Machine Intelligence

Emotion recognition is one of the important highlights of human emotional intelligence and has long been studied to be incorporated with machine intelligence argued to make machines even more intelligent. This paper aims to contribute to this field of study by enabling machines to recognize emotion from facial electromyogram (EMG) signals. This includes a compilation of the groups attempt to recognize basic facial expressions namely happy, angry, and sad through the use of EMG signals from facial muscles. The group extracted features from the three EMG signals from the face of two human subjects, a male and a female, and analyzed these features to serve as feature templates. Using a minimum-distance classifier, recognition rates exceeded the target accuracy - 85 percent - reaching 94.44 percent for both the male and female subjects.

Mind<>Computer: Attempts to mimic human intelligence through methods of classical computing have failed because implementing basic elements of rationality has proven obstinate to the design criterion of machine intelligence. A radical definition of Consciousness describing awareness, as the dynamic representation of a noumenon comprised of three base states; and not itself fundamental as generally defined in the current reductionistic view of the standard model, which has created an intractable hard problem of consciousness as defined by Chalmers. By clarifying the definition of matter a broader ontological quantum theory removes immateriality from the Cartesian split bringing mind into the physical realm for pragmatic investigation. Evidence suggests that the brain is a naturally occurring quantum computer, but the brain not being paramount to awareness does not itself evanesce consciousness without the interaction of a nonlocal conscious process; because Mind <> computer and cannot be reduced to brain states alone. The proposed cosmology of consciousness is indicative of a teleological principle as an inherent part of a conscious universe. By applying the parameters of quantum brain dynamics to the stack of a specialized hybrid electronic optical quantum computer with a heterosoric molecular crystal core, consciousness evanesces through entrainment of the non local conscious processes. This 'extracellular containment of natural intelligence' probably represents the only viable direction for AI to simulate 'conscious computing' because true consciousness = life.

The study of frog is important since the chemical compound in their skin extracts peptides with antimicrobial activity becomes a valuable tool for pharmacology and biochemistry research. Instead of depending on physically observation procedure to identify the particular species, this study proposes an automated frog sound identification system for recognizing frog species. 990 data species that collected from internet database and recorded from Malaysia forest are sampled. Two features i.e. Mel Frequency Cepstrum Coefficient (MFCC) and Linear Predictive Coding (LPC) have been used in the feature extraction techniques. Subsequently Support Vector Machine (SVM) and k Nearest Neighbor (kNN) are obtained to evaluate the performance in the identification system. The experimental results show that kNN gives better performance with accuracy up to 80.33% to 98.4%compare than SVM.

Mind<>Computer: Attempts to mimic human intelligence through methods of classical computing have failed because implementing basic elements of rationality has proven obstinate to the design criterion of machine intelligence. A radical definition of Consciousness describing awareness, as the dynamic representation of a noumenon comprised of three base states; and not itself fundamental as generally defined in the current reductionistic view of the standard model, which has created an intractable hard problem of consciousness as defined by Chalmers. By clarifying the definition of matter a broader ontological quantum theory removes immateriality from the Cartesian split bringing mind into the physical realm for pragmatic investigation. Evidence suggests that the brain is a naturally occurring quantum computer, but the brain not being paramount to awareness does not itself evanesce consciousness without the interaction of a nonlocal conscious process; because Mind <> computer and cannot be reduced to brain states alone. The proposed cosmology of consciousness is indicative of a teleological principle as an inherent part of a conscious universe. By applying the parameters of quantum brain dynamics to the stack of a specialized hybrid electronic optical quantum computer with a heterosoric molecular crystal core, consciousness evanesces through entrainment of the non local conscious processes. This 'extracellular containment of natural intelligence' probably represents the only viable direction for AI to simulate 'conscious computing' because true consciousness = life.

The idea is to conceive an intelligent computing system of decision-making aid, in this context of teaching training, the quantification of the human behaviors is the key to this system's innovation. Seeing that it gives opportunity of measuring the professional acts of the teachers in class. This makes it possible to locate these practices compared to the pedagogical theories, into force and especially compared to the constructive approach. In addition, this system helps to identify the student's styles of learning; therefore it places, at the disposal, information which facilitates to the teachers the adaptation of their pedagogical methods so that the student benefit a maximum assimilation. For what concerns them, the students also profit from an innovating alternative: result of the web based performances analysis in summative evaluation. It is a generated report, for each pupil, automatically at the end of the seizure of the marks stipulating in details their weak points and specifying the adequate remedies in terms of activities to be realized to proceed to reflexive regulations in order to raise their levels of assimilation. An able system, starting from observable behaviors in classrooms and of performances seized remotely via the Web, to quantify in a rational way the cognitive and emotional states of the actors of the context of teaching training. An expert system whose general objectives would be: classifying the profiles of the teachers starting from their professional competences based on their pedagogy, behavior and attitude, classifying the learners' profiles by analyzing their interactions and analyzing their performances in summative evaluations, adapting the resources of the teacher's pedagogical methods in order to have a better coherence with the profiles of identities, the profiles of motivation and the profiles of comprehension of learner.

The success of genetic algorithm in nding relatively good solutions to NP-complete problems such as the traveling salesman problem and job-shop scheduling problem provided a good starting point for a machine intelligent method of nding Golomb Rulers. These rulers have been applied to radio astronomy, X-ray crystallography, circuit layout and geographical mapping. Currently the shortest lengths of the rst sixteen rulers are known. The nature of NP-complete makes the search for higher order rulers difcult and very time consuming. While the shortest lengths for each order are important as a mathematical exercise, nding relatively short high order valid rulers has a more important impact on real world applications. Genetic algorithm has shown good results in nding usable Golomb Rulers in minutes or hours instead of weeks or months.

A s the subtitle indicates, the book by David Fogel is not just a technical monograph about evolutionary computation, a subfield of computer science that deals with algorithms gleaned from the model of organic evolution, but also emphasizes a philosophical view of machine intelligence (artificial intelligence) and its relation to evolutionary processes. The definition of intelligence as "the capability of a system to adapt its behavior to meet its goals in a range of environments…" (p. 24) dates back to the '60s, when Lawrence J. Fogel (David's father) developed the evolutionary programming algorithm, one of the three basic paradigms of evolutionary computation (the other two are evolution strategies and genetic algorithms ; also see Bäck for an overview of evolutionary algorithms. Both authors convincingly argue that the goal-driven adaptation of behavior is achieved by Darwinian evolution, which can appropriately be emulated on a computer by evolutionary algorithms. Moreover, Fogel claims that "Evolution provides the solution to the problem of how to solve problems." (p. 259), provided that evolutionary computation relies on the careful observation and abstraction of the process of natural evolution.

Machine intelligence has been a research hotspot in mechatronics in recent years. This research presents a 2D/3D object recognition system for enhancing the intelligence of an industrial robot (KUKA robot). The image processing and object recognition algorithms were developed using software packages VisionPro and LabVIEW. Experiments were carried out to verify the performance of the system. It can be concluded that the system is able to recognise any general 2D object within a time of six seconds. The performance of the system in 3D object recognition is slower compared to 2D objects, which is largely affected by the number of trained images stored in the database, the complexity of the object, and the presence of similar objects in the database. Despite the complexity of the objects being recognised, both the overall accuracy and success rate of the system are close to 100%. The developed system proved to be robust and allows for automatic recognition of objects in the manufacturing environment described in this paper.

Attribution of paintings is a critical problem in art history. This study extends machine learning analysis to surface topography of painted works. A controlled study of positive attribution was designed with paintings produced by a class of art students. The paintings were scanned using a chromatic confocal optical profilometer to produce surface height data. The surface data were divided into virtual patches and used to train an ensemble of convolutional neural networks (CNNs) for attribution. Over a range of square patch sizes from 0.5 to 60 mm, the resulting attribution was found to be 60-96% accurate, and, when comparing regions of different color, was nearly twice as accurate as CNNs using color images of the paintings. Remarkably, short length scales, even as small as a bristle diameter, were the key to reliably distinguishing among artists. These results show promise for real-world attribution, particularly in the case of workshop practice.

This paper reviews the field of artificial intelligence focusing on embodied artificial intelligence. It also considers models of artificial consciousness, agent-based artificial intelligence and the philosophical commentary on artificial intelligence. It concludes that there is almost no consensus nor formalism in the field and that the achievements of the field are meager.

I will, in my project, go into the discovery of artificial intelligence technology, its development, today’s application fields and assess possible threats it imposes if it remains unchecked. 

(Projemde, yapay zekâ teknolojisinin ortaya çıkışını, gelişmesini, günümüzde yarar sağladığı alanları ve kontrol edilmediği takdirde gelecekte vereceği zararları incelemekteyim.)

The notion of computation has changed the world more than any previous expressions of knowledge. However, as know-how in its particular algorithmic embodiment, computation is closed to meaning. Therefore, computer-based data processing can only mimic life's creative aspects, without being creative itself. AI's current record of accomplishments shows that it automates tasks associated with intelligence, without being intelligent itself. Mistaking the abstract (computation) for the concrete (computer) has led to the religion of "everything is an output of computation"-even the humankind that conceived the computer. The hypostatized role of computers explains the increased dependence on them. The convergence machine called deep learning is only the most recent form through which the deterministic theology of the machine claims more than what it actually is: extremely effective data processing. A proper understanding of complexity, as well as the need to distinguish between the reactive nature of the artificial and the anticipatory nature of the living are suggested as practical responses to the challenges posed by machine theology.

In the 1950s, Alan Turing proposed his influential test for machine intelligence, which involved a teletyped dialogue between a human player, a machine, and an interrogator. Two readings of Turing's rules for the test have been given. According to the standard reading of Turing's words, the goal of the interrogator was to discover which was the human being and which was the machine, while the goal of the machine was to be indistinguishable from a human being. According to the literal reading, the goal of the machine was to simulate a man imitating a woman, while the interrogator -unaware of the real purpose of the test -was attempting to determine which of the two contestants was the woman and which was the man. The present work offers a study of Turing's rules for the test in the context of his advocated purpose and his other texts. The conclusion is that there are several independent and mutually reinforcing lines of evidence that support the standard reading, while fitting the literal reading in Turing's work faces severe interpretative difficulties. So, the controversy over Turing's rules should be settled in favor of the standard reading.

Nowadays the work of Alan Turing and Kurt Gödel rightly takes center stage in discussions of the relationships between computability and the mind. After decades of comparative neglect, Turing's 1936 paper "On Computable Numbers" is now regarded as the foundation stone of computability theory, and it is the fons et origo of the concept of computability employed in modern theoretical computer science. Moreover, Turing's 1950 essay, "Computing Machinery and Intelligence," sparked a rich literature on the mind-machine issue. Gödel's 1931

To make machines look intelligent it was necessary that the sources of their power, the labor force which surrounded and ran them, be rendered invisible. -Simon Schaffer If a machine is expected to be infallible, it cannot also be intelligent. -Alan Turing

Granular Rough Computing is a paradigm within Rough Computing which in turn can be set within the realm of Cognitive Informatics, i.e. machine intelligence tools which emulate cognitive processes in living organisms; its aim is to compute with granules of knowledge that are collective objects formed from individual objects by means of a similarity measure. In this work, we apply the formalism for granule formation proposed by us and studied over last few years which is based on similarity measures called Rough Inclusions. A proper study of rough inclusions has been done within Rough Mereology, a paradigm for approximate reasoning that is rooted in the theory of concepts called Mereology based on the notion of a part instead on the notion of an element like the naive concept theory. We give an outline of granulation theory based on rough inclusions; then, we discuss the most important consequence of similarity among objects in a granule, viz., the hypothesis that granules represent new objects, which preserve the most important features of objects in a granule. This leads to the notion of a granular decision system obtained by means of granulation from an original decision system. The hypothesis that granular decision systems reflect properties of original decision systems to a satisfactory degree, put forth by the author at 2005 and 2006 IEEE GrC conferences, has been tested with very good results. We include here some results of those tests.

The present article endeavors to study Stephen Baxter’s two novels, namely World Engines: Destroyer (2019)
and World Engines: Creator (2020), in order to analyze the role of agential realism of the autopoietic, selfaware machines in creating and sustaining a post-anthropocentric and posthuman world. Also, since the novels
are set against the backdrop of a continuously proliferating manifold of parallel worlds, the study will attempt
to show how adopting a post-anthropocentric viewpoint can help us in ascribing equal agency to each entity
whether it be human or non-human or biological or mechanical. Also, it will be shown that even though the
machine minds appear to be quite godlike in their virtually limitless technological capabilities, still they are
not given the unquestioned and absolute supremacy as befits a Creator God; rather they are placed alongside
and on an equal pedestal with the humans, or specifically the posthuman descendants of mankind.

Alan Turing’s influence on subsequent research in artificial intelligence is undeniable. His proposed test for intelligence remains influential. In this paper, I propose to analyze his conception of intelligence by relying on traditional close reading and language technology. The Turing test is interpreted as an instance of conceptual engineering that rejects the role of the previous linguistic usage, but appeals to intuition pumps instead. Even though many conceive his proposal as a prime case of operationalism, it is more plausibly viewed as a stepping stone toward a future theoretical construal of intelligence in mechanical terms. To complete this picture, his own conceptual network is analyzed through the lens of distributional semantics over the corpus of his written work. As it turns out, Turing’s conceptual engineering of the notion of intelligence is indeed quite similar to providing a precising definition with the aim of revising the usage of the concept. However, that is not its ultimate aim: Turing is after a rich theoretical understanding of thinking in mechanical, i.e., computational, terms.

In this paper, handwritten Kannada and Telugu digits recognition system is proposed based on zone features. The digit image is divided into 64 zones. For each zone, pixel density is computed. The KNN and SVM classifiers are employed to classify the Kannada and Telugu handwritten digits independently and achieved average recognition accuracy of 95.50%, 96.22% and 99.83%, 99.80% respectively. For bilingual digit recognition the KNN and SVM classifiers are used and achieved average recognition accuracy of 96.18%, 97.81% respectively.

Convolutional neural network (CNN) is one of the most prominent architectures and algorithm in Deep Learning. It shows a remarkable improvement in the recognition and classification of objects. This method has also been proven to be very effective in a variety of computer vision and machine learning. As in other deep learning, however, training this approach is interesting yet challenging. Recently, some metaheuristic algorithms have been used to optimize CNN using Genetic Algorithm, Particle Swarm Optimization, Simulated Annealing and Harmony Search. In this paper, another type of metaheuristic algorithms with different strategy has been proposed, i.e. Microcanonical Annealing to optimize Convolutional Neural Network. The performance of the proposed method is tested using the MNIST and CIFAR-10 datasets. Although experiment results of MNIST dataset indicate the increase in computation time (1.02x-1.38x), nevertheless this proposed method can considerably enhance the performance of the original CNN (up to 4.60%). On the CIFAR10 dataset, currently, state of the art is 96.53% using fractional pooling, while this proposed method achieves 99.14%.

Character recognition (CR) has been extensively studied in the last half century and progressed to a level sufficient to produce technology driven applications. Now, the rapidly growing computational power enables the implementation of the present CR methodologies and creates an increasing demand on many emerging application domains, which require more advanced methodologies.

— In this proposal, we present an idea of implementing the concept of Haptic Feedback on humanoid robots for pick and place operations in the industries. This idea helps in rendering human actions through a haptic technology and clone it with the Baxter Research Robot/Robot arm. Accelerometer sensors placed on the palm, forearm and the shoulder helps in determining the position of different joints with respect to X, Y, Z axes. Also Force sensitive resistors are used in order to determine the change in the value of resistance when force is applied on every finger thereby helping in the pick and place functionality.

The workplace influences the safety, health, and productivity of workers at multiple levels. To protect and promote total worker health, smart hardware, and software tools have emerged for the identification, elimination, substitution, and control of occupational hazards. Wearable devices enable constant monitoring of individual workers and the environment, whereas connected worker solutions provide contextual information and decision support. Here, the recent trends in commercial workplace technologies to monitor and manage occupational risks, injuries, accidents, and diseases are reviewed. Workplace safety wearables for safe lifting, ergonomics, hazard identification, sleep monitoring, fatigue management, and heat and cold stress are discussed. Examples of workplace productivity wearables for asset tracking, augmented reality, gesture and motion control, brain wave sensing, and work stress management are given. Workplace health wearables designed for work-related musculoskeletal disorders, functional movement disorders, respiratory hazards, cardiovascular health, outdoor sun exposure, and continuous glucose monitoring are shown. Connected worker platforms are discussed with information about the architecture, system modules, intelligent operations, and industry applications. Predictive analytics provide contextual information about occupational safety risks, resource allocation, equipment failure, and predictive maintenance. Altogether, these examples highlight the ground-level benefits of real-time visibility about frontline workers, work environment, distributed assets, workforce efficiency, and safety compliance.

Hex is a challenging strategy board game for two players. To enhance students' progress in acquiring understanding and practical experience with complex machine intelligence and programming concepts we developed the Machine Intelligence Hex (MIHex) project. The associated undergraduate student assignment is about designing and implementing Hex players and evaluating them in an automated tournament of all programs developed by the class. This article surveys educational aspects of the MIHex project. Additionally, fundamental techniques for game programming as well as specific concepts for Hex board evaluation are reviewed. The MIHex game server and possibilities of tournament organisation are described. We summarise and discuss our experiences from running the MIHex project assignment over four consecutive years. The impact on student motivation and learning benefits are evaluated using questionnaires and interviews.

The Virtual Flute is a web service that provides many thousands of machine-predicted alternative fingerings. Alternative fingerings can offer variations in intonation and timbre, and can be easier to play in different musical contexts. Many play multiphonics. An advanced fingering guide is invaluable when exotic effects and demanding passages are required of the player. The Virtual Flute uses an expert system that predicts musical properties from acoustic impedance spectra. Impedance spectra for the 39,744 acoustic configurations of the flute are generated by a physical model of the instrument, some parameters of which are machinelearned. We report the construction and use of The Virtual Flute

Attribution of paintings is a critical problem in art history. This study extends machine learning analysis to surface topography of painted works. A controlled study of positive attribution was designed with paintings produced by a class of art students. The paintings were scanned using a chromatic confocal optical profilometer to produce surface height data. The surface data were divided into virtual patches and used to train an ensemble of convolutional neural networks (CNNs) for attribution. Over a range of square patch sizes from 0.5 to 60 mm, the resulting attribution was found to be 60–96% accurate, and, when comparing regions of different color, was nearly twice as accurate as CNNs using color images of the paintings. Remarkably, short length scales, even as small as a bristle diameter, were the key to reliably distinguishing among artists. These results show promise for real-world attribution, particularly in the case of workshop practice.

Mind<>Computer: Attempts to mimic human intelligence through methods of classical computing have failed because implementing basic elements of rationality has proven obstinate to the design criterion of machine intelligence. A radical definition of Consciousness describing awareness, as the dynamic representation of a noumenon comprised of three base states; and not itself fundamental as generally defined in the current reductionistic view of the standard model, which has created an intractable hard problem of consciousness as defined by Chalmers. By clarifying the definition of matter a broader ontological quantum theory removes immateriality from the Cartesian split bringing mind into the physical realm for pragmatic investigation. Evidence suggests that the brain is a naturally occurring quantum computer, but the brain not being paramount to awareness does not itself evanesce consciousness without the interaction of a nonlocal conscious process; because Mind <> computer and cannot be reduced to brain states alone. The proposed cosmology of consciousness is indicative of a teleological principle as an inherent part of a conscious universe. By applying the parameters of quantum brain dynamics to the stack of a specialized hybrid electronic optical quantum computer with a heterosoric molecular crystal core, consciousness evanesces through entrainment of the non local conscious processes. This 'extracellular containment of natural intelligence' probably represents the only viable direction for AI to simulate 'conscious computing' because true consciousness = life.

Vision Computing is a branch of artificial intelligence that focuses on providing computers with the functions typical of human vision. Due to recent advances made in technology, scientists have incorporate portable imagers into computer vision systems that can be connected via wireless networks. Advances in Vision Computing: An International Journal (AVC) is an open access, peer-reviewed journal that publishes articles which contribute new results in all areas of the advanced vision computing. The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on understanding advances in vision computing and establishing new collaborations in these areas. Authors are solicited to contribute to the journal by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the areas of vision computing.

The next major feature of the age of conversational services is chatbots in the new era of technology. A Chatbot
framework is a software program that uses natural language to communi- cate with users. Chatbots is a virtual entity that can
effectively explore the use of digital textual competencies with any hu- man being. Recently, their growth as a medium of
conversa- tion between people and computers has taken a great step for- ward. The aim of the chatbot framework for machine
learn- ing and artificial intelligence is to simulate a human conver-sation, maybe through text or speech. Natural Language Processing understands one or more human languages through chatbot software. To simulate informal chat communication, the
chatbot structure combines a language model and compu- tational algorithms covering enormous natural language pro- cessing
techniques. This paper discusses other applications that may be useful for chatbots, such as a computer conversa- tion system,
virtual agent, dialogue system, retrieval of infor- mation, industry, telecommunications, banking, health, cus- tomer call centers,
and e-commerce. It also offers an overview of cloud-based chatbots technology along with the program- ming of chatbots and
programming problems in the chatbot’s present and future periods.

One of the enduring concerns of moral philosophy is deciding who or what is deserving of ethical consideration. Much recent attention has been devoted to the "animal question"--consideration of the moral status of nonhuman animals. In this book, David Gunkel takes up the "machine question": whether and to what extent intelligent and autonomous machines of our own making can be considered to have legitimate moral responsibilities and any legitimate claim to moral consideration.

The machine question poses a fundamental challenge to moral thinking, questioning the traditional philosophical conceptualization of technology as a tool or instrument to be used by human agents. Gunkel begins by addressing the question of machine moral agency: whether a machine might be considered a legitimate moral agent that could be held responsible for decisions and actions. He then approaches the machine question from the other side, considering whether a machine might be a moral patient due legitimate moral consideration. Finally, Gunkel considers some recent innovations in moral philosophy and critical theory that complicate the machine question, deconstructing the binary agent–patient opposition itself.

Technological advances may prompt us to wonder if the science fiction of computers and robots whose actions affect their human companions (think of HAL in 2001: A Space Odyssey) could become science fact. Gunkel's argument promises to influence future considerations of ethics, ourselves, and the other entities who inhabit this world.

CEUR-WS.org, online This volume constitutes the refereed proceedings of the 9th International Conference on ICT in Education, Research, and Industrial Applications, held in Kherson, Ukraine, in June 2013. The 49 papers were carefully reviewed and selected from 124 submissions. The volume opens with the contributions of the invited speakers. Further, the part of the volume containing the papers of the main ICTERI conference is structured in four

Financial fraud is an issue with far reaching consequences in the finance industry, government, corporate sectors, and for ordinary consumers. In-creasing dependence on new technologies such as cloud and mobile computing in recent years has compounded the problem. Traditional methods of detection involve extensive use of auditing, where a trained individual manually observes reports or transactions in an attempt to discover fraudulent behaviour.  This method is not only time consuming, expensive and inaccurate, but in the age of big data it is also impractical. Not surprisingly, financial institutions have turned to automated processes using statistical and computational methods.  This paper presents a comprehensive investigation on financial fraud detection practices using such data mining methods, with a particular focus on computational intelligence-based techniques. Classification of the practices based on key aspects such as detection algorithm used, fraud type investigated, and success rate have been covered.  Issues and challenges associated with the current practices and potential future direction of research have also been identified.

In this era any type of digital media such as image, text, audio and video, all are easily accessible and transferable through
the use of high speed internet. As the use of internet increased, the need of security and authenticity also increased. To secure the
multimedia data such as image, audio, text and video researcher has developed a watermarking technique which gives a watermark
embedding and watermark extraction algorithm, and later it is used for proof of ownership. Here we are proposing a technique on video
watermarking using 2D DWT and 2-level SVD technique. In this paper first we are taking a video which is decomposed into number of
frames and embedding a watermark image on each frame. First 2-D DWT is applied on each frame. Dwt decompose each frame into
low frequency, mid frequency and in high frequency (LL, LH, HL, HH) band then we applied SVD on LL and HL sub-band called it
dual band. SVD convert it into three matrices as U1S1V1’ of single matrix. A watermark image is taken which converted in to gray scale
from RGB scale then resized it. And embed this watermark image into host frames with some scaling factor. After that we again applied
SVD on this watermarked frames which further convert this single matrix into three matrices as U2S2V2’ now multiply S2 matrix with
U1 and V1 matrix component to make it more secure. To demonstrate the authenticity of this watermarked video we applied some
attacks such as Gaussian filtering, median filtering, frame rotation, contrast adjustment and sharpness attack which show its PSNR and
NCC value in comparison with the original video.

Mind<>Computer: Attempts to mimic human intelligence through methods of classical computing have failed because implementing basic elements of rationality has proven obstinate to the design criterion of machine intelligence. A radical definition of Consciousness describing awareness, as the dynamic representation of a noumenon comprised of three base states; and not itself fundamental as generally defined in the current reductionistic view of the standard model, which has created an intractable hard problem of consciousness as defined by Chalmers. By clarifying the definition of matter a broader ontological quantum theory removes immateriality from the Cartesian split bringing mind into the physical realm for pragmatic investigation. Evidence suggests that the brain is a naturally occurring quantum computer, but the brain not being paramount to awareness does not itself evanesce consciousness without the interaction of a nonlocal conscious process; because Mind <> computer and cannot be reduced to brain states alone. The proposed cosmology of consciousness is indicative of a teleological principle as an inherent part of a conscious universe. By applying the parameters of quantum brain dynamics to the stack of a specialized hybrid electronic optical quantum computer with a heterosoric molecular crystal core, consciousness evanesces through entrainment of the non local conscious processes. This 'extracellular containment of natural intelligence' probably represents the only viable direction for AI to simulate 'conscious computing' because true consciousness = life.

Even after more than a half a century of research on machine intelligence, humans remain far better than our strongest computing machines at a wide range of natural cognitive tasks, such as object recognition, language comprehension, and planning and acting in contextually appropriate ways. While progress is being made in many of these areas, computers still lack the fluidity, adaptability, open-endedness, creativity, purposefulness, and insightfulness we associate with the supreme achievements of human cognitive ability. Reasons for this and prospects for overcoming these limitations are discussed.

Artificial Emotional Intelligence is a newly growing sub-field of artificial intelligence that deals with recognition of human emotions and representation of human like emotion by machines. This paper intends to explain the interaction of human beings with machines and the method by which it can be enriched by adding artificial emotional intelligence to machines. In addition to it, the paper explains the implementation of artificial emotional intelligence in machine along with the flow chart and implementation algorithm. The paper also focuses on working procedure of emotionally sensitive machines and how they can help user to increase productivity of user and keeping him emotionally stable.

Evaluating the output of NLG systems is notoriously difficult, and performing assessments of text quality even more so. A range of automated and subject-based approaches to the evaluation of text quality have been taken, including comparison with a putative gold standard text, analysis of specific linguistic features of the output, expert review and task-based evaluation. In this paper we present the results of a variety of such approaches in the context of a case study application. We discuss the problems encountered in the implementation of each approach in the context of the literature, and propose that a test based on the Turing test for machine intelligence offers a way forward in the evaluation of the subjective notion of text quality.