The 2024 Nobel Prizes reveal the transformative impact of artificial intelligence (AI) in modern science. The contributions of AI are reshaping the boundaries of scientific disciplines and raising new questions about how human-AI collaboration will be recognized in scientific discoveries in the future.
This year's Nobel Prizes in physics and chemistry clearly demonstrate that AI has revolutionized not only the technological world but also the fundamental building blocks of science. These developments raise the following questions: In the future, will the Nobel Prizes blur the lines between fields such as physics, chemistry, and medicine? As the role of AI in research increases, how will we distinguish between the contributions of scientists and the collaboration of AI, or is there even a need to distinguish?
The Impact of Artificial Intelligence on the 2024 Nobel Prizes
AI played a critical role in the discoveries honored with this year's Nobel Prizes in Physics and Chemistry. The physics award was given to physicist John Hopfield from Princeton University and AI pioneer Geoffrey Hinton from the University of Toronto.
The chemistry prize went to biochemist David Baker from the University of Washington and DeepMind representatives Demis Hassabis and John Jumper. These three individuals were recognized for their AI-based methods in predicting protein structures.
The AI methods developed in both fields have become indispensable parts of their respective sciences. The back-propagation algorithm developed by Hinton forms the foundation of modern neural networks, while DeepMind's AlphaFold algorithm is revolutionizing how proteins fold.
What is the Connection Between the 2024 Nobel Prize in Physics and Physics?
This year's Nobel Prize in Physics surprised everyone, including physicists and AI experts, because AI and machine learning are not typically directly associated with physics. In fact, some researchers criticized the awarding of this prize to AI studies by arguing that there is no direct connection between AI and physics. For instance, some physicists referred to the awarding of this prize to AI work as "falling into the hype of artificial intelligence". However, the work of Hopfield and Hinton is based on applying collective behaviors observed in physical systems to neural networks.
Hopfield modeled the learning processes in neural networks using physical concepts such as the "spin glass" explained by interactions of magnetic particles. Hinton, inspired by principles of statistical mechanics, developed Boltzmann machines.
Like physical systems, neural networks demonstrate the ability to learn and form memories through complex interactions of simple units. Such physics-based approaches laid the groundwork for modern AI and machine learning algorithms. For example, Hinton's back-propagation algorithm is one of the most important techniques in deep learning.
This connection to physics has ensured that applications of neural networks and machine learning are utilized not only in biological and computational sciences but also in fields such as simulating physical processes and data analysis. For instance, the AI algorithms used during the discovery of the Higgs boson at CERN played a key role in analyzing large datasets. Therefore, the 2024 Nobel Prize in Physics honors this profound relationship between AI and physics.
A simple artificial neural network architecture.
Artificial Neural Networks and High-Tech Entrepreneurship
The neural network research of John Hopfield and Geoffrey Hinton opened the door to revolutionary innovations in many domains. The back-propagation algorithm developed by Hinton laid the foundation for AI-based health applications that enable early diagnosis of chronic diseases by processing large datasets.
For example, the Istanbul-based venture Virasoft offers AI-supported solutions in the field of digital pathology using structures similar to Hinton's algorithms. This accelerates the diagnostic processes by providing doctors with highly accurate analyses for diseases such as cancer.
Various analyses developed by Virasoft with AI support.
The success of neural networks in data analytics is also evident in financial technologies. Switzerland-based Sentifi analyzes large amounts of financial data using AI algorithms inspired by Hinton's neural network models, providing guidance on predicting market risks for investors.
The work of Hopfield and Hinton has enabled companies not only to accelerate data analysis but also to make high-accuracy predictions that were previously impossible, creating transformations across various sectors.
AlphaFold and Revolution in Chemistry
In the field of chemistry, AlphaFold developed by Demis Hassabis and John Jumper is another example of the impact of AI on scientific discoveries. The deep learning algorithms of AlphaFold made it possible to predict protein structures. Proteins are the building blocks of life, and their functions depend on how they fold. Predicting how proteins fold based on their amino acid sequences has been one of the greatest challenges in modern biology.
DeepMind's AlphaFold 2 system solved this 50-year-old problem with unprecedented accuracy. AlphaFold outperformed all other methods in competitions like the Critical Assessment of Structure Prediction (CASP), proving AI's superiority in solving biological puzzles.
David Baker used computational tools to design new proteins, which enabled the production of proteins that can be used in various areas from combating viruses to drug delivery. Both AlphaFold and Baker's computational methods demonstrate that AI is not only accelerating scientific research but also redefining the boundaries of what can be achieved in chemistry and biology.
The predicted structure of crRNA and CasLambda (Cas12l), part of the CRISPR subsystem.
AlphaFold and High-Tech Entrepreneurship
Thanks to its high accuracy in protein structure prediction, AlphaFold creates unique opportunities in biotechnology and drug development ventures. Biotechnology companies working on complex diseases like cancer and Alzheimer’s can utilize AlphaFold's protein structure prediction capabilities to pre-solve the structures of target proteins and design new drugs.
For example, the US-based company Insitro developed a platform capable of predicting the effects of drugs by examining mutations in the human genome, leveraging AlphaFold technology. Recursion Pharmaceuticals, which works on infectious diseases, can quickly analyze the structure of viruses using this technology, identifying protein targets for treatment and enabling rapid drug development processes. These possibilities offered by AlphaFold to biotechnology ventures not only reduce costs and speed up the drug discovery process but also add a new dimension to high-tech entrepreneurship.
Nobel Categories, the Role of Artificial Intelligence, and High-Tech Entrepreneurship
This year's Nobel prizes reveal a growing challenge for the Nobel Committee: Which scientific discipline should we include discoveries in? Both Hinton and Hassabis are researchers rooted in AI, yet the impact of their work is evident in the fields of physics and chemistry. In the future, we may see a shift in how scientific achievements are categorized. Rigid boundaries between fields such as physics, chemistry, and medicine are becoming increasingly meaningless due to interdisciplinary research enabled by AI.
The 2024 prizes also raise important questions regarding the role of AI in scientific discoveries. In the past, scientific tools were seen merely as assistants in the service of human genius. However, systems like AlphaFold and AI-based technologies like the back-propagation algorithm now directly provide insights, placing them at the heart of scientific processes. In this era of AI striving to surpass human intelligence, it may be necessary to rethink who will receive credit for scientific discoveries.
High-tech entrepreneurship is playing a significant role in resolving these questions by rapidly commercializing the potential of AI to transform science. Enterprises operating in the fields of AI and biotechnology are not only innovating but also translating insights derived from fundamental scientific research into practice, creating a broad impact on society. The awarding of Nobel Prizes to AI-based works signals that high-tech entrepreneurship has become a guiding force in science and has taken a key role in pioneering interdisciplinary inventions. The entrepreneurial ecosystem now serves as a bridge between fundamental sciences and commercial applications, enabling AI-supported discoveries to quickly touch human lives across various sectors such as health, agriculture, and energy.
In the future, it may also be possible for AI tools to be awarded alongside humans. Perhaps in this era of increasing collaboration between humans and machines, new categories may emerge in the Nobel Prizes.
