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.     

Intentionality and computationalism: minds,machines, Searle and Harnad

Abstract

Searle (1980, 1989) has produced a number of arguments purporting to show that computer programs, no matter how intelligently they may act, lack ‘ intentionality’ Recently, Harnad (1989) has accepted Searle' s arguments as having ‘ shaken the foundations of Artificial Intelligence’ (p. 5). To deal with Searle' s arguments, Harnad has introduced the need for ‘ noncomputational devices’ (e.g. transducers) to realize ‘ symbol grounding’ This paper critically examines both Searle' s and Hamad' s arguments and concludes that the foundations of AT remain unchanged by these arguments, that the Turing Test remains adequate as a test of intentionality, and that the philosophical position of computationalism remains perfectly reasonable as a working hypothesis for the task of describing and embodying intentionality in brains and machines.     

视频图像理解的一般性框架研究*

视频图像理解侧重于对视频序列进行解释,既涉及到图像的空间特性,也涉及到视频序列的时间特性,是目前计算机视觉领域的一个研究热点。回顾了视频图像理解方法的研究现状,提出视频图像理解的一般性框架,包括层次结构、涉及的技术领域和应用的系统结构,并以一个实际应用作为示例解释该框架的层次结构。     

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.     

“三维视觉—语言”推理技术的前沿研究与最新趋势

三维视觉推理的核心思想是对点云场景中的视觉主体间的关系进行理解。非专业用户难以向计算机传达自己的意图,从而限制了该技术的普及与推广。为此,研究人员以自然语言作为语义背景和查询条件反映用户意图,进而与点云的信息进行交互以完成相应的任务。此种范式称做“三维视觉—语言”推理,在自动驾驶、机器人导航以及人机交互等众多领域广泛应用,已经成为计算机视觉领域中备受瞩目的研究方向。过去几年间,“三维视觉—语言”推理技术迅猛发展,呈现出百花齐放的趋势,但是目前依然缺乏对最新研究进展的全面总结。本文聚焦于两类最具代表性的研究工作,锚框预测和内容生成类的“三维视觉—语言”推理技术,系统性概括领域内研究的最新进展。首先,本文总结了“三维视觉—语言”推理的问题定义和现存挑战,同时概述了一些常见的骨干网络。其次,本文按照方法所关注的下游场景,对两类“三维视觉—语言”推理技术做了进一步细分,并深入探讨了各方法的优缺点。接下来,本文对比分析了各类方法在不同基准数据集上的性能。最后,本文展望了“三维视觉—语言”推理技术的未来发展前景,以期促进该领域的深入研究与广泛应用。     

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.     

国内外隐喻知识库建设综述

就国外的几个隐喻知识库,包括Master Metaphor List、Senseframe、MetaBank、Metalude、Hamburg Metaphor Database、ATTMeta以及国内的隐喻句库的建设进行综述,并对上述隐喻知识库作了简单评述。最后提出了一种建立面向计算的大规模汉语隐喻知识库构建的设想。     

Neural Networks for Omni-View Road Image Understanding

This paper presents a new approach to the outdoor roa scene understanding by using omni-view images and backpropagation networks.Both the road directions used for vehicle heading and th road categories used for vehicle localization are determined by the integrated system.There are three main features about the work.First,an omni-view image sensor is used to extract image samples,and the original image is preprocessed so that the inputs of the network is rotation-invariant and simple.Second,the problem of the network size,especially the number of the hidden units,is decided by the analysis of systematic experimental results.Finally,the internal representation,which reveals the properties of the neural network,is analyzed in the view point of visual signal processing.Experimental results with real scene images are encouraging.     

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.     

面向智能服务系统的时间语义理解

针对智能服务制定与提供过程中时间语义处理难的问题,提出一种面向智能服务系统的时间语义信息理解模型。在自然语言描述的服务消息文本上,实现对时间信息的抽取、映射和语义建模,从而为一般的智能服务系统提供通用的时间语义表达模式。首先,模型采用启发式策略自动抽取时间短语并构建时间信息知识库,无需人工干预;然后,提出一种基于时间基元的时间信息映射方法,实现了绝对时间的量化表达以及相对时间的逻辑推理;最后,综合利用时间信息与上下文信息构建时间语义模型。实验结果表明,该模型在服务自然语言文本测试集上,时间信息抽取准确率高达97.58%,时间信息映射准确率高于85%,语义建模效果良好。     

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.     

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。     

Architectural layer recovery for software system understanding and evolution

This paper presents an approach to identify software layers for the understanding and evolution of software systems implemented with any object‐oriented programming language. The approach first identifies relations between the classes of a software system and then uses a link analysis algorithm (i.e. the Kleinberg algorithm) to group them into layers. Additionally to assess the approach and the underlying techniques, the paper also presents a prototype of a supporting tool and the results from a case study. Copyright © 2010 John Wiley & Sons, Ltd.     

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.     

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.     

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.