The development of innovative technology has reshaped the way we consume, access, process, and distribute information. Academic and research libraries are adopting new technology, searching to improve their services and competitive advantage. Artificial intelligence has been a major force driving this change, and in this article we are going to answer: how is AI shaping the world of libraries and researchers?
What is AI? And what are the different categories of artificial intelligence?
Artificial intelligence is a wide-ranging branch of computer science that attempts to build smart machines with human intelligence aspects. It’s so far one of the most complex and impressive human inventions but the field remains mostly unexplored and with huge growth potential. AI is divided into 3 categories:
Artificial narrow intelligence (ANI)
Referred to as weak AI with a narrow range of abilities, it is the only type of AI we have available for now. Artificial narrow intelligence is used in facial recognition, speech recognition/voice assistants, and driving cars.
Artificial general intelligence (AGI)
Referred to as strong AI with the ability to mimic human intelligence or behaviors to solve any problem. For now, strong or deep AI is not yet available but currently researchers are working on improving machines’ ability to see, understand, and learn as humans do.
Artificial superintelligence (ASI)
It is the hypothetical AI that surpasses human intelligence and abilities. It has always been a source of inspiration for science fiction in which robots take over the world. Having powerful and self-aware super-intelligent machines may be an exciting idea, but their impact on humanity remains uncertain. For now, there are still many years before artificial superintelligence will be achievable.
How AI will change the job of librarians
AI has been implemented in more and more libraries, and here are some ways in which AI will have a significant impact:
1. Content indexing
Up until today, indexing has been a tedious and manual task. It is done partly by publishers and partly by authors. Indexing provides an overview of the context in which the book, journal, or paper was originally thought up. However, indexing says very little about, for example, other fields the information could potentially be useful for, and human-made labeling and indexing is hampering interdisciplinary discovery. It also limits the literature’s ability to stay relevant over time because the indexing was done in a specific category in a specific context, and over time that context of what we know about the world will change.
AI tools for indexing will improve consistency and quality. It can identify concepts and assign them corresponding keywords. Index automation will also help the reader discover new literature and navigate through different disciplines, which is not applicable through manual indexing. This type of AI tools will surpass human capabilities in indexing by providing more specific and accurate material for the readers and as a result, help university librarians improve their job.
2. Document matching
AI machines are better at processing documents fast and accurately than humans. Thanks to automatic proper indexing, AI tools are now identifying similarities and differences between documents or patents. Matching documents with similar ones or connecting sections that are describing the same topics, solutions, or phenomena is now possible. When a document can be indexed based on its actual content, it means that you can compare the content of thousands of documents that are contextually relevant to the search topic. It can be limited to only sections of a document, such as certain book chapters or research paper sections. Then you compare the content in these sections to find exactly what you’re looking for in the literature rather than doing a five keyword summary in the indexing. It is an essential operation that helps researchers and libraries to get to their knowledge easier and faster.
3. Death of citation
The citation system can be perceived as a popularity contest, but it doesn’t do much more than providing a very biased overview of a researcher network. When doing research landscape mapping and literature reviews, it is clear that using the citation system for snowballing is not an ideal method for covering everything. AI algorithms, which are based on the actual content of papers, will create far better mapping systems of the actual research, and be of major help to librarians and researchers alike (as opposed to the network of researchers presented in the citation system).
4. Content summarization
Automatic content summarization is about condensing documents to a shorter version, independently from human interference, while preserving the key elements and the meaning of the original text. Instead of summarising the whole article or book, AI tools are able to summarize just a section of a book or five documents into three sentences. AI tools for content summarizations are already available online and gaining popularity as well as machine learning algorithms that are continuously improving this task.
There are two types of automatic summarization: extraction and abstraction
Extractive summarization depends on extracting sentences from the original text based on a scoring function. It selects the most important sections of the input based on the statistical survey and rearranges them together to produce a new condensed version of the document.
Abstractive summarization used advanced natural language techniques to produce a new summarized version of the document that is different from the original one. It aims at preserving the most important sentences while rephrasing them and incorporating critical information, like a human-written summary.
Most of the summarizations today use the extractive approach as it is easier and requires less linguistic analysis.
5. Quality of service
AI has penetrated the world of librarians and researchers in the form of chatbots that can answer directional or simple questions, alert when a new book is published, and direct a customer to specific library resources. The automation of conversations between a user and a machine will enable librarians to embed their focus on more difficult questions and save time answering repetitive ones. This will also enable libraries to extend the opening hours of both in-person and online services.
6. The Impact Factor of the Future
The impact factor is a measure of the relative importance and quality of the individual publication, journal, or researcher to literature. In the future, the algorithm will be capable of breaking down scientific research into arguments and validating them against other pieces of research. Or it could build for each document a truth tree of arguments and evidence, verify each branch, and then find the overall validity score. Having validated or rephrased research is more important than the number of its readers, as it is the solid and validated research that deserves a broader readership.
7. Better Operational Efficiency
Libraries can identify and magnify operational efficiency by improving service effectiveness and reducing operational costs with process automation, optimized research data management, and digital asset management (DAM). Implementation of machine learning in the library’s processes and digital resources can optimize collection analysis, visualization, and preservation, and reduce expenses associated with the provision of services. The adoption of advanced library service platforms can help in the development of operational efficiency.
The road ahead for libraries
Artificial intelligence is changing the information landscape while disrupting librarians’ traditional jobs. They are required to embrace AI not as a user but as an active leader to better serve the new upcoming generations. However, some reservations hinder the integration of AI in the world of libraries. The fear of being replaced by AI robots is totally understandable but we cannot neglect that advanced technologies will open up new horizons for librarians. It will help them maintain new innovative positions and roles, solve current challenges, and prevent them from becoming old fashioned. The focus on traditional tasks should be shifted to a new direction that embraces the advanced technologies and assists the upcoming generation with their evolving needs.
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