在科技与经济的交汇点上，一场关于人工智能的深度对话正在悄然展开。机器之心独家专访了2011年诺贝尔经济学奖得主托马斯·萨金特教授，探讨了人工智能大模型在信息处理和理论创新方面所扮演的角色，以及它是否能像历史上伟大的科学家伽利略、牛顿、爱因斯坦那样，从数据中提炼出全新的理论，实现真正的科学发现。
### 科学的比喻：费曼与大模型
物理学家理查德·费曼的比喻——自然世界如同众神的游戏，既激发了人们对科学探索的无限遐想，也揭示了科学研究的本质：在纷繁复杂的现象背后，寻找规律与秩序。这一比喻同样适用于经济学，以及如今炙手可热的领域——人工智能。
### 从数据到知识：人工智能的进化
随着数据为中心的人工智能（Data-Centric AI）的兴起，人类正在经历信息处理与组织方式的革命。人工智能，尤其是近年来迅速发展的大模型，正以惊人的速度改变着信息获取与利用的模式。从文本生成、图像识别到复杂决策支持，大模型展现出了强大的学习与预测能力，使得人类能够处理和理解前所未有的数据量。
### 机器学习与科学发现
托马斯·萨金特教授认为，人工智能和机器学习的核心理念，其根源可以追溯到伽利略时代。通过构建世界模型并基于模型进行预测和决策，机器学习实现了从数据到知识的飞跃。正如历史上伟大的科学家通过实验和观察构建理论模型，进而推动科学进步，现代人工智能通过数据驱动的方式，同样在探索数据背后的规律，甚至可能揭示出新的理论框架。
### 大模型的科学发现潜力
萨金特教授指出，大模型在处理大量复杂数据时展现出的深度学习能力，为科学研究提供了前所未有的工具。通过分析海量数据，大模型能够揭示出传统方法难以捕捉的模式和趋势，甚至在某些情况下，能够基于数据驱动的发现，提出新的理论假设，从而推动科学理论的革新。
### 结语：通往未来的桥梁
在托马斯·萨金特教授的视角中，人工智能大模型不仅是对现有知识的补充，更是通往未来科学发现的桥梁。随着技术的不断进步，人类与人工智能的合作将愈发紧密，共同探索未知，揭示自然与社会的深层规律。这场关于大模型是记忆还是理解的对话，不仅关乎技术本身，更是对未来科学与人类知识边界拓展的深刻思考。
英语如下：
### Nobel Laureate Sargent: Big Models Reshaping the Future of Economics
Keywords: Big Models, Nobel Prize in Economics, Scientific Discovery
### Exclusive Interview with Nobel Laureate: Are Big Models Memory or Understanding? Machine Intelligence’s Unique Dialogue with Professor Thomas Sargent
In the convergence of technology and economy, a profound dialogue about artificial intelligence is unfolding quietly. Machine Intelligence conducted an exclusive interview with Professor Thomas Sargent, the Nobel Prize in Economics laureate of 2011, exploring the role of artificial intelligence’s big models in information processing and theoretical innovation. The discussion delves into whether these models, like the great scientists Galileo, Newton, and Einstein, can extract new theories from data and make genuine scientific discoveries.
### The Scientific Metaphor: Feynman and Big Models
Physicist Richard Feynman’s metaphor—Nature as a game played by gods—sparks boundless imagination for scientific exploration and reveals the essence of scientific research: uncovering patterns and order from the complex phenomena. This metaphor applies equally to economics and the hot field of artificial intelligence today.
### From Data to Knowledge: The Evolution of AI
With the rise of data-centric AI, humanity is undergoing a revolution in information processing and organization. Artificial intelligence, particularly the rapidly advancing big models, are transforming the way we acquire and utilize information at an astonishing pace. From text generation, image recognition, to complex decision support, big models demonstrate their powerful learning and predictive capabilities, enabling us to handle and understand unprecedented data volumes.
### Machine Learning and Scientific Discovery
Professor Thomas Sargent posits that the core principles of AI and machine learning have their roots in the era of Galileo. By constructing world models and making predictions and decisions based on these models, machine learning achieves a leap from data to knowledge. Much like how great scientists built theoretical models through experiments and observations to drive scientific progress, modern AI, through data-driven methods, is exploring the patterns behind data and even potentially revealing new theoretical frameworks.
### Potential for Scientific Discovery in Big Models
Sargent highlights that big models’ deep learning abilities in processing vast amounts of complex data provide unprecedented tools for scientific research. By analyzing massive data, big models can reveal patterns and trends that traditional methods often miss, and in some cases, propose new theoretical hypotheses based on data-driven discoveries, thus driving the evolution of scientific theories.
### Conclusion: A Bridge to the Future
From Professor Thomas Sargent’s perspective, big models are not just a supplement to existing knowledge but a bridge to future scientific discoveries. As technology advances, human collaboration with AI will become increasingly intimate, exploring the unknown together and uncovering the deep laws of nature and society. This dialogue about whether big models are memory or understanding is not only about the technology itself but a profound reflection on the expansion of scientific knowledge and human boundaries.
【来源】https://www.jiqizhixin.com/articles/2024-07-22-5
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