AI-Powered Robots for Libraries: Exploratory Questions

With recent developments in machine learning, a subfield of artificial intelligence (AI), it seems no longer extraordinary to think that we will be soon living in the world with many robots. While the term, ‘a robot’ conjures up the image of a humanoid machine, a robot can take many forms ranging from a drone, an autonomous vehicle, to a therapeutic baby seal-bot. But what counts as a robot, and what kind of robots should we expect to see at libraries? AI has made it possible to make a robot intelligent and autonomous in performing tasks not only mechanical but also cognitive, such as driving, natural language processing, translation, and face recognition. The capability of AI-powered robots far exceeds that of other simpler and less sophisticated machines. How we will be interacting with these robots once they came to be in the world with us is an interesting question. Humans have a strong tendency to anthropomorphize creatures and objects they interact with, many of which are less complex than a robot. This suggests that we will be quite susceptible to projecting motives, emotions, and other human traits onto robots. For this reason, the adoption of robots raises unique concerns regarding their safety, morality, their impact on social relationships and norms, and their potential to be used as a means for manipulation and deception. This paper explores these concerns related to the adoption of robots. It also discusses what kind of robots we may come to see at libraries in the near future, what kind of human-robot interactions may take place at libraries, and what type of human-robot relationship may facilitate or impede a library robot’s involvement in our information-seeking activities

Moving Forward with Digital Disruption: What Big Data, IoT, Synthetic Biology, AI, Blockchain, and Platform Businesses Mean to Libraries

Digital disruption, also known as “the fourth industrial revolution,” is blurring the lines between the physical, digital, and biological spheres. This issue of Library Technology Reports (vol. 56, no. 2) examines today’s leading-edge technologies and their disruptive impacts on our society through examples such as extended reality, Big Data, the Internet of Things (IoT), synthetic biology, 3-D bio-printing, artificial intelligence (AI), blockchain, and platform businesses in the sharing economy. This report explains how new digital technologies are merging the physical and the biological with the digital; what kind of transformations are taking place as a result in production, management, and governance; and how libraries can continue to innovate with new technologies while keeping a critical distance from the rising ideology of techno-utopianism and at the same time contributing to social good

AI and Creating the First Multidisciplinary AI Lab

In this chapter, contributing author Bohyun Kim discuss artificial intelligence (AI), machine learning, and deep learning and why they are important for libraries. Kim shares how the University of Rhode Island created the first multidisciplinary AI lab, which launches in the fall of 2018. She discusses how the AI lab will be used to further research, discussion, and exploration of AI, and shares how such an environment can help facilitate multidisciplinary collaboration and foster interdisciplinary thinking. Kim shares the future hopes of the AI lab and AI

President’s Message: Rebuilding Our Identity, Together

This is the President\u27s message column from LITA President Bohyun Kim regarding the current discussion of forming a new division from LITA, ALCTS, and LLAMA that embraces the breakdown of silos and positive risk-taking to better collaborate and move our profession forward

A Practical Study of Longitudinal Reference Based Compressed Sensing for MRI

Compressed sensing (CS) is a new signal acquisition paradigm that enables the reconstruction of signals and images from a low number of samples. A particularly exciting application of CS is Magnetic Resonance Imaging (MRI), where CS significantly speeds up scan time by requiring far fewer measurements than standard MRI techniques. Such a reduction in sampling time leads to less power consumption, less need for patient sedation, and more accurate images. This accuracy increase is especially pronounced in pediatric MRI where patients have trouble being still for long scan periods. Although such gains are already significant, even further improvements can be made by utilizing past MRI scans of the same patient. Many patients require repeated scans over a period of time in order to track illnesses and the prior scans can be used as references for the current image. This allows samples to be taken adaptively, based on both the prior scan and the current measurements. Work by Weizman has shown that so-called reference based adaptive-weighted temporal Compressed Sensing MRI (LACS-MRI) requires far fewer samples than standard Compressed Sensing (CS) to achieve the same reconstruction signal-to-noise ratio (RSNR). The method uses a mixture of reference-based and adaptive-sampling. In this work, we test this methodology by using various adaptive sensing schemes, reconstruction methods, and image types. We create a thorough catalog of reconstruction behavior and success rates that is interesting from a mathematical point of view and is useful for practitioners. We also solve a grayscale compensation toy problem that supports the insensitivity of LACS-MRI to changes in MRI acquisition parameters and thus showcases the reliability of LACS-MRI in possible clinical situations

Personal Branding For New Librarians: Standing Out And Stepping Up

If you blog, tweet, use LinkedIn, Facebook, ALA Connect, or other social sites, you may have already begun building your personal brand. Learn about the recent trend of social media use and its role in developing and maintaining a personal brand and professional reputation. Find out how librarians utilize social media to develop an online presence and a support network and to participate in the conversation of librarianship

MACHINE LEARNING APPROACHES FOR IDENTIFICATION OF PARKINSON’S DISEASE SEVERITY USING MULTIMODAL FEATURES

This study aimed to create a digital biomarker for assessing the severity of Parkinson\u27s disease (PD) using multimodal features from 50 PD patients and 50 healthy controls. They underwent clinical tests, gait analysis using a motion capture system, postural and functional evaluations, and lifestyle questionnaires. These multimodal features underwent dimensionality reduction techniques such as logistic regression and principal component analysis to identify PD severity using the MDS-UPDRS total score. The results developed six models using machine learning algorithms (Linear Regression and Random Forest), with Model 1 performing the best; spatiotemporal variables from gait analysis were crucial in identifying PD severity. We aim to identify important features correlated with MDS-UPDRS and expect to be applied in clinical settings to monitor the severity of PD

POSSIBILITY OF EARLY DETECTION OF PARKINSON’S DISEASE USING CONVOLUTIONAL NEURAL NETWORK DURING SIX-MINUTE WALK TEST

This study aimed to determine the accuracy of distinguishing patients with early Parkinson’s disease (PD) (n=27) from healthy controls (n=50) using a convolutional neural network (CNN) technique with an artificial intelligence deep learning algorithm based on a 6-minute walk test (6MWT) using wearable sensors. After wearing the six sensors, the participants performed the 6MWT, and the time-series data were converted into new images. The main results demonstrated the highest discrimination accuracy of 72% on the left arm gyroscope data. The results confirmed the possibility of using CNN models to distinguish between individuals with early PD and controls. Moreover, the 6MWT using sensors may contribute to early diagnosis as an objective indicator in clinical settings

A Practical Study of Longitudinal Reference Based Compressed Sensing for MRI

Compressed sensing (CS) is a new signal acquisition paradigm that enables the reconstruction of signals and images from a low number of samples. A particularly exciting application of CS is Magnetic Resonance Imaging (MRI), where CS significantly speeds up scan time by requiring far fewer measurements than standard MRI techniques. Such a reduction in sampling time leads to less power consumption, less need for patient sedation, and more accurate images. This accuracy increase is especially pronounced in pediatric MRI where patients have trouble being still for long scan periods. Although such gains are already significant, even further improvements can be made by utilizing past MRI scans of the same patient. Many patients require repeated scans over a period of time in order to track illnesses and the prior scans can be used as references for the current image. This allows samples to be taken adaptively, based on both the prior scan and the current measurements. Work by Weizman [20] has shown that so-called reference based adaptive-weighted temporal Compressed Sensing MRI (LACS-MRI) requires far fewer samples than standard Compressed Sensing (CS) to achieve the same reconstruction signal-to-noise ratio (RSNR). The method uses a mixture of reference-based and adaptive-sampling. In this work, we test this methodology by using various adaptive sensing schemes, reconstruction methods, and image types. We create a thorough catalog of reconstruction behavior and success rates that is interesting from a mathematical point of view and is useful for practitioners. We also solve a grayscale compensation toy problem that supports the insensitivity of LACS-MRI to changes in MRI acquisition parameters and thus showcases the reliability of LACS-MRI in possible clinical situations