategy to avoid sharing what he knew with mortals. It's pretty clear that the
relationship between content and form is central to this story.
Well, in
2003, Baker managed to create "designer proteins," non-existent in
nature, which allows "creating" new proteins that can be used in
drugs, materials, and even as sensors, while in 2020 Hassabis and Jumper
presented AlphaFold, an AI that manages to predict the structure of known
proteins, which in turn opens the door to a better understanding of, for
example, the reasons and processes by which antibiotic resistance occurs, one of
the topics that were discussed at a high-level meeting during the week of the
general debate of the UN General Assembly a month ago, or create images of
enzymes that can break down plastics, which becomes a significant contribution
to "decontaminate" our planet.
Baker has
carried out his work in academia, but Hassabis and Jumper have done it at
DeepMind, a company that jumped to fame in 2016 for defeating the world champions of Go, a strategy board game with more
move options than chess.
Meanwhile,
Han Kang was multi-awarded, from obtaining the Young Artist of the Year award
in 2000 to the Booker Prize she received in London in 2016 for her book The
Vegetarian. The last award she received before the Nobel, just a few months
ago, was the Ho-Am Prize, created in 1990 by Kun-Hee Lee, Director of Samsung,
and awarded in five categories: science, engineering, medicine, arts, and
community service.
Kang writes
in her novel Greek Lessons about communication, using two
characters, one who cannot speak and another who is losing sight, to create a
story of communication and uncommunication where touch and gaze come to occupy
places traditionally reserved for other protagonists. In a passage from her
novel, she says, "Eight years ago, when her son started speaking, she
dreamed of a single word that synthesized all languages. It was such a vivid
nightmare that she woke up with her back soaked in sweat. It was a word solidly
compressed by an enormous density and gravitational force. In the instant that
someone pronounced it, that language would explode and expand like the matter
of primordial times."
Greek
Lessons can be read
as a novel about the fragility of communication (and also of uncommunication), languages
and their forms, and how our humanity is compromised by it. Under a style
marked by the art of fiction, it puts us in front of essential elements to
think about new languages and encounters and, why not, new forms of humanity,
issues that are not far from the fears and opportunities, concerns and new
languages that challenge us when thinking about a world and a humanity that
reconverts its relationships with technology.
Han Kang's
Nobel brings the quota of humanity that those of chemistry and physics hide and
carry in a sublimated way, the emotions behind the zeros and ones, the humanity
of the technologies that produce changes, and our fragile relationship with it.
It is this
same humanity, framed by the use of nuclear technology in its most destructive
form and the need to communicate the horror accessible only to those who lived
the experience directly, that forms the basis of the 2024 Nobel Peace Prize.
This award comes at a time when AI applications in military technology pose new
dangers and reveal, as seen in the Summit of the Future, the reluctance of
states to allow control regulations to reach their development. Such is the
link between the Nobel Peace and Literature Prizes that a phrase taken from the
book The Greek Class by Hu Kang can perfectly express the essence of the
testimony of those who form the Nihon Hidankyo association: "Death and
extinction are the opposite of ideas from the beginning. Snow that melts and
turns into mud cannot be an idea."
Finally,
the importance of institutions and the rule of law in generating well-being.
The centrality of institutions as barriers of containment and protection, but
also as engines of well-being, cannot be more helpful or timely than now when
we must move decisively in building a global and shared governance of AI that
provides us with security but also opportunities for enhanced and shared global
prosperity.
Once again,
the Nobel Prizes speak to us about politics as much as or more than science.
They talk to us about politics when they open the opportunity to draw a line
that connects them through their connections with AI; they speak to us about
politics when the winners in "hard" sciences are men working in
Northern countries, while the only woman on the list is associated with art and
is not Western. They speak to us about politics when we see winners who do
their work in the private sector appear increasingly frequently.
The Nobel
Prizes speak to us, but we are not always sensitive enough to hear the message.
