From the digital data revolution to digital health and digital economy toward a digital society: Pervasiveness of Artificial Intelligence

Technological progress has led to powerful computers and communication technologies that penetrate nowadays all areas of science, industry and our private lives. As a consequence, all these areas are generating digital traces of data amounting to big data resources. This opens unprecedented opportunities but also challenges toward the analysis, management, interpretation and utilization of these data. Fortunately, recent breakthroughs in deep learning algorithms complement now machine learning and statistics methods for an efficient analysis of such data. Furthermore, advances in text mining and natural language processing, e.g., word-embedding methods, enable also the processing of large amounts of text data from diverse sources as governmental reports, blog entries in social media or clinical health records of patients. In this paper, we present a perspective on the role of artificial intelligence in these developments and discuss also potential problems we are facing in a digital society.

[1]  M. Dehmer,et al.  An Introductory Review of Deep Learning for Prediction Models With Big Data , 2020, Frontiers in Artificial Intelligence.

[2]  Peter Herrlich The responsibility of the scientist , 2013, EMBO reports.

[3]  John W. Sutherland,et al.  Predictive Maintenance of Machine Tool Systems Using Artificial Intelligence Techniques Applied to Machine Condition Data , 2019, Procedia CIRP.

[4]  Jeffrey Dean,et al.  Distributed Representations of Words and Phrases and their Compositionality , 2013, NIPS.

[5]  F Emmert-Streib,et al.  Local network-based measures to assess the inferability of different regulatory networks. , 2010, IET systems biology.

[6]  Chao Liu,et al.  A novel adversarial learning framework in deep convolutional neural network for intelligent diagnosis of mechanical faults , 2019, Knowl. Based Syst..

[7]  Mark S. Granovetter Economic Action and Social Structure: The Problem of Embeddedness , 1985, American Journal of Sociology.

[8]  J. Kvedar,et al.  Artificial intelligence powers digital medicine , 2018, npj Digital Medicine.

[9]  Matthias Dehmer,et al.  Explainable artificial intelligence and machine learning: A reality rooted perspective , 2020, WIREs Data Mining Knowl. Discov..

[10]  Mina J. Hanna,et al.  User Data Privacy: Facebook, Cambridge Analytica, and Privacy Protection , 2018, Computer.

[11]  Robert J. Kauffman,et al.  Understanding the paradigm shift to computational social science in the presence of big data , 2014, Decis. Support Syst..

[12]  Matthias Dehmer,et al.  Ensuring Quality Standards and Reproducible Research for Data Analysis Services in Oncology: A Cooperative Service Model , 2019, Front. Cell Dev. Biol..

[13]  A Min Tjoa,et al.  Current Advances, Trends and Challenges of Machine Learning and Knowledge Extraction: From Machine Learning to Explainable AI , 2018, CD-MAKE.

[14]  Ron S. Jarmin,et al.  Measuring the Digital Economy , 1999 .

[15]  Kagermann Henning Recommendations for implementing the strategic initiative INDUSTRIE 4.0 , 2013 .

[16]  Hui Li,et al.  Digital mammographic tumor classification using transfer learning from deep convolutional neural networks , 2016, Journal of medical imaging.

[17]  Yang Lu,et al.  Industry 4.0: A survey on technologies, applications and open research issues , 2017, J. Ind. Inf. Integr..

[18]  Yuxi Li,et al.  Deep Reinforcement Learning: An Overview , 2017, ArXiv.

[19]  Richard Heeks,et al.  Defining, Conceptualising and Measuring the Digital Economy , 2017, International Organisations Research Journal.

[20]  Timothy Baldwin,et al.  An Empirical Evaluation of doc2vec with Practical Insights into Document Embedding Generation , 2016, Rep4NLP@ACL.

[21]  Matthias Dehmer,et al.  Ensuring the Robustness and Reliability of Data-Driven Knowledge Discovery Models in Production and Manufacturing , 2020, ArXiv.

[22]  Weng Marc Lim,et al.  The Sharing Economy: A Marketing Perspective , 2020, Australasian Marketing Journal.

[23]  O. A. Sawy,et al.  Digital business strategy: toward a next generation of insights , 2013 .

[24]  E. Petricoin,et al.  SELDI-TOF-based serum proteomic pattern diagnostics for early detection of cancer. , 2004, Current opinion in biotechnology.

[25]  Matthias Dehmer,et al.  A clarification of misconceptions, myths and desired status of artificial intelligence , 2020, ArXiv.

[26]  M. Kosinski,et al.  Psychological targeting as an effective approach to digital mass persuasion , 2017, Proceedings of the National Academy of Sciences.

[27]  Spyros Makridakis,et al.  The Forthcoming Artificial Intelligence (AI) Revolution: Its Impact on Society and Firms , 2017 .

[28]  Bill Franks,et al.  Taming The Big Data Tidal Wave: Finding Opportunities in Huge Data Streams with Advanced Analytics , 2012 .

[29]  Honglak Lee,et al.  Unsupervised feature learning for audio classification using convolutional deep belief networks , 2009, NIPS.

[30]  Matthias Dehmer,et al.  High-Dimensional LASSO-Based Computational Regression Models: Regularization, Shrinkage, and Selection , 2019, Mach. Learn. Knowl. Extr..

[31]  V. Marx Biology: The big challenges of big data , 2013, Nature.

[32]  M. Turakhia,et al.  Characteristics of Digital Health Studies Registered in ClinicalTrials.gov. , 2019, JAMA internal medicine.

[33]  Mumbai,et al.  Internet of Things (IoT): A Literature Review , 2015 .

[34]  Antoine Grall,et al.  Continuous-time predictive-maintenance scheduling for a deteriorating system , 2002, IEEE Trans. Reliab..

[35]  Bradley Malin,et al.  Anonymising and sharing individual patient data , 2015, BMJ : British Medical Journal.

[36]  Mehmed Kantardzic,et al.  Learning from Data , 2011 .

[37]  Dhavan V. Shah,et al.  Big Data, Digital Media, and Computational Social Science , 2015 .

[38]  Dirk Helbing,et al.  Thinking Ahead - Essays on Big Data, Digital Revolution, and Participatory Market Society , 2015, Springer International Publishing.

[39]  Laura Shafner,et al.  Using Artificial Intelligence to Reduce the Risk of Nonadherence in Patients on Anticoagulation Therapy , 2017, Stroke.

[40]  P O Skobelev,et al.  On the way from Industry 4.0 to Industry 5.0: from digital manufacturing to digital society , 2017 .

[41]  Colin Lankshear,et al.  Introduction: digital literacies: concepts, policies and practices , 2008 .

[42]  Matthias Dehmer,et al.  The Process of Analyzing Data is the Emergent Feature of Data Science , 2016, Front. Genet..

[43]  Rob Kitchin,et al.  The data revolution : big data, open data, data infrastructures & their consequences , 2014 .

[44]  Eric J. Topol,et al.  Digital medicine, on its way to being just plain medicine , 2018, npj Digital Medicine.

[45]  Mónica Aguilar-Igartua,et al.  Smart city for VANETs using warning messages, traffic statistics and intelligent traffic lights , 2012, 2012 IEEE Intelligent Vehicles Symposium.

[46]  Alagan Anpalagan,et al.  Efficient Energy Management for the Internet of Things in Smart Cities , 2017, IEEE Communications Magazine.

[47]  Feng Xia,et al.  From machine-to-machine communications towards cyber-physical systems , 2013, Comput. Sci. Inf. Syst..

[48]  David Stuart,et al.  The Data Revolution: Big Data, Open Data, Data Infrastructures and Their Consequences , 2015, Online Inf. Rev..

[49]  Ronald W. Davis,et al.  Quantitative Monitoring of Gene Expression Patterns with a Complementary DNA Microarray , 1995, Science.

[50]  Patty Kostkova,et al.  Grand Challenges in Digital Health , 2015, Front. Public Health.

[51]  Ronald P. S. Mahler,et al.  Statistical Multisource-Multitarget Information Fusion , 2007 .

[52]  M. Gerstein,et al.  RNA-Seq: a revolutionary tool for transcriptomics , 2009, Nature Reviews Genetics.

[53]  Yu-Chee Tseng,et al.  Pervasive and Mobile Computing ( ) – Pervasive and Mobile Computing Review from Wireless Sensor Networks towards Cyber Physical Systems , 2022 .

[54]  Sergey Levine,et al.  Deep reinforcement learning for robotic manipulation with asynchronous off-policy updates , 2016, 2017 IEEE International Conference on Robotics and Automation (ICRA).

[55]  Yannis Charalabidis,et al.  IoT and AI for Smart Government: A Research Agenda , 2019, Gov. Inf. Q..

[56]  Gregory Cohen,et al.  EMNIST: an extension of MNIST to handwritten letters , 2017, CVPR 2017.

[57]  Matthias Dehmer,et al.  Data Analytics Applications for Streaming Data From Social Media: What to Predict? , 2018, Front. Big Data.

[58]  Veda C. Storey,et al.  Business Intelligence and Analytics: From Big Data to Big Impact , 2012, MIS Q..

[59]  Shane Legg,et al.  Human-level control through deep reinforcement learning , 2015, Nature.

[60]  Joy Bill,et al.  Why the future doesn’t need us , 2003 .

[61]  Ming-Wei Chang,et al.  BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding , 2019, NAACL.

[62]  Sebastian Thrun,et al.  Dermatologist-level classification of skin cancer with deep neural networks , 2017, Nature.

[63]  Ivan Stojmenovic,et al.  Machine-to-Machine Communications With In-Network Data Aggregation, Processing, and Actuation for Large-Scale Cyber-Physical Systems , 2014, IEEE Internet of Things Journal.

[64]  Mingming Cheng Sharing economy: A review and agenda for future research , 2016 .

[65]  Matthias Dehmer,et al.  Applied Statistics for Network Biology: Methods in Systems Biology , 2011 .

[66]  Dirk Helbing,et al.  Societal, Economic, Ethical and Legal Challenges of the Digital Revolution: From Big Data to Deep Learning, Artificial Intelligence, and Manipulative Technologies , 2015, Towards Digital Enlightenment.

[67]  Marina Jirotka,et al.  Ethical governance is essential to building trust in robotics and artificial intelligence systems , 2018, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[68]  Zoran Milosevic,et al.  Ethics in Digital Health: A Deontic Accountability Framework , 2019, 2019 IEEE 23rd International Enterprise Distributed Object Computing Conference (EDOC).

[69]  Yi Wang,et al.  Industry 4.0: a way from mass customization to mass personalization production , 2017 .

[70]  M. Braga,et al.  Exploratory Data Analysis , 2018, Encyclopedia of Social Network Analysis and Mining. 2nd Ed..

[71]  Matthias Dehmer,et al.  Evaluation of Regression Models: Model Assessment, Model Selection and Generalization Error , 2019, Mach. Learn. Knowl. Extr..

[72]  Yon Dohn Chung,et al.  Utility-preserving anonymization for health data publishing , 2017, BMC Medical Informatics and Decision Making.

[73]  Stephen Hailes,et al.  Security of smart manufacturing systems , 2018 .

[74]  Min Chen,et al.  Machine-to-Machine Communications: Architectures, Standards and Applications , 2012, KSII Trans. Internet Inf. Syst..

[75]  Gabriel-Miro Muntean,et al.  A Communications-Oriented Perspective on Traffic Management Systems for Smart Cities: Challenges and Innovative Approaches , 2015, IEEE Communications Surveys & Tutorials.

[76]  Matthias Dehmer,et al.  Understanding Statistical Hypothesis Testing: The Logic of Statistical Inference , 2019, Mach. Learn. Knowl. Extr..

[77]  Qiang Yang,et al.  A Survey on Transfer Learning , 2010, IEEE Transactions on Knowledge and Data Engineering.

[78]  Subhashini Venugopalan,et al.  Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs. , 2016, JAMA.

[79]  Li Da Xu,et al.  Industry 4.0: state of the art and future trends , 2018, Int. J. Prod. Res..

[80]  Max Welling,et al.  Semi-supervised Learning with Deep Generative Models , 2014, NIPS.

[81]  Detlef Schoder,et al.  Consolidated, systemic conceptualization, and definition of the “sharing economy” , 2020, J. Assoc. Inf. Sci. Technol..

[82]  D. Hoon,et al.  Emerging technologies for studying DNA methylation for the molecular diagnosis of cancer , 2015, Expert review of molecular diagnostics.

[83]  Andreas Holzinger,et al.  Interactive machine learning for health informatics: when do we need the human-in-the-loop? , 2016, Brain Informatics.

[84]  D. Ruppert The Elements of Statistical Learning: Data Mining, Inference, and Prediction , 2004 .

[85]  Rachel Botsman,et al.  What's Mine Is Yours: The Rise of Collaborative Consumption , 2010 .

[86]  Avi Ma'ayan,et al.  Lean Big Data integration in systems biology and systems pharmacology. , 2014, Trends in pharmacological sciences.

[87]  Jukka Huhtamäki,et al.  Conceptualizing Big Social Data , 2017, Journal of Big Data.

[88]  Francesco Buccafurri,et al.  Comparing Twitter and Facebook user behavior: Privacy and other aspects , 2015, Comput. Hum. Behav..

[89]  Dongyan Zhao,et al.  Explainable AI: A Brief Survey on History, Research Areas, Approaches and Challenges , 2019, NLPCC.

[90]  Michael Marien,et al.  Book Review: The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies , 2014 .

[91]  B. Ripley,et al.  Pattern Recognition , 1968, Nature.

[92]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[93]  J. Nadal,et al.  Manifesto of computational social science , 2012 .

[94]  知秀 柴田 5分で分かる!? 有名論文ナナメ読み:Jacob Devlin et al. : BERT : Pre-training of Deep Bidirectional Transformers for Language Understanding , 2020 .

[95]  Ernesto Damiani,et al.  Augmented reality technologies, systems and applications , 2010, Multimedia Tools and Applications.

[96]  Pierre Baldi,et al.  Autoencoders, Unsupervised Learning, and Deep Architectures , 2011, ICML Unsupervised and Transfer Learning.

[97]  Robert Tibshirani,et al.  The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd Edition , 2001, Springer Series in Statistics.

[98]  M. Ienca,et al.  Digital Medicine and Ethics: Rooting for Evidence , 2018, The American journal of bioethics : AJOB.

[99]  Liping Zhu,et al.  A Review on Dimension Reduction , 2013, International statistical review = Revue internationale de statistique.

[100]  Wayne H. Wolf,et al.  Cyber-physical Systems , 2009, Computer.

[101]  Mathias Schmitt,et al.  Human-machine-interaction in the industry 4.0 era , 2014, 2014 12th IEEE International Conference on Industrial Informatics (INDIN).

[102]  Tomi Dufva,et al.  Grasping the future of the digital society , 2019, Futures.

[103]  Hanlee P. Ji,et al.  Next-generation DNA sequencing , 2008, Nature Biotechnology.

[104]  Allen Newell,et al.  The psychology of human-computer interaction , 1983 .

[105]  Matthias Dehmer,et al.  Named Entity Recognition and Relation Detection for Biomedical Information Extraction , 2020, Frontiers in Cell and Developmental Biology.

[106]  Amina Adadi,et al.  Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI) , 2018, IEEE Access.

[107]  K. Coombes,et al.  Deriving chemosensitivity from cell lines: Forensic bioinformatics and reproducible research in high-throughput biology , 2009, 1010.1092.

[108]  Araz Taeihagh,et al.  Crowdsourcing, Sharing Economies and Development , 2017, ArXiv.

[109]  J. Pope,et al.  What's Mine is Yours , 2016, Journal of occupational and environmental medicine.

[110]  Sahil R. Kalra,et al.  Big Challenges? Big Data … , 2015 .