The Constructibility of Artificial Intelligence (as Defined by the Turing Test)

The Turing Test (TT), as originally specified, centres on theability to perform a social role. The TT can be seen as a test of anability to enter into normal human social dynamics. In this light itseems unlikely that such an entity can be wholly designed in anoff-line mode; rather a considerable period of training insitu would be required. The argument that since we can pass the TT,and our cognitive processes might be implemented as a Turing Machine(TM), that consequently a TM that could pass the TT could be built, isattacked on the grounds that not all TMs are constructible in a plannedway. This observation points towards the importance of developmentalprocesses that use random elements (e.g., evolution), but in these casesit becomes problematic to call the result artificial. This hasimplications for the means by which intelligent agents could bedeveloped.     

BORIS—An experiment in in-depth understanding of narratives

boris is an experimental story understanding and question answering system which deals with the specification and interaction of many sources of knowledge. Unlike skimmers, which simply extract the “gist” of a story in a top-down manner and ignore everything else, boris attempts to understand everything that it reads to as great a depth as possible. This report focuses on how the boris program handles a complex story involving a divorce.     

图像理解原理的数学评价

图像是一种客观存在的事物,它以不同的模式向人们传递信息.图像理解就是模式分析和理解,是一门研究用计算机系统分析和理解图像,实现类似人类视觉系统理解外部世界的学科.介绍了图像理解的基本概念、原理以及与相关学科的联系和区别,综合分析总结了目前在图像恢复、图像分割及特征提取中常见的多种算法的特点,并且对图像理解的应用发展进行了一定的展望.     

Video understanding for complex activity recognition

This paper presents a real-time video understanding system which automatically recognises activities occurring in environments observed through video surveillance cameras. Our approach consists in three main stages: Scene Tracking, Coherence Maintenance, and Scene Understanding. The main challenges are to provide a robust tracking process to be able to recognise events in outdoor and in real applications conditions, to allow the monitoring of a large scene through a camera network, and to automatically recognise complex events involving several actors interacting with each others. This approach has been validated for Airport Activity Monitoring in the framework of the European project AVITRACK.     

一种高效的基于约束网络的工程图符号识别方法

将工程图中所有符号的几何特征转换为一个约束网格,用结点记录判断条件,边指示判断次序,实现以层次化约束判断为核心的过滤式识别方法,基于逐步简化的思想,该方法引入多层优先级和边属性的定义,通过优先级调整和边属性查询的配合,优化网络搜索;通过将巳经过约束检查的图元编号记录在结点中而大幅度减少图元遍历和判断。该方法应用在建筑工程图识别中效率较高,对符号种类多、图低内容复杂等情况适应能力较强。     

Informative PLS score-loading plots for process understanding and monitoring

Principal component regression (PCR) based on principal component analysis (PCA) and partial least squares regression (PLSR) are well known projection methods for analysis of multivariate data. They result in scores and loadings that may be visualized in a score-loading plot (biplot) and used for process monitoring. The difficulty with this is that often more than two principal or PLS components have to be used, resulting in a need to monitor more than one such plot. However, it has recently been shown that for a scalar response variable all PLSR/PCR models can be compressed into equivalent PLSR models with two components only. After a summary of the underlying theory, the present paper shows how such two-component PLS (2PLS) models can be utilized in informative score-loading biplots for process understanding and monitoring. The possible utilization of known projection model monitoring statistics and variable contribution plots is also discussed, and a new method for visualization of contributions directly in the biplot is presented. An industrial data example is included.     

An extension of an over-dispersion test for count data

While over-dispersion in capture-recapture studies is well known to lead to poor estimation of population size, current diagnostic tools to detect the presence of heterogeneity have not been specifically developed for capture-recapture studies. To address this, a simple and efficient method of testing for over-dispersion in zero-truncated count data is developed and evaluated. The proposed method generalizes an over-dispersion test previously suggested for un-truncated count data and may also be used for testing residual over-dispersion in zero-inflation data. Simulations suggest that the asymptotic distribution of the test statistic is standard normal and that this approximation is also reasonable for small sample sizes. The method is also shown to be more efficient than an existing test for over-dispersion adapted for the capture-recapture setting. Studies with zero-truncated and zero-inflated count data are used to illustrate the test procedures.     

Visible speech improves human language understanding: Implications for speech processing systems

Evidence from the study of human language understanding is presented suggesting that our ability to perceive visible speech can greatly influence our ability to understand and remember spoken language. A view of the speaker's face can greatly aid in the perception of ambiguous or noisy speech and can aid cognitive processing of speech leading to better understanding and recall. Some of these effects have been replicated using computer synthesized visual and auditory speech. Thus, it appears that when giving an interface a voice, it may be best to give it a face too.     

A Bayesian network-based framework for semantic image understanding

Current research in content-based semantic image understanding is largely confined to exemplar-based approaches built on low-level feature extraction and classification. The ability to extract both low-level and semantic features and perform knowledge integration of different types of features is expected to raise semantic image understanding to a new level. Belief networks, or Bayesian networks (BN), have proven to be an effective knowledge representation and inference engine in artificial intelligence and expert systems research. Their effectiveness is due to the ability to explicitly integrate domain knowledge in the network structure and to reduce a joint probability distribution to conditional independence relationships. In this paper, we present a general-purpose knowledge integration framework that employs BN in integrating both low-level and semantic features. The efficacy of this framework is demonstrated via three applications involving semantic understanding of pictorial images. The first application aims at detecting main photographic subjects in an image, the second aims at selecting the most appealing image in an event, and the third aims at classifying images into indoor or outdoor scenes. With these diverse examples, we demonstrate that effective inference engines can be built within this powerful and flexible framework according to specific domain knowledge and available training data to solve inherently uncertain vision problems.     

Other bodies,other minds: A machine incarnation of an old philosophical problem

Any attempt to explain the mind by building machines with minds must confront the other-minds problem: How can we tell whether any body other than our own has a mind when the only way to know is by being the other body? In practice we all use some form of Turing Test: If it can do everything a body with a mind can do such that we can't tell them apart, we have no basis for doubting it has a mind. But what is “everything” a body with a mind can do? Turing's original “pen-pal” version of the Turing Test (the TT) only tested linguistic capacity, but Searle has shown that a mindless symbol-manipulator could pass the TT undetected. The Total Turing Test (TTT) calls instead for all of our linguistic and robotic capacities; immune to Searle's argument, it suggests how to ground a symbol manipulating system in the capacity to pick out the objects its symbols refer to. No Turing Test, however, can guarantee that a body has a mind. Worse, nothing in the explanation of its successful performance requires a model to have a mind at all. Minds are hence very different from the unobservables of physics (e.g., superstrings); and Turing Testing, though essential for machine-modeling the mind, can really only yield an explanation of the body.     

自然语言理解在Web数据挖掘中的应用

Internet的迅猛发展,使其日益成为人们查找有用数据的重要来源。一般的搜索引擎是基于关键字的查询,命中率较低,且不能针对特定用户给出特定服务。提出了将自然语言理解技术与Web数据挖掘相结合,根据用户的特殊需求定制个性化的Web数据挖掘系统,给出了面向新闻挖掘这一特定领域的Web挖掘系统News-Miner的应用方案及设计实现。初步实验结果表明该方案是可行的。该方法可方便地扩展到其它专业应用领域。     

一种智能型流量计

在流量计中嵌入单片微型计算机,使之具有了一定的人工智能,用于测量各种流体的流量,并可实现由上位微型计算机对组网的智能型流量计进行集中管理。本文简要地介绍了这种智能型流量计的硬件和软件的工作原理及设计方法。     

The Status and Future of the Turing Test

The standard interpretation of the imitation game is defended over the rival gender interpretation though it is noted that Turing himself proposed several variations of his imitation game. The Turing test is then justified as an inductive test not as an operational definition as commonly suggested. Turing's famous prediction about his test being passed at the 70% level is disconfirmed by the results of the Loebner 2000 contest and the absence of any serious Turing test competitors from AI on the horizon. But, reports of the death of the Turing test and AI are premature. AI continues to flourish and the test continues to play an important philosophical role in AI. Intelligence attribution, methodological, and visionary arguments are given in defense of a continuing role for the Turing test. With regard to Turing's predictions one is disconfirmed, one is confirmed, but another is still outstanding.     

一种文稿理解算法

提出一种模式识别方法,它能有效地用于有监督的连续语音识别、文稿理解和数据文件分析。在PⅡ350微机上的实际运行表明,无论是有监督的连续语音识别、还是文稿理解或数据文件分析,其结果都人脑的判别一致。     

The design and implementation of a distributed image understanding system

Computer vision is concerned with extracting information about a scene by analyzing images of that scene. Performing any computer vision task requires an enormous amount of computation. Exploiting parallelism appears to be a promising way to improve the performance of computer vision systems. Past work in this area has focused on applying parallel processing techniques to image-operator level parallelism. In this article, we discuss the parallelism of computer vision in the control level and present a distributed image understanding system (DIUS).In DIUS, control-level parallelism is exploited by a dynamic scheduler. Furthermore, two levels of rules are used in the control mechanism. Meta-rules are concerned mainly with which strategy should be driven and the execution sequence of the system; control rules determine which task needs to be done next. A prototype system has been implemented within a parallel programming environment, Strand, which provides various virtual architectures mapping to either a shared-memory machine, Sequent, or to the Sun network.     

图形语义理解的三维重建技术在相图中的应用

分析了目前进行金属相图的三维重建时面临的困难和存在的问题,介绍了基于图形语义理解的三维重建技术在相图领域中的应用方法,指出方法的可行性和必要性。最后,指明了目前研究的不足之处,以及下一步研究的方向和重点。