How Does AI Make Libraries Smart?: A Case Study of Hangzhou Public Library

How Does AI Make Libraries Smart?: A Case Study of Hangzhou Public Library

Bing Nie, Ting Wang, Brady Daniel Lund, Fengping Chen
Copyright: © 2022 |Pages: 16
DOI: 10.4018/978-1-7998-8942-7.ch003
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In the past two decades, the rapid development of information technology has been widely used in individuals' daily lives, leading to a 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.
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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).

Key Terms in this Chapter

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.

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.

Reference Service: Reference services provide patrons with personal help by making the best use of collection resources to meet their information needs.

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.

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.

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.

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.

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