Geoffrey Hinton and John Hopfield received Nobel Prize in the field of physics for pioneering work in neural networksWhile Demis Hassabis, John Jumper and David Baker got home Chemistry Award for the use of artificial intelligence to solve long -term problems with the protein structure. These transitions based on AI have ignited discussions about the role of AI in traditional science and whether Nobelka's categories, established over a hundred years ago, must evolve to reflect the impact of interdisciplinary technology.
For decades, AI is an indispensable tool in many scientific disciplines, but its recognition in two categories of the Nobel Prize in one week signals a wider change in the perception of its role. The Nobel Prize in the field of physics was awarded to two men who helped put the foundation of machine learning. John Hopfield, an American physicist, developed the Hopfield network in the 1980s, one of the earliest types of artificial neural networks that influenced future AI research. Meanwhile, Geoffrey Hinton, a British-Canadian IT specialist, often called one of the “godparents of AI”, developed a background propagation algorithm that remains crucial in training modern neural networks. Although their studies are based on the concepts of physics, it was initially not clear to some in this field, why AI deserved a physics prize.
AI once again took a central place, because the chemistry award was awarded to Demis Hassabis Deepminind and John Jumper, along with Biochemist David Baker. Their work, especially Hassabis and the development of Alphafold, the AI system, which broke the long -lasting problem of predicting protein structures, was hailed as a breakthrough of the game in biological sciences. The breakthrough of Alphafold was based on the principles of Hinton's machine learning, emphasizing mutual connections between the prizes of physics and chemistry. And once again, while the award met with excitement, it also caused a debate about the place of AI in traditional scientific fields.
However, the role of AI in chemistry, especially in computational chemistry, seems less controversial. Andy Cooper, a professor of chemistry from the University of Liverpool, emphasized that AI's ability to predict protein structures opens the door to countless applications in biology, medicine and more. “AI will also affect other areas of chemistry,” Cooper said, indicating that the field of protein research is extremely adapted to AI due to the large, well-chestnized data sets and relatively simple composition of proteins.
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Despite the fears of many scientists, they accept the potential of AI to revolutionize research. Virginia Dignum, a professor at the Umeå University in Sweden, described Nobel recognition as “triumph of interdisciplinarity” AI. She suggested that the categories of the Nobel Prize alone may need to evolve, because the boundaries between the disciplines are becoming more and more smooth with the growth of AI. Dignum suggested that software engineering and cyber security may also deserve recognition as their social contribution increases.
The controversy regarding the recognition of AI in physics and chemistry reveal a deeper question: did Nobel categories, which remained largely unchanged since their creation in 1895, adapted to the changing landscape of modern science? Some say that creating a new category for artificial intelligence may be necessary because technology plays a more significant role in discoveries in many fields.
Hassabis himself dealt with this problem during a press conference after winning the chemistry prize, emphasizing that although AI tools are extremely powerful, they still depend on human ingenuity. “It's too premature to talk about AI engaged in all awards,” he noted. He explained that AI, above all, analyzes data and cannot generate hypotheses or ask critical questions that cause a scientific question. However, as the AI systems evolve, the border between research based on man and AI can blur.
AI Nobel Prizes this year emphasize the growing importance of technology, not only in crossing the limits of scientific knowledge, but also to transform the way of thinking about science itself. While some are skeptical about AI in disciplines such as physics, others perceive this as a natural evolution of scientific progress. Since AI will develop, its impact will probably expand to more research areas, raising new questions about how we assign scientific achievements and whether traditional prize categories can keep pace with rapid technology changes.
