Constructivist foundations of modeling—A Kantian perspective

By linking Knowledge Engineering to Kantian Philosophy, this paper attempts to elaborate a potential theoretical foundation for understanding the nature of expertise and the processes of modeling. The established way of modeling is criticized for presuming the relation between the real objective world and the AI software models being a “mapping” (abstraction, copy, representation, etc.) relation. The paper argues that this aspect of the foundations of modeling in Knowledge Engineering could be improved by employing the standpoint presented in Kant's Critique of Pure Reason. Two hypotheses and ten principles of a constructivist modeling paradigm based on Kant's work are proposed. The term Constructivist here refers to the hypothesis that a model cannot “correspond to” reality but merely be “viable in” (i.e., “fit into”) reality. © John Wiley & Sons, Inc.     

BagGMM: Calling copy number variation by bagging multiple Gaussian mixture models from tumor and matched normal next-generation sequencing data

Copy number variations (CNVs) contribute significantly to human genomic variability, some of which lead to diseases. However, effective detection of CNVs from whole genome next generation sequencing data (NGS) remains challenging. Here, we present BagGMM, a new method to call CNVs using tumor-normal matched samples from NGS data. BagGMM extracts read depth ratios of tumor samples to normal samples, divides the genomic sequences into segments by sliding windows to count the average coverage ratio of each segment, filters candidate deletions and duplications based on a coarse criterion of coverage ratio, and then builds Gaussian Mixture Model (GMM) for remaining ratios to identify the remaining ambiguous copy number states after filtration. Bagging multiple GMMs makes false positive calls descent instead of using one GMM, thus enhancing the detection power of BagGMM. Considering the computation speed of GMMs and false positive calls, we employ a segmentation procedure “large window and then small windows”, which is also helpful to determine boundary of CNV regions. We apply BagGMM to three simulation datasets and two groups of human whole genome sequencing (WGS) data for breast cancer patients and ovarian cancer patients to identify CNVs, respectively. All performed experiments demonstrate that BagGMM has the capability of robustly identification of CNVs with different sizes and states. The performance of this tool is compared to four peer existing CNV detection methods. BagGMM shows a significant improvement in both sensitivity and specificity for detecting both copy number gains and losses.     

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.     

Tool condition monitoring using artificial intelligence methods

This paper describes an application of three artificial intelligence (AI) methods to estimate tool wear in lathe turning. The first two are “conventional” AI methods—the feed forward back propagation neural network and the fuzzy decision support system. The third is a new artificial neural network based-fuzzy inference system with moving consequents in if–then rules. Tool wear estimation is based on the measurement of cutting force components. This paper discusses a comparison of usability of these methods in practice.     

An online framework for catching top spreaders and scanners

Flow level information is important for many applications in network measurement and analysis. In this work, we tackle the “Top Spreaders” and “Top Scanners” problems, where hosts that are spreading the largest numbers of flows, especially small flows, must be efficiently and accurately identified. The identification of these top users can be very helpful in network management, traffic engineering, application behavior analysis, and anomaly detection.We propose novel streaming algorithms and a “Filter-Tracker-Digester” framework to catch the top spreaders and scanners online. Our framework combines sampling and streaming algorithms, as well as deterministic and randomized algorithms, in such a way that they can effectively help each other to improve accuracy while reducing memory usage and processing time. To our knowledge, we are the first to tackle the “Top Scanners” problem in a streaming way. We address several challenges, namely: traffic scale, skewness, speed, memory usage, and result accuracy. The performance bounds of our algorithms are derived analytically, and are also evaluated by both real and synthetic traces, where we show our algorithm can achieve accuracy and speed of at least an order of magnitude higher than existing approaches.     

Methods of intelligent diagnosis for control of flexible moving craft

A new approach to the control of moving flexible mechanical systems based on the methods of intelligent diagnosis and forecast was proposed. While observing the requirements on stability and performance of control of the main (“rigid”) craft motion, this approach also stabilizes its elastic oscillations. A notion of the “model” function of the effect of basic control on the elastic oscillations in the presence of the dominating mode was introduced. The loop for adaptive adjustment of the basic algorithm of power-optimal control of the flexible craft motion was designed. For the proposed method of control of the elastic multifrequency moving craft such as the large space structure, some results of modeling were presented.     

Trends in artificial intelligence applications for real-time control (a speculative study)

Some of the theory-bound evolutional trends in real-time AI applications are pointed out based on analysis of essential properties of real-time systems as well as AI based systems. The evolutional mainstream is increasing interdisciplinary integration. Three subtrends are illustrated on examples: mechanical combination of methods, AI methods used for approximate solution of “classical” problems, and abstract methods applied in new domains. In addition similarity between integrated circuits and real-time systems design and increased use of formal verification at the early stages of systems development are pointed out.     

Inductive hierarchical nonnegative graph embedding for “verb–object” image classification

Most existing image classification algorithms mainly focus on dealing with images with only “object” concepts. However, in real-world cases, a great variety of images contain “verb–object” concepts, rather than only “object” ones. The hierarchical structure embedded in these “verb–object” concepts can help to enhance classification. However, traditional feature representation methods cannot utilize it. To tackle this problem, we present in this paper a novel approach, called inductive hierarchical nonnegative graph embedding. By assuming that those “verb–object” concept images which share the same “object” part but different “verb” part have a specific hierarchical structure, we integrate this hierarchical structure into the nonnegative graph embedding technique, together with the definition of inductive matrix, to (1) conduct effective feature extraction from hierarchical structure, (2) easily transfer each new testing sample into its low-dimensional nonnegative representation, and (3) perform image classification of “verb–object” concept images. Extensive experiments compared with the state-of-the-art algorithms on nonnegative data factorization demonstrate the classification power of proposed approach on “verb–object” concept images classification.     

“范式变革”引领与“信息转换”担纲:机制主义通用人工智能的理论精髓

世界人工智能研究至今一直沿用着物质科学的科学范式(科学观和方法论),因此被分解为结构主义人工智能(人工神经网络)、功能主义人工智能(物理符号系统/专家系统)和行为主义人工智能(感知动作系统/智能机器人) 3个各自为战互不相容的学派。虽然各个学派都获得了一些精彩的局部性专用性成果,却没有通用性整体性的人工智能应用,更无法形成通用的人工智能整体理论,这成为人工智能研究与发展的最大痛点。目前,通用性整体性的人工智能理论越来越成为社会的紧迫需求。为此,本文依据作者四十多年研究的积累,总结和提出了“机制主义通用人工智能理论”,特别强调了“范式变革”和“信息转换”,希望引起学界的研讨和批评。     

Artificial intelligence as a discursive practice: the case of embodied software agent systems

In this paper, I explore some of the ways in which Artificial Intelligence (AI) is mediated discursively. I assume that AI is informed by an “ancestral dream” to reproduce nature by artificial means. This dream drives the production of “cyborg discourse”, which hinges on the belief that human nature (especially intelligence) can be reduced to symbol manipulation and hence replicated in a machine. Cyborg discourse, I suggest, produces AI systems by rhetorical means; it does not merely describe AI systems or reflect a set of prevailing attitudes about technology. To support this argument, I analyse a set of research articles about an “embodied conversational agent” called the Real Estate Agent (REA). The articles about REA mobilise a set of rhetorical strategies that systematically downplay the system’s artificiality and bolster its humanlike qualities. Within the context of the dream of AI to produce humanlike machines, and given our strong bias for human-human interaction, the designers’ claim to REA’s humanness in their research articles, as I argue in the final section of this paper, needs little justification.     

人-AI交互: 实现“以人为中心AI”理念的跨学科新领域

AI技术造福了人类,也给研发带来了挑战,如果开发不当,会伤害人类和社会。目前国内外还没有系统的跨学科工作框架来有效地应对这些新挑战。为顺应学科发展的交叉趋势,中国国家自然科学基金委2020年成立了交叉科学部。在这样的背景下,本文分析AI系统研发面临的新挑战,进一步阐述我们在2019年提出的“以人为中心AI”（human-centered AI,HCAI）研发理念和设计目标。目前,HCAI研发理念在国外是AI界的热门课题之一,为推动 HCAI 理念的落实,本文系统地提出了人?人工智能交互（human-AI interaction,HAII）的跨学科新领域,定义了其目的、范围、研究和应用重点等。通过文献综述和分析,本文总结了国内外HAII研究和应用的重点,提出了今后的主要研究方向。最后,针对今后HCAI理念和HAII领域的工作,提出了一系列对策和建议。     

The Revenge of Ecological Rationality: Strategy-Selection by Meta-Induction Within Changing Environments

According to the paradigm of adaptive rationality, successful inference and prediction methods tend to be local and frugal. As a complement to work within this paradigm, we investigate the problem of selecting an optimal combination of prediction methods from a given toolbox of such local methods, in the context of changing environments. These selection methods are called meta-inductive (MI) strategies, if they are based on the success-records of the toolbox-methods. No absolutely optimal MI strategy exists—a fact that we call the “revenge of ecological rationality”. Nevertheless one can show that a certain MI strategy exists, called “AW”, which is universally long-run optimal, with provably small short-run losses, in comparison to any set of prediction methods that it can use as input. We call this property universal access-optimality. Local and short-run improvements over AW are possible, but only at the cost of forfeiting universal access-optimality. The last part of the paper includes an empirical study of MI strategies in application to an 8-year-long data set from the Monash University Footy Tipping Competition.     

A computational approach to measuring the correlation between expertise and social media influence for celebrities on microblogs

Social media influence analysis, sometimes also called authority detection, aims to rank users based on their influence scores in social media. Existing approaches of social influence analysis usually focus on how to develop effective algorithms to quantize users’ influence scores. They rarely consider a person’s expertise levels which are arguably important to influence measures. In this paper, we propose a computational approach to measuring the correlation between expertise and social media influence, and we take a new perspective to understand social media influence by incorporating expertise into influence analysis. We carefully constructed a large dataset of 13,684 Chinese celebrities from Sina Weibo (literally ”Sina microblogging”). We found that there is a strong correlation between expertise levels and social media influence scores. Our analysis gave a good explanation of the phenomenon of “top across-domain influencers”. In addition, different expertise levels showed influence variation patterns: e.g., (1) high-expertise celebrities have stronger influence on the “audience” in their expertise domains; (2) expertise seems to be more important than relevance and participation for social media influence; (3) the audiences of top expertise celebrities are more likely to forward tweets on topics outside the expertise domains from high-expertise celebrities.     

Nonlinear parametric identification based on the criterion of minimum probability of estimation error

The importance of studies of alternative (as compared to traditional) methods of nonlinear stochastic parametric identification is demonstrated. Synthesis of a procedure of identification of the parameter, “discrete observer,” with the use of the criterion, “minimum probability of estimation error,” is proposed for the first time. A model example that demonstrates the efficiency of the proposed approach is considered.     

Decision Analytics mit Heatmap-Visualisierung von mehrschrittigen Ensembledaten

Today’s forecasting techniques, which are integrated into several information systems, often use ensembles that represent different scenarios. Aggregating these forecasts is a challenging task: when using the mean or median (common practice), important information is lost, especially if the underlying distribution at every step is multimodal. To avoid this, the authors present a heatmap visualization approach. It is easy to visually distinguish regions of high activity (high probability of realization) from regions of low activity. This form of visualization allows to identify splitting paths in the forecast ensemble and adds a “third alternative” to the decision space. Most forecast systems only offer “up” or “down”: the presented heatmap visualization additionally introduces “don’t know”. Looking at the heatmap, regions can be identified in which the underlying forecast model cannot predict the outcome. The authors present a software prototype with interactive visualization to support decision makers and discuss the information gained by its use. The prototype has already been presented to and discussed with researchers and practitioners.     

15 challenges for AI: or what AI (currently) can’t do

AI & SOCIETY - The current “AI Summer” is marked by scientific breakthroughs and economic successes in the fields of research, development, and application of systems with...     

Analytical Network Process for logistics management: A case study in a small electronic appliances manufacturer

The purpose of this study is to present an application of Analytical Network Process (ANP) for determination of best logistics partnership strategy of a small electronic appliances manufacturer operating in Turkey. The model includes both qualitative and quantitative factors which have been determined by literature review and the expertise of the company managers. Three different logistics management alternatives, “In-house Logistics”, “Third Party Arrangements”, and “Strategic Alliance”, are evaluated by an ANP model. Pairwise comparisons have been obtained through a survey conducted with the company personnel and sector managers. The results provide a decision support for the company courting strategies for logistics management under the aspects of “Law and Regulations”, “Corporate Structure”, and “Economical Factors”; given in “Customer”, “Finance” and “Technology” Dimensions.     

Deferring to expertise versus the prima donna syndrome: a manager’s dilemma

Deference to operational or engineering expertise is considered critical for maintaining safety in many industries. At the same time, legitimating specialized knowledge can help create “prima donnas,” expert operators who attain considerable organizational status and informal power. Safety can be used as a lever to gain industrial advantage or maintain inequitable perquisites. This paper first considers the common consensus about the need to defer to expertise in safety–critical organizations and industries and assesses available research on the relationship between deference to expertise and safety. Then, it reviews two psychological literatures that illuminate some of the cognitions, behaviors and organizational dynamics behind a prima donna syndrome: one on entitlement and another on organizational narcissism. Conclusions and recommendations center on how to defer to expertise (not necessarily experts) while dealing with “prima donnas.”