Innovative Intelligence Research Articles

COMPUTER VISION FOR HUMAN-MACHINE INTERACTION-REVIEW

The paper is a review on the computer vision that is helpful in the interaction between the human and the machines. The computer vision that is termed as the subfield of the artificial intelligence and the machine learning is capable of training the computer to visualize, interpret and respond back to the visual world in a similar way as the human vision does. Nowadays the computer vision has found its application in broader areas such as the heath care, safety security, surveillance etc. due to the progress, developments and latest innovations in the artificial intelligence, deep learning and neural networks. The paper presents the enhanced capabilities of the computer vision experienced in various applications related to the interactions between the human and machines involving the artificial intelligence, deep learning and the neural networks.

Reflections on the Ingredients for Success in AI Research: An Interview with Arvind Gupta

This article contains the observations of Arvind Gupta, who has over 22 years of experience in leadership, policy, and entrepreneurial roles, in both the Silicon Valley and India. Gupta was recently interviewed about the factors that could influence successful artificial intelligence research. At the time of the interview, Gupta was the chief executive officer of MyGov, India. During our interview, he shared with the editorial team his perspectives on investing in artificial intelligence innovations for business and society, in India. The interviewers included members of the special track edito­rial team from IBM (Biplav Srivastava, Daby Sow, and Ching-Hua Chen) and Rensselaer Polytechnic Institute (Oshani Seneviratne).

Care pathways for glaucoma detection and monitoring in the UK.

Glaucoma presents considerable challenges in providing clinically and cost-effective care pathways. While UK population screening is not seen as justifiable, arrangements for case finding have historically been considered relatively ineffective. Detection challenges include an undetected disease burden, whether from populations failing to access services or difficulties in delivering effective case-finding strategies, and a high false positive rate from referrals via traditional case finding pathways. The enhanced General Ophthalmic Service (GOS) in Scotland and locally commissioned glaucoma referral filtering services (GRFS) elsewhere have undoubtedly reduced false positive referrals, and there is emerging evidence of effectiveness of these pathways. At the same time, it is recognised that implementing GRFS does not intrinsically reduce the burden of undetected glaucoma and late presentation, and obvious challenges remain. In terms of diagnosis and monitoring, considerable growth in capacity remains essential, and non-medical health care professional (HCP) co-management and virtual clinics continue to be important solutions in offering requisite capacity. National guidelines, commissioning recommendations, and the Common Clinical Competency Framework have clarified requirements for such services, including recommendations on training and accreditation of HCPs. At the same time, the nature of consultant-delivered care and expectations on the glaucoma specialist's role has evolved alongside these developments. Despite progress in recent decades, given projected capacity requirements, further care pathways innovations appear mandated. While the timeline for implementing potential artificial intelligence innovations in streamlining care pathways is far from established, the glaucoma burden presents an expectation that such developments will need to be at the vanguard of future developments.

Research and Design of Knowledge System Construction System Based on Natural Language Processing

The digital processing of content resources has subverted the traditional paper content processing model and has also spread widely. The digital resources processed by text structure need to be structured and processed by professional knowledge, which can be saved as a professional digital content resource of knowledge base and provide basic metadata for intelligent knowledge service platform. The professional domain-based knowledge system construction system platform explored in this study is designed based on natural language processing. Natural language processing is an important branch of artificial intelligence, which is the application of artificial intelligence technology in linguistics. The system first extracts the professional thesaurus and domain ontology in the digital resources and then uses the new word discovery algorithm based on the label weight designed by artificial intelligence technology to intelligently extract and clean the new words of the basic thesaurus. At the same time, the relationship system between knowledge points and elements is established to realize the association extraction of targeted knowledge points, and finally the output content is enriched from knowledge points into related knowledge systems. In order to improve the scalability and universality of the system, the extended architecture of the thesaurus, algorithms, computational capabilities, tags, and exception thesaurus was taken into account when designing. At the same time, the implementation of “artificial intelligence [Formula: see text] manual assistance” was adopted. On the basis of improving the system availability, the experimental basis of the optimization algorithm is provided. The results of this research will bring an artificial intelligence innovation after the digitization to the publishing industry and will transform the content service into an intelligent service based on the knowledge system.

Estimation of Blast-Induced Air Overpressure in Quarry Mines Using Cubist-Based Genetic Algorithm

In the present work, blast-induced air overpressure is estimated by an innovative intelligence system based on the cubist algorithm (CA) and genetic algorithm (GA) with high accuracy, called GA–CA model. Herein, CA initialization model was developed first and the hyper-parameters of the CA model were selected randomly. Subsequently, the GA procedure was applied to perform a global search for the optimized values of the hyper-factors of the CA model. Root-mean-square error (RMSE) is utilized as a compatibility function to determine the optimal CA model with the lowest RMSE. Gaussian process (GP), conditional inference tree (CIT), principal component analysis (PCA), hybrid neural fuzzy inference system (HYFIS) and k-nearest neighbor (k-NN) models are also developed as the benchmark models in order to compare and analyze the quality of the proposed GA–CA algorithm; 164 blasting works were investigated at a quarry mine of Vietnam for this aim. The results revealed that GA significantly improved the performance of the CA model. Based on the statistical indices used for model assessment, the proposed GA–CA model was confirmed as the most superior model as compared to the other models (i.e., GP, CIT, HYFIS, PCA, k-NN). It can be applied as a robust soft computing tool for estimating blast-induced air overpressure.

Preparing for Artificial Intelligence: Systems-Level Implications for the Medical Imaging and Radiation Therapy Professions

Innovations in artificial intelligence (AI) are driving a new industrial revolution, and as a result, the medical radiation sciences is experiencing transformational, open, beneficial, yet disruptive changes. Many studies have already been published on specific frontline examples where AI will improve cancer care, but discussions of how AI will change the medical radiation sciences and its associated professions at the systems level have been comparatively sparse. In this article, the system-level implications of AI on the medical radiation sciences are discussed, and recommendations are made on how professionals in this field may need to adapt in preparation for a future where AI will be an integral part of the health care system at all levels.

Intelligence as a Psychological Mechanism for Ecotheory of Creativity

Evolution causes biological and cultural diversity through adaptation to environmental conditions. This idea forms the cornerstone of recent research on the ecological origins of innovation and creativity, advanced prominently by Van de Vliert and Murray (2018) as the ecotheory of creativity. Van de Vliert and Murray propose that heat demands and cold demands impose different selection pressures on human creativity, which are further moderated by wealth resources. This commentary tests the hypothesis that population-level variation in intelligence serves as one of the mechanisms that links atmospheric cold demands to population-level variation in creativity. The results of a mediation analysis show that 70.7% of the cross-nationally significant effect of cold demands on national creative culture (r = .55, p < .001, n = 155 nations) is mediated by intelligence. This finding responds to Van de Vliert and Murray’s research call for a more detailed understanding of the psychobehavioral mechanisms underpinning human adaptation to harsh environments. Overall, the results complement existing research which indicates that intelligence and cultural innovations may form two interlinked constituents of a suite of human adaptations or adaptive responses to cold climate.

Limitations to Text and Data Mining and Consumer Empowerment: Making the Case for a Right to Machine Legibility

The paper focuses on the current legal barriers to text and data mining (TDM) in the context of smart disclosure systems (SDSs) whose aim is to provide consumers with improved access to the data needed to make informed decisions. The use of intellectual property rights and contracts, combined with technological protection measures, can hinder TDM and the deployment of SDSs. Further, those legal constraints can negatively impact artificial intelligence innovation that requires improved access to data. There are thus various arguments for enhanced “machine legibility”. However, the TDM exception included in the draft Copyright in the DSM Directive and the various amendments proposed by the European Parliament or the Council do not appear to clear the way for enhanced “machine legibility”. In relation to SDSs, we also argue that the principle of transparency, embedded in consumer and data protection laws, can serve as a last line of defence against prohibition of TDM.

What Should We Do with Plasticity? An Interview with Catherine Malabou

This interview with the contemporary French philosopher Catherine Malabou explores Malabou's central concept of ‘plasticity’ across the interdisciplinary contexts through which it is elaborated, including continental philosophy, neuroscience, genetics and literature, among other domains. In particular, the interview maps how the concept of plasticity has developed most recently in response to new innovations in technology, informatics and artificial intelligence; how the concept proposes a methodology to other researchers and practitioners seeking to explore the transformability and mutability of their own areas; the emotional and affective terrain of self-exploration proposed by a philosophy of plasticity; and avenues for engagements with the concept in practical, clinical settings. Navigating a concept as mutable as it is multiple, the interview is guided by the central question: what should we do with plasticity?

Limitations to Text and Data Mining and Consumer Empowerment: Making the Case for a Right to “Machine Legibility”

This paper focuses on the current legal barriers to text and data mining (TDM) in the context of smart disclosure systems (SDSs) whose aim is to provide consumers with improved access to the data needed to make informed decisions. The use of intellectual property rights and contracts, combined with technological protection measures, can hinder TDM and the deployment of SDSs. Further, those legal constraints can negatively impact on artificial intelligence innovation, because that requires improved access to data. There are thus various arguments for enhanced “machine legibility”. However, the TDM exceptions included in the recently approved Directive on Copyright in the Digital Single Market do not appear to clear the way for enhanced “machine legibility”. In relation to SDSs, we also argue that the principle of transparency, which is embedded in consumer and data protection laws, can serve as a last line of defence against prohibition of TDM.

Hypermodernity, automated uncertainty, and education policy trajectories

ABSTRACT This paper examines how elite transnational policy and research organizations are framing emergent technologies as a hypermodern risk. It outlines how innovations in artificial intelligence and machine learning are feeding into global policy imaginaries and responses oriented to education and skills as adaption and minimization of potential disruption flowing from unpredictable workforce transitions. Drawing on research reports by the Organization for Economic Cooperation and Development, World Economic Forum, and McKinsey, the paper suggests that this emphasis on risk and uncertainty represents a shift in elite policy discourse. The paper discusses how automated uncertainty is feeding into educational policy trajectories that seek to mitigate disruption through digital learning and work synergies via agile learners of risk. The cognitive structuring of these policy trajectories reflects a closed ideological loop deflecting analysis from political economy and alternative policy futures within hypermodern capitalism.

The Impact of Intellectual Property in Fostering Innovation

This article aims to examine the role of intellectual property within the scope of innovation by highlighting the principal connections between intellectual property and innovation. In this sense, this article emphasizes on the massive contribution of intellectual property rights (just for patent, trade secret and copyright) in innovation and also criticizes the role of intellectual property in developing concepts such as big data, artificial intelligence and green innovation. In addition, it is extremely significant to demonstrate the differences among the traditional definitions, understandings and historical background of intellectual property and innovation and their current situations in our era. Having said that, it is also necessary to see the present and future challenges faced as to the role of intellectual property rights in the era of innovation where the boundaries are more flexible, the information is more accessible, and the limits can be easily exceeded.

Innovative Behavior And Emotional Intelligence Of Managers In Managing A Catering Business

The main problem of SME Catering family business in Makassar City is low competitiveness which has an impact on financial performance. This study investigates the causes of low competitiveness through the role of the manager's innovative behavior and emotional intelligence. Sampling uses purposive sampling by setting the owner and manager as respondents. Data was transformed from the score to scale data through SolAnd 1.9 software application and data analysis using WarpPLS 3.0. This study found that Catering's financial performance is good because ownership is increasing. The more catering ownerships are the better innovation of managers and better financial performance. Although innovative managers are good, the emotional intelligence of managers is still low, financial performance is also low. The catering business is vulnerable to the risk of raw materials. Therefore, managers who are able to innovate and have personal qualities to work are needed indeed the competitiveness of this business increases.

Computational intelligence techniques for efficient delivery of healthcare

Computational intelligence innovation and the use of computers have changed the entire healthcare delivery system. Nurses are the leading crew of healthcare organization. But, these nurses are either lacking in computer usage or automated analysis generated by computers. Therefore, it motivates to study the use of computers and information technology by nurses in Indian healthcare system. Further, it is essential to identify the chief factors where these nurses are lacking while using computers and information technology. This will help the management to take necessary measure to train them and make the healthcare industry more productive in perception with usage of computer and information technology. To this end, data has collected from nurses in hospitals in the state of Tamilnadu, India. Data collection is not beneficial unless it is analyzed and meaningful information obtained from it. In this paper, we hybridize rough set and formal concept analysis to arrive at chief factors affecting the decisions. Rough set is used to analyze the data and to generate rules. These generated rules further passed into formal concept analysis to identify the chief characteristics affecting the decisions. This in turn help the organization to provide adequate training to the nurses and the healthcare system will move further to the next stage.

Analysis of Patent Technology Transfer in China

Taking the status of China's patent technology transfer as the research goal, select the representative Guangdong Province as the research object, Using the incopat innovative intelligence platform as a data source, 157,178 related content were retrieved, And from patent technology transfer trends, technology ownership, applicants, geography, legal and operational status, patent agents and other aspects of analysis. Research shows that the development of China's patent technology transfer is strong, but there are some problems, in particular, the transfer of patent technology in research institutes and research institutions needs to be strengthened, and there is a large gap in regional patent technology transfer. Therefore, it is necessary to create a favorable environment for patent licensing and technology transfer and transformation policies, and actively guide the industrialization of patent technology in universities and research institutions, Strengthen the transfer of scientific and technological achievements to the market, train and expand the technical transfer of professional talents and so on, further promote the integrated development of science and technology and the economy, and accelerate the transformation of scientific and technological achievements into actual productivity. China's patent technology research has certain reference value for other countries.

AUTOMATED MEDICAL DATA ANALYSES OF DISEASES USING BIG DATA

Diagnosis of different diseases is a growing concern and one of the most difficult challenges for modern medicine. Current diagnosis technologies (e.g. magnetic resonance imaging, electroencephalogram) produce huge quantity data (in size and dimension) for detection, monitoring and treatment of neurological diseases. In general, analysis of those medical big data is performed manually by experts to identify and understand the abnormalities. It is really difficult task for a person to accumulate, manage, analyse and assimilate such large volumes of data by visual inspection. As a result, the experts have been demanding computerised diagnosis systems, called “computer-aided diagnosis (CAD)” that can automatically detect the neurological abnormalities using the medical big data. This system improves consistency of diagnosis and increases the success of treatment, save lives and reduce cost and time. Recently, there are some research works performed in the development of the CAD systems for management of medical big data for diagnosis assessment. Such data analyses to realize diagnosis is very interesting for diabetes and autism. Many companies and research groups are working to treat diabetes, but preventing the disease will have a greater impact on health in at-risk groups. A team of US researchers are using data analytics to create a precision medicine approach to prevention of diabetes that steers efforts towards those who are at highest risk of developing the disease and who would benefit most from drug treatment or preventive lifestyle strategies. The analyses yielded most important 17 factors that were assessed that could predict an individual’s risk of diabetes. Autism Spectrum Disorder (ASD) is characterized by difficulties in social communication, social interactions, and repetitive behaviors. It is diagnosed during the first three years of life. Early and intensive interventions have been shown to improve the developmental trajectory of the affected children. The earlier the diagnosis, the sooner the intervention therapy can begin, thus, making early diagnosis is set as our important research goal. Because ASD is not a neurodegenerative disorder, many of the core symptoms can improve as the individuals learn to cope with their environments under the right conditions. The earlier the age at which intervention can be started, the better their learning and daily function can be facilitated. Recent Big Data software packages and innovations in Artificial Intelligence have tremendous potential to assist with early diagnosis and improve intervention programs. The research study will focus on methodological evaluation of emerging technologies and will investigate by comparing different data sets and find a pattern that can be established as prognosis system. The research study investigated peer-reviewed studies in order to understand the current status of empirically-based evidence on the clinical applications in the diagnosis and treatment of Autism Spectrum Disorders (ASD). Also a survey and investigation on different sensing technologies for ASD like: eye trackers, movement trackers, electrodermal activity monitors, tactile sensors, vocal prosody and speech detectors. We assess their effectiveness and study their limitations. We also examine the challenges faced by this growing field that need to be addressed before these technologies can perform up to their theoretical potential In some cases, a technology is unable to deliver up to its potential, not due to the hardware but due to the inefficiency of the accompanied algorithms, as in the case of classifiers for repetitive behavior detection. Therefore, equal emphasis needs to be placed on the improvement of all aspects of a tracking technology. The nature of the sensors makes the tracked data very sensitive to experimental and systematic errors, often causing the collected data to be discarded due to unreliability. Efforts to reduce such inaccuracies can significantly improve the performance and potential of the overall technology. By collecting specific data, these sensors may be able to acquire objective measures that can be used to identify symptoms specific to ASD. The contribution of the analyses will assist not only the therapists and clinicians in their selection of suitable tools, but to also guide the developers of the technologies and devise new algorithm in prediction of autism.

Using generative adversarial networks to improve deep-learning fault interpretation networks

Deep learning is arguably one of the most important innovations in artificial intelligence in recent times. It allows for computational solutions to problems that are not easily characterized by a mathematical model or deterministic algorithm. It also allows for automated solutions to problems that are inherently subjective. Both of these criteria are endemic in the earth sciences, so innovative solutions to these challenges should be welcomed. We demonstrate a recent refinement to a deep-learning fault identification process that improves the continuity and compactness of predicted fault planes in areas where faults intersect. Historically, predictions from both deep learning and traditional algorithmic approaches were characterized by “blurry” clouds of intermediate probability values that extended well beyond the fault plane. To remediate this blurring problem and enhance confidence of inferences, we demonstrate a preprocessing technique in the image domain by using generative adversarial networks (GANs) that sharpen the seismic image prior to training and prediction. This sharpening solution consists of two neural networks. A feature-extraction network is used for extracting both local and global features from an unrelated, high-quality “donor” seismic survey. Then, the data set of interest is sent through a donor reconstruction network where a generator architecture creates plausible-looking images at a denser sampling rate with high perceptual quality. In the study presented here, we create our sharpening network using a modern, high-fidelity, deepwater 3D survey with well-imaged faults as the donor. The resulting generator architecture is then applied to our data set of interest — an altogether separate deepwater data set in the Gulf of Mexico. Similar in intent to a 5D interpolation, the GANs-based supersampled data contain three times the inline and crossline trace density, and the sampling interval is upsampled by a factor of three. This approach aims to preserve spatial and temporal frequency content of the parent data while providing a denser data set for deep-learning applications. The supersampled data is then deployed into our deep-learning training regimen to enhance the performance of our fault detection network. By introducing a preprocessing sharpening step, the predicted faults are less blurry, more compact, and more amenable to programmatic attempts to segment them into discrete features.

US Defense Innovation and Artificial Intelligence

The United States seeks conventional military overmatch in the strategic competition with China and Russia through efforts to develop advanced technology for military application and new concept of operations as well as pursue organizational adaptation and human talent pool development. The current article will trace the development of US defense innovation efforts from the Obama administration to the Trump administration and focus on the technological dimension of defense innovation by illustrating ways through which the U.S. Department of Defense is attempting to apply “narrow” artificial intelligence for military purposes such as situational awareness, cyber, military logistics, command and control, and swarm technology and tactics for autonomous unmanned systems. The article will conclude by making two caveats regarding the actual introduction of artificial intelligence into the US battle network, and very briefly point to implications for US allies.

WAGE INEQUALITY AND INNOVATIVE INTELLIGENCE-BIASED TECHNOLOGICAL CHANGE

In this paper, “innovative intelligence–biased technological change” (IIBTC) is examined as an alternative to the traditional concept of skill-biased technological change (SBTC) as a source of increases in wage inequality. The innovative intelligence of ordinary or average workers is an important element in productivity and can be heterogeneous across workers. Because technologies are heterogeneous in that they have different characteristics and are used in different situations, some technologies are “innovative intelligence-biased” and are advantageous for workers with relatively high innovative intelligence. If IIBTC prevails over a certain period of time, these workers become additionally advantaged and thereby wage inequality will increase during the period.

Weakly Supervised Biomedical Image Segmentation by Reiterative Learning.

Recent advances in deep learning have produced encouraging results for biomedical image segmentation; however, outcomes rely heavily on comprehensive annotation. In this paper, we propose a neural network architecture and a new algorithm, known as overlapped region forecast, for the automatic segmentation of gastric cancer images. To the best of our knowledge, this report for the first time describes that deep learning has been applied to the segmentation of gastric cancer images. Moreover, a reiterative learning framework that achieves superior performance without pretraining or further manual annotation is presented to train a simple network on weakly annotated biomedical images. We customize the loss function to make the model converge faster while avoiding becoming trapped in local minima. Patch boundary errors were eliminated by our overlapped region forecast algorithm. By studying the characteristics of the model trained using two different patch extraction methods, we train iteratively and integrate predictions and weak annotations to improve the quality of the training data. Using these methods, a mean Intersection over Union coefficient of 0.883 and a mean accuracy of 91.09% were achieved on the partially labeled dataset, thereby securing a win in the 2017 China Big Data and Artificial Intelligence Innovation and Entrepreneurship Competition.