Managing artificial intelligence projects: Key insights from an AI consulting firm

While organisations are increasingly interested in artificial intelligence (AI), many AI projects encounter significant issues or even fail. To gain a deeper understanding of the issues that arise during these projects and the practices that contribute to addressing them, we study the case of Consult, a North American AI consulting firm that helps organisations leverage the power of AI by providing custom solutions. The management of AI projects at Consult is a multi-method approach that draws on elements from traditional project management, agile practices, and AI workflow practices. While the combination of these elements enables Consult to be effective in delivering AI projects to their customers, our analysis reveals that managing AI projects in this way draw upon three core logics, that is, commonly shared norms, values, and prescribed behaviours which influence actors' understanding of how work should be done. We identify that the simultaneous presence of these three logics—a traditional project management logic, an agile logic, and an AI workflow logic—gives rise to conflicts and issues in managing AI projects at Consult, and successfully managing these AI projects involves resolving conflicts that arise between them. From our case findings, we derive four strategies to help organisations better manage their AI projects.     

Shifting ML value creation mechanisms: A process model of ML value creation

Advancements in artificial intelligence (AI) technologies are rapidly changing the competitive landscape. In the search for an appropriate strategic response, firms are currently engaging in a large variety of AI projects. However, recent studies suggest that many companies are falling short in creating tangible business value through AI. As the current scientific body of knowledge lacks empirically-grounded research studies for explaining this phenomenon, we conducted an exploratory interview study focusing on 56 applications of machine learning (ML) in 29 different companies. Through an inductive qualitative analysis, we uncover three broad types and five subtypes of ML value creation mechanisms, identify necessary but not sufficient conditions for successfully leveraging them, and observe that organizations, in their efforts to create value, dynamically shift from one ML value creation mechanism to another by reconfiguring their ML applications (i.e., the shifting practice). We synthesize these findings into a process model of ML value creation, which illustrates how organizations engage in (resource) orchestration by shifting between ML value creation mechanisms as their capabilities evolve and business conditions change. Our model provides an alternative explanation for the current high failure rate of ML projects.     

A survey of business process reengineering practices in Singapore

The changing economic environment has led to an increasing interest in improving organizational processes to enhance business performance. This paper presents the results of a survey of the business process reengineering (BPR) practices followed by firms in Singapore. The paper highlights the status of BPR projects, motives behind their efforts, the functional areas targeted for reengineering, roles of various organizational members in BPR programs, use of IT in BPR, and the main problems faced in the efforts of Singapore firms. The results show that about 50% of firms surveyed were engaged in BPR projects, with as many as 37% of the firms indicating their intention to take up BPR projects in the next few years. Main problems faced by the Singapore firms are the lack of human and financial resources, lack of internal IT expertise and capabilities, and lack of a champion for BPR efforts. These findings are compared to prior studies in the US and elsewhere. The paper concludes with a discussion of the implications based on the findings of the survey.     

Characterising Artificial Intelligence technology for international transfer

One of the central factors influencing the process and the outcome of technology transfer is the nature of the technology being transferred. This paper identifies and discusses the main characteristics of Artificial Intelligence (AI) technology from the point of view of international technology transfer. It attempts to indicate the peculiarities of AI in this context and move towards a framework to assist recipient decision makers in optimising the formulation of their policies on AI technology transfer.The five AI characteristics identified here relate to complexity, localisation, uncertainty, capital intensiveness and awareness. Some of these features are in principle common with other high technologies, but still bear some aspects specific to AI, albeit only the intensity with which they characterise the technology. The complexity of AI technology partially stems from its multi-disciplinary nature. Certain sub-areas of AI are locally bound and therefore need local innovative capabilities to develop on the fundamental concepts. The uncertainty in AI projects stems mainly from the two factors of high rate of change and the difficulties of quantifying the gains of transferring cognitive load from human to machine. Expensive human skill and enabling technologies are required to develop, maintain and use AI systems efficiently, making such projects highly capital intensive. Finally, AI is a young field and is not always completely open about itself which makes the decision makers' awareness an important issue to be addressed in the growth of AI applications.     

创新型人工智能教学改革与实践

人工智能是计算机科学与技术专业的一门重要专业课程,是一门研究运用计算机模拟和延伸人脑功能的综合性学科。探讨就3个方面内容进行:启发式传授人工智能解决问题的非结构化的思想;成体系的实验训练;与毕业论文、学校大学生科研项目资助计划、国家大学生创新性实验计划相对接的科研训练。上述3个方面层层递进、环环相扣,是体系完整的创新型人工智能教学实践。     

Machine intelligence-the animat path to intelligent adaptivebehaviour

The animat path to artificial intelligence (AI), a bottom-up approach to creating intelligent systems, is described. Using this approach, artificial creatures or agents animats are constructed in an environment. They begin simply and are gradually made more and more complex, exhibiting more and more complex behaviors at each step. The goals and accomplishments of five research projects incorporating the animat path to AI methodology are reviewed     

A survey of modern knowledge modeling techniques

A major characteristic regarding developments in the broad field of artificial intelligence (AI) during the 1990s has been an increasing integration of AI with other disciplines. A number of other computer science fields and technologies have been used in developing intelligent systems, starting from traditional information systems and databases, to modern distributed systems and the Internet. This paper surveys the knowledge modeling techniques that have received most attention in recent years among developers of intelligent systems, AI practitioners and researchers. The techniques are described from two perspectives, theoretical and practical. Hence the first part of the paper presents major theoretical and architectural concepts, design approaches, and research issues. The second part deals with several practical systems, applications, and ongoing projects that use and implement the techniques described in the first part.     

住宅智能化系统分析

随着国内智能大厦建设风潮的渐趋平静,智能住宅小区又掀新潮,有关技术标准也在不断完善,目前在很多地区已成为开发商开发新项目的重要卖点。智能住宅、小区是住宅、小区发展的方向,美国、日本、新加坡都有根据这些标准建立的智能住宅和小区的示范工程。     

Artificial intelligence: A Far East perspective

Today the world's fastest economic growth area is in the Far East. Besides Japan, the four Small Dragons of Asia; Hong Kong, Singapore, South Korea and Taiwan are achieving very outstanding results in industry and trade. Their large foreign incomes will enable them to carry out substantial technology development programmes. An AI programme is certainly a possible candidate, considering Japan's Fifth Generation Project. This paper surveys the backgrounds of these Dragons from many perspectives: economical, cultural, political, historical ... and speculates on the future roles which AI may play in these nations. China, which has so many ties with these Dragons is also included in the discussion.     

TensorFlow开源软件社区中贡献修订的实证研究

人工智能(artificial intelligence, AI)的飞速发展得益于开源社区的开放协同,大量的开发者通过提交PR(pull-request)为AI开源软件做贡献.然而,外部贡献者所提交的PR质量参差不齐,开源项目管理团队需要对PR进行代码审查,并要求贡献者根据审查意见对PR进行修订. PR的修订过程对AI开源软件的质量有着重要的影响,因此对该过程进行更加全面、深入的实证研究很有必要.首先,从TensorFlow开源软件社区中收集一组PR的修订历史,通过对PR的代码提交信息以及审查评论进行定性分析,归纳总结PR修订类型的分类体系.其次,根据此分类体系人工标注一组修订数据集,并基于此数据集定量分析不同修订类型的频率分布、次序分布以及关联关系.研究结果表明:TensorFlow开源社区中的PR存在3大类共11种不同类型的修订,其中完善类修订出现的频率最高;此外,相比于其他类修订和完善类修订,修正类修订更常发生在PR的早期更新中;与结构相关的修订更有可能与其他类型的修订同现或邻现,配置修订以及变基修订有较大概率会接连出现.实证研究结果可帮助AI开源实践者和研究者更好地理解PR的修...     

源码处理场景下人工智能系统鲁棒性验证方法

人工智能(artificial intelligence, AI)技术的发展为源码处理场景下AI系统提供了强有力的支撑.相较于自然语言处理,源码在语义空间上具有特殊性,源码处理相关的机器学习任务通常采用抽象语法树、数据依赖图、控制流图等方式获取代码的结构化信息并进行特征抽取.现有研究通过对源码结构的深入分析以及对分类器的灵活应用已经能够在实验场景下获得优秀的结果.然而,对于源码结构更为复杂的真实应用场景,多数源码处理相关的AI系统出现性能滑坡,难以在工业界落地,这引发了从业者对于AI系统鲁棒性的思考.由于基于AI技术开发的系统普遍是数据驱动的黑盒系统,直接衡量该类软件系统的鲁棒性存在困难.随着对抗攻击技术的兴起,在自然语言处理领域已有学者针对不同任务设计对抗攻击来验证模型的鲁棒性并进行大规模的实证研究.为了解决源码处理场景下AI系统在复杂代码场景下的不稳定性问题,提出一种鲁棒性验证方法 (robustness verification by Metropolis-Hastings attack method, RVMHM),首先使用基于抽象语法树的代码预处理工具提取模型的变量池,然后利...     

Reconciliation between factions focused on near-term and long-term artificial intelligence

Artificial intelligence (AI) experts are currently divided into “presentist” and “futurist” factions that call for attention to near-term and long-term AI, respectively. This paper argues that the presentist–futurist dispute is not the best focus of attention. Instead, the paper proposes a reconciliation between the two factions based on a mutual interest in AI. The paper further proposes realignment to two new factions: an “intellectualist” faction that seeks to develop AI for intellectual reasons (as found in the traditional norms of computer science) and a “societalist faction” that seeks to develop AI for the benefit of society. The paper argues in favor of societalism and offers three means of concurrently addressing societal impacts from near-term and long-term AI: (1) advancing societalist social norms, thereby increasing the portion of AI researchers who seek to benefit society; (2) technical research on how to make any AI more beneficial to society; and (3) policy to improve the societal benefits of all AI. In practice, it will often be advantageous to emphasize near-term AI due to the greater interest in near-term AI among AI and policy communities alike. However, presentist and futurist societalists alike can benefit from each others’ advocacy for attention to the societal impacts of AI. The reconciliation between the presentist and futurist factions can improve both near-term and long-term societal impacts of AI.     

人工智能中的封闭性和强封闭性——现有成果的能力边界、应用条件和伦理风险

针对现有人工智能技术的两种代表性途径——暴力法和训练法，以及它们结合的一种典型方式，给出了规范化描述，AI研究中的知识被重新定义为从模型到现实场景的完闭降射，进而提出人工智能的封闭性准则和强封闭性准则。封闭性准则刻画了暴力法和训练法在理论上的能力边界；强封闭性准则刻画了暴力法和训练法在工程中的应用条件。两项准则还为开放性人工智能技术的进一步研究提供了新的概念基础。     

Human activity recognition in artificial intelligence framework: a narrative review

Human activity recognition (HAR) has multifaceted applications due to its worldly usage of acquisition devices such as smartphones, video cameras, and its ability to capture human activity data. While electronic devices and their applications are steadily growing, the advances in Artificial intelligence (AI) have revolutionized the ability to extract deep hidden information for accurate detection and its interpretation. This yields a better understanding of rapidly growing acquisition devices, AI, and applications, the three pillars of HAR under one roof. There are many review articles published on the general characteristics of HAR, a few have compared all the HAR devices at the same time, and few have explored the impact of evolving AI architecture. In our proposed review, a detailed narration on the three pillars of HAR is presented covering the period from 2011 to 2021. Further, the review presents the recommendations for an improved HAR design, its reliability, and stability. Five major findings were: (1) HAR constitutes three major pillars such as devices, AI and applications; (2) HAR has dominated the healthcare industry; (3) Hybrid AI models are in their infancy stage and needs considerable work for providing the stable and reliable design. Further, these trained models need solid prediction, high accuracy, generalization, and finally, meeting the objectives of the applications without bias; (4) little work was observed in abnormality detection during actions; and (5) almost no work has been done in forecasting actions. We conclude that: (a) HAR industry will evolve in terms of the three pillars of electronic devices, applications and the type of AI. (b) AI will provide a powerful impetus to the HAR industry in future.

     

Intelligent control of external software systems

This paper focuses on the relatively unexplored set of issues that arises when an intelligent agent attempts to use external software systems (EESs). The issues are illustrated initially in the context of the complex agent-ESS interactions in an engineering design example. Approaching the area from the perspective of artificial intelligence (AI) research, we find that in general, agent-ESS interactions vary widely. We characterize the possible variations in terms of performance capabilities required, skill levels at which performance is exhibited, and knowledge sources from which capabilities can be acquired. We are exploring these variations using Soar as our candidate AI agent; the document briefly describes seven Soar-based projects in early stages of development, in which agent-ESS issues are addressed. We conclude by placing agent-ESS research in the context of other work on software technology, and discuss the research agenda we have set for ourselves in this area.     

Three fundamental misconceptions of Artificial Intelligence

In discussions on the limitations of Artificial Intelligence (AI), there are three major misconceptions, identifying an AI system with an axiomatic system, a Turing machine, or a system with a model-theoretic semantics. Though these three notions can be used to describe a computer system for certain purposes, they are not always the proper theoretical notions when an AI system is under consideration. These misconceptions are not only the basis of many criticisms of AI from the outside, but also responsible for many problems within AI research. This paper analyses these misconceptions, and points out the common root of them: treating empirical reasoning as mathematical reasoning. Finally, an example intelligent system called NARS is introduced, which is neither an axiomatic system nor a Turing machine in its problem-solving process, and does not use model-theoretic semantics, but is still implementable in an ordinary computer.     

Information fusion as an integrative cross-cutting enabler to achieve robust,explainable, and trustworthy medical artificial intelligence

Medical artificial intelligence (AI) systems have been remarkably successful, even outperforming human performance at certain tasks. There is no doubt that AI is important to improve human health in many ways and will disrupt various medical workflows in the future. Using AI to solve problems in medicine beyond the lab, in routine environments, we need to do more than to just improve the performance of existing AI methods. Robust AI solutions must be able to cope with imprecision, missing and incorrect information, and explain both the result and the process of how it was obtained to a medical expert. Using conceptual knowledge as a guiding model of reality can help to develop more robust, explainable, and less biased machine learning models that can ideally learn from less data. Achieving these goals will require an orchestrated effort that combines three complementary Frontier Research Areas: (1) Complex Networks and their Inference, (2) Graph causal models and counterfactuals, and (3) Verification and Explainability methods. The goal of this paper is to describe these three areas from a unified view and to motivate how information fusion in a comprehensive and integrative manner can not only help bring these three areas together, but also have a transformative role by bridging the gap between research and practical applications in the context of future trustworthy medical AI. This makes it imperative to include ethical and legal aspects as a cross-cutting discipline, because all future solutions must not only be ethically responsible, but also legally compliant.     

DAO to HANOI via DeSci: AI Paradigm Shifts from AlphaGo to ChatGPT

From AlphaGo to ChatGPT,the field of AI has launched a series of remarkable achievements in recent years.Analyzing,comparing,and summarizing these achievements at the paradigm level is important for future AI innovation,but has not received sufficient attention.In this paper,we give an overview and perspective on machine learning paradigms.First,we propose a paradigm taxonomy with three levels and seven dimensions from a knowledge perspective.Accordingly,we give an overview on three basic and tw...     

Online dispute resolution: an artificial intelligence perspective

Litigation in court is still the main dispute resolution mode. However, given the amount and characteristics of the new disputes, mostly arising out of electronic contracting, courts are becoming slower and outdated. Online Dispute Resolution (ODR) recently emerged as a set of tools and techniques, supported by technology, aimed at facilitating conflict resolution. In this paper we present a critical evaluation on the use of Artificial Intelligence (AI) based techniques in ODR. In order to fulfill this goal, we analyze a set of commercial providers (in this case twenty four) and some research projects (in this circumstance six). Supported by the results so far achieved, a new approach to deal with the problem of ODR is proposed, in which we take on some of the problems identified in the current state of the art in linking ODR and AI.