Artificial Intelligence Science Research Articles

Geography's Quantitative Revolutions: Edward A. Ackerman and the Cold War Origins of Big Data by Elvin Wyly

Reviewed by: Geography's Quantitative Revolutions: Edward A. Ackerman and the Cold War Origins of Big Data by Elvin Wyly Russell S. Kirby Geography's Quantitative Revolutions: Edward A. Ackerman and the Cold War Origins of Big Data. Elvin Wyly. Morgantown: West Virginia University Press, 2019. Pp. xx+171, notes, index. $99.99, hardcover, ISBN 978-1-949199-08-6. $22.99, paperback, ISBN 978-1-949199-09-3. This slender monograph uses the career and writings of Edward Ackerman as a mirror to reflect on the evolution of the discipline of geography from the mid-twentieth century to the present, with some speculations into the future. Using Ackerman's work to center the discourse, Elvin Wyly weaves a narrative that links the origins of geography's quantitative revolution to the cadre of geographers involved in intelligence efforts in the Office of Strategic Services (OSS) during World War II and its transition aft er the war to the Central Intelligence Agency (better known to Americans as the CIA). While this is to some extent an oversimplification, those geographers involved in the OSS played an important role in amplifying the use of mathematical [End Page 191] modeling and hypothesis testing in a wide array of topics in human geography. Along the way, Wyly's discourse examines elements of the debate concerning the nature of geography from the perspective of Hartshorne to geography as a quantitative spatial science informed by general systems theory. He then extends this to the emerging science of big data, machine learning, and artificial intelligence, all the while centering Ackerman as a pivotal figure in what was a more gradual transition over decades. The monograph consists of six chapters. The first chapter uses Ackerman's address as honorary president of the Association of American Geographers in 1963, titled "Where Is a Research Frontier," as a point of embarkation for a detailed discussion of geography's scientific revolutions that continue to the present day. The second chapter chronicles and contextualizes Ackerman's career to the early 1960s, touching on his critical roles at OSS, Harvard University in the 1940s, and the Carnegie Institute of Washington. From the 1940s to the early 1960s, Ackerman was an essential figure in the early stages of geography's transition as a quantitative science, through both his research and writing and his leadership roles in the national scientific community. The narrative quickly dives into the history of philosophy of science in the next chapter, as the author examines the underpinnings of some of Ackerman's arguments from the perspective of Kant, viewed through the lens of Hartshorne. Ackerman argued for a "systematic geography" and sought to align geography with general systems theory. Several pages are devoted to discussing a controversy around a comprehensive report Ackerman spearheaded on Japanese Natural Resources in 1949–50, in which some conclusions were deemed in our present parlance as being "politically incorrect," though based on sound scientific reasoning. In the words of the title of the next chapter, Ackerman became in some ways a "militant neo-Kantian" in seeking to impose a systems perspective on the discipline of geography. Wyly develops this line of thought in a lengthy chapter that shows how Ackerman's thinking began with insights from the philosophy of Kant, articulated by Hartshorne. Ackerman transformed these into a worldview that supported the delineation of scientific laws governing the spatial organization of human activity within a logical positivist framework. [End Page 192] While the chapter's focus on Ackerman's career in the context of the mid-twentieth century evolution of academic geography in the US may be of most interest to historical geographers, readers should pay special attention to chapter 5, "The New Evolution of Geographic Thought." In this chapter Wyly makes a strong connection between Ackerman's new frontier of systems theory for geography, and the interconnected web of internet, telecommunications, apps, marketing, social networking, and sharing of information and misinformation. The result is not a fully nuanced view—there is no mention of humanist or phenomenological approaches in geographic thinking or research, nor are its leading proponents referenced. Even so, Wyly gives geographers more than enough to digest and reflect on concerning the current...

Real Estate Data Marketplace

The Real estate financing, construction, and management are being revolutionised by the data science and infrastructure technologies of Artificial Intelligence (AI), Internet of Things (IoT), Big Data, Digital Object Identifiers (DOI) and Blockchain. The key to automation and integration is a real estate data marketplace across the lifespan of finance, planning, construction, regulation, management, even refurbishment and demolition. An integration of digital 4.0 and industry 4.0. Real estate is becoming increasingly digital, automated and integrated; embracing connected-buildings, wearable personal devices, integrated transportation systems, buildings and infrastructure data management and smart cities. The key to promoting the use of data driven techniques in the Real Estate sector is the introduction of a Data Marketplace which promotes validation of data and tokenisation to create value for the owners of the data, Blockchain security and governance for users of the data and the application of Artificial Intelligence for data analytics. The technical capability exists today for a complete end-to-end reshaping of the Real Estate Market based on data. This is data-driven real estate (cf. Google, Amazon, Alibaba, Tencent). The contributions of this review paper are three-fold: a) to provide a vision of a fully digital real estate marketplace; b) to brief the industry on digital technology and also stimulate engagement with the Computer Science community; and c) to encourage debate amongst professionals, academics and politicians of the benefits of full automation and integration of real estate

Боткинские чтения по биоэтике, этике науки и этике искусственного интеллекта в условиях COVID-19

Боткинские чтения по биоэтике, этике науки и этике искусственного интеллекта в условиях COVID-19

Question answering system for agriculture domain using machine learning techniques: literature survey and challenges

Natural language processing (NLP) is a part of artificial intelligence and computer science. Question answering system that provides an interaction between computers and human languages, it is role to program the computers to process and analyse the large amounts of human language data. It is one of the important aspects in all domains and helps in every domain to satisfy the people requirements. Now a day's almost all people are literate and using the mobile phone to receive the up to date information as per their requirement. QAS can be used to provide succinct information for the questions that are being asked by user and it provides answers to users based on some rules which are stored in the data base. This survey paper details about what is question answering system and its previous related work with respect to methods, technologies or approaches that were used. It provides research gaps and future scope to the researches in the reviewed papers, which helps researchers to choose a suitable solution to their problems wherein available comparative analyses have been provided.

Research on the emotional cognitive evaluation model based on artificial neural network

the neural network, fuzzy set theory and evolutionary algorithm in artificial intelligence are all intelligent information processing theories that follow the biological processing mode. These theories are realized by rational logical thinking mode without considering the role of human perceptual thinking in the information processing process, such as emotion and cognition. Among them, the neural network mainly imitates the function of the mental system of human, adopts the method from the bottom to the top, and processes the difficult language pattern information through a large number of complicated connections of neurons. Artificial neural network (Ann) is a cross research field of artificial intelligence and life science. This theory mainly imitates the information processing mechanism of organisms in nature and is mainly used in intelligent information processing systems that can adapt to long-term changes in the environment. Therefore, neural network has important application significance in the research of intelligence, robot and artificial emotion.

The Displacement of Human Judgment in Science: The Problems of Biomedical Research in an Age of Big Data

The Displacement of Human Judgment in Science:The Problems of Biomedical Research in an Age of Big Data Paul Scherz (bio) in the human condition, hannah arendt lamented the state of the mathematical natural sciences. While recognizing their power to transform the world, she thought that they leave much to be desired in terms of providing an explanation for the world once they lose contact with human senses and intuitions. Surveying fields like quantum mechanics in which we still do not have a good picture of the world described by the equations, Arendt suggested that science was being reduced to mere technical intervention into nature rather than an understanding of it. "But the mathematization of physics … had in its last stages the unexpected and yet plausible consequence that every question man puts to nature is answered in terms of mathematical patterns to which no model can ever be adequate" (Arendt 1998, 287). Understanding is lost, leaving "us a universe of whose qualities we know no more than the way they affect our measuring instruments" (261). Of course, similar critiques were levelled throughout the mid-twentieth century by many other scholars—Edmund Husserl, Martin Heidegger, Hans Jonas, and Georges Canguilhem. As Husserl put it, "This arithmetization of geometry leads almost automatically, in a certain way, to the emptying of its meaning" (1970, 44). Physical science had lost hold of the original [End Page 957] intuitions underlying the quantification of nature, driving it from the grasp of human understanding. To many scholars who shared this critique, one natural science seemed like an exception: biology. Heidegger argued that "all sciences concerned with life must necessarily be inexact just in order to remain rigorous. A living thing can indeed also be grasped as a spatio-temporal magnitude of motion, but then it is no longer apprehended as living" (1977, 120). Because of the rich complexity of organisms, it was not thought that they could be understood purely mathematically. Instead, one needed to understand their purposes, aims, and goals; that is, their teleology. Even those like Ernst Mayr, attempting to mathematize biology as part of the Modern Synthesis of evolution, understood this need, although he preferred the cybernetically derived term "teleonomy" to the older "teleology" (Mayr 1989). For example, a complete, detailed description of the chemical and signaling reactions that lead to birds migrating would still be a deficient explanation if we did not also include an account of why birds migrate. Because it deals with living things, biology requires richer forms of judgment and explanation than other natural sciences. This exceptional feature of biological explanation had been recognized already by Immanuel Kant, who argued that science needed something beyond the mathematics of Newtonian physics. While he would have liked to submit all of nature to that mathematical form of description, it was impossible because living organisms demanded this richer teleological description (Kant 2000, 386–88). Most philosophers in prior generations shared Kant's view. A science of purely mathematical relations between entities could not capture the complexity and goal-directedness of biological life. Then, two decades ago, the Human Genome Project brought a sea change. Biology began to be inundated by massive amounts of genetic and other data, much of which was produced and interpreted by machines. Analogous to developments in other fields like medicine and education, human judgment began to look inadequate to the task of analyzing this data and using it to develop the therapies that [End Page 958] had been promised in exchange for the massive public and private investment in the machine infrastructure. Scientists began suggesting that these vast accumulations of data, "mined" and interpreted by machines, could reveal knowledge. For instance, an editorial in Nature entitled "Can Biological Phenomena Be Understood by Humans?" quoted a biologist arguing that scientists "have to free [themselves] from the hypothesis-driven approach" and rely more on computer models (Anonymous 2000). After 2000, practicing scientists and philosophers of biology joined a broad debate over the relationship between the scientist, computer-analyzed data, and human reason in biological knowledge production (Allen 2001a, b; Gillies 2001; Smal-heiser 2002; Kell and Oliver 2004). The argument for the seeming promise of artificial intelligence in science was put...

Editorial: Special Issue on Scholarly Data Analysis (Semantics, Analytics, Visualisation)

The increasing interest in analysing, describing, and improving the research process requires the development of new forms of scholarly data publication and analysis that integrates lessons and approaches from the field of Semantic Technologies, Science of Science, Digital Libraries, and Artificial Intelligence. This editorial summarises the content of the Special Issue on Scholarly Data Analysis (Semantics, Analytics, Visualisation), which aims to showcase some of the most interesting research efforts in the field. This issue includes an extended version of the best papers of the last two editions of the “Semantics, Analytics, Visualisation: Enhancing Scholarly Dissemination” (SAVE-SD 2017 and 2018) workshop at The Web Conference.

Special Feature on Advanced Mobile Robotics

Mobile robots and their applications are involved with many research fields including electrical engineering, mechanical engineering, computer science, artificial intelligence and cognitive science [...]

THE IMPACT OF ATTITUDES TOWARDS GOVERNMENT AND CORPORATIONS ON TRUST IN TECHNOLOGY

Understanding public distrust of technology is both theoretically and practically important, yet while previous research has focused on the association between political ideology and trust in science, it is at best an inconsistent predictor. This study shall demonstrate that two dimensions of political ideology, attitudes towards governments and corporations, can more precisely predict trust in technology across issues. We will conduct an online survey on the science of radio frequency electromagnetic fields (RF-EMF) and Artificial Intelligence (AI) applications to test our hypotheses that trust in technology varies across issues and that attitudes towards government and corporations are important predictors of this trust.

Sentiment Analysis of the News Media on Artificial Intelligence Does Not Support Claims of Negative Bias Against Artificial Intelligence.

Artificial intelligence (AI) is a hot topic in digital health, as automated systems are being adopted throughout the health care system. Because they are still flexible, emerging technologies can be shaped significantly by media representations as well as public engagement with science. In this context, we examine the belief that negative news media coverage of AI-and specifically, the alleged use of imagery from the movie Terminator-is to blame for public concerns about AI. This belief is identified as a potential barrier to meaningful engagement of AI scientists and technology developers with journalists and the broader public. We name this climate of risk perception the "Terminator Syndrome"-not because of its origins in the movie of the same name per se, but because such unchecked beliefs can terminate broad public engagement on AI before they even begin. Using both quantitative and qualitative approaches, this study examined the hypothesis that the news media coverage of AI is negative. We conducted a sentiment analysis of news data spanning over six decades, from 1956 to 2018, using the Google Cloud Natural Language API Sentiment Analysis tool. Contrary to the alleged negative sentiment in news media coverage of AI, we found that the available evidence does not support this claim. We conclude with an innovation policy-relevant discussion on the current state of AI risk perceptions, and what critical social sciences offer for responsible AI innovation in digital health, life sciences, and society.

How AI can Change/Improve/Influence Music Composition, Performance and Education: Three Case Studies

The use of artificial intelligence in science is happening more and more frequently, and often artificial intelligence can be seen in different approaches to creating music and art. In this paper, I will present some of the research that has been carried out, which involve the use of artificial intelligence in the field of composition, performance, and music education. The main focus in the field of composition will be on AIVA – the first virtual composer created with artificial intelligence, which is registered with an author’s rights society. In the field of performance, we’ll mostly talk about Yamaha's experiment where the world-renowned dancer Kaiji Moriyama controls a piano with his body movements, and in the context of education, this paper reviews some of the possibilities in a variety of artificial intelligence approaches to music education. Lastly, I will conclude the paper by presenting the direction of and possible future for the use of artificial intelligence in music.

The Role of Artificial Intelligence in Decision Making in Small Businesses

Background/Objectives: The science of support for decision-making has undergone many changes thanks to the remarkable development in the science of networks and Artificial Intelligence. Decision support was born through the integration of management science, operational research and cognitive science. Now this science is growing in line with the results of advanced research in the field of database management, Internet networks, intranet networks, extranet networks, good systems and neural networks, genetic algorithms and gas logic. Method: Management Science, Operations Research and Cognitive Science are the basic concepts of decision support. We find the concept of decision, the decision process and the concept of different models to study the decision-making process. We find how to simulate and simulate real systems (modeling, descriptive models and methods). Mathematical models used in solving these models, such as linear and nonlinear programming methods and the theory of frames and rows. Artificial Intelligence (AI) became one of the hottest topic and a promising methodology trending in many industries and businesses. Artificial Intelligence is a computer-based science developed to analyze data, tackle problems, and discover trends and similarities. Findings: This study shed the light on the importance of using AI in supporting the big and small businesses through adapting the novel advantages gained by Artificial Intelligence to overcome routine or complicated problems that usually take a lot of effort and resources such as data analysis and forecasting markets and customers’ preferences. Also, in this article, the experience of several entrepreneurs who have augmented us with their own knowledge on how they used Artificial Intelligence in their corporations will be demonstrated. Application/Improvements: The application of this study will be on internet technology that has been greatly contributed to the development of collective decision support systems through virtual meeting sessions and Brainstorming sessions without the need to be physically present in one place. Keywords: AI, Decision Making and Marketing, Enterprise, Intelligent Systems, ML

The Emergence of Abstract Sciences and Transdisciplinary Advances: Developments in Systems, Man, and Cybernetics

Transdisciplinary studies in systems, man, and cybernetics (SMC) are an advanced approach to generate new knowledge and novel perceptions on persistent challenges and emerging technologies across the edges of traditional disciplines. An unprecedented phenomenon in science history in the past decade is the emergence of abstract sciences (ASs) as a counterpart of classic concrete sciences (CSs). It leads to a new perception of SMC as well as its transdisciplinary foundations and the impacts on classic sciences and engineering disciplines. This article presents the emergence of AS underpinned by SMC and denotational mathematics (DMs). It explores the transdisciplinary theories of system science, cognitive cybernetic foundations of ASs, and hybrid human-machine societies driven by the fast development of artificial intelligence (AI), intelligence science, and knowledge science as well as their engineering applications.

Inteligências artificiais e os limites da computação

Much has been heard about the of artificial intelligence (AI) and the intelligent machines, but very little about the limits of computation that make or not possible the simulations of intelligences in artificial systems. If, on the one hand, computer hardware has advanced a lot, that is to say, hardware, which actually became faster, smaller and more powerful, on the other, there is little or nothing advance in the engineering techniques of the software that make them work computer technical supports, that is, software, as well as the conceptual theories that underlie and structure them. The aim of this article is to explore the mistake made by the mass media, and, ultimately, by the AI scientists and enthusiasts: the fact that the emergence of systems cybernetic, informational, intelligent, supposedly living, conscious of themselves and the world around them. A belief that definitely has no echo in the factual reality, much less considering the current limits of computing.

RetainVis: Visual Analytics with Interpretable and Interactive Recurrent Neural Networks on Electronic Medical Records.

We have recently seen many successful applications of recurrent neural networks (RNNs) on electronic medical records (EMRs), which contain histories of patients' diagnoses, medications, and other various events, in order to predict the current and future states of patients. Despite the strong performance of RNNs, it is often challenging for users to understand why the model makes a particular prediction. Such black-box nature of RNNs can impede its wide adoption in clinical practice. Furthermore, we have no established methods to interactively leverage users' domain expertise and prior knowledge as inputs for steering the model. Therefore, our design study aims to provide a visual analytics solution to increase interpretability and interactivity of RNNs via a joint effort of medical experts, artificial intelligence scientists, and visual analytics researchers. Following the iterative design process between the experts, we design, implement, and evaluate a visual analytics tool called RetainVis, which couples a newly improved, interpretable, and interactive RNN-based model called RetainEX and visualizations for users' exploration of EMR data in the context of prediction tasks. Our study shows the effective use of RetainVis for gaining insights into how individual medical codes contribute to making risk predictions, using EMRs of patients with heart failure and cataract symptoms. Our study also demonstrates how we made substantial changes to the state-of-the-art RNN model called RETAIN in order to make use of temporal information and increase interactivity. This study will provide a useful guideline for researchers that aim to design an interpretable and interactive visual analytics tool for RNNs.

CODATA and global challenges in data-driven science

This synthesis report presents the scientific results of the international conference Global Challenges and Data-Driven Science which took place in St. Petersburg, Russian Federation from 8 October to 13 October 2017. This event facilitated multidisciplinary scientific dialogue between leading scientists, data managers and experts, as well as Big Data researchers of various fields of knowledge. The St. Petersburg conference covered a wide range of topics related to data science. It featured discussions covering the collection and processing of large amounts of data, the implementation of system analysis methods into data science, machine learning, data mining, pattern recognition, decision-making robotics and algorithms of artificial intelligence. The conference was an outstanding event in the field of scientific diplomacy and brought together more than 150 participants from 35 countries. It's success ensured the effective data science dialog between nations and continents and established a new platform for future collaboration.

Complexity Sciences and Artificial Intelligence for Improving Lives through Convergent Innovation

This panel symposium brings together a unique portfolio of scientists in management, artificial intelligence and complexity science to help address questions bearing on how organizations and organi...

Application of Artificial Intelligence and Meta-heuristic Algorithms in Civil Health Monitoring Systems

After the discovery and manufacturing of every accomplishment, the mankind tends to make it sustainable in terms of different aspects that one of them can be its durability. Nowadays, a science titled “health monitoring” has provided such a consideration in different fields. For example, civil engineering sciences, in various branches, aim to construct various civil engineering accomplishments, followed by the higher goals of making them durable and healthy. The present study tries to give an account about the various study fields like structural engineering, bridge construction, dam construction, hydraulic and on-beach constructions, road engineering and building, water resources management and so on along with the mentioning of the various methods extant for the implementation of such study fields. But, in between, there is an appropriate method that provides such objectives as cost-effectiveness, access to the entire required details, awareness of the civil infrastructures in order to estimate the remained lifetime of the structure in line with the continuation and/or change of the uses. Also, it has high precision and minimally influenced by the environment, so, it can be said that it has very little error in its collection of information. For instance, this method can be used to evaluate the ruination of the structures based on modal properties, which can have static or dynamic foundations such that the current state of the structure is compared to its ideal state to monitor the degree of the structure’s ruination or its soundness. In present study, it was tried to investigate the artificial intelligence science as one of the richest methods possessing all the prerequisites as well as having more traits in common with the various sub-disciplines of civil engineering so that it can be utilized more comprehensively and in a more centralized manner.

Señales de gentrificación a través de la Inteligencia Artificial: identificación mediante el censo de vivienda

La gentrificación no siempre es detectada por la sociedad, la política y la planificación a tiempo de interpretar sus dinámicas y de llevar a cabo intervenciones que mitiguen sus efectos adversos. Sus implicaciones son tan importantes en la fisionomía social de las ciudades, que será relevante toda herramienta que permita pronosticar o evidenciar cualquier tipo de señal de la gentrificación. La investigación trata de evaluar la viabilidad de la detección de ámbitos vinculados a procesos de gentrificación, incipientes o asentados, mediante el uso de fuentes de información comunes en las ciudades, como son los censos de viviendas. Para ello se propone el uso de metodologías de extracción de información basadas en técnicas de minería de datos procedentes de las ciencias de la Inteligencia Artificial. La metodología se evalúa experimentalmente en un territorio complejo y extenso, la costa mediterránea peninsular española. Los resultados permiten identificar un perfil urbano que incluye todas las barriadas a las que el estado del arte atribuye gentrificación, resultando la proporción de viviendas en alquiler determinante. Se concluye que la metodología propuesta es útil para evidenciar territorios con señales similares a los entornos urbanos con gentrificación, permitiendo la detección temprana de procesos semejantes en otros ámbitos.

A review of semantic similarity approach for multiple ontologies

Measuring semantic similarity between concepts is an important step in information retrieval and information integration which requires semantic content matching. Semantic similarity has attracted great concern for a long time in artificial intelligence, psychology and cognitive science. Many methods have been proposed. This paper contains a review on the state of art approaches including structure-based approach, information content-based approach, feature-based approach and hybrid-based approach. We also discussed the similarity according to their advantages, disadvantages and issues related to multiple ontologies. Besides that, we also concentrated on methods in feature-based approach which we will be using as a mechanism to measure the similarity for multiple ontologies.