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Virtue ethics has many times been suggested as a promising recipe for the construction of artificial moral agents due to its emphasis on moral character and learning. However, given the complex nature of the theory, hardly any work has de facto attempted to implement the core tenets of virtue ethics in moral machines. The main goal of this paper is to demonstrate how virtue ethics can be taken all the way from theory to machine implementation. To achieve this goal, we critically explore the possibilities and challenges for virtue ethics from a computational perspective. Drawing on previous conceptual and technical work, we outline a version of artificial virtue based on moral functionalism, connectionist bottom–up learning, and eudaimonic reward. We then describe how core features of the outlined theory can be interpreted in terms of functionality, which in turn informs the design of components necessary for virtuous cognition. Finally, we present a comprehensive framework for the technical development of artificial virtuous agents and discuss how they can be implemented in moral environments. [ABSTRACT FROM AUTHOR]

For millions of years, biological creatures have dealt with the world without being able to see it; however, the change in the atmospheric condition during the Cambrian period and the subsequent increase of light, triggered the sudden evolution of vision and the consequent evolutionary benefits. Nevertheless, how from simple organisms to more complex animals have been able to generate meaning from the light who fell in their eyes and successfully engage the visual world remains unknown. As shown by many psychophysical experiments, biological visual systems cannot measure the physical properties of the world. The light projected onto the retina is, in fact, unable to specify the physical properties of the world in which humans and other visually 'intelligent' animals behave; however, visual behaviours are habitually successful. Through psychophysical evidence, examples of the functioning of Artificial Neural Networks (ANNs) and a reflection upon visual appreciation in the cultural and artistic context, this paper shows (a) how vision emerged by random trial and error during evolution and lifetime learning; (b) how the functioning of ANNs may provide evidence and insights on how machine and human vision works; and (c) how rethinking vision theory in terms of trial and error may offer a new approach to better understand vision—biological and artificial—and reveal new insights into why we like what we like. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to develop Artificial Neural Network (ANN)-based apple classifier. Testing effort is calculated using ANN method. The complete system is divided into two modules. In the first module, input (surface level apple quality parameter) from the different sources is collected by the software developed in Visual Basic through different input device like web camera, weight machine, etc. In the second module, the input data are used by ANN simulator to classify the apple according to their quality. The final result of an ANN model for apple classification is discussed; however, the modeling results showed that there is excellent agreement between the experimental data and predicted values. A low level of error prediction confirmed the fact that the Neural Network model is an effective instrument of the apple quality estimation. There is not any misclassification during testing. The paper presents alternative method for quality assessment of apple and provides consumers with a safer food supply. [ABSTRACT FROM AUTHOR]

This paper describes a new apple classification system based on machine vision and artificial neural network (ANN), which classifies apple in real time on the basis of physical parameters of apple such as size, color and external defects. A specific hardware subsystem has been developed and described for every stage of input and output. The hardware subsystem is interfaced with the software to make the whole system automatic. The purpose of this paper is to automate apple classification. Presently, ANN is used in a wide range of classification applications. We have trained a back-propagation neural network to classify apple. Two sets of variables are used for the training purpose. First set is the independent variable, which is the surface level apple quality parameter. Second set is the dependent variable, which is the quality of the apple. The results of ANN model are discussed; however, the modeling results showed that there is an excellent agreement between the experimental data and predicted values, with a high determination coefficient, very good performance, fewer parameters, shorter calculation time and lower prediction error. The classification accuracy achieved is high, showing that a neural network is capable of making such classification. A low level of errors in classification confirmed that the neural network models are an effective instrument for apple classification. This model might be an alternative method for assessing the quality of apple and provide consumers with a safer food supply. [ABSTRACT FROM AUTHOR]

Computational imagination (CI) conceives imagination as an agent's simulated sensorimotor interaction with the environment in the absence of sensory feedback, predicting consequences based on this interaction (Marques and Holland in Neurocomputing 72:743-759, ). Its bedrock is the simulation hypothesis whereby imagination resembles seeing or doing something in reality as both involve similar neural structures in the brain (Hesslow in Trends Cogn Sci 6(6):242-247, ). This paper raises two-forked doubts: (1) neural-level equivalence is escalated to make phenomenological equivalence. Even at an abstract level, many imagined and real actions turn out to be dissimilar. More so, some imagined actions have no corresponding real actions and vice versa, even though neural regions involved in imaginings and real action-perception are the same (Sect. 1). (2) At the implementation level, the hypothesis presents a mutually exclusive view of imagination and perception whereby imagination functions in the absence of the sensory feedback and is action based. Both these issues are contested here: Neither imagination functions in the absence of perception nor all forms of imaginings are action based; it is, rather, about conceiving possibilities which emerge during the perceptual stage itself (Sect. 2). For the modal aspect to arise, it is submitted that an integrative framework is required which Kant can provide for whom imagination is an indispensable part of perception. Kant's views on concept-formation are presented here to illustrate this aspect (Sect. 3). The Paper is concluded with emphasizing the relevance of Kant's views to the problems identified in the two sections. [ABSTRACT FROM AUTHOR]

As development of the speech recognition system entirely depends upon the spoken language used for its development, and the very fact that speech technology is highly language dependent and reverse engineering is not possible, there is an utmost need to develop such systems for Indian languages. In this paper we present the implementation of a time delay neural network system (TDNN) in a modular fashion by exploiting the hidden structure of previously phonetic subcategory network for recognition of Hindi consonants. For the present study we have selected all the Hindi phonemes for srecognition. A vocabulary of 207 Hindi words was designed for the task-specific environment and used as a database. For the recognition of phoneme, a three-layered network was constructed and the network was trained using the back propagation learning algorithm. Experiments were conducted to categorize the Hindi voiced, unvoiced stops, semi vowels, vowels, nasals and fricatives. A close observation of confusion matrix of Hindi stops revealed maximum confusion of retroflex stops with their non-retroflex counterparts. [ABSTRACT FROM AUTHOR]

The prime objective of this paper is to conduct phoneme categorization experiments for Indian languages. In this direction a major effort has been made to categorize Hindi phonemes using a time delay neural network (TDNN), and compare the recognition scores with other languages. A total of six neural nets aimed at the major coarse of phonetic classes in Hindi were trained. Evaluation of each net on 350 training tokens and 40 test tokens revealed a 99% recognition rate for vowel classes, 87% for unvoiced stops, 82% for voiced stops, 94.7% for semi vowels, 98.1% for nasals and 96.4% for fricatives. A new feature vector normalisation technique has been proposed to improve the recognition scores. [ABSTRACT FROM AUTHOR]

There are many studies conducted on recommendation systems, most of which are focused on recommending items to users and vice versa. Nowadays, social networks are complicated due to carrying vast arrays of data about individuals and organizations. In today's competitive environment, companies face two significant problems: supplying resources and attracting new customers. Even the concept of supply-chain management in a virtual environment is changed. In this article, we propose a new and innovative combination approach to recommend organizational people in social networks based on organizational communication and SCM. The proposed approach uses a hybrid strategy that combines basic collaborative filtering and demographic recommendation systems, using data mining, artificial neural networks, and fuzzy techniques. The results of experiments and evaluations based on a real dataset collected from the LinkedIn social network showed that the hybrid recommendation system has higher accuracy and speed than other essential methods, even substantially has eliminated the fundamental problems with such systems, such as cold start, scalability, diversity, and serendipity. [ABSTRACT FROM AUTHOR]

Computed tomography images are widely used in the diagnosis of intracranial hematoma and hemorrhage. This paper presents a new approach for automated diagnosis based on classification of the normal and abnormal images of computed tomography. The computed tomography images used in the classification consists of non-enhanced computed tomography images. The proposed method consists of four stages namely pre-processing, feature extraction, feature reduction and classification. The discrete wavelet transform coefficients are the features extracted in this method. The essential coefficients are selected by the principal component analysis. The features derived are used to train the binary classifier, which infer automatically whether the image is that of a normal brain or a pathological brain, suffering from brain lesion. The proposed method has been evaluated on a dataset of 80 images. A classification with a success of 92, 97 and 98 % has been obtained by artificial neural network, k-nearest neighbor and support vector machine, respectively. This result shows that the proposed technique is robust and effective. [ABSTRACT FROM AUTHOR]

Inspired by the operation of human social organisation, this paper presents a new architecture—a pyramid-committee—for developing society-oriented intelligence, whose structure imitates the organisation of human society in its decision making. The system takes a pyramid-like hierarchical structure with links in the pyramid forming a semi-lattice, which relate not only to nodes in the same layer, but also to others in different layers. The output of the system is a result of the negotiation and balancing of different interests. For such a system to function, the main difficulties concern the complicated relationships between different factors or agents. Focussing on the airport environment audit, we discuss the development of a model framework and the role of neural networks. [ABSTRACT FROM AUTHOR]

Elon Musk's plan to make Twitter's algorithms open source in order to increase public trust in the company may not be an effective strategy. While open source algorithms provide transparency, they may not be accessible or understandable to the average person. Trustworthiness is essential before placing trust, and accessibility is a key criterion for judging trustworthiness. Instead, the author suggests that Explainable AI (XAI) methods, which aim to make complex algorithms intelligible to relevant users, may be a more promising solution. Open-sourcing algorithms alone may not be sufficient to build trust between Twitter and the general public. [Extracted from the article]

These AI human faces are both faces of missing humans (who do not exist in the actual world) and faces of algorithmically-generated ghosts, as paradoxically shown by the project I DoppelGANger.agency i (Azar [3]), which turns AI-generated faces into street posters for missing persons (Fig. Computer vision is not, in general, meant to be a simulation of a biological visual system; that is not the epistemic role of a theory of human vision for the computer vision community. They establish a conceptual and material link between photographic practice and deep learning computer vision, and the implied "optical perspective of the computer vision system itself". It includes conventional private companies which sell machine vision for profit - but also corporate-funded AI labs which publish research openly in the scientific community; privately-funded open-source software libraries, on which much of AI depends; and infrastructure, from cloud computing to hard silicon. [Extracted from the article]

Artificial intelligence (AI) has been adopted in a wide range of domains. This shows the imperative need to contribute to making citizens insightful actors in debates and decisions involving the adoption of AI mechanisms. Currently, existing approaches to the teaching of basic AI concepts through programming treat machine intelligence as an external element/module. After being trained, that external module is coupled to the main application. Combining block-based programming and WiSARD weightless artificial neural networks, this article presents the conceptualization and design of a new methodology, AI from concrete to abstract (AIcon2abs), to endow general people (including children) with a minimum understanding of what AI means. The main strategy adopted was to include AI training and classification primitives as blocks that compose an otherwise conventional computer program. This way, the programmer can use these blocks as he/she uses other programming constructs. In order to achieve this purpose, we also propose BlockWiSARD, a block-based programming environment designed to promote the demystification of artificial intelligence via practical activities related to the development of learning machines, as well as through the observation of their learning process. As a beneficial side effect of BlockWiSARD, the difference between a program capable of learning from data and a conventional computer program becomes more evident. In addition, the simplicity of the WiSARD weightless artificial neural network model enables easy visualization and understanding of training and classification tasks internal realization. [ABSTRACT FROM AUTHOR]

This article offers a critical inquiry of contemporary neural network models as an instance of machine learning, from an interdisciplinary perspective of AI studies and performativity. It shows the limits on the architecture of these network systems due to the misemployment of 'natural' performance, and it offers 'context' as a variable from a performative approach, instead of a constant. The article begins with a brief review of machine learning-based natural language processing systems and continues with a concentration on the relevant model of recurrent neural networks, which is applied in most commercial research such as Facebook AI Research. It demonstrates that the logic of performativity is not brought into account in all recurrent nets, which is an integral part of human performance and languaging, and it argues that recurrent network models, in particular, fail to grasp human performativity. This logic works similarly to the theory of performativity articulated by Jacques Derrida in his critique of John L. Austin's concept of the performative. Applying Jacques Derrida's work on performativity, and linguistic traces as spatially organized entities that allow for this notion of performance, the article argues that recurrent nets fall into the trap of taking 'context' as a constant, of treating human performance as a 'natural' fix to be encoded, instead of performative. Lastly, the article applies its proposal more concretely to the case of Facebook AI Research's Alice and Bob. [ABSTRACT FROM AUTHOR]

Looking back on the development of computer technology, particularly in the context of manufacturing, we can distinguish three big waves of technological exuberance with a wave length of roughly 30 years: In the first wave, during the 1950s, mainframe computers at that time were conceptualized as “electronic brains” and envisaged as central control unit of an “automatic factory” (Wiener). Thirty years later, during the 1980s, knowledge-based systems in computer-integrated manufacturing (CIM) were adored as the computational core of the “unmanned factory”. Both waves dismally stranded on the contumacies of reality. Nevertheless, again thirty years later, we now experience the departure of the “smart factory” based on networks of “artificially intelligent” multi-agent or “cyber-physical systems” (often also addressed as “internet of things”). From the very beginning, these technological exuberances rooted in mistaken metaphors describing the artifacts (e.g. “electronic brain”, “knowledge-based” or “intelligent systems”) and, hence, in delusions about the true nature of computer systems. The behaviour of computers is, as computing science teaches us, strictly restrained to executing computable functions by means of algorithms, it thus neither resembles the performance of a brain as part of a complex sensitive living body nor is it in any meaningful sense “knowledgeable” or “intelligent” (this predicate remaining reserved for the programmer designing the algorithms). When the delusion of being able to implement “smart factories”, despite the countless accomplishment failures before, gains momentum anew, it appears absolutely essential to reflect on underlying misconceptions. [ABSTRACT FROM AUTHOR]

This simulation study is a fuzzy model-based neural network control method. The basic idea is to consider the application of a special type of neural networks based on radial basis function, which belongs to a class of associative memory neural networks. The novelty of this approach is the use of an RBF neural network controller in a model reference adaptive control architecture, based on a one-step-ahead Takagi-Sugeno fuzzy model. The objective is to control the concentration in a continuous stirred-tank reactor highly non-linear system and to assure its stability by limiting the temperature rise generated from the irreversible exothermic reaction. This contribution will help to reduce environmental impact of chemical waste. [ABSTRACT FROM AUTHOR]

Artificial Intelligence (AI) is now a days gaining immense importance and is becoming a key technology in many fields ranging from banking industry, to travel industry, to communication industry, and to robotic industry. The use of AI in medical diagnosis too is becoming increasingly popular and has been widely used in the diagnosis of tumors, cancers, hepatitis, lung diseases, etc. Numerous algorithms have been designed that help in the process of decision making by analyzing the hidden patterns in previously held information. The main objective of this manuscript is to apply multiple algorithms to a problem in the domain of medical diagnosis and analyze their efficiency in predicting the results. The problem selected for the study is the diagnosis of diabetes. Authors have identified ten parameters that play an important role in diabetes and prepared a rich database of training data which served as the backbone of the prediction algorithms. Keeping in view this training data, authors implemented three algorithms [Naïve Bayes, artificial neural networks (ANN), and K-nearest neighbors (KNN)] and developed prediction models. To calculate the efficiency, the results of prediction system were compared with the actual medical diagnosis of the subjects. The results indicate that the ANN is the best predictor with the accuracy of about 96 % which was followed by Naïve Bayes networks having an accuracy of about 95 % and the KNN came to be the worst predictor having an accuracy of about 91 %. [ABSTRACT FROM AUTHOR]

Does connectionism spell doom for folk psychology? I examine the proposal that cognitive representational states such as beliefs can play no role if connectionist models—interpreted as radical new cognitive theories—take hold and replace other cognitive theories. Though I accept that connectionist theories are radical theories that shed light on cognition, I reject the conclusion that neural networks do not represent. Indeed, I argue that neural networks may actually give us a better working notion of cognitive representational states such as beliefs, and in so doing give us a better understanding of how these states might be instantiated in neural wetware. [ABSTRACT FROM AUTHOR]