From Intelligent to Wise Machines: Why a Poem Is Worth More Than 1 Million Tweets

In recent years, the interest in artificial intelligence and big data has grown exponentially, and the amount of data produced every day is truly staggering. Data are considered to be the “new oil” making algorithms capable of delivering meaningful information, which makes them more “intelligent.” In this position paper, we review the DIKW pyramid model, shedding a new light on each component. In particular, examining the engineering point of view, we focus on the definition of information , giving it a new conceptual structure. If tradition has always considered information as data-bearing meaning, in this paper, we argue that information is not meaningful. In fact, from the analysis of Shannon’s studies in communication engineering, we highlight how the notion of meaning is not necessary for the definition of information. It follows that we need to explore other paths in order to find a sustainable conceptual theory able to provide a new insight. Therefore, we show how it will not only be necessary to carry out a semiotic revolution to be able to introduce meaning into the communicative act, but it is also necessary to introduce the figure of an interpreting agent. Thanks to the interaction between such interpretative acts, which take place in conscious freedom à la Eco , cultural units emerge. Thus, we should address part of today’s research into new forms of data in order to facilitate a semiotic revolution. In particular, digital humanities and cultural heritage can funnel a new type of data for which semiotic representativeness has a greater degree of quality. Knowledge and wisdom are the next steps to truly craft intelligent machines. In den letzten Jahren ist das Interesse an künstlicher Intelligenz und Big Data exponentiell gewachsen. Die Menge an Daten, die tagtäglich produziert wird, ist wirklich erschütternd. Daten werden als das „neue Öl“ bezeichnet und erlauben es den Algorithmen, aussagekräftige Informationen zu liefern und dadurch „intelligenter“ zu werden. In diesem Thesenpapier überprüfen wir das DIKW-Pyramidenmodell und werfen ein neues Licht auf jede einzelne Komponente. Aus der Perspektive der Technik konzentrieren wir uns insbesondere auf die Definition von Information und geben ihr eine neue konzeptionelle Struktur. Obwohl in der Tradition Information immer als bedeutungsträchtige Daten angesehen wurde, argumentieren wir in diesem Artikel, dass Information nicht an und für sich aussagefähig ist. Aus der Analyse von Shannons Studien in der Kommunikationstechnik zeigen wir sogar, dass der Begriff „Bedeutung“ für die Definition von Information nicht notwendig ist. Daraus folgt, dass wir andere Wege erforschen müssen, um eine tragfähige konzeptuelle Theorie zu finden, die neue Erkenntnisse liefern kann. Wir zeigen, dass eine semiotische Revolution nicht genug ist, um Bedeutung in den kommunikativen Akt einzuführen. Es ist hingegen notwendig, die Figur eines interpretierenden Agenten einzuführen. Nur durch die Interaktion zwischen solchen Deutungsakten, die in bewusster Freiheit à la Eco geschehen, ist das Entstehen kultureller Einheiten möglich. Daher sollte sich ein Teil der heutigen Forschung nach neuen Datenformen richten, um die semiotische Revolution zu unterstützen. Insbesondere die Digital Humanities und das kulturelle Erbe können eine neue Art von Daten hervorbringen, mit einer höheren Qualität der semiotischen Repräsentativität. Wissen und Weisheit sind die nächsten Schritte, um wahrhaft intelligente Maschinen herzustellen.

[1]  B. Turner,et al.  The Computer and the Mind: An Introduction to Cognative Science, P.N. Johnson-Laird. 1987. Harvard University Press, Cambridge, MA. 448 pages. ISBN: 0-674-15615-3. $29.50 , 1988 .

[2]  C. P. Goodman,et al.  The Tacit Dimension , 2003 .

[3]  Dorothy E. Leidner,et al.  Knowledge Management Systems: Issues, Challenges, and Benefits , 1999, Commun. Assoc. Inf. Syst..

[4]  P. Johnson-Laird The computer and the mind: an introduction to cognitive science , 1988 .

[5]  Thomas H. Davenport,et al.  Book review:Working knowledge: How organizations manage what they know. Thomas H. Davenport and Laurence Prusak. Harvard Business School Press, 1998. $29.95US. ISBN 0‐87584‐655‐6 , 1998 .

[6]  Magnus Sahlgren,et al.  The Distributional Hypothesis , 2008 .

[7]  Marcis Pinnis,et al.  Latvian Tweet Corpus and Investigation of Sentiment Analysis for Latvian , 2018, Baltic HLT.

[8]  Claude E. Shannon,et al.  Recent Contributions to The Mathematical Theory of Communication , 2009 .

[9]  野中 郁次郎,et al.  The Knowledge-Creating Company: How , 1995 .

[10]  K. Wiig Knowledge Management Foundations: Thinking About Thinking - How People and Organizations Represent, Create and Use Knowledge , 1994 .

[11]  Henry David Thoreau,et al.  Walden: or Life in the Woods , 2019 .

[12]  Philip N. Johnson-Laird,et al.  The computer and the mind - An introduction to cognitive science [Book Review] , 1989, Proceedings of the IEEE.

[13]  David Fernbach,et al.  Aristotle's "Nicomachean ethics" , 2010 .

[14]  Brian Detlor,et al.  Web Work: Information Seeking and Knowledge Work on the World Wide Web , 2000 .

[15]  U. Eco The Open Work , 1989 .

[16]  Ashok Jashapara,et al.  Knowledge Management: An Integrated Approach , 2010 .

[17]  Jennifer E. Rowley,et al.  The wisdom hierarchy: representations of the DIKW hierarchy , 2007, J. Inf. Sci..

[19]  G. Lakoff,et al.  Where mathematics comes from : how the embodied mind brings mathematics into being , 2002 .

[20]  Stuart J. Barnes,et al.  Knowledge Management Systems: Theory and Practice , 2001 .

[21]  M. Engelmann The Philosophical Investigations , 2013 .

[22]  Mounir Zrigui,et al.  Using Tweets and Emojis to Build TEAD: an Arabic Dataset for Sentiment Analysis , 2018, Computación y Sistemas.

[23]  F. D. Saussure Cours de linguistique générale , 1924 .

[24]  Anthony Debons,et al.  Interrogative theory of information and knowledge , 1999, SIGCPR '99.

[25]  Umberto Eco,et al.  A theory of semiotics , 1976, Advances in semiotics.

[26]  Andrew Greasley,et al.  Business Information Systems: Technology, Development and Management for the Modern Business , 2018 .

[27]  Tracy Brown,et al.  The Embodied Mind: Cognitive Science and Human Experience , 2002, Cybern. Hum. Knowing.

[28]  J. Lee,et al.  Data, information, and knowledge. , 2002, The Lancet. Oncology.

[29]  D. Stenmark Information vs. knowledge: the role of intranets in knowledge management , 2002, Proceedings of the 35th Annual Hawaii International Conference on System Sciences.

[30]  Upkar Varshney,et al.  Communications of the Association for Information Systems , 2000 .

[31]  C. Peirce,et al.  Collected Papers of Charles Sanders Peirce , 1936, Nature.

[32]  Abdulmohsen Al-Thubaity,et al.  Sentiment lexicon for sentiment analysis of Saudi dialect tweets , 2018, ACLING.

[33]  Ashok Jashapara,et al.  Knowledge Management: An Integral Approach , 2004 .

[34]  Rajdeep Niyogi,et al.  Sentiment extraction and classification for the analysis of users' interest in tweets , 2018, Int. J. Web Inf. Syst..

[35]  Dorothy E. Leidner,et al.  Review: Knowledge Management and Knowledge Management Systems: Conceptual Foundations and Research Issues , 2001, MIS Q..

[36]  Sang Joon Kim,et al.  A Mathematical Theory of Communication , 2006 .