Dr Ritika Mansotra
ritikamansotra444@gmail.com
Science has reached a turning point. For the first time, the work on artificial intelligence (AI) has been honored with the Nobel Prize in 2024, marking a historic shift in the way discoveries are recognized. No longer just a supporting actor in research, AI took center stage, with two awards going to scientists whose breakthroughs in AI have transformed the very process of doing science. In Physics, John J. Hopfield and Geoffrey E. Hinton were honored for their groundbreaking work on artificial neural networks- the foundation of modern machine learning. In Chemistry, Demis Hassabis and John M. Jumper of DeepMind, alongside David Baker, were recognized for their AI-driven breakthroughs in predicting and designing protein structures. Their achievements solved one of biology’s most stubborn mysteries, opening new horizons in medicine, agriculture, and biotechnology.
It is hard to overstate the significance. What once took years of painstaking research can now be done in hours. DeepMind’s AlphaFold has already mapped the structures of nearly all known proteins- a feat that is set to transform the search for cures and therapies. Beyond biology, AI is accelerating breakthroughs in climate modeling, materials science, and even space exploration. The shift in science is undeniable: machines are now solving problems once thought unsolvable. The story is not all bright. With such breakthroughs come deep concerns. AI is not magic. It depends on the data it is trained on. If that data is flawed or biased, the results can mislead and in science, a misleading answer can be dangerous. A wrong protein model could waste years of research. A faulty medical prediction could affect lives.
The greater danger lies in how we, as a society, treat AI. Too often, there is a cultural temptation to trust the make- the AI output- more than the maker, the human scientist behind it. Science has always thrived on questioning, testing, and refining ideas. AI can produce answers, but it cannot ask better questions, nor can it carry the burden of ethics, responsibility, or human values. Without careful oversight, we risk replacing human judgment with blind faith in algorithms.
The last Nobel recognition of AI is both a triumph and a warning. It shows what humanity can achieve when creativity meets computation. But it also asks us to pause: How do we make sure these tools are transparent, fair, and used responsibly? Who is accountable when an AI system makes a mistake?
This is the shift in science. AI is not replacing scientists, but it is changing how science is done. If we guide it wisely, it could accelerate progress in ways we can barely imagine. But if we surrender judgment to algorithms, we risk losing what makes science trustworthy: the human capacity for doubt, accountability, and integrity.
Great minds have long warned us about this balance. Alan Turing, the father of computer science, once said: “Instead of trying to produce a program to simulate the adult mind, why not rather try to produce one which simulates the child’s?” His words remind us that machines can learn, but they still need guidance. Richard Feynman also warned, “What I cannot create, I do not understand.” Blindly trusting what AI creates risks losing the deeper scientific understanding that comes only from human inquiry.
Geoffrey Hinton, often referred to as one of the “godfathers of AI” and a 2024 Nobel laureate, reflecting on the power and unpredictability of artificial intelligence, has noted
“We are creating systems that can think faster than us, smarter than us, and we don’t fully understand how they work. That is a dangerous place to be.”
The Nobel Committee’s choice in 2024 carries a dual message: AI has redefined what science can achieve, but humanity must remain in charge of its direction. The future of science is here. Whether it leads to greater discoveries or greater dangers depends not on the machines, but on us- the makers.
We must ensure AI serves humanity, not the other way around. If science is to serve humanity, then AI must remain our partner, not our master. AI may be the engine of tomorrow’s discoveries, but humans must remain the drivers.
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