人工智能驱动的科学研究(AI for Science)被视为科学发现的第五范式的曙光。依循演绎主义的科学研究逻辑,梳理了人工智能在科学假设生成、数据收集以及分析挖掘中的应用。人工智能“数据算法算力”三原则,对科学数据的质量、算法的复杂...人工智能驱动的科学研究(AI for Science)被视为科学发现的第五范式的曙光。依循演绎主义的科学研究逻辑,梳理了人工智能在科学假设生成、数据收集以及分析挖掘中的应用。人工智能“数据算法算力”三原则,对科学数据的质量、算法的复杂性以及计算能力提出了更高的要求。AI for Science时代预计会出现科技巨头、AI专家、软硬件工程师、政府以及教育机构等紧密协同的新型科研模式。然而,AI算法的黑箱特性对科学研究的可解释性和可重复性构成潜在威胁。因此,在推进人工智能驱动的科学研究的发展过程中,必须坚持伦理优先的原则,注重科学数据的安全性管理,防范化解大模型分布外泛化带来的解释性弱等问题。展开更多
近年来,在算法、数据、算力三大引擎驱动下,人工智能(artificial intelligence,AI)发展迅速,并在AlphaFold3、核聚变智能控制、新冠药物设计等前沿领域取得诸多令人瞩目的成果。AI驱动的科学研究(AI for Science,AI4S)解决了科学数据分...近年来,在算法、数据、算力三大引擎驱动下,人工智能(artificial intelligence,AI)发展迅速,并在AlphaFold3、核聚变智能控制、新冠药物设计等前沿领域取得诸多令人瞩目的成果。AI驱动的科学研究(AI for Science,AI4S)解决了科学数据分析维度高、尺度跨度大以及局限性科研实验制约大规模跨学科科研活动的瓶颈问题,促进科学研究迈向以“平台协作”为主要特征的新模式。分析了AI4S的国际态势,梳理了当前我国农业数字化发展现状及现实困境,将文献、统计数据、调研案例分析相结合,提出推动AI4S赋能我国农业发展的实践路径。AI4S将成为撬动农业生产从“看天、看地、看庄稼”的传统模式向智能感知、智能决策、可视化管理等模式转变的强力引擎,推动科学研究从单打独斗的“小农作坊模式”迈向“安卓模式”的平台科研。在此平台上,科研人员共享算力、模型、算法、数据库和知识库等基础设施,围绕农业全产业链全生命周期研发应用,通过“滚雪球效应”加速科研创新和成果应用。利用AI技术赋能农业生产数字化、网络化和智能化,为支撑理论-实验的在线迭代,还需要完善高质量农业科学数据资源体系、适度超前推进AI关键技术与基础设施、优化新范式下的交叉创新科研生态、加强农业数据安全监管、制定完善的配套政策和激励机制等措施来打通数据壁垒,推动AI+农业落地,从源头强化农业科技创新,推动农业强国建设。展开更多
人工智能科学(AI for science,AI4S)为代表的新范式正在重塑科学研究。作为AI4S的关键技术,大语言模型在教育智能体建模与仿真、教育过程挖掘、教育数据增广等方面展现出了巨大潜力。研究立足科学哲学领域的“问题-方法-过程”框架,剖...人工智能科学(AI for science,AI4S)为代表的新范式正在重塑科学研究。作为AI4S的关键技术,大语言模型在教育智能体建模与仿真、教育过程挖掘、教育数据增广等方面展现出了巨大潜力。研究立足科学哲学领域的“问题-方法-过程”框架,剖析了大语言模型引发的教育研究范式变革图景:在问题维度,大语言模型基于海量数据形成的“世界知识”拓宽了教育研究的问题视野;在方法维度,大语言模型依托其“泛思维链”能力,为情境建模、模拟仿真、因果推断等方法创新提供新的可能;在过程维度;大语言模型为“端到端”和“人在回路”理念在教育研究中的融合提供了理想的技术载体,开启了人机协同的新范式;结合教育研究范式演进的历史维度,当前AI4S引领的变革是社会需求牵引和技术进步双重驱动的必然,既延续了数字时代教育研究范式的演进逻辑,还在智能维度、生成式范式、跨界整合等方面实现了独特突破。需要指出的是,这场范式变革虽然前景广阔,但其复杂性也不容忽视。研究对教育知识生产“单一文化”、理解错觉加剧、模型黑箱效应等潜在风险作了深度探讨,提出了重塑教育研究的反思性、审慎评估大语言模型适用边界的策略实施,为应对AI4S时代的教育机遇与挑战提供了新思路。展开更多
Artificial intelligence, often referred to as AI, is a branch of computer science focused on developing systems that exhibit intelligent behavior. Broadly speaking, AI researchers aim to develop technologies that can ...Artificial intelligence, often referred to as AI, is a branch of computer science focused on developing systems that exhibit intelligent behavior. Broadly speaking, AI researchers aim to develop technologies that can think and act in a way that mimics human cognition and decision-making [1]. The foundations of AI can be traced back to early philosophical inquiries into the nature of intelligence and thinking. However, AI is generally considered to have emerged as a formal field of study in the 1940s and 1950s. Pioneering computer scientists at the time theorized that it might be possible to extend basic computer programming concepts using logic and reasoning to develop machines capable of “thinking” like humans. Over time, the definition and goals of AI have evolved. Some theorists argued for a narrower focus on developing computing systems able to efficiently solve problems, while others aimed for a closer replication of human intelligence. Today, AI encompasses a diverse set of techniques used to enable intelligent behavior in machines. Core disciplines that contribute to modern AI research include computer science, mathematics, statistics, linguistics, psychology and cognitive science, and neuroscience. Significant AI approaches used today involve statistical classification models, machine learning, and natural language processing. Classification methods are widely applicable to problems in various domains like healthcare, such as informing diagnostic or treatment decisions based on patterns in data. Dean and Goldreich, 1998, define ML as an approach through which a computer has to learn a model by itself from the data provided but no specification on the sort of model is provided to the computer. They can then predict values for things that are different from the values used in training the models. NLP looks at two interrelated concerns, the task of training computers to understand human languages and the fact that since natural languages are so complex, they lend themselves very well to serving a number of very useful goals when used by computers.展开更多
文摘人工智能驱动的科学研究(AI for Science)被视为科学发现的第五范式的曙光。依循演绎主义的科学研究逻辑,梳理了人工智能在科学假设生成、数据收集以及分析挖掘中的应用。人工智能“数据算法算力”三原则,对科学数据的质量、算法的复杂性以及计算能力提出了更高的要求。AI for Science时代预计会出现科技巨头、AI专家、软硬件工程师、政府以及教育机构等紧密协同的新型科研模式。然而,AI算法的黑箱特性对科学研究的可解释性和可重复性构成潜在威胁。因此,在推进人工智能驱动的科学研究的发展过程中,必须坚持伦理优先的原则,注重科学数据的安全性管理,防范化解大模型分布外泛化带来的解释性弱等问题。
文摘近年来,在算法、数据、算力三大引擎驱动下,人工智能(artificial intelligence,AI)发展迅速,并在AlphaFold3、核聚变智能控制、新冠药物设计等前沿领域取得诸多令人瞩目的成果。AI驱动的科学研究(AI for Science,AI4S)解决了科学数据分析维度高、尺度跨度大以及局限性科研实验制约大规模跨学科科研活动的瓶颈问题,促进科学研究迈向以“平台协作”为主要特征的新模式。分析了AI4S的国际态势,梳理了当前我国农业数字化发展现状及现实困境,将文献、统计数据、调研案例分析相结合,提出推动AI4S赋能我国农业发展的实践路径。AI4S将成为撬动农业生产从“看天、看地、看庄稼”的传统模式向智能感知、智能决策、可视化管理等模式转变的强力引擎,推动科学研究从单打独斗的“小农作坊模式”迈向“安卓模式”的平台科研。在此平台上,科研人员共享算力、模型、算法、数据库和知识库等基础设施,围绕农业全产业链全生命周期研发应用,通过“滚雪球效应”加速科研创新和成果应用。利用AI技术赋能农业生产数字化、网络化和智能化,为支撑理论-实验的在线迭代,还需要完善高质量农业科学数据资源体系、适度超前推进AI关键技术与基础设施、优化新范式下的交叉创新科研生态、加强农业数据安全监管、制定完善的配套政策和激励机制等措施来打通数据壁垒,推动AI+农业落地,从源头强化农业科技创新,推动农业强国建设。
文摘人工智能科学(AI for science,AI4S)为代表的新范式正在重塑科学研究。作为AI4S的关键技术,大语言模型在教育智能体建模与仿真、教育过程挖掘、教育数据增广等方面展现出了巨大潜力。研究立足科学哲学领域的“问题-方法-过程”框架,剖析了大语言模型引发的教育研究范式变革图景:在问题维度,大语言模型基于海量数据形成的“世界知识”拓宽了教育研究的问题视野;在方法维度,大语言模型依托其“泛思维链”能力,为情境建模、模拟仿真、因果推断等方法创新提供新的可能;在过程维度;大语言模型为“端到端”和“人在回路”理念在教育研究中的融合提供了理想的技术载体,开启了人机协同的新范式;结合教育研究范式演进的历史维度,当前AI4S引领的变革是社会需求牵引和技术进步双重驱动的必然,既延续了数字时代教育研究范式的演进逻辑,还在智能维度、生成式范式、跨界整合等方面实现了独特突破。需要指出的是,这场范式变革虽然前景广阔,但其复杂性也不容忽视。研究对教育知识生产“单一文化”、理解错觉加剧、模型黑箱效应等潜在风险作了深度探讨,提出了重塑教育研究的反思性、审慎评估大语言模型适用边界的策略实施,为应对AI4S时代的教育机遇与挑战提供了新思路。
文摘Artificial intelligence, often referred to as AI, is a branch of computer science focused on developing systems that exhibit intelligent behavior. Broadly speaking, AI researchers aim to develop technologies that can think and act in a way that mimics human cognition and decision-making [1]. The foundations of AI can be traced back to early philosophical inquiries into the nature of intelligence and thinking. However, AI is generally considered to have emerged as a formal field of study in the 1940s and 1950s. Pioneering computer scientists at the time theorized that it might be possible to extend basic computer programming concepts using logic and reasoning to develop machines capable of “thinking” like humans. Over time, the definition and goals of AI have evolved. Some theorists argued for a narrower focus on developing computing systems able to efficiently solve problems, while others aimed for a closer replication of human intelligence. Today, AI encompasses a diverse set of techniques used to enable intelligent behavior in machines. Core disciplines that contribute to modern AI research include computer science, mathematics, statistics, linguistics, psychology and cognitive science, and neuroscience. Significant AI approaches used today involve statistical classification models, machine learning, and natural language processing. Classification methods are widely applicable to problems in various domains like healthcare, such as informing diagnostic or treatment decisions based on patterns in data. Dean and Goldreich, 1998, define ML as an approach through which a computer has to learn a model by itself from the data provided but no specification on the sort of model is provided to the computer. They can then predict values for things that are different from the values used in training the models. NLP looks at two interrelated concerns, the task of training computers to understand human languages and the fact that since natural languages are so complex, they lend themselves very well to serving a number of very useful goals when used by computers.
