=Paper=
{{Paper
|id=Vol-1509/inv_paper3
|storemode=property
|title=Expectations from Artificial Intelligence: What Changed During the Decades?
|pdfUrl=https://ceur-ws.org/Vol-1509/ITALIA2015_invpaper_3.pdf
|volume=Vol-1509
|dblpUrl=https://dblp.org/rec/conf/aiia/Orio15
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==Expectations from Artificial Intelligence: What Changed During the Decades?==
https://ceur-ws.org/Vol-1509/ITALIA2015_invpaper_3.pdf
Expectations from Artificial Intelligence: What
Changed During the Decades?
Nicola Orio
Department of Cultural Heritage, University of Padua, Italy
nicola.orio@unipd.it
Abstract. Artificial intelligence has been often perceived as a promis-
ing field that can provide methods and tools that can be extended to
other research areas and exploited in industrial applications. Digital li-
braries and archives can thus have a twofold benefit. Since research and
evaluation can hardly be carried out without the development of a work-
ing system, based on a real collection accessed by real users, it could be
expected that artificial intelligence plays a central role in the design and
development of the many components of a digital library or of a digital
archive. This short communication starts from a review of what was ex-
pected from artificial intelligence in the research areas that converge in
the interdisciplinary field of digital libraries, concluding with a number
of considerations on what could be today’s expectations from the variety
of persons – researchers, stakeholders, librarians, users – involved in the
design, development and access to organized digital content.
1 Introduction
Methods and tools developed in the field of Artificial Intelligence (AI) are per-
vasive in many other research areas. Advancements in very different fields, from
robotics to multimedia indexing were possible thanks to AI research results. AI
is an interdisciplinary field both in the scientific background of researchers who
contribute to the field and in the applications of its results.
Among other fields, it is probably particularly relevant to investigate the
impact of AI in the development of Digital Libraries and Archives (DLAs). This
because DLAs are themselves multidisciplinary both when considered as existing
institutions and when considered as an applicative research area. The issue can
be faced by comparing the early expectations, about how AI could contribute
to the advancement of disciplines related to DLAs, with nowadays expectations,
about the directions in which user experience can be improved using AI.
2 Early Expectations from Artificial Intelligence
One of the first concepts taught to university students in computer science and
computer engineering is the, apparently simple, equation data + context = in-
formation. The context in which this important piece of information is delivered
is usually a course on database systems, where its direct implications in software
design are more evident. In this case the goal is mainly to provide the user with
all the necessary background to contextualize and give meaning to the data, but
since the early days of automatic information processing the idea of exploiting
AI techniques to automatize this process has been considered as an important
issue.
For instance, already in 1980, Schank published a short communication [7]
in which he envisaged that Information Retrieval (IR) effectiveness could be
improved by a “system able to analyze and understand the natural language of
both new text inputs and queries to the data base”. The idea of applying AI to
IR, and more in general to a number of aspects related to DLAs such as human-
computer interfaces and automatic data entry, has been further elaborated in
1983 by DeJong [1] where a number of examples are given where the focus is
again on understanding and deducing information.
The integration of database and AI research has been discussed in the liter-
ature since a long time, as witnessed by the introduction of the term intelligent
database [6] already in 1989. An intelligent database should be able to go beyond
the classical boolean searches and introduces the idea of a semantic represen-
tation of data. There is a close relationship between the two disciplines. For
instance, decision support and data mining are research areas based on the inte-
gration of AI and database. The relationship is so tight that there has even been
an effort to integrate university courses on the two fields in a single teaching
offer [12].
Another research field that is usually perceived as tightly connected with
AI is Human-Computer Interaction (HCI). Also in this case, the relationship is
witnessed by the introduction of the term intelligent user interfaces, although
the main focus was probably on the development of natural language processing
techniques. Yet, it has been argued [3] that AI researchers usually have limited
interest in interface design and that in general HCI are more interested in human
factors ergonomic research.
As reagards DLAs, the idea of exploring how AI can contribute to the field
motivated the organization, already in 1997, of the international workshop “Arti-
ficial Intelligence and Digital Libraries”, which was part of the International Joint
Conference on Artificial Intelligence (IJCAI). It is interesting to note that in the
same year it was presented a paper on the integration of machine learning tech-
niques directly targeted to the development of an intelligent digital library [9].
One of the outcomes of the workshop was a special issue of the International
Journal on Digital Libraries, edited by Ferguson and Durfee in 1998 [2]. The
main themes raised by the editorial of that special issue are valid still nowadays:
– Information discovery and retrieval;
– User interface design;
– Classification and indexing;
– Architectural design and issues.
As it can be seen, these four main points are all related to the research
areas previously described, from HCI to IR and database research. It can be
noted that most of these expectations have been, at least partially, fulfilled. For
instance, machine learning plays a central role in the management of multimedia
content, which is increasingly present in DLAs. Recent approaches to automatic
identification and classification of images, music and video largely relies on AI
techniques [4]. The interaction with the user is increasingly based on a holistic
approach, which takes into account user needs, behavior and sentiments [8].
3 Nowadays Expectations from Artificial Intelligence
Given the centrality of users in a DL system, the integration of AI research re-
sults in working digital libraries has to take into account nowadays expectation
of all the types of users, from stakeholders and librarians to the general public.
These expectations are not supposed to be the main driver of individual re-
search projects, but can highlight the general direction to which projects might
converge. Even if there is a time span of 45 years, nowadays expectations can
probably be expressed by a famous quote by Marvin Minsky, in a 1970 interview
to Life magazine:
In from three to eight years we will have a machine with the general
intelligence of an average human being. I mean a machine that will be
able to read Shakespeare, grease a car, play office politics, tell a joke,
have a fight. At that point the machine will begin to educate itself with
fantastic speed. In a few months it will be at genius level and a few
months after that its powers will be incalculable
This sentence actually summarizes the abilities of the main character of the
movie Her (Spike Jonze, 2013), a hand-free operative system voiced by Scarlett
Johansson. Apart from being able to compose music and create jokes, as envis-
aged by Minsky, the operative system in Her seems to understand the content
of documents and actively use this ability to filter out irrelevant mails, assign
priorities to messages, and even spontaneously select texts based on their artistic
quality in order to publish a book. Of course, science fiction always looks ahead
of the actual time, but at the same it is an important litmus test on which com-
puter abilities are considered crucial for an effective improvement of the user
experience.
Thus, today’s expectations are probably not very different from the ones en-
visaged in the eighties and early nineties: AI should provide results that help
computer programs to automatically infer a context in order to transform struc-
tured and unstructured data into understandable information.
To this general idea, it could probably be added another facet on the concept
of understandability. Users, in particular scholars, are willing to understand why
a system recommends, suggests, filters, a particular set of documents [11]. That
is, not only an AI-powered system should be able to understand documents
content, but the process has to be understood by the users. Understanding the
underling process, even if they might not understand the actual formal models
and algorithms, allows users to predict the system behavior and to direct the
interaction towards the expected results. In a sense, this can be considered as
another variant of the well-known Turing test. Instead of asking “Can machines
think?”, users of an AI-powered DL might ask “Can I understand, and thus
trust, the results of machine thinking?”.
4 Conclusions
The need for integrating intelligent techniques in all the many components
of DLAs, from search tools to data management and user interfaces, emerged
decades ago and resulted in the development of many intelligent tools that are
used to manage large digital multimedia collections. Yet, very few digital library
systems dare to call them selves intelligent [5]. This is probably due to the com-
plexity of a DLA system, where the existence of a number of tools that implement
AI results is not sufficient to perceive a complete digital library or digital archive
as an intelligent actor to interact with. The design of a system centered on the
final user experience can be an important goal for future research.
References
1. G. DeJong. Artificial Intelligence Implications for Information Retrieval. In Proc.
of ACM SIGIR, pages 10–17, 1983.
2. I.A.. Ferguson and E.H. Durfee. Artificial Intelligence in Digital Libraries: Moving
from Chaos to (More) Order. International Journal on Digital Libraries, 2(1):1–2,
1998.
3. J. Grudin. Turing Maturing: The Separation of Artificial Intelligence and Human-
Computer Interaction. Interactions, 13(5):54–57, 2006.
4. A.-E. Hassanien, A. Abraham and J. Kacprzyk. Computational Intelligence in Mul-
timedia Processing: Recent Advances. Springer Publishing Company Inc., 2008.
5. J.P. Leidig and E.A. Fox. Intelligent Digital Libraries and Tailored Services. Journal
of Intelligent Information Systems, 43(3):463–480, 2014.
6. K. Parsaye et al. Intelligent Databases: Object-Oriented, Deductive Hypermedia
Technologies. John Wiley & Sons Inc., 1989.
7. R.C. Schank. Artificial Intelligence and Information Retrieval. In Proc. of ACM
Annual Conference, pages 157–17, 1980.
8. B.W. Schuller. Modeling User Affect and Sentiment in Intelligent User Interfaces:
A Tutorial Overview. In Proc. of International Conference on Intelligent User In-
terfaces, pages 443–446, 2015.
9. G. Semeraro et al. IDL: A Prototypical Intelligent Digital Library Service. In Proc.
of the Congress of the Italian Association for Artificial Intelligence on Advances in
Artificial Intelligence, pages 447–450, 1997.
10. L.C. Smith. Artificial Intelligence in Information Retrieval Systems. Information
Processing and Management, 12(3):189–222, 1976.
11. C.M. Steiner et al. Evaluating a Digital Humanities Research Environment: The
CULTURA Approach. International Journal on Digital Libraries, 15(1):53–70, 2014.
12. P.A. Taylor and D.L. Wyatt. Database and Artificial Intelligence Integration: A
Challenge to Academia. ACM SIGCSE Bulletin, 24(4):35–42, 1992.