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Breaking down silos: Researchers highlight Nobel-winning AI breakthroughs and call for interdisciplinary innovation
In 2024, the Nobel Prize in physics was awarded to John Hopfield and Geoffrey Hinton for their foundational work in artificial intelligence (AI), and the Nobel Prize in chemistry went to David Baker, Demis Hassabis, and John Jumper for using AI to solve the protein-folding problem, a 50-year grand challenge problem in science. A new article by researchers at Carnegie Mellon University and Calculation Consulting examines the convergence of physics, chemistry, and AI, highlighted by recent Nobel Prizes. It traces the historical development of neural networks, emphasizing the role of interdisciplinary research in advancing AI. The authors advocate for nurturing AI-enabled polymaths to bridge the gap between theoretical advancements and practical applications, driving progress toward artificial general intelligence. The article is published in Patterns. "With AI being recognized in connections to both physics and chemistry, practitioners of machine learning may wonder how these sciences relate to AI and how these awards might influence their work," explained Ganesh Mani, Professor of Innovation Practice and Director of Collaborative AI at Carnegie Mellon's Tepper School of Business, who coauthored the article. "As we move forward, it is crucial to recognize the convergence of different approaches in shaping modern AI systems based on generative AI." In their article, the authors explore the historical development of neural networks. By examining the history of AI development, they contend, we can understand more thoroughly the connections among computer science, theoretical chemistry, theoretical physics, and applied mathematics. The historical perspective illuminates how foundational discoveries and inventions across these disciplines have enabled modern machine learning with artificial neural networks. Then they turn to key breakthroughs and challenges in this field, starting with Hopfield's work, and go on to explain how engineering has at times preceded scientific understanding, as is the case with the work of Jumper and Hassabis. The authors conclude with a call to action, suggesting that the rapid progress of AI across diverse sectors presents both unprecedented opportunities and significant challenges. To bridge the gap between hype and tangible development, they say, a new generation of interdisciplinary thinkers must be cultivated. These "modern-day Leonardo da Vincis," as the authors call them, will be crucial in developing practical learning theories that can be applied immediately by engineers, propelling the field toward the ambitious goal of artificial general intelligence. This calls for a paradigm shift in how scientific inquiry and problem-solving are approached, say the authors, one that embraces holistic, cross-disciplinary collaboration and learns from nature to understand nature. By breaking down silos between fields and fostering a culture of intellectual curiosity that spans multiple domains, innovative solutions can be identified to complex global challenges like climate change. Through this synthesis of diverse knowledge and perspectives, catalyzed by AI, meaningful progress can be made and the field can realize the full potential of technological aspirations. "This interdisciplinary approach is not just beneficial but essential for addressing the many complex challenges that lie ahead," suggests Charles Martin, Principal Consultant at Calculation Consulting, who coauthored the article. "We need to harness the momentum of current advancements while remaining grounded in practical realities." The authors acknowledge the contributions of Scott E. Fahlman, Professor Emeritus in Carnegie Mellon's School of Computer Science.
[2]
Researchers highlight Nobel-winning AI breakthroughs and call for interdisciplinary innovation
In 2024, the Nobel Prize in physics was awarded to John Hopfield and Geoffrey Hinton for their foundational work in artificial intelligence (AI), and the Nobel Prize in chemistry went to David Baker, Demis Hassabis, and John Jumper for using AI to solve the protein-folding problem, a 50-year grand challenge problem in science. A new article, written by researchers at Carnegie Mellon University and Calculation Consulting, examines the convergence of physics, chemistry, and AI, highlighted by recent Nobel Prizes. It traces the historical development of neural networks, emphasizing the role of interdisciplinary research in advancing AI. The authors advocate for nurturing AI-enabled polymaths to bridge the gap between theoretical advancements and practical applications, driving progress toward artificial general intelligence. The article is published in Patterns. "With AI being recognized in connections to both physics and chemistry, practitioners of machine learning may wonder how these sciences relate to AI and how these awards might influence their work," explained Ganesh Mani, Professor of Innovation Practice and Director of Collaborative AI at Carnegie Mellon's Tepper School of Business, who coauthored the article. "As we move forward, it is crucial to recognize the convergence of different approaches in shaping modern AI systems based on generative AI." In their article, the authors explore the historical development of neural networks. By examining the history of AI development, they contend, we can understand more thoroughly the connections among computer science, theoretical chemistry, theoretical physics, and applied mathematics. The historical perspective illuminates how foundational discoveries and inventions across these disciplines have enabled modern machine learning with artificial neural networks. Then they turn to key breakthroughs and challenges in this field, starting with Hopfield's work, and go on to explain how engineering has at times preceded scientific understanding, as is the case with the work of Jumper and Hassabis. The authors conclude with a call to action, suggesting that the rapid progress of AI across diverse sectors presents both unprecedented opportunities and significant challenges. To bridge the gap between hype and tangible development, they say, a new generation of interdisciplinary thinkers must be cultivated. These "modern-day Leonardo da Vincis," as the authors call them, will be crucial in developing practical learning theories that can be applied immediately by engineers, propelling the field toward the ambitious goal of artificial general intelligence. This calls for a paradigm shift in how scientific inquiry and problem solving are approached, say the authors, one that embraces holistic, cross-disciplinary collaboration and learns from nature to understand nature. By breaking down silos between fields and fostering a culture of intellectual curiosity that spans multiple domains, innovative solutions can be identified to complex global challenges like climate change. Through this synthesis of diverse knowledge and perspectives, catalyzed by AI, meaningful progress can be made and the field can realize the full potential of technological aspirations. "This interdisciplinary approach is not just beneficial but essential for addressing the many complex challenges that lie ahead," suggests Charles Martin, Principal Consultant at Calculation Consulting, who coauthored the article. "We need to harness the momentum of current advancements while remaining grounded in practical realities." The authors acknowledge the contributions of Scott E. Fahlman, Professor Emeritus in Carnegie Mellon's School of Computer Science.
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Researchers from Carnegie Mellon University and Calculation Consulting examine the convergence of physics, chemistry, and AI in light of recent Nobel Prizes, advocating for interdisciplinary approaches to advance artificial intelligence.
The 2024 Nobel Prizes in physics and chemistry have underscored the growing influence of artificial intelligence (AI) across scientific disciplines. John Hopfield and Geoffrey Hinton were awarded the Nobel Prize in Physics for their foundational work in AI, while David Baker, Demis Hassabis, and John Jumper received the Nobel Prize in Chemistry for using AI to solve the long-standing protein-folding problem 12.
Researchers from Carnegie Mellon University and Calculation Consulting have published an article in Patterns examining the convergence of physics, chemistry, and AI highlighted by these recent Nobel Prizes. The study traces the historical development of neural networks and emphasizes the crucial role of interdisciplinary research in advancing AI 12.
The authors explore the historical development of neural networks, revealing the intricate connections among computer science, theoretical chemistry, theoretical physics, and applied mathematics. This historical lens illuminates how foundational discoveries across these disciplines have enabled modern machine learning with artificial neural networks 12.
The article delves into significant breakthroughs and challenges in the field, beginning with Hopfield's work. It also explains how engineering has sometimes preceded scientific understanding, as exemplified by the work of Jumper and Hassabis on the protein-folding problem 12.
The researchers advocate for nurturing "AI-enabled polymaths" or "modern-day Leonardo da Vincis" to bridge the gap between theoretical advancements and practical applications. These interdisciplinary thinkers are seen as crucial for developing practical learning theories that can be immediately applied by engineers, driving progress toward artificial general intelligence 12.
The authors call for a paradigm shift in scientific inquiry and problem-solving, emphasizing the need for holistic, cross-disciplinary collaboration. They argue that by breaking down silos between fields and fostering intellectual curiosity across multiple domains, innovative solutions to complex global challenges like climate change can be identified 12.
Ganesh Mani, Professor of Innovation Practice and Director of Collaborative AI at Carnegie Mellon's Tepper School of Business, notes the importance of understanding how physics and chemistry relate to AI and how these Nobel awards might influence the work of machine learning practitioners 12.
Charles Martin, Principal Consultant at Calculation Consulting, emphasizes that this interdisciplinary approach is not just beneficial but essential for addressing the complex challenges that lie ahead. The researchers stress the need to harness the momentum of current advancements while remaining grounded in practical realities 12.
By synthesizing diverse knowledge and perspectives, catalyzed by AI, the authors believe that meaningful progress can be made, and the field can realize the full potential of its technological aspirations, potentially leading to breakthroughs in artificial general intelligence and solutions to global challenges.
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