Abstract
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.
Zusammenfassung
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.
Similar content being viewed by others
Notes
https://trends.google.com/trends/explore?date=all&q=%2Fm%2F01hyh_,%2Fm%2F0bs2j8q accessed on 28/11/2018.
These tweets are retrieved using the hashtag #ThisIsWhatDepressionFeelsLike at https://twitter.com/hashtag/ThisIsWhatDepressionFeelsLik?src=hash.
References
Abdellaoui H, Zrigui M (2018) Using tweets and emojis to build TEAD: an arabic dataset for sentiment analysis. Comp Sist 22(3):777–786. https://doi.org/10.13053/cys-22-3-3031
Al-Thubaity A, Alqahtani Q, Aljandal A (2018) Sentiment lexicon for sentiment analysis of Saudi dialect tweets. Procedia Computer Science 142:301–307. https://doi.org/10.1016/j.procs.2018.10.494
Alavi M, Leidner D (1999) Knowledge management systems: Issues, challenges, and benefits. Communications of the association for information systems 1. https://doi.org/10.17705/1CAIS.00107
Alavi M, Leidner D (2001) Review: knowledge management and knowledge management systems: conceptual foundations and research issues. MIS Quarterly 25(1):107–136. https://doi.org/10.2307/3250961
Dastin J (2018) Amazon scraps secret AI recruiting tool that showed bias against women. Reuters. https://www.reuters.com/article/us-amazon-com-jobs-automation-insight/amazon-scraps-secret-ai-recruiting-tool-that-showed-bias-against-women-idUSKCN1MK08G
Bartlett RC, Collins SD (2012) Aristotle’s Nicomachean ethics. Chicago: University of Chicago Press
Atkin A (2015) Peirce. Basingstoke: Taylor & Francis Ltd
Barnes S (2002) Knowledge management systems: theory and practice. Cengage Learning EMEA
Bocij P, Greasley A, Hickie S (2019) Business information systems: Technology, development and management for the modern business. Harlow: Pearson Education Limited
Burgin M (2004) Data, Information and Knowledge. Information Yamaguchi 7:47–58
Toonders J (2014) Data is the new oil of the digital economy. Wired https://www.wired.com/insights/2014/07/data-new-oil-digital-economy/
Choo CW, Detlor B, Turnbull D (2011) Web work: Information seeking and knowledge work on the World Wide Web. Dordrecht: Springer
Davenport TH, Prusak L (2010) Working knowledge: How organizations manage what they know. Boston, Mass: Harvard Business School Press
Davenport TH (1997) Information ecology, OUP USA
Spek R, Spijkervet A (1997) Knowledge management: Dealing intelligently with knowledge. Utrecht: Knowledge Management Network
Eco U (1979) A theory of semiotics. Bloomington: Indiana University Press
Eco U (1989) The open work. Cambridge, Mass: Harvard University Press
Jashapara A (2010) Knowledge management: An integrated approach. Harlow: Financial Times Prentice Hall
Johnson-Laird PN (1987) The computer and the mind: An introduction to cognative science. Harvard University Press, Cambridge, MA
Lakoff G, Nez RE (2001) Where mathematics comes from: how the embodied mind brings mathematics into being. Basic Books Publisher, New York
Marr Bernard (2018) How much data do we create every day? The mind-blowing stats everyone should read. https://www.forbes.com/sites/bernardmarr/2018/05/21/how-much-data-do-we-create-every-day-the-mind-blowing-stats-everyone-should-read/#7b8b3a8460ba
Perez S (2016) Microsoft silences its new A.I. bot Tay, after Twitter users teach it racism (updated). Techcrunch. https://techcrunch.com/2016/03/24/microsoft-silences-its-new-a-i-bot-tay-after-twitter-users-teach-it-racism/
Milani A, Franzoni V, Rajdeep N, Mangal N, Mudgal RK (2018) Sentiment extraction and classification for the analysis of users’ interest in tweets. Int J Web Inf Sys 14(1):29–40
Nonaka I, Takeuchi H (1995) The knowledge-creating company: How Japanese companies create the dynamics of innovation. Oxford University Press, New York
Peirce CS, Hartshorne C, Weiss P, Burks AW (1998) Collected papers of Charles Sanders Peirce. Thoemmes Press, Bristol
Pinnis M (2018) Latvian tweet corpus and investigation of sentiment analysis for Latvian. Frontiers in Artificial Intelligence and Applications 307:112–119
Polanyi M (1983) The Tacit Dimension. Peter Smith, Gloucester
Quigley EJ, Debons A (1999) Interrogative theory of information and knowledge. In Proceedings of the 1999 ACM SIGCPR conference on Computer personnel research (SIGCPR ’99). Association for Computing Machinery, New York, NY, USA, 4–10. https://doi.org/10.1145/299513.299602
Rowley J (2007) The wisdom hierarchy: representations of the DIKW hierarchy. J Inf Sci 33(2):163–180. https://doi.org/10.1177/0165551506070706
Sahlgren M (2008) The distributional hypothesis. Italian J Linguist 20(1):33–53
Saussure F (1983) Cours de linguistique générale. Payot, Paris
Shannon CE (1948) A mathematical theory of communication. Bell System Technical J 27:3
Stenmark D (2002) Information vs. knowledge: The role of intranets in knowledge management. https://doi.org/10.1109/HICSS.2002.994043
Econimist (2017) The world’s most valuable resource is no longer oil, but data. The Economist
Thoreau HD (1908) Walden, or, Life in the woods. JM Dent, London
Varela FJ, Rosch E, Thompson E (1992) The embodied mind: Cognitive science and human experience. Cambridge, Massachusetts: MIT
Weaver W (1953) Recent contributions to the mathematical theory of communication. ETC: A Review of General Semantics 10(4):261–281
Desjardins J (2017) What happens in an internet minute in 2017? Weforum. https://www.weforum.org/agenda/2017/08/what-happens-in-an-internet-minute-in-2017
Wiig KM (1998) Knowledge management foundations: Thinking about thinking – How people and organizations create, represent, and use knowledge. Schema Press, Arlington
Wilson T (2013) Trends and issues in information science – a general survey. In: Media, knowledge and power. Routledge, 407–422
Wittgenstein L (1978) Philosophical investigations. Oxford
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Picca, D. From Intelligent to Wise Machines: Why a Poem Is Worth More Than 1 Million Tweets. Informatik Spektrum 43, 28–39 (2020). https://doi.org/10.1007/s00287-020-01245-8
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00287-020-01245-8