AI for Science:科研应用及其带来的革新与挑战

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。

AI for Science在农业领域的应用研究

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。

The User Analysis of Amazon Using Artificial Intelligence at Customer Churn

Customer churns remains a key focus in this research, using artificial intelligence-based technique of machine learning. Research is based on the feature-based analysis four main features were used that are selected on the basis of our customer churn to deduct the meaning full analysis of the data set. Data-set is taken from the Kaggle that is about the fine food review having more than half a million records in it. This research remains on feature based analysis that is further concluded using confusion matrix. In this research we are using confusion matrix to conclude the customer churn results. Such specific analysis helps e-commerce business for real time growth in their specific products focusing more sales and to analyze which product is getting outage. Moreover, after applying the techniques, Support Vector Machine and K-Nearest Neighbour perform better than the random forest in this particular scenario. Using confusion matrix for obtaining the results three things are obtained that are precision, recall and accuracy. The result explains feature-based analysis on fine food reviews, Amazon at customer churn Support Vector Machine performed better as in overall comparison.

Big Data 4.0: The Era of Big Intelligence

Big data has had significant impacts on our lives,economies,academia and industries over the past decade.The current equations are:What is the future of big data?What era do we live in?This article addresses these questions by looking at meta as an operation and argues that we are living in the era of big intelligence through analyzing from meta(big data)to big intelligence.More specifically,this article will analyze big data from an evolutionary perspective.The article overviews data,information,knowledge,and intelligence(DIKI)and reveals their relationships.After analyzing meta as an operation,this article explores Meta(DIKE)and its relationship.It reveals 5 Bigs consisting of big data,big information,big knowledge,big intelligence and big analytics.Applying meta on 5 Bigs,this article infers that 4 Big Data 4.0=meta(big data)=big intelligence.This article analyzes how intelligent big analytics support big intelligence.The proposed approach in this research might facilitate the research and development of big data,big data analytics,business intelligence,artificial intelligence,and data science.

AI for Science时代下的电池平台化智能研发

在AI for Science时代,电池设计自动化智能研发(battery design automation,BDA)平台通过整合先进的人工智能技术,为电池研发领域带来了革命性进展。BDA平台覆盖了文献调研、实验设计、合成制备、表征测试和分析优化这五个电池研发的关键环节,利用机器学习、多尺度建模、预训练模型等先进算法,结合软件工程开发用户交互友好的工具,加速从理论设计到实验验证的整个电池研发周期。通过自动化的实验设计、合成制备、表征测试和性能优化,BDA平台不仅提升了研发效率,还提高了电池设计的精确度和可靠性,推动了电池技术向更高能量密度、更长循环寿命和更低成本的方向发展。

From Brain Science to Artificial Intelligence

Reviewing the history of the development of artificial intelligence(AI)clearly reveals that brain science has resulted in breakthroughs in AI,such as deep learning.At present,although the developmental trend in AI and its applications has surpassed expectations,an insurmountable gap remains between AI and human intelligence.It is urgent to establish a bridge between brain science and AI research,including a link from brain science to AI,and a connection from knowing the brain to simulating the brain.The first steps toward this goal are to explore the secrets of brain science by studying new brain-imaging technology;to establish a dynamic connection diagram of the brain;and to integrate neuroscience experiments with theory,models,and statistics.Based on these steps,a new generation of AI theory and methods can be studied,and a subversive model and working mode from machine perception and learning to machine thinking and decision-making can be established.This article discusses the opportunities and challenges of adapting brain science to AI.

Towards a Theoretical Framework of Autonomous Systems Underpinned by Intelligence and Systems Sciences

Autonomous systems are an emerging AI technology functioning without human intervention underpinned by the latest advances in intelligence,cognition,computer,and systems sciences.This paper explores the intelligent and mathematical foundations of autonomous systems.It focuses on structural and behavioral properties that constitute the intelligent power of autonomous systems.It explains how system intelligence aggregates from reflexive,imperative,adaptive intelligence to autonomous and cognitive intelligence.A hierarchical intelligence model(HIM)is introduced to elaborate the evolution of human and system intelligence as an inductive process.The properties of system autonomy are formally analyzed towards a wide range of applications in computational intelligence and systems engineering.Emerging paradigms of autonomous systems including brain-inspired systems,cognitive robots,and autonomous knowledge learning systems are described.Advances in autonomous systems will pave a way towards highly intelligent machines for augmenting human capabilities.

Some issues for discipline of intelligence science

The general frame for the system of intelligence science was proposed, the common features of the researching objects of the intelligence science were summarized. The intelligence science consists of three portions: scientific foundation, technical methodology and application fields. The common features of intelligence science include complexity, intersection, nonlinearity, anthropomorphic property, uncertainty, incompleteness and distribution etc. The new proposed scientific branch would reflect the new height, new thought and new way for developing the control science and intelligent systems from one angle, and present a strong wish for establishing a new branch of intelligence science.

Artificial Intelligence Based Optimal Functional Link Neural Network for Financial Data Science

In present digital era,data science techniques exploit artificial intelligence(AI)techniques who start and run small and medium-sized enterprises(SMEs)to have an impact and develop their businesses.Data science integrates the conventions of econometrics with the technological elements of data science.It make use of machine learning(ML),predictive and prescriptive analytics to effectively understand financial data and solve related problems.Smart technologies for SMEs enable allows the firm to get smarter with their processes and offers efficient operations.At the same time,it is needed to develop an effective tool which can assist small to medium sized enterprises to forecast business failure as well as financial crisis.AI becomes a familiar tool for several businesses due to the fact that it concentrates on the design of intelligent decision making tools to solve particular real time problems.With this motivation,this paper presents a new AI based optimal functional link neural network(FLNN)based financial crisis prediction(FCP)model forSMEs.The proposed model involves preprocessing,feature selection,classification,and parameter tuning.At the initial stage,the financial data of the enterprises are collected and are preprocessed to enhance the quality of the data.Besides,a novel chaotic grasshopper optimization algorithm(CGOA)based feature selection technique is applied for the optimal selection of features.Moreover,functional link neural network(FLNN)model is employed for the classification of the feature reduced data.Finally,the efficiency of theFLNNmodel can be improvised by the use of cat swarm optimizer(CSO)algorithm.A detailed experimental validation process takes place on Polish dataset to ensure the performance of the presented model.The experimental studies demonstrated that the CGOA-FLNN-CSO model has accomplished maximum prediction accuracy of 98.830%,92.100%,and 95.220%on the applied Polish dataset Year I-III respectively.

Artificial Intelligence and Human Intelligence——On Human-Computer Competition from the Five-Level Theory of Cognitive Science

It is generally accepted that the human mind and cognition can be viewed at five levels; nerves, psychology, language, thinking and culture. Artificial intelligence(AI) simulates human intelligence at all five levels of human cognition, however, AI has yet to outperform human intelligence, although it is making progress. Presently artificial intelligence lags far behind human intelligence in higher-order cognition, namely, the cognitive levels of language, thinking and culture. In fact, artificial intelligence and human intelligence fall into very different intelligence categories. Machine learning is no more than a simulation of human cognitive ability and therefore should not be overestimated. There is no need for us to feel scared even panic about it. Put forward by John R. Searle, the"Chinese Room"argument, a famous AI model and standard, is not yet out of date. According to this argument, a digital computer will never acquire human intelligence. Given that, no artificial intelligence will outperform human intelligence in the foreseeable future.

Foundations of Intelligence Science

In order to make significant progress toward achievement of human level machine intelligence a paradigm shift is needed. More specifically, the natural intelligence and artificial intelligence should be closely interacted in Intelligence Science study, instead of separate from each other. In order to reach the paradigm, brain science, cognitive science, artificial intelligence and others should cross-research together. Brain science explores the essence of brain, research on the principle and model of natural intelligence in molecular, cell and behavior level. Cognitive science studies human mental activity, such as perception, learning, memory, thinking, consciousness etc. Artificial intelligence attempts simulation, extension and expansion of human intelligence using artificial methodology and technology. All together pursue to explore the mechanism and principle of intelligence which is the engine of advanced science and technology. The paper will give the definition of intelligence and discuss ten big issues of Intelligence Science. The conclusion and perspective will be given in last section.

Call for Papers Journal of Electronic Science and Technology Announcing a Special Issue on Artificial Intelligence with Rough Sets

Submission Deadline： 10 December 2010Since the introduction of rough sets in 1982 by Professor Zdzislaw Pawlak, we have witnessed great advances in both theory and applications. In order to promote development of rough sets, we are preparing a special issue on ＂Artificial Intelligence with Rough Sets＂ published by JEST （International）, Journal of Electronic Science and Technology, which is a refereed international journal focusing on IT area. The aim of this special issue is to present the current state of the research in this area, oriented towards both theoretical and applications aspects of rough sets.

MatChat: A large language model and application service platform for materials science

The prediction of chemical synthesis pathways plays a pivotal role in materials science research. Challenges, such as the complexity of synthesis pathways and the lack of comprehensive datasets, currently hinder our ability to predict these chemical processes accurately. However, recent advancements in generative artificial intelligence(GAI), including automated text generation and question–answering systems, coupled with fine-tuning techniques, have facilitated the deployment of large-scale AI models tailored to specific domains. In this study, we harness the power of the LLaMA2-7B model and enhance it through a learning process that incorporates 13878 pieces of structured material knowledge data.This specialized AI model, named Mat Chat, focuses on predicting inorganic material synthesis pathways. Mat Chat exhibits remarkable proficiency in generating and reasoning with knowledge in materials science. Although Mat Chat requires further refinement to meet the diverse material design needs, this research undeniably highlights its impressive reasoning capabilities and innovative potential in materials science. Mat Chat is now accessible online and open for use, with both the model and its application framework available as open source. This study establishes a robust foundation for collaborative innovation in the integration of generative AI in materials science.

Evaluation of the Relation between Cognitive Science and Embodied Cognition

This article delves into the intricate relationship between cognitive science and embodied cognition, offering transformative philosophical insights with profound implications for our understanding of the mind-body connection. In response to the journal’s feedback, we have enhanced the abstract to provide a more comprehensive overview of our study. Background: We trace the historical evolution of ideas, from the inception of cognitive science rooted in analytic philosophy to the groundbreaking contributions of Rodney Brooks and others in the field of artificial intelligence. We also explore the work of scholars such as Agre, Chapman, and Dreyfus, shedding light on the role of cognitive metaphor and the concept of the cognitive unconscious in shaping our understanding of embodied cognition. Purpose: Our study aims to shed light on the central theme that unites these various strands of thought—the rejection of the traditional, transcendental view of the subject in favor of the concept of an embodied subject. This embodied subject actively engages with its environment, shaping consciousness and cognition. This shift in perspective challenges classical epistemological theories and opens new avenues for inquiry. Method: We have conducted a comprehensive literature review to explore the historical development and key concepts in the field of embodied cognition, with a particular focus on the philosophical underpinnings and their integration into cognitive science. Results: Our examination of embodied cognition reveals that the mind is intimately connected to the body, with cognition emerging through interactions with the environment and perceptual experiences. This perspective challenges reductionist notions and demonstrates that mental states cannot be reduced to brain states alone. We also explore the relationship between functionalism and computational states of the brain, illustrating that mental states can be understood in the context of mathematical functions. Conclusion: In conclusion, this paper highlights the profound implications of embodied cognition and suggests that the mind is not isolated from the body but intimately tied to it. This perspective provides a fresh approach to the mind-body problem, emphasizing the role of the environment and perceptual experiences in shaping cognition. We invite further research into the practical applications of embodied cognition in fields like artificial intelligence, robotics, and psychology, and encourage investigations into the intersections between cognitive science and various branches of philosophy, offering valuable insights into the nature of consciousness and cognition. In essence, this study provides a comprehensive overview of the evolution and implications of embodied cognition, laying the groundwork for further research and fostering a deeper appreciation of the profound shifts in perspective that this theory brings to our understanding of the human mind.

通用人工智能时代信息资源管理学科的发展方向

本文梳理了人工智能与通用人工智能的70年发展历史,回顾了信息资源管理的50年历程,剖析通用人工智能对信息资源管理知识体系、教育体系、事业体系的深刻、多面的影响,并为信息资源管理在通用人工智能时代的发展方向提出三点建议。

新时代“情报学+”数智化人才的培育理路

[目的/意义]数智化时代背景下,更要求情报学学科融入新时代,积极探索数智化人才培育路径。[研究设计/方法]从认知到应用全方位策划“情报学+”数智化人才的培育理路,明晰育人体系,重点描述人才核心素养模型的搭建以及对人才培育路径的思考,展现情报学育人的本土化、特色化、智慧化。[结论/发现]情报学学科未来应致力于培育服务新时代各工作领域和研究情境的复合应用型情报学人才,不断探索更完整的人才培育闭环。[创新/价值]以中国哲学思想赋能新时代情报人才培育的理论遵循和实践路径,体现哲学思想现实价值的同时,为情报学学科的未来发展提供参考。

Data,Analytics,and Intelligence

We are living in an age of big data,analytics,and artificial intelligence(AI).After reviewing a dozen different books on big data,data analytics,data science,AI,and business intelligence(BI),there are the current questions:(1)What are the relationships between data,analytics,and intelligence?(2)What are the relationships between big data and big data analytics?(3)What is the relationship between BI and data analytics?This article first discusses the heuristics of the Greek philosopher Plato and French mathematician Descartes and how to reshape the world.Then it addresses the above questions based on a Boolean structure,which destructs big data,data analytics,data science,and AI into data,analytics,and intelligence as the Boolean atoms.Data,analytics,and intelligence are reorganized and reassembled,based on the Boolean structure,to data analytics,analytics intelligence,data intelligence,and data analytics intelligence.The research will analyse each of them after examining the system intelligence.The proposed approach in this research might facilitate the research and development of big data,data analytics,AI,and data science.

人工智能时代社会科学研究的“变”与“不变”

人工智能的发展推动社会科学研究在广度与深度上产生了质的飞跃,基础研究、知识创造将更加呈现跨学科交叉的趋势,形成了人工智能驱动的社会科学研究范式变革,对社会科学的知识生产和传播产生了革命性影响。一方面,人工智能以其强大的数据和算法全面赋能社会科学的发展;另一方面,人工智能也显著改变着人类的行为和决策方式,引发了道德、伦理、隐私、规范等新的社会问题,使人工智能治理成为社会科学的重要研究对象。当前,有必要在阐释人工智能赋能社会科学的深层次机理的基础上,剖析人工智能时代社会科学的“变”与“不变”,并就进一步促进人工智能与社会科学的融合发展提出对策建议。

数智时代人文社科数据资源开发利用的跃迁:从观念到行动

近年来数据资源开发利用的热潮已然在人文社科领域扩散开来,人文社科的数据资源观正在从传统的有限样本资料、“小数据”向大数据、智慧数据转变。针对当前人文社科数据资源开发利用仍然存在的“保护”“深闺”“孤岛”“同质”“展示”现象,本文构建了以主体层、内容层、保障层为核心的人文社科数据资源开发利用路径框架:主体层强调顶层规划主体、建库主体、数据用户之间的协同合作;内容层凸显逻辑维、资源维、方法维、验证维的组合路径;保障层涉及政策法规、资金项目、人才资源、文化氛围等保障措施。面向未来,人文社科数据资源开发利用需要积极打造人文社科数据资源“治理术”,在基本理念上需要从“资源基础论”向“治理能力论”转变,在具体行动上可从数据基础设施体系搭建、多学科数据资源共生共长机制构建、数据资源产权与安全有效保护、基于数据资源的服务链建构等方面着力推进。图1。参考文献15。

伦理先行:生成式人工智能的治理策略

生成式人工智能的迅猛发展为数字经济发展提供新动力,为社会民生创造新福祉,同时也引发了一系列现实挑战。中国现行立法已为生成式人工智能的发展划定了基本的安全保障底线,但该技术的发展方向和产业发展均处在高度不确定性当中,部分安全风险可能是被制造的焦虑,以国家立法形式进行规制为时尚早。生成式人工智能的治理需要伦理先行,坚持激励相容精神,为人工智能新技术新应用创新留下充分空间,以伦理治理的方式推动技术社区、产业群体、社会组织、社会公众、治理机构共同进行探索磨合,凝聚共识,待实践经验充足、立法时机成熟时,再将共识性规范纳入法律框架之中。