See discussions, stats, and author profiles for this publication at: https://www.researchgate.net/publication/357890833
How Does AI Make Libraries Smart?: A Case Study of Hangzhou Public Library
Chapter · January 2022
DOI: 10.4018/978-1-7998-8942-7.ch003
CITATIONS
READS
0
547
4 authors, including:
Ting Wang
Brady D. Lund
Emporia State University
University of North Texas
27 PUBLICATIONS 121 CITATIONS
101 PUBLICATIONS 292 CITATIONS
SEE PROFILE
Some of the authors of this publication are also working on these related projects:
Nigerian_US Project View project
Anonymous Web and Libraries and Information Organizations View project
All content following this page was uploaded by Ting Wang on 24 January 2022.
The user has requested enhancement of the downloaded file.
SEE PROFILE
This article has been published in
Technological Advancements in Library
Service Innovation, published by IGI
Global
How does AI make libraries smart?:
A case study of Hangzhou Public Library
Bing Nie
Zhejiang Tongji Vocational College of Science and Technology, China
Ting Wang
Emporia State University, USA
Brady Lund
Emporia State University, USA
Fengping Chen
Hangzhou Public Library, China
ABSTRACT
In the past two decades, the rapid development of information technology has been widely
used in individuals’ daily life leads to their lives’ sea change. As a city at the forefront of
Artificial Intelligence (AI) technology in China, Hangzhou first applied the technology to
public library services to relieve the shortage of reference and circulation service resources
caused by a large number of regular patrons. This chapter first introduced the application
model of AI in various libraries worldwide, then focused on its application in a public library
in China, a developing country. This chapter may shed light on the application of AI in other
libraries in developing countries.
Keywords: Artificial Intelligence, Deep Learning, Machine Learning, Reference Robot,
Reference Service, Service Efficiency, Service Transform, Hangzhou Public Library,
Developing Countries
INTRODUCTION
From the computer Deep Blue developed in 1997 to the computer program
AlphaGo developed in 2016, board game battles between robots and humans
constantly push Artificial Intelligence (AI) technology into the public’s view. Humans
have mixed emotions about the victories of AI-based robots in games against humans.
On the one hand, human beings are excited about the sea change that AI will bring to
our lives. On the other hand, the possibilities of AI taking over most human beings’
work may leave humans with limited prospects for employment and economic
growth. Due to the high-sensitive requirements of librarians as a profession and the
fact that Chinese librarians have been in the shadow of the libraries declining for
years, we paid close attention to the impact of AI on libraries.
The origin of AI is much earlier than the game robots, such as Deep Blue and
AlphaGo. As early as 1950, Alan Turing, often referred to as the father of computer
science (Beavers, 2013), started discussing machine thinking and artificial
intelligence in his article Computing Machinery and Intelligence (Turning, 1950). The
first proof of concept for Artificial Intelligence (AI) technology was demonstrated at
John McCarthy’s first AI conference at Dartmouth college in 1956 (McCarthy et al.,
2006). McCarthy and his colleagues stated that research about AI was to “proceed on
the basis of the conjecture that every aspect of learning or any other feature of
intelligence can in principle be so precisely described that a machine can be made to
simulate it” (McCarthy et al., 2006, p.2). It is related to the similar task of using
computers to understand human intelligence, but AI does not have to confine itself to
biologically observable methods (McCarthy, 2004).
In the half-century since the term AI was coined, the research and application of
AI developed slowly, with ups and downs. Regardless of the symbolism genre, based
on rules, symbols, algorithms, or the connectionism genre based on neural networks,
it has encountered significant obstacles or even reached a dead end (Crevier, 1993;
Nilsson, 1998; Russell & Norvig, 2002). What ultimately revived the neural network
approach and AI was a change in the the explosive growth of sample data and
computer capabilities, coupled with a significant technological breakthrough—deep
learning (Li, 2018. Deep learning is a subset of machine learning (Guo et al., 2016),
essentially a neural network with three or more layers (Rani & Kumar, 2019). These
neural networks attempt to simulate the behavior of the human brain, allowing it to
learn from large amounts of data (Bashar, 2019). While a neural network with only
one layer can make approximate predictions, additional hidden layers help to optimize
and refine for accuracy (IBM Cloud Education, 2020). Despite the success of deep
learning research, AI systems are still in the early stage of narrow AI, indicating they
can only handle single or limited tasks (Ben, 2017).
At present, machine learning, natural language processing, computer vision, and
other technologies involved in AI are being integrated into various aspects of our lives
(Meel, 2021; Section, 2020; Tyagi, 2021). In addition to bringing subversive changes
to daily life fields such as speech recognition (The Signal, 2021), facial recognition
(Klosowski, 2020), and machine translation (Madhavan, 2019), AI also shows
excellent application prospects in various fields, such as medical treatment
(Greenfield, 2019), transportation (Conde & Twinn, 2019), manufacturing
(Manufacturer, 2021).
As part of a broader trend, AI is also making its way into libraries in different
forms. In the early 1990s, Metzler (1990) and Bailey (1991) discussed how AI could
be applied to library collections and (partial) services in the future. Later, Hauptmann
et al. (1997) introduced AI technologies, such as speech recognition, natural language
processing, and image analysis, in Carnegie Mellon University’s Infomedia Digital
Video Library to help patrons navigate and browse the database and locate in-need
information effectively. In the 21st century, especially in the last decade, many
scholars worldwide introduced the application of AI in libraries (Asemi & Asemi,
2018; Finley, 2019; Massis, 2018). In China, the adoption of artificial intelligence and
machine learning in academic and public libraries has been rapidly growing in recent
years (Liu, Q., 2019; Qian & Wen, 2017).The following sections will discuss how AI
technology makes libraries smarter and take Hangzhou Public Library as an example
that AI brings new values to customer services.
BACKGROUND
Even though various types of libraries have their specific servicing patron groups
and goals and have experienced the development from initial paper-based libraries,
automated libraries, to the current electronic libraries (Lewis, 2016), making library
services efficient has always been one of the essential goals. The concept of
smartness, derived from IBM's Smarter Planet and Smarter City initiatives in 2008,
aims to use technologies and intelligent systems to improve society's functions and
operations (IBM, n.d.). As part of the Smarter City concept, the term Smart Library
has been used more frequently in recent years to describe the vision of future libraries
(Freybeg, 2018). The term was first coined by Aittola et al. in 2003. Zimmerman and
Chang (2018) believed that the smart library is an institution that integrates books,
information, and digital and space resources based on a library's comprehensive
“informationization”. Big data intelligent analysis platforms make the library
management and services intelligent and individualized to meet patrons' needs with
the goal of improving user experience. Baryshev et al. (2015) described the
necessities for libraries to achieve efficient communications between patrons and
libraries and to be flexible to adapt to changing needs and technology in modern
society. As a high-level development stage of digital libraries, smart libraries need to
introduce modern scientific and technological means, such as AI, to increase readers'
experience and enhance readers' services (Aithal, 2016).
Li (2021) stated two approaches about the fusion between AI and libraries and
five AI applications in libraries. The fusion approaches include the fusion between AI
and library elements (e.g., library subject and object, library environment) and the
fusion between AI and library operation and management (e.g., library service
process, library management evaluation process, and library service model
construction) (Li, 2021). Five AI applications in libraries are:
o AI+ library environment. The application of artificial intelligence in the
library environment helps realize the personalized temperature control,
lighting, and objects and makes the physical library environment have a
situational awareness function to create a smart space (Wang & Wang, 2018).
o AI+ service process. Using machine learning to analyze user data, identify
user characteristics, predict user interests, and provide intelligent information
push and personalized accurate services (Wang, 2019).
o AI+ service design. Providing individualized services by conducting service
data analysis, user behavior analysis, and intelligent evaluation (Wang, Yuan,
&Lei, 2019).
o AI+ library management. Using AI technology to predict the possibility of
library collection borrowing, assist the library in the collection management
and future collection purchasing decisions (Fu et al., 2018).
o AI+ service scenario, such as providing service with Augment Reality, Virtual
Reality, and library robots (Tan, Xiang, & Zuo, 2019).
Li (2021) also believed that the new generation of artificial intelligence
technology, as an Energizing, Enabling, and Empowering Technology in the
development of libraries, was an approach to improve libraries’ service efficiency.
Energizing Technology is the process of making breakthroughs from nothing with
technical innovations that empower the subject and object with capabilities that they
did not initially have (Costanzo & Masotti, 2017). In smart libraries, artificial
intelligence, as an energizing technology, is deeply integrated with librarians, patrons,
library environment and resources, and library service processes to achieve
breakthroughs in librarian service capabilities and improvement of reader experiences.
Enabling Technology facilitates libraries to provide equal access to information
for patrons with disabilities to improve accessibility, which can be achieved by
providing assistive technology for reading and learning (Li, 2021). Providing services
for vulnerable groups and patrons with disabilities is essential in library services and
libraries’ social responsibilities (Wu, 2018). Empowering Technology promotes library
services by empowering subjects and objects in the library system or the library
environment with richer functions and powerful capabilities (Li, 2021).
Using AI in Libraries
Intelligent warehouse management
AI has made significant progress in developing and applying Automated Storage
and Retrieval Systems (ASRS) for lesser-used books and documents storage
(Atkinson, 2020). However, its adoption in libraries is still in its infancy (Guth &
Vander Meer, 2017). The first ASRS, installed at California State University, consists
of over 13,000 steel bins, occupying 8,000 square feet and standing 40 feet high. The
storage facility holds over 857,200 volumes, including bins containing 15,000-linearfoot archival and special collection materials (Heinrich & Willis, 2014). The ASRS
intelligent robot system owned by Sonoma State University Library is a large capacity
intelligent intensive storage system equipped with three large robot arms, realizing
intelligent positioning automatic extraction and tracking 2 million records.
In Japan, the Library of Meiji University uses the ASRS intelligent robot system
to manage books and other documents, preserve dense literature in the collection, and
complete a series of work such as transmission, handling, extracting records (Li et al.,
2021). In addition to ASRS, existing application cases, such as the Automated Guided
Vehicle (AGV) of the Humboldt University Library in Germany, the Automotive
Research Center (ARC) in the Willard Merlot Library in the United States, and the
Autonomous Robotic Shelf Scanning System (AuroSS) in the National Library of
Singapore, are bold attempts and innovations of intelligent warehouse management
(Chen & Zhang, 2018). In 2017, Nanjing University Library in China developed an
automatic inventory robot based on UHF-RFID technology that scans and checks the
bookshelves to realize inventory at night (in the dark) and compile and generate
incorrect-shelf reports lists with 97% accuracy (Fan & Shao, 2018).
Intelligent consulting service
Intelligent consulting service develops from traditional reference and consulting
services that are empowered by artificial intelligence. It breaks through the limitations
of time, space, system, and resources of traditional reference and consulting services
to provide patrons with convenient and friendly round-the-clock intelligent consulting
services a year around. The self-learning functions by big data analysis improve the
efficiency of the consulting services (Chen & Zhang, 2018). Hugh, a robot launched
by Aberystwyth University in Wales in 2016, can intelligently recognize readers'
language requests and carry out relevant consulting services with a chat-based
interaction (Chant, 2016). Ina, a virtual network robot of Hamburg Public Library in
Germany, is designed based on artificial intelligence and can communicate with
readers by answering questions about library services (Ke & Liu, 2012). The
University of Oklahoma Library launched Bizzy in 2019, an intelligent chatbot,
interacts with readers by answering daily service-related questions, completing
catalog searches, and providing 24-hour consulting services (Jeffrey, 2019). In China,
a chatbot named Xiaotu was designed by Tsinghua University Library based on the
Artificial Linguistic Internet Computer Entity open-source software in 2011, receiving
wide attention as a new attempt for real-time virtual reference consulting services
(Yao et al., 2011).
Intelligent security services
Librarianship involves providing information services to humans. Its inclusive
nature indicates that anyone can enter a library. The positive perspective of
inclusiveness is that every patron has equal access to library services. However, the
negative side may involve some users’ (e.g., gang members or sexual predators who
prefer children) behaviors that may hurt library staff and other library users (Albrecht,
2015, p. xi). At present, professional security personnel and some library staff provide
most library security services, but there is a workforce shortage from time to time.
Bob, developed by the University of Birmingham and the security company G4S
became the first to be tested in the security services sector. Bob can monitor its
surroundings to gather information, such as the cleanliness of desks and the closeness
of fire doors (Taylor, 2014; Yorke & White, 2014). In China, many academic libraries
and public libraries have adopted face recognition technology to manage user access.
During the COVID-19 pandemic, body temperature measurement was performed
simultaneously as face recognition, which better protected the safety of librarians and
readers in the library (Li & Zhao, 2020).
Other innovative services and humanoid robots
As mentioned earlier in the chapter, AI as an enabling technology helps libraries
meet the particular needs expressed by patrons with disabilities. In Ireland, the
Limerick University Library uses the robot Lucas to provide assistive services for
patrons with disabilities or cognitive impairments (Behan & O’Keeffe, 2008). The
robot Friend, launched by the Library of The University of Bremen in Germany, can
reach the books on higher shelves, deliver them to patrons with disabilities, and put
them back after using (Heyer, Enjarini, Fragkopoulos, & Graeser, 2012).
In addition to the daily works, many libraries have developed various innovative
services based on AI and robotics. The robot Finch of the Chicago Public Library
(Chicago Public Library, 2014) and the robot Dash of the Frankfurt Public Library in
Germany (Zhao, 2018) have diverse built-in programming languages to assist in
programming learning and teaching activities, cultivate logical thinking, and stimulate
readers' interest in learning computer coding. Australian public libraries have widely
adopted humanoid robots developed by Aldebaran Robotics, named NAO and Pepper,
in recent years (Nguyen, 2019). The robots can perform various tasks include
greetings patrons, storytelling to children, assisting programming languages,
performing entertainments, and answering Wikipedia-based reader questions, which
make the robots successfully play the roles of community builders, teachers,
assistants, and challengers (Linh, 2019).
Application of AI during the pandemic
With the spread of COVID-19 in the recent two years, some scholars began to
turn their attention to the application of AI in libraries to provide services to library
patrons during the pandemic continually. Nawaz et al. (2020) described using AI in
academic libraries to recognize patrons’ voice, collect patrons’ identity information,
track IP to monitor remotely operated service access, provide 24/7 automatic
response, external book delivery, and provide online tutorial resources. Fernadez
(2020) discussed the increased importance of new technologies due to the pandemic
and the possibilities of using various AI technologies to meet the information needs of
communities. Maidment-Otlet (2021) argued that while online learning, teaching, and
research with the latest digital technologies such as AI and blockchain are widely used
during the pandemic, libraries are central to providing the support structures, which
are critical to the success of more hybrid online teaching, learning, and research. Hu
et al. (2020) discussed that libraries took AI as a tool for intelligent reference services,
information push, and augmented reading during the pandemic to meet patrons’
explosive demand for online services. Liu (2021) proposed a robot that combines
temperature measurement with face recognition to reduce hardware costs to meet
price-sensitive patrons’ needs with deep learning and reinforcement learning.
MAIN FOCUS OF THE CHAPTER
Issues, Controversies, Problems
Hangzhou Public Library (HPL), founded in 1958, is a non-profit institution
established by the Hangzhou Government. The new library building opened in 2008
with over 46,000 square feet, 3,900 reading seats, and a collection of over 5.96 billion
books. The library is located in Qianjiang New District – Hangzhou central business
district. As the most prominent and best-equipped public library in Hangzhou, and the
first public library offering free services in China, the library aims to provide patrons
with diverse and equitable services according to its principle: equal, free, and nobarrier. The library is equipped with a first-class automatic system. For instance,
besides providing adequate terminals for reference services, the library is well
covered with Wi-Fi and adopted Radio Frequency Identification for automated
circulation management. Despite its advanced facilities and nearly 700 librarians,
HPL still faces difficulties meeting the needs of over 4 million patrons and circulation
services for over 2 million books a year. Therefore, the library has turned its sights to
AI to help alleviate the current dilemma.
As the headquarters of Alibaba, one of the most significant AI Research,
development, and application companies in China, Hangzhou has a high degree of
acceptance for AI by its citizens. Various AI projects acquired the city government’s
supports. On October 13, 2016, Hangzhou Municipal Government and Alibaba
launched the City Brain AI project for urban traffic management. The project aims to
assist the city management system to think and make decisions similar to human
thinking to alleviate traffic congestion through big data analysis and increase the
management system’s self-regulation ability and interactions with citizens (Li, 2019).
Alibaba has actively expanded its AI business to other public institutions and private
corporations, including HPL.
Among various problems HPL seeks solutions from AI, reference and circulation
overloads are the most pressing, caused by over 4 million regular patrons. The issues
significantly affect patrons’ service experiences. Considering the maturity of the AI
product and the urgency of providing services to patrons, HPL decided to introduce
the intelligent consulting robot into the reference service.
At HPL, traditional reference approaches include face-to-face, talking through
phones, voice mail on phones, or leaving a message on the library's official WeChat
account (a social media chatting tool similar to Facebook Messenger). Reference
services through phone are limited to the library staff's working hours. Although there
is no time limit for acquiring reference services through WeChat, it usually lacks realtime responses. Therefore, HPL librarians believe that it is urgent to launch an online
real-time consultation platform. Ant Financial, a subsidiary of Alibaba, promoted its
intelligent customer service platforms while the library staff was seeking a possible
solution. The service platforms applied in e-commerce websites, and insurance
industries have become stable and developed and are being promoted and tested in
various industries. HPL was the first in China to use an intelligent service platform
developed by the AI development team for the cultural industry. In early November
2016, Hangzhou Library began negotiating with Ant Financial to introduce and debug
AI referencing robots at the cost between 10,000 to 20,000 USD. By June 26, 2017,
the intelligent reference service platform was officially launched on the official
WeChat account and promoted with posters and reading areas in the library.
Currently, patrons can access the online reference platform through various
approaches, such as the library's official website, the library's APP, and the library's
official WeChat account. Patrons with reference service requests first come to the AI
self-service platform. Then the AI robot identifies user identity and service demands,
then conducts service data mining, intelligent analysis, and decision making. When
the AI robot fails to provide the requested services or patrons would like to
communicate with a librarian, access to talking with a librarian option will be
available. For instance, a patron needs to access the AI robot and type in book (which
is a recommended setting by the robot system to find books) and the book title when
s/he would like to find a book in the library. The robot will check the patron's
identification and the book's information to determine whether the patron has access.
After confirming, the robot retrieves all relevant information about the book in the
library's collection database and presents the results to the patron, who can click on
the results for more information and book located in the library.
In the service process, the robot conducts cluster analysis according to users’
reference in a time and sorts out the types of users’ reference questions and their
suggestions on library services. Library staff can manage library services according to
the data analysis and machine learning results, such as adding information consulted
by users but not in the library database to improve robot service quality. This
operation optimizes the services which users expect to achieve well human-machine
interaction. The robot also makes it possible to decrease librarians’ repetitive work
and think more about improving services.
SOLUTIONS AND RECOMMENDATIONS
AI reference robots are fundamentally different from the online knowledge
market, such as Baidu Knows and Google Answers. The latter is based on massive
databases to search for relevant questions and answers by keywords. The core of AI
referencing robots is AI, which allows the machine to locate the corresponding
answering methods and information representation based on patrons' descriptions and
the robots' independent judgment. Compared with the intelligent customer service
technology provided in the market, the most prominent advantage of the AI
referencing robot HPL uses is that the robust machine learning technologies lead to
higher Q&A accuracy. On the one hand, the robot understands user intentions
accurately through deep learning, human natural language understanding, and
dialogue state tracking technologies. On the other hand, it has a complete data link
closed-loop and uses data mining technologies to extract high user frequency
keywords from session records. To better use AI referencing robots in a library, it is
necessary to constantly optimize the robot’s knowledgebase and improve its problemsolving abilities to in line with patrons’ needs. Following lists AI referencing robot
functions:
o Rich and flexible service access. a) Flexible and Convenient login. Patrons
can access the AI referencing robot through the smartphone APP, official
WeChat account, scanning a QR code, and visiting the library's official
website. Currently, the HPL set up a robot referencing section in the
official WeChat account. QR codes for accessing AI referencing are set on
the library's official website and service halls on each floor of the library
building for users to scan, log in, and consult. The library also develops PC
and mobile access addresses for accessing with browsers. b) Open service
interface. The library docked the OPAC query interface to the robot
referencing platform and set the book as a search command according to
the patrons' service needs. Users search for collection information by
typing in book and book titles.
o Machine learning and accurate responses. The intelligent algorithms
supported by Ant Financial enable the referencing robot to understand
patrons’ intentions and provide answers accurately. Deep learning and data
mining technologies enable the robots to continuously improve its ability
of answering patrons’ questions by internal data analysis. While patrons
seek assistance from intelligent robots through the chat window embedded
on the library’s website or APP, the robot filters and classifies the
questions raised by patrons for future maintenance, which mainly involves
three items—chat window, AI robots, and machine learning. In the process
of AI robot maintenance, librarians can add numbers of robots according to
needs to provide and improve individualized reference services.
o Unified knowledge base management. The standardized information in the
knowledge base is convenient for the AI robots to answer patrons’
questions correctly. Externally, the knowledge base supports the
knowledge delivering of multiple service channels such as online services
and AI robots and has various functions, such as collection management,
image management, attachment management, and providing feedback.
Internally, it provides a separate knowledge base display, distinguishes
external display content to achieve data isolation that the internal and
external data do not interfere with each other. Robust intelligent retrieval
and independent learning functions help the robots supplement the
knowledge base and improve accuracy. The robots also support multiscenario display and content isolation, facilitating users to reuse and
separate information in different scenarios. The unified categories on
reference platforms provide a data basis for comprehensive data analysis
and reports monitoring.
o Visual data analysis. Data drives and supports decision-making to reduce
blindness and uncertainty. The data display platform presents an overview
of the data collected by the intelligent robot in the form of charts, such as
the times of referencing services provided on the day, times of
communicating between patrons and reference librarians, and service
evaluation. The platform also provides a list of referencing topics to
intuitively understand the common referencing questions among patrons
and provides data support for the background maintenance personnel.
o In addition to providing intelligent advice, the robot can also provide fun
chat, weather forecasts, stock market quotes, and knowledge base
functions for users to ask other daily questions.
o Multi-channel intelligent services. Combining robot self-service with
librarian reference service is an approach to improve service quality. On
the one hand, the service cost of a self-service robot is lower than that of a
librarian reference service, which reduces the service cost significantly. On
the other hand, librarian reference service is superior to robot self-service
in problem-solving. When an intelligent robot cannot meet a patron's need,
the librarian reference service is a great supplementation.
By late summer of 2021, HPL's intelligent referencing robot had logged more
than 7,000 reference questions, averaging 1,500 per month. Compared with traditional
referencing methods, the robots provide the services to more patrons and reduce HPL
librarians' repetitive workload, freeing them for more creative works. At the same
time, this technology also requires librarians to master artificial intelligence skills to
maintain an intelligent referencing knowledge base regularly.
HPL librarians are generally optimistic about new technologies, including
artificial intelligence, to improve their working methods and efficiency. However,
they also expressed anxiety and pressure about the rapid information technology
development. Patrons believe that intelligent consulting robots can provide more
convenient and efficient consulting services and meet the timeliness requirements of
consulting services. However, communications with AI robots are inevitably rigid,
which results in a less satisfying service experience than the reference services
provided by librarians.
In 2017, HPL and credit management institutions, Zhima Credit, jointly launched
the service of binding personal ID cards to a payment service platform to improve
circulation efficiency through methods such as card-free circulation. That is, patrons
who are Hangzhou residents with specific credit scores enjoy the library credit
circulation service. Later, non-residents were also eligible for credit circulation if their
Zhima credit score reached the required scores. Further, HPL reduced the Zhima
Credit score requirement for library credit circulation. The combination of online and
offline platforms launched the credit service started a new circulation mode. Zhima
Credit score is a total score that objectively presents individual credit status based on
the dimensions of user credit history, behavioral preference, performance ability,
identity and characteristics, and interpersonal relationship by using AI. Continuous
data tracking indicates that the higher the Zhima Credit score, the better the credit
level and the lower the default probability in financial lending and life services.
To increase the interaction between the library and patrons, and present collection
resources to patrons in various ways, HPL takes AI technology to make ancient
paintings dynamic with the name Antic Can Talk (HPL, 2021). With two AI
technologies, human portrait dynamic modeling and video generation technology, the
project enables the characters in ancient books or paintings to have facial expressions
and actions and speak like natural persons. The operation method is to extract the
original portrait from an ancient book or painting and a reciter’s video, followed by
conducting dynamic portrait modeling and aligning and synthesizing the two items. It
is necessary to synthesize the new video by extracting the sequence of the key points
of the reciter's video and the target portrait through dynamic modeling. It is usually
challenging to preserve the composite video while maintaining the details and textures
in ancient books and paintings because their definition is usually low. In this process,
super-resolution reconstruction is usually conducted to generate videos with its charm
and high definition.
In the future, HPL will be based on current RFID technology to provide more
intelligent services, such as with the assistance of big data, +5G, and Internet of
Things (IoT), using in-depth mining technology to explore the relationship between
patrons and books to provide more customized services, such as individualized book
recommendation and intelligent knowledge service. The services may facilitate to
increase collections check out and make libraries a part of patrons’ lives.
FUTURE RESEARCH AND DIRECTIONS
The social background in which HPL uses AI as an auxiliary tool to provide
patrons services—the large population of China. As described previously, HPL has
about four million regular patrons, with only 700 librarians, a common challenge
faced by public libraries in China. In Beijing, the National Library of China, with
around 4.8 million regular patrons, provided reference services to nearly nine million
patrons in 2019 (National Library of China, 2021). The Guangzhou Public Library,
located in Guangdong Province, was visited by 9.2 million patrons in 2019 (National
Digital Library of China, 2020). In southwest China, where population density is
relatively low, the Kunming Public Library received 4.4 million visits and 4.43
million books in circulation in 2019 (Zhaotong News, 2021). Therefore, using AI to
provide reference and circulation services to replace human labor may be more costeffective when the library serves a large population, such as India and China in
developing countries and large cities in developed countries (e.g., New York and Los
Angeles).
For academic libraries, Li et al. (2020) proposed to use 5G and its group
technology to embed in library construction to promote scholarly communication. In
the future, academic lectures and conferences can break the geographical restrictions
by using 5G to display 3D lecture contents and transmit 4K/8K high-resolution
videos, allowing patrons to interact with speakers and enhance the real sense. The
library can also import academic lectures to VR/AR goggles, allowing students to use
5G+VR/AR simulation to investigate various new fields (Li et al., 2020). The
applications can also be extended to small libraries or libraries located in rural areas to
facilitate communication worldwide.
In future research, the application of AI in public libraries in developing countries
and densely populated cities in developed countries and 5G technology to improve the
service quality of academic libraries and public libraries in rural areas can be more
widely discussed.
CONCLUSION
The application of AI has helped HPL increase its social visibility and reputation.
It also plays a significant role in enhancing HPL’s demand perception, response
flexibility, and service efficiency. Nevertheless, while AI has allowed libraries to
transform, it has also brought an enormous impact and influence on traditional library
management and service models. Accordingly, there are still many issues that need to
consider and explore. Jiao and Liu (2021) pointed out in their article on reflection and
criticism of AI that the application of intelligent technology in the library destroyed
the spirit of the library fundamentally. Knowledge acquisition based on AI technology
shows prominent characteristics of granulation, dispersion, and passivity, which
violates human knowledge acquisition's autonomy, systematization, and accuracy. It
reduces the humanistic value contained in knowledge and the pleasure in the process
of knowledge acquisition. More likely, it may amplify the existing information gaps
and result in racial, religious, and gender discrimination (Yang, Yang, & Xu, 2019).
The application of AI can be view as a threat to libraries services and librarians
job positions (Massis, 2018). According to a survey report titled Libraries Lose A
Quarter of Staff as Hundreds Close released by BBC on March 29, 2016, over 8,000
jobs, or about a quarter of the total, disappeared from libraries between 2010 and
2016. Although such situation is caused by multiple factors, how to cope with such a
grim prospect and transforming mechanical and repetitive shallow intellectual labor
into creative and flexible work, initiate and organize creative events (Lund et al.,
2020), will be a daunting task facing all librarians in the future. In addition, the
application of AI in libraries will involve obtaining user information and inevitably
generating a large amount of user behavior data—issues such as how to manage the
data and protect readers' privacy need to be taken seriously (Cox et al., 2019).
Therefore, applying AI in libraries should be subject to clear ethical standards, such as
those spelled out in the IFLA Code of Ethics for Librarians and other Information
Workers: AI applications that rely on extensive data collection should not override
patron privacy or equity (IFLA, 2020). The better application of AI technology in
libraries will need governments, libraries, and library associations to make joint
efforts from all levels of the system, technology, concept, and service, to better realize
the application and improvement of artificial intelligence technology in the field of
the library.
Going back to the match between Ke Jie and AlphaGo on May 23, 2017, after
several hours of fierce battle, facing the impeccable AlphaGo, Ke Jie finally
concealed his face and wept in the natural human emotions such as self-confidence,
hope, anxiety, and fear. However, after a short break, he continued the game that was
doomed to fail and tried his best to show the fighting spirit that only belongs to
humanity and the love and respect for Go and its history (Li, 2018). AI will soon
surpass human capabilities in various fields, including libraries, but humans will
always have advantages. Since the progress of science and technology cannot be
stopped, what we need to learn is how to wrap the hardcore of the machine with frail
humanity to create a more diverse, rich, and exciting world.
REFERENCES
Aithal, P. S. (2016). Smart Library Model for Future Generations. Social Science
Electronic Publishing, 1(1):693-703.
Aittola M., Ryhänen T., Ojala T. (2003) SmartLibrary-Location-Aware Mobile
Library Service. In: Chittaro L. (eds) Human-Computer Interaction with Mobile
Devices and Services. Mobile HCI 2003. Lecture Notes in Computer Science, vol
2795. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-452331_38
Albrecht, S. (2015). Library Security: Better Communication, Safer Facilities.
Chicago: American Library Association.
Asemi, A., & Asemi, A. (2018). Artificial Intelligence (AI) application in Library
Systems in Iran: A taxonomy study. Library Philosophy and Practice, 1840.
Atkinson, J. (2020). Technology, change and the academic library: case studies,
trends and reflections. Oxford: Chandos Publishing.
Baryshev, R. A., Verkhovets, S.V. & Babina, O.I. (2018). The smart library project:
development of information and library services for educational and scientific
activity. The Electronic Library, 36(3), 535-549.
Bashar, A. (2019). Survey on evolving deep learning neural network architectures.
Journal of Artificial Intelligence, 1(02), 73-82.
BBC. (2016). Libraries lose a quarter of staff as hundreds close. Accessed on Oct 16,
2021 from: http://www.bbc.com/news/uk-england-35707956.
Beavers, A. (2013). Alan turing: Mathematical mechanist. Cooper, S. Barry; van
Leeuwen, Jan. Alan Turing: His Work and Impact. Waltham: Elsevier, 481-485.
Behan, J., & O’Keeffe, D.T. (2008). The development of an autonomous service
robot. Implementation: “Lucas”—The library assistant robot. Intelligent Service
Robotics, 1, 73–89.
Ben, Dickson. (2017). What is Narrow, General and Super Artificial Intelligence.
Accessed on Oct 16, 2021 from: https://bdtechtalks.com/2017/05/12/what-isnarrow-general-and-super-artificial-intelligence/
Chant, I. Library Robot Coming to Welsh University. Library Journal. Accessed on
Oct 16, 2021 from: https://www.libraryjournal.com/?detailStory=library-robotcoming-to-welsh-university
Chen, F. P. (2018). Application and Exploration of Artificial Intelligence Consulting
Robots in Public Libraries: Taking Hangzhou Library as an Example. Library
Science Research & Work, 11, 73-76.
Chen, M., & Zhang, J. (2018). Practice innovation and thinking of library service base
on artificial intelligence. Library, 12, 8-16.
Conde, M. L., & Twinn, I. (2019). How Artificial Intelligence is Making Transport
Safer, Cleaner, More Reliable and Efficient in Emerging Markets. World Bank
Group.
Costanzo, A., & Masotti, D. (2017). Energizing 5G:Near-and far-field wireless energy
and data transfer as an enabling technology for the 5G IoT. IEEE Microwave
Magazine, 18(3), 125-136.
Cox, A. M., Pinfield, S., & Rutter, S. (2019). The intelligent library: Thought leaders’
views on the likely impact of artificial intelligence on academic libraries. Library
Hi Tech, 37(3), 418-435.
Chicago Public Library. (2014). Finch Robots Land at CPL. Accessed on Oct 17,
2021 from: https://www.chipublib.org/news/finch-robots-land-at-cpl/
Crevier, D. (1993). AI: the tumultuous history of the search for artificial intelligence.
Basic Books, Inc.
Dong, T. Q., & Tang, X. W. (2020). 5G+AI: building a new generation of library
service platform in the era of intelligence. Research on Library Science, 5,81-86.
Fernandez, P. (2020). “Through the looking glass: envisioning new library
technologies” pandemic response technologies: tracking technologies and
artificial intelligence. Library Hi Tech News, 37(10), 17-20.
Finley, T. K. (2019). The democratization of artificial intelligence: One library’s
approach. Information Technology and Libraries, 38(1), 8-13.
Freyberg, L. (2018). Smart Libraries - buzz word or tautology?. Elephant in the Lab.
https://doi.org/10.5281/zenodo.1302988
GMW. (2020). The first Smart Library was officially opened in Yuhang, Hangzhou.
Accessed on September 19, 2021 from https://m.gmw.cn/baijia/202005/29/1301249270.html
Greenfield, D. (2019). Artificial intelligence in medicine: Applications, implications,
and limitations. Accessed on Sep 19, 2021 from:
https://sitn.hms.harvard.edu/flash/2019/artificial-intelligence-in-medicineapplications-implications-and-limitations/
Guo, Y., Liu, Y., Oerlemans, A., Lao, S., Wu, S., & Lew, M. S. (2016). Deep learning
for visual understanding: A review. Neurocomputing, 187, 27-48.
Guth, L., & Vander Meer, P. (2017). Telepresence robotics in academic library: A
study of exposure and adaption among patrons and employees. Library Hi Tech,
35(3), 408-420.
Hauptmann, A. G., Witbrock, M. J., & Christel, M. G. (1997). Artificial intelligence
techniques in the interface to a digital video library. Proceedings of the CHI-97
Computer-Human Interface Conference, 2-3.
Heinrich, H., & Willis, E. (2014). Automated storage and retrieval system: A timetested innovation. Library Management, 35(6/7), 444-453.
HPL. (2021). DAMO Academy’s AI Technology "Resurrects" the Characters in
Ancient Paintings. Accessed on Oct 17, 2021 from:
https://baijiahao.baidu.com/s?id=1711565003074129715&wfr=spider&for=pc
Hu, L., Lin, S., & Xiao, G. Y. (2020). Research on the use of AI to develop online
services in college libraries during the COVID-19 prevention and control period.
Software Guide, 19(10), 25-28.
IBM. (n.d.). IBM builds a smarter plant. Accessed on Oct 19, 2021 from:
https://www.ibm.com/smarterplanet/us/en/
IBM Cloud Education. (2020). What is Deep Learning?. Accessed on Sep 22, 2021
from: https://www.ibm.com/cloud/learn/deeplearning#:~:text=Deep%20learning%20and%20IBM%20For%20decades%20no
w%2C%20IBM,the%20development%20of%20IBM%20Watson%2C%20IBM%
27s%20AI%20chatbot
IFLA. (2020). IFLA Statement on Libraries and Artificial Intelligence. Accessed on
Sep 19, 2021 https://www.ifla.org/wpcontent/uploads/2019/05/assets/faife/ifla_statement_on_libraries_and_artificial_i
ntelligence.pdf
Jiao, Y. P., Liu, W. (2021). Knowledge acquisition, artificial intelligence and library
spirit. Journal of Library Science in China, 47(05), 1-10.
Ke, P., & Liu, L. (2012). Virtual librarian: new type of librarians in the Lib3.0
Environment. Journal of Academic Libraries, 3, 21-29.
Klosowski, T. (2020). Facial recognition is everywhere. Here’s what we can do about
it. Accessed on Sep 19, 2021 from:
https://www.nytimes.com/wirecutter/blog/how-facial-recognition-works/
Lewis, D. W. (2016). Reimagining the academic library. Maryland: Rowman &
Littlefield.
Li, H., Zhao, X. J. (2020). Research on the Aapplication of face recognition
technology in domestic libraries. Shanxi Library Journal, 181(6),18-21.
Li, J., Wang, N., & Duan, C. (2020). The design of smart library based on 5G.
Journal of Physics: Conference Series, 1606, 1, 012011.
Li, K. F. (2018). AI the future. Beijing: Zhejiang People's Publishing House.
Li, P. (2019). Discussion on application of artificial intelligence technology in library
service. Information Research, 257(3):102-106.
Li, Y., Shi, L., Yao, T. H., & Zhang, Z. (2021). Current situation of library robot
application and its opportunities and challenges from an international perspective.
Library, 9, 34-41.
Li, Q. (2021). A new ecology of smart libraries under the environment of new
generation artificial intelligence +5G technology. Library Theory and Practice,
2021(3), 52-57.
Linh, N. (2019). An Investigation of Humanoid Robots and Their Implications for
Australian Public Libraries: Research Report. Accessed on Oct 17, 2021 from:
https://read.alia.org.au/investigation-humanoid-robots-and-their-implicationsaustralian-public-libraries-research-report
Liu, Q. (2019). The application of AI in academic libraries. Information Recording
Materials, 20(7), 81-83.
Lu, X. Y. (2021). Research on temperature measurement service robot for smart
libraries. Computer Era, 9, 10-13.
Lund, B. D., Omame, I., Tijani, S., & Agbaji, D. (2020). Perceptions toward Artificial
Intelligence among Academic Library Employees and Alignment with the
Diffusion of Innovations’ Adopter Categories. College & Research Libraries,
81(5), 865.
Madhavan, R. (2019). Machine translation – 14 current applications and services.
Accessed on Sep 19, 2021 from: https://emerj.com/ai-sector-overviews/machinetranslation-14-current-applications-and-services/
Maidment-Otlet, R. (2021). Digital-first approaches and the library brand in a postpandemic world. In Libraries, Digital Information, and COVID (pp. 103-110).
Chandos Publishing.
Manufacturer. (2021). How artificial intelligence is transforming manufacturing.
Accessed on Sep 19, 2020 from: https://www.themanufacturer.com/articles/aitransforming-manufacturing/
Massis, B. (2018). Artificial intelligence arrives in the library. Information and
Learning Science, 119(7/8), 456-459.
McCarthy, J., Minsky, M. L., Rochester, N., & Shannon, C. E. (2006). A proposal for
the dartmouth summer research project on artificial intelligence, august 31, 1955.
AI magazine, 27(4), 12-14.
McCarthy, J. (2004). What is artificial intelligence?. Accessed on Sep 19, 2021 from:
http://www-formal.stanford.edu/jmc/whatisai/
Meel, V. (2021). Fifty-six most popular computer vision applications in 2021.
Accessed on Sep 19, 2021 from: https://viso.ai/applications/computer-visionapplications/
Metzler, D. P. (1990). Artificial intelligence: What will they think of next?. In
Lancaster, F.W., & Smith, L. C. (Eds.), Artificial intelligence and expert system:
Will they Change the Library? (pp.2-49). Graduate School of Library and
Information Science, University of Illinois at Urbana-Champaign.
National Digital Library of China. (2020). Guangzhou Library in Guangdong
province has an average of 25,000 daily visitors. Accessed on Oct 31, 2021 from:
http://www.nlc.cn/newtsgj/yjdt/2020n/1y/202001/t20200117_187203.htm
National Library of China. (2021). National Library of China yearbook 2020.
Accessed on Oct 31, 2021 from:
http://www.nlc.cn/dsb_footer/gygt/xxgk/202105/P020210507531622392315.pdf
Nawaz, N., Gomes, A. M., & Saldeen, M. A. (2020). Artificial intelligence (ai)
applications for library services and resources in covid-19 pandemic. Artificial
intelligence (AI), 7(18), 1951-1955.
Nguyen, L. (2019). An investigation of humanoid robots and their implications for
Australian public libraries. Accessed on Oct 30, 2021 from:
https://eprints.qut.edu.au/131451/23/HumanoidRobotsinLibs_FinalReport_V2.pd
f
Nilsson, N. J., & Nilsson, N. J. (1998). Artificial intelligence: a new synthesis.
Morgan Kaufmann.
Rani, S., & Kumar, P. (2019). Deep learning based sentiment analysis using
convolution neural network. Arabian Journal for Science and Engineering, 44(4),
3305-3314.
Russell, S., & Norvig, P. (2003). Artificial intelligence: a modern approach. Prentice
Hall.
Section. (2020). Five real life use cases of Natural Language Processing (NLP).
Accessed on Sep 19, 2021 from: https://www.section.io/engineeringeducation/five-real-life-use-cases-of-natural-language-processing-nlp/
Sohu. (2019). You keep your word, I don't need the deposit! Look at the sample of
library service innovation in Zhejiang. Accessed on September 18, 2021 from:
https://www.sohu.com/a/310056076_160257?sec=wd
Stefan Heyer, Bashar Enjarini, Christos Fragkopoulos and Axel Graeser. (2012).
Book Detection and Grasping in Library Scenario. Proceedings of ROBOTIK
2012. Accessed on Oct 17, 2021 from: http://www.iat.unibremen.de/sixcms/media.php/81/FinalPaper_Robotik_BookDetection_Grasping.p
df
Tan, W. J., Xiang, L. W., & Zuo, Y. Q. (2019). Integration and reconstruction: the
service logic and path of smart library driven by AI technology. Library Work
and Study, 3, 29-33.
Taylor, R. (2014). ‘Students Awed by New Security Robot’. Accessed on Oct 19,
2021 from: http://www.redbrick.me/tech/slider-tech/students-awed-by-newsecurity-robot/
The Signal. (2021). Role of Artificial Intelligence and machine learning in speech
recognition. Accessed on Sep 19, 2021 from: https://signalscv.com/2021/07/roleof-artificial-intelligence-and-machine-learning-in-speech-recognition/
Turing, A. M. (2009). Computing Machinery and Intelligence. In Parsing the turning
test (pp. 23-65). Springer, Dordrecht.
Tyagi, N. (2021). Seven popular applications of machine learning in daily life.
Accessed on Sep 19, 2021 from: https://www.analyticssteps.com/blogs/7popular-applications-machine-learning-daily-life
Wang, H., Yuan, X. S., & Lei, J. X. (2019). Artificial Intelligence: the remodeling of
library application architecture and service mode. Journal of Modern
Information, 39(9), 101-108.
Wang, S. W. (2019). Five questions on Artificial Intelligence and library service
reshaping: re-discussion on Artificial Intelligence and library service rebuilding.
Library & Information, 1, 80-90.
Wang, X. W., & Wang, T. N. (2018). Library space reengineering and service based
on Artificial Intelligence. Library & Information, 3, 50-55.
Wu, H. X. (2018). Probe into the precise services for the disabled in public libraries
under the background of national reading. Library Work and Study, 10, 113-117.
Yang, J. L., Yang, Y. K., Xu, B. H. (2019). The theoretical logic, practical difficulties
and path prospects of artificial intelligence in library application. Library and
Information Service, 63(4), 32-38.
Yao, F., Ji, L., Zhang, C. Y., & Chen, W. (2011). New Attempt on Real time Virtual
Reference Service: The Smart Chat Robot of Tsinghua University Library.
Modern Library and Information Technology, 204(4), 77-81.
Yao, Q., & Wen,R. (2017). Application of AI in libraries. Science & Technology
Information, 24, 222-222.
Yorke, H. & White, D. (2014). Meet Bob the Birmingham Library Robot. Accessed
on Oct 20, 2021 at http://thetab.com/uk/birmingham/2014/11/24/meet-boblibrary-robot-15102.
Young, J. R. (2019). Bots in the Library? Colleges Try AI to Help Researchers (But
With Caution). Edsurge. Accessed on Oct 17, 2021 from:
https://www.edsurge.com/news/2019-06-14-bots-in-the-library-colleges-try-ai-tohelp-researchers-but-with-caution
Zhao, M. F. (2018). Current status and enlightenment of artificial intelligence
application in foreign libraries. Lantai World, 11, 131-135.
Zhaotong News. (2021). Kunming takes the lead in the construction of public cultural
service system among provincial capitals. Accessed on Oct 31, 2021 from:
https://www.ztnews.net/article/show-367520.html
Zimmerman, T. and Chang, H.C. (2018), “Getting smarter: Definition, scope, and
implications of smart libraries”, Proceedings of the 18th ACM/IEEE on Joint
Conference on Digital Libraries, Association for computer Machinery, University
of North TX, Fort Worth, 3-7 June, 403-404, doi: 10.1145/3197026.3203906.
Key Terms and Definitions
o Artificial Intelligence: Artificial intelligence is a branch of computer science
that utilizes machine learning and deep learning techniques to produce an
intelligent machine that can process and interact similarly to human
intelligence.
o Deep Learning: Deep learning is a branch of machine that typically utilizes
an artificial neural network for data representation learning. It is usually used
for speech recognition and image classification.
o Machine Learning: Machine learning is the core of artificial intelligence,
specializing in studying how computers can emulate human learning behavior
to acquire new knowledge or skill and reorganize the existing knowledge
structure to improve performance continuously.
o Reference Robot: A reference robot is a human-machine online
communication tool developed based on artificial intelligence and customer
service chat corpus. It can provide automatic reference services by identifying
patrons’ textual and vocal referencing contents and replying with
corresponding answers.
o Reference Service: Reference services provide patrons with personal help by
making the best use of collection resources to meet their information needs.
o Service Efficiency: Service efficiency in this chapter refers to the balance
between the library labor and technical input and service output in providing
services to patrons.
o Service Transformation: Service transformation in this chapter refers to
changing the delivery mode of library services to the public through modern
technological approaches, such as AI.
View publication stats