Showing papers on "Human intelligence published in 2018"
Posted Content•
TL;DR: It is argued that combinatorial generalization must be a top priority for AI to achieve human-like abilities, and that structured representations and computations are key to realizing this objective.
Abstract: Artificial intelligence (AI) has undergone a renaissance recently, making major progress in key domains such as vision, language, control, and decision-making. This has been due, in part, to cheap data and cheap compute resources, which have fit the natural strengths of deep learning. However, many defining characteristics of human intelligence, which developed under much different pressures, remain out of reach for current approaches. In particular, generalizing beyond one's experiences--a hallmark of human intelligence from infancy--remains a formidable challenge for modern AI.
The following is part position paper, part review, and part unification. We argue that combinatorial generalization must be a top priority for AI to achieve human-like abilities, and that structured representations and computations are key to realizing this objective. Just as biology uses nature and nurture cooperatively, we reject the false choice between "hand-engineering" and "end-to-end" learning, and instead advocate for an approach which benefits from their complementary strengths. We explore how using relational inductive biases within deep learning architectures can facilitate learning about entities, relations, and rules for composing them. We present a new building block for the AI toolkit with a strong relational inductive bias--the graph network--which generalizes and extends various approaches for neural networks that operate on graphs, and provides a straightforward interface for manipulating structured knowledge and producing structured behaviors. We discuss how graph networks can support relational reasoning and combinatorial generalization, laying the foundation for more sophisticated, interpretable, and flexible patterns of reasoning. As a companion to this paper, we have released an open-source software library for building graph networks, with demonstrations of how to use them in practice.
2,170 citations
TL;DR: An important implication from the theory is that analytical skills will become less important, as AI takes over more analytical tasks, giving the “softer” intuitive and empathetic skills even more importance for service employees.
Abstract: Artificial intelligence (AI) is increasingly reshaping service by performing various tasks, constituting a major source of innovation, yet threatening human jobs We develop a theory of AI job repl
1,176 citations
TL;DR: This article used the final release of the Young Adult Human Connectome Project, providing a full hour of resting-state fMRI per subject; controlled for gender, age and brain volume; and derived a reliable estimate of general intelligence from scores on multiple cognitive tasks.
Abstract: Individual people differ in their ability to reason, solve problems, think abstractly, plan and learn. A reliable measure of this general ability, also known as intelligence, can be derived from scores across a diverse set of cognitive tasks. There is great interest in understanding the neural underpinnings of individual differences in intelligence, because it is the single best predictor of long-term life success. The most replicated neural correlate of human intelligence to date is total brain volume; however, this coarse morphometric correlate says little about function. Here, we ask whether measurements of the activity of the resting brain (resting-state fMRI) might also carry information about intelligence. We used the final release of the Young Adult Human Connectome Project (N = 884 subjects after exclusions), providing a full hour of resting-state fMRI per subject; controlled for gender, age and brain volume; and derived a reliable estimate of general intelligence from scores on multiple cognitive tasks. Using a cross-validated predictive framework, we predicted 20% of the variance in general intelligence in the sampled population from their resting-state connectivity matrices. Interestingly, no single anatomical structure or network was responsible or necessary for this prediction, which instead relied on redundant information distributed across the brain.This article is part of the theme issue 'Causes and consequences of individual differences in cognitive abilities'.
214 citations
TL;DR: Recent neuroscience evidence is surveyed to elucidate how general intelligence, g, emerges from individual differences in the network architecture of the human brain.
206 citations
TL;DR: Using a cross-validated predictive framework, 20% of the variance in general intelligence in the sampled population was predicted from their resting-state connectivity matrices, and no single anatomical structure or network was responsible or necessary for this prediction, which instead relied on redundant information distributed across the brain.
Abstract: Individual people differ in their ability to reason, solve problems, think abstractly, plan and learn. A reliable measure of this general ability, also known as intelligence, can be derived from scores across a diverse set of cognitive tasks. There is great interest in understanding the neural underpinnings of individual differences in intelligence, since it is the single best predictor of long-term life success. The most replicated neural correlate of human intelligence to date is total brain volume; however, this coarse morphometric correlate says little about function. Here we ask whether measurements of the activity of the resting brain (resting-state fMRI) might also carry information about intelligence. We used the final release of the Young Adult Human Connectome Project (N=884 subjects after exclusions), providing a full hour of resting-state fMRI per subject; controlled for gender, age, and brain volume; and derived a reliable estimate of general intelligence from scores on multiple cognitive tasks. Using a cross-validated predictive framework, we predicted 20% of the variance in general intelligence in the sampled population from their resting-state connectivity matrices. Interestingly, no single anatomical structure or network was responsible or necessary for this prediction, which instead relied on redundant information distributed across the brain.
88 citations
TL;DR: It is demonstrated that access to variable neural states predicts complex behavioral performance, and specifically shows that entropy derived from neuroimaging signals at rest carries information about intellectual capacity.
Abstract: Human intelligence comprises comprehension of and reasoning about an infinitely variable external environment. A brain capable of large variability in neural configurations, or states, will more easily understand and predict variable external events. Entropy measures the variety of configurations possible within a system, and recently the concept of brain entropy has been defined as the number of neural states a given brain can access. This study investigates the relationship between human intelligence and brain entropy, to determine whether neural variability as reflected in neuroimaging signals carries information about intellectual ability. We hypothesize that intelligence will be positively associated with entropy in a sample of 892 healthy adults, using resting-state fMRI. Intelligence is measured with the Shipley Vocabulary and WASI Matrix Reasoning tests. Brain entropy was positively associated with intelligence. This relation was most strongly observed in the prefrontal cortex, inferior temporal lobes, and cerebellum. This relationship between high brain entropy and high intelligence indicates an essential role for entropy in brain functioning. It demonstrates that access to variable neural states predicts complex behavioral performance, and specifically shows that entropy derived from neuroimaging signals at rest carries information about intellectual capacity. Future work in this area may elucidate the links between brain entropy in both resting and active states and various forms of intelligence. This insight has the potential to provide predictive information about adaptive behavior and to delineate the subdivisions and nature of intelligence based on entropic patterns.
86 citations
TL;DR: Artificial intelligence algorithms are a rich set of algorithms used to perform AI tasks, notably those pertaining to perception and cognition that involve learning from data and experiences simulating human intelligence.
Abstract: Artificial intelligence (AI) is a branch of computer science and a technology aimed at developing the theories, methods, algorithms, and applications for simulating and extending human intelligence. Modern AI enables going from an old world-where people give computers rules to solve problems-to a new world-where people give computers problems directly and the machines learn how to solve them on their own using a set of algorithms. An algorithm is a self-contained sequence of instructions and actions to be performed by a computational machine. Starting from an initial state and initial input, the instructions describe computational steps, which, when executed, proceed through a finite number of well-defined successive states, eventually producing an output and terminating at a final ending state. AI algorithms are a rich set of algorithms used to perform AI tasks, notably those pertaining to perception and cognition that involve learning from data and experiences simulating human intelligence.
71 citations
Posted Content•
TL;DR: What AI of today is capable of doing, why it still cannot reach human intelligence and what are the open challenges existing in front of AI to reach and outperform human level of intelligence are critically analyzed.
Abstract: AI technology has a long history which is actively and constantly changing and growing. It focuses on intelligent agents, which contain devices that perceive the environment and based on which takes actions in order to maximize goal success chances. In this paper, we will explain the modern AI basics and various representative applications of AI. In the context of the modern digitalized world, AI is the property of machines, computer programs, and systems to perform the intellectual and creative functions of a person, independently find ways to solve problems, be able to draw conclusions and make decisions. Most artificial intelligence systems have the ability to learn, which allows people to improve their performance over time. The recent research on AI tools, including machine learning, deep learning and predictive analysis intended toward increasing the planning, learning, reasoning, thinking and action taking ability. Based on which, the proposed research intends towards exploring on how the human intelligence differs from the artificial intelligence. Moreover, we critically analyze what AI of today is capable of doing, why it still cannot reach human intelligence and what are the open challenges existing in front of AI to reach and outperform human level of intelligence. Furthermore, it will explore the future predictions for artificial intelligence and based on which potential solution will be recommended to solve it within next decades.
61 citations
01 Jan 2018
TL;DR: This chapter will look at a phenomenon known as stereotype threat, which can relate to Chapter 12: Social Psychology about how different cultures perform on American intelligence tests.
Abstract: (1) To what degree intelligence is due to heredity or to the environment (is it inherited or learned) • Should we collect sperm from Nobel Prize winners • Should we sterilize “morons” to keep them from the possibility of producing other “morons” • Should we improve public education and strive to create a positive learning environment? (2) To what extent does ethnicity and culture play in intelligence and intelligence testing? • For example, this chapter will look at a phenomenon known as stereotype threat. This can relate to Chapter 12: Social Psychology • How do different cultures perform on American intelligence tests? • How do Americans perform on different cultures’ intelligence tests? (3) What are the appropriate and inappropriate uses of intelligence tests?
52 citations
TL;DR: This paper introduces two applications of cognition-based communications, including user-centric cognitive communications, and cognitive internet of vehicles, which can better meet users’ needs, provide them with a higher Quality of Experience (QoE), and achieve higher energy efficiency.
44 citations
TL;DR: To navigate the ethical issues raised by suicide risk prediction, recommendations in three areas are provided—communication, consent, and controls—for both providers and researchers.
Abstract: In the United States, suicide increased by 24% in the past 20 years, and suicide risk identification at point-of-care remains a cornerstone of the effort to curb this epidemic (1). As risk identification is difficult because of symptom under-reporting, timing, or lack of screening, healthcare systems rely increasingly on risk scoring and now artificial intelligence (AI) to assess risk. AI remains the science of solving problems and accomplishing tasks, through automated or computational means, that normally require human intelligence. This science is decades-old and includes traditional predictive statistics and machine learning. Only in the last few years has it been applied rigorously in suicide risk prediction and prevention. Applying AI in this context raises significant ethical concern, particularly in balancing beneficence and respecting personal autonomy. To navigate the ethical issues raised by suicide risk prediction, we provide recommendations in three areas-communication, consent, and controls-for both providers and researchers (2).
TL;DR: For example, Warne et al. as mentioned in this paper analyzed the content of 29 of the most popular introductory psychology textbooks to learn the most frequently taught topics related to human intelligence, the accuracy of information about human intelligence and the presence of logical fallacies about intelligence research.
Abstract: Human intelligence is an important construct in psychology, with far-reaching implications, providing insights into fields as diverse as neurology, international development, and sociology. Additionally, IQ scores can predict life outcomes in health, education, work, and socioeconomic status. Yet, students of psychology are often exposed to human intelligence only in limited ways. To ascertain what psychology students typically learn about intelligence, we analyzed the content of 29 of the most popular introductory psychology textbooks to learn (a) the most frequently taught topics related to human intelligence, (b) the accuracy of information about human intelligence, and (c) the presence of logical fallacies about intelligence research. We found that 79.3% of textbooks contained inaccurate statements and 79.3% had logical fallacies in their sections about intelligence. The five most commonly taught topics were IQ (93.1% of books), Gardner’s multiple intelligences (93.1%), Spearman’s g (93.1%), Sternberg’s triarchic theory (89.7%), and how intelligence is measured (82.8%). Conversely, modern models of intelligence were only discussed in 24.1% of books, with only one book discussing the Carroll three-stratum model by name and no book discussing bifactor models of intelligence. We conclude that most introductory psychology students are exposed to some inaccurate information and may have the mistaken impression that nonmainstream theories (e.g., Sternberg’s or Gardner’s theories) are as empirically supported as g theory. This has important implications for the undergraduate curriculum and textbook authors. Readers should be aware of the limitations of the study, including the choice of standards for accuracy for the study and the inherent subjectivity required for some of the data collection process. S C I E N T I F I C A B S T R A C T Human intelligence is an important concept in psychology because it provides insights into many areas, including neurology, sociology, and health. Additionally, IQ scores can predict life outcomes in health, education, work, and socioeconomic status. Yet, most students of psychology do not have an opportunity to take a class on intelligence. To learn what psychology students typically learn about intelligence, we analyzed 29 textbooks for introductory psychology courses. We found that over 3/4 of textbooks contained inaccurate statements. The five most commonly taught topics were IQ (93.1% of books), Gardner’s multiple intelligences (93.1%), Spearman’s g (93.1%), Sternberg’s triarchic theory (89.7%), This article was published February 26, 2018. Russell T. Warne, Mayson C. Astle, and Jessica C. Hill, Department of Behavioral Science, Utah Valley University. This research was previously presented at the Utah Conference on Undergraduate Research on February 17, 2017, in Orem, UT; the National Conference on Undergraduate Research on April 7, 2017, in Memphis, TN; the annual conference of the Rocky Mountain Psychological Association on April 7, 2017, in Salt Lake City, UT; the annual conference of the International Society for Intelligence Research on July 15, 2017, in Montreal, Canada; and the annual conference of the American Psychological Association on August 3, 2017, in Washington, DC. The authors have made available for use by others the data that underlie the analyses presented in this paper (see Warne, 2017), thus allowing replication and potential extensions of this work by qualified researchers. Next users are obligated to involve the data originators in their publication plans, if the originators so desire. This project was financially supported by a Grant for Engaged Learning and an Undergraduate Research Scholarly and Creative Activities Grant from Utah Valley University. Copyright of this manuscript belongs to the author(s). The author(s) grant(s) the American Psychological Association the exclusive right to publish this manuscript first, identify itself as the original publisher, and claim all commercial exploitation rights. Upon publication, the manuscript is available to the public to copy, distribute, or display under a Creative Commons Attribution-Noncommercial 3.0 Unported License (http://creativecommons.org/licenses/by-nc/3.0/), which permits use, distribution, and reproduction in any medium, provided that the original work is properly cited and is not used for commercial purposes. Please use APA’s Online Permissions Process (Rightslink®) at http://www.apa.org/about/contact/copyright/seek-permission.aspx to request commercial reuse of this content. Correspondence concerning this article should be addressed to Russell T. Warne, Department of Behavioral Science, Utah Valley University, 800 West University Parkway MC 115, Orem, UT 84058. E-mail: rwarne@uvu.edu Archives of Scientific Psychology 2018, 6, 32–50 © 2018 The Author(s) DOI: http://dx.doi.org/10.1037/arc0000038 2169-3269 Archives of Scientific Psychology www.apa.org/pubs/journals/arc and how intelligence is measured (82.8%). We learned that most introductory psychology students are exposed to some inaccurate information about intelligence and may have the mistaken impression that nonmainstream theories (e.g., Sternberg’s or Gardner’s theories) are as empirically supported mainstream theories (such as Spearman’s g).
08 Feb 2018
TL;DR: The prospects for automating intelligence versus augmenting human intelligence, and an overall architecture for providing this functionality, are addressed, including how it will make deep learning explainable to decision makers.
Abstract: This article addresses the prospects for automating intelligence versus augmenting human intelligence. The evolution of artificial intelligence (AI) is summarized, including contemporary AI and the...
11 Jan 2018
TL;DR: In this paper, the 19 most highly cited psychological scientists in the leading textbooks on human intelligence to share their research programs and fi ndings, each chapter answers a standardized set of questions on the measurement, investigation, and development of intelligence and the outcome represents a wide range of substantive and methodological emphases including psychometric, cognitive, expertise-based, developmental, neuropsychological, genetic, cultural, systems, and groupdifference approaches.
Abstract: Th e study of human intelligence features many points of consensus, but there are also many diff erent perspectives. In this unique book Robert J. Sternberg invites the 19 most highly cited psychological scientists in the leading textbooks on human intelligence to share their research programs and fi ndings. Each chapter answers a standardized set of questions on the measurement, investigation, and development of intelligence – and the outcome represents a wide range of substantive and methodological emphases including psychometric, cognitive, expertisebased, developmental, neuropsychological, genetic, cultural, systems, and groupdiff erence approaches. Th is is an exciting and valuable course book for upperlevel students to learn from the originators of the key contemporary ideas in intelligence research about how they think about their work and about the fi eld.
TL;DR: The use of AI method for cellular therapy and regenerative medicine is reviewed and its potential to further the progress in these fields of medicine is emphasized.
Book•
27 Jul 2018
01 Aug 2018
TL;DR: The architecture of artificial intelligence must emphasize on evaluation and redesign the nature of design process, and data science is also trending now and analytically deals to solve complex problems.
Abstract: Artificial intelligence is a field which requires multidisciplinary expertise where the final goal is to automate all the human activities that presently require human intelligence. The major problem is to develop a method which works exactly the way how a human brain works. The architecture of artificial intelligence must emphasize on evaluation and redesign the nature of design process. Data science is also trending now and analytically deals to solve complex problems. Data is divided into smaller parts and its trends, behaviors are understood. The main problem in data science is to handle large quantities of data. Though there is significant increase in terms of research opportunities few challenges like lack of compute power, people power still remains a big challenge.
TL;DR: It is concluded that AI and smart contract will have a huge impact in future for Islamic Finance industry.
Abstract: This study examines the two important aspect of latest technology issues in Islamic finance that related to artificial intelligence (AI) and smart contract. AI refers to the ability of machines to understand, think, and learn in a similar way to human beings, indicating the possibility of using computers to simulate human intelligence. Smart contract is a computer code running on top of a block-chain containing a set of rules under which the parties to that smart contract agree to interact with each other. The main objectives of this article is to evaluate the operations of AI and smart contract. Our study also highlights the comparison between the operations of AI and smart contract. This article concludes that AI and smart contract will have a huge impact in future for Islamic Finance industry.
Patent•
12 Mar 2018
TL;DR: In this paper, an Artificial Intelligence-assisted Highway Stewardship System (HAI) using deep convolutional neural networks and machine vision-based algorithms is proposed to provide complete solution to safe, legal and comfortable parking, driving and riding for commuters on the roadways.
Abstract: The present disclosure relates to artificial intelligence based systems and method for determination of traffic violations. The present disclosure provides systems and methods that use deep convolutional neural networks and machine vision based algorithms to perform a task of detection and recognition to provide complete solution to safe, legal and comfortable parking, driving and riding for commuters on the roadways. Roadway stewardship systems, Parking management systems when made on-demand and crowdsourced, can play a very strong role in regulating driving conditions in cities and highways. By allowing the on-demand, crowdsourced, roadway stewardship system to be automated, through the use of Artificial Intelligence (AI) sub-systems, users can be trained to recognize and be educated as well in the laws & regulations around the use of roadways; can help the process through an interactive console/game-play, which can also be used for monetization for individuals to earn money for their contribution. The AI assisted with Human Intelligence (HI) together called HAI in particular, can play a valuable role in reducing traffic density, traffic movement restrictions and fuel and time waste in large cities. Also proper driving on the roads can lead to faster and safer commute. In Addition, multiple other objects of interest can also be identified and trained to be recognized using the Stewardship System disclosed herein.
TL;DR: It is suggested that a tendency to overrate one’s cognitive abilities may be a stable feature of human psychology and not always unrealistic: more educated people were more likely to think their intelligence is above average.
Abstract: Psychologists often note that most people think they are above average in intelligence. We sought robust, contemporary evidence for this “smarter than average” effect by asking Americans in two independent samples (total N = 2,821) whether they agreed with the statement, “I am more intelligent than the average person.” After weighting each sample to match the demographics of U.S. census data, we found that 65% of Americans believe they are smarter than average, with men more likely to agree than women. However, overconfident beliefs about one’s intelligence are not always unrealistic: more educated people were more likely to think their intelligence is above average. We suggest that a tendency to overrate one’s cognitive abilities may be a stable feature of human psychology.
Proceedings Article•
25 Apr 2018
TL;DR: The Structural Affinity method, a computational account that first learns the structure of a Raven’s problem and then predicts the solution by computing the probability of the correct answer by recognizing patterns corresponding to Carpenter et al.
Abstract: Graphical models offer techniques for capturing the structure of many problems in real-world domains and provide means for representation, interpretation, and inference. The modeling framework provides tools for discovering rules for solving problems by exploring structural relationships. We present the Structural Affinity method that uses graphical models for first learning and subsequently recognizing the pattern for solving problems on the Raven's Progressive Matrices Test of general human intelligence. Recently there has been considerable work on computational models of addressing the Raven's test using various representations ranging from fractals to symbolic structures. In contrast, our method uses Markov Random Fields parameterized by affinity factors to discover the structure in the geometric analogy problems and induce the rules of Carpenter et al.'s cognitive model of problem-solving on the Raven's Progressive Matrices Test. We provide a computational account that first learns the structure of a Raven's problem and then predicts the solution by computing the probability of the correct answer by recognizing patterns corresponding to Carpenter et al.'s rules. We demonstrate that the performance of our model on the Standard Raven Progressive Matrices is comparable with existing state of the art models.
01 Oct 2018
TL;DR: Cognitive architecture of autonomous intelligent agent is proposed as an artificial neural network with a feedback loop in meaningful processing of information through the self-referential Processing of information with consideration of itself as a meaningful model.
Abstract: Taking into account that the human intelligence is the only available intelligence we will find the functional relationship between neuronal processes and psychic phenomena to reproduce intelligence in artificial system. The autonomous behavior of an agent may be the consequence of a gap between physical processes and self-referential meaningful processing of information which is related but not determined by physical processes. This indeterminism can be reproduced in a cognitive architecture through the self-referential processing of information with consideration of itself as a meaningful model. We propose embodiment of cognitive architecture of autonomous intelligent agent as an artificial neural network with a feedback loop in meaningful processing of information.
Posted Content•
TL;DR: In this paper, the authors discuss few of the challenges facing the accounting profession nowadays and shed light on some possible trends of its future development in the artificial intelligence context, drawing conclusions related to accounting education will be drawn from the perspective of the new intelligent technologies and their business applications.
Abstract: Recent developments in robotics and artificial intelligence and their applications have started to reshape our world, bringing it closer to what famous futurologists as Jules Verne and Isaac Asimov have predicted some decades ago. Many aspects of our lives are changing due to technological innovations. Businesses are now exploring new opportunities for raising profitability and return on capital ratio through revenue increase, cost reduction and new sources of value creation, trying to overcome the negative long-lasting effects of the World Financial Turmoil from 2007 - 2008 and aiming to become more competitive and sustainable. The “artificial vs. human intelligence” dilemma widely debated among academics and practitioners, encompasses many controversial issues related to the future prospects of some occupations, the required new skill set and competences, the way humans and machines could work efficiently and effectively together. Accounting profession is on its way to make a great change of the role it plays in the organization and the functions it currently performs. The proponents of artificial intelligence revolution view this development as a step-ahead and embrace the challenges of the future. The opponents, on the other hand, consider it a step-back as many accountants will fail to adapt to this new business environment and will drop behind. This paper is aiming to discuss few of the challenges facing the accounting profession nowadays and to shed light on some possible trends of its future development in the artificial intelligence context. Some conclusions related to accounting education will be drawn from the perspective of the new intelligent technologies and their business applications.
TL;DR: The model of human intelligence that is most widely adopted derives from psychometrics and behavioral genetics as mentioned in this paper, which conceives intelligence as a general cognitive ability that is genetically highly heritable and describable using quantitative traits analysis.
Abstract: The model of human intelligence that is most widely adopted derives from psychometrics and behavioral genetics. This standard approach conceives intelligence as a general cognitive ability that is genetically highly heritable and describable using quantitative traits analysis. The paper analyzes intelligence within the debate on natural kinds and contends that the general intelligence conceptualization does not carve psychological nature at its joints. Moreover, I argue that this model assumes an essentialist perspective. As an alternative, I consider an HPC theory of intelligence and evaluate how it deals with essentialism and with intuitions coming from cognitive science. Finally, I highlight some concerns about the HPC model as well, and conclude by suggesting that it is unnecessary to treat intelligence as a kind in any sense.
TL;DR: This article presents a position highlighting the importance of combining artificial intelligence (AI) approaches with natural intelligence, in other words, involvement of humans, to solve problems of societal significance, stemming from complex, sensitive domains.
Abstract: This article presents a position highlighting the importance of combining artificial intelligence (AI) approaches with natural intelligence, in other words, involvement of humans To do so, we specifically focus on problems of societal significance, stemming from complex, sensitive domains We first discuss our prior work across a series of projects surrounding social media and mental health, and identify major themes wherein augmentation of AI systems and techniques with human feedback has been and can be fruitful and meaningful We then conclude by noting the implications, in terms of opportunities as well as challenges, that can be drawn from our position, both relating to the specific domain of mental health, and those for AI researchers and practitioners
01 Jan 2018
TL;DR: Researchers found that consumers are aware of the AI enabled tools and the tools under study are all effective and consumers are also loyal towards the tools.
Abstract: Artificial Intelligence (AI) is finally ready to challenge the human intelligence. Computers defeated human in complicated board games like Chess and Chinese game Go. AI is now ready to write poems and complex codes, predict choices, interact with human in real time, mining trillions of data and providing solutions in less than milliseconds. Today AI is driving almost every business unit and Customer Relationship Management (CRM) is one area that is benefitting the most in leveraging better customer experience (CX). In this research paper, researchers studied various AI enabled tools and finalized five AI enabled tools to study the Consumer Awareness, Effectiveness and Loyalty. They used survey based research methodology and collected primary data through Google form. After the data analysis researchers found that consumers are aware of the AI enabled tools and the tools under study are all effective and consumers are also loyal towards the tools.
10 Oct 2018
TL;DR: The role and contributions of Service-Oriented computing (SOC) to elaborate a Smart University in order to provide relevant and equitable Smart Services for all the stakeholder of a university, in term of accessibility, self-study, collaboration, and partnerships are investigated.
Abstract: Digitization is a catalyst for transforming a traditional university when human intelligence is mainly invoked in learning practices, towards an intelligence university that dematerialized learning by using Artificial Intelligence (AI) and which integrates intelligent learning platforms namely MOOCs. Admittedly, the traditional university boasts of human intelligence that has served many learning techniques for many years. However, the content and the organization of higher education should change with the evolution and needs of the society, these requirements have emerged a Smart University System (SUS) that conforms to the Smart City (SC) referential, in terms of infrastructure, interactions, reasoning, and visualization. In this paper, we investigate the role and contributions of Service-Oriented computing (SOC) to elaborate a Smart University in order to provide relevant and equitable Smart Services for all the stakeholder of a university, in term of accessibility, self-study, collaboration, and partnerships. Through the integration of service concept as a principal component in the Smart University System (SUS) that promotes the reuse and the composition of services.
TL;DR: Methods commonly taught in statistics classes like linear regression, discriminant analysis, principal components analysis, and so on are used to find structure or patterns in data and can be considered as algorithms for machine learning.
Abstract: We kindly thank Drs Ramachandran and van den Heuvel for their interest in our article.1 2 We broadly defined machine learning (the field concerned with algorithms to find structure or patterns in data) as a set of techniques to enable artificial intelligence (the field concerned with programming computers to mimic human intelligence). Methods commonly taught in statistics classes like linear regression, discriminant analysis, principal components analysis, and so on are used to find structure or patterns in data and can be considered as algorithms for machine learning. It is thus difficult to define a fine line between where statistics ends and where machine learning begins—some of the methods have both flavours …
06 Mar 2018
TL;DR: An overview of artificial intelligence, machine learning & deep learning techniques and compare these techniques is given and a particular kind of machine learning that achieves great power and flexibility is achieved.
Abstract: There is a lot of confusion these days about Artificial Intelligence (AI), Machine Learning (ML) and Deep Learning (DL). A computer system able to perform tasks that normally require human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages. Artificial Intelligence has made it possible. Deep learning is a subset of machine learning, and machine learning is a subset of AI, which is an umbrella term for any computer program that does something smart. In other words, all machine learning is AI, but not all AI is machine learning, and so forth. Machine Learning represents a key evolution in the fields of computer science, data analysis, software engineering, and artificial intelligence. Machine learning (ML)is a vibrant field of research, with a range of exciting areas for further development across different methods and applications. These areas include algorithmic interpretability, robustness, privacy, fairness, inference of causality, human-machine interaction, and security. The goal of ML is never to make “perfect” guesses, because ML deals in domains where there is no such thing. The goal is to make guesses that are good enough to be useful. Deep learning is a particular kind of machine learning that achieves great power and flexibility by learning to represent the world as nested hierarchy of concepts, with each concept defined in relation to simpler concepts, and more abstract representations computed in terms of less abstract ones. This paper gives an overview of artificial intelligence, machine learning & deep learning techniques and compare these techniques.
TL;DR: A taxonomy and classification of the broader domain of human-enhanced computing systems as it assimilates crowdsourcing, hybrid intelligence, and mixed elastic systems is presented.
Abstract: State-of-the-art practices have recognized the utility of leveraging human intervention as a crucial aspect of modern computing systems. The emerging crowdsourcing paradigm is based on harnessing human intelligence, effort and rational behaviors to augment computation and analysis. In addition to the crowdsourcing paradigm, new techniques have emerged that incorporate machine and human computational resources together forming a hybrid intelligence when addressing complex problems and tasks. This combined technique is particularly impactful if human and machine contributions can scale automatically in response to their respective efficiency and effectiveness when addressing subsets of a bigger problem – an approach that we have named mixed elastic systems . In this survey, we highlight state-of-the-art projects that investigate crowdsourcing, hybrid intelligence systems and mixed elastic systems. We also present a taxonomy and classification of the broader domain of human-enhanced computing systems as it assimilates crowdsourcing, hybrid intelligence, and mixed elastic systems.