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Machine Learning and Data Science in Industries
Published in Sandeep Misra, Chandana Roy, Anandarup Mukherjee, Introduction to Industrial Internet of Things and Industry 4.0, 2021
Sandeep Misra, Chandana Roy, Anandarup Mukherjee
Data science is a scientific approach to provide meaningful insight from data using algorithms, methods, and processes. In order to process, analyze, and store a huge volume of data generated from industrial processes, data science is indispensable. The raw data collected from various industrial processes, machines, and devices may be in structured, semi-structured, and unstructured form. The information extracted from the data collected helps AI to identify hidden patterns and derive meaningful insights from them. AI is the capability of machines to operate and act intelligently, bordering toward human intelligence [184]. Machine learning (ML) and deep learning are the two common tools of AI that assist in making industrial technologies smarter. ML is a subset of AI, which predicts or provides decisions based on collected data. Similarly, deep learning is a form of ML based on the concept of dense artificial neural networks (ANNs). The relationship between AI, ML, and deep learning is depicted in Fig. 13.1. Typically, AI can be categorized as strong and weak AI. Strong AI is designed to be context-aware, cognitive, and capable of making decisions on their own. In contrast, weak AI is mostly dependent on algorithms and programmatic responses. For example, Alexa is a virtual assistant, developed by Amazon for voice interactions, setting alarms, music playback, and providing real-time information. However, Alexa is based on weak AI and processes data using a request-response behavior.
IoT-Tangle Enhanced Security Systems
Published in Uzzal Sharma, Parmanand Astya, Anupam Baliyan, Salah-ddine Krit, Vishal Jain, Mohammad Zubair Khan, Advancing Computational Intelligence Techniques for Security Systems Design, 2023
Falak Bhardwaj, Raj Shyam, Arti Saxena
Among the different applications of IoT, an suitable real-life example of the IoT is Amazon's own virtual assistant, Alexa. Initially installed in Amazon's home assistant device, Echo, Alexa has become very popular over a short amount of time. As for Alexa, other tech giants have also developed their own voice assistant that interacts with people and other services (both software and hardware) to make services more accessible and surroundings more interactive. For instance, Apple has Siri, Microsoft has Cortona, and Facebook has come up with their assistant known as “M”; Google also has its own virtual assistant.
Design and implementation of a VoIP PBX integrated Vietnamese virtual assistant: a case study
Published in Journal of Information and Telecommunication, 2023
Hai Son Hoang, Anh Khoa Tran, Thanh Phong Doan, Huu Khoa Tran, Ngoc Minh Duc Dang, Hoang Nam Nguyen
Integrating developing technology trends into customer care is considered an inevitable future development. The number of calling customers consistently overloads traditional PBXs, and the traditional automated handling and interaction systems typically used to control simple interactions and long calls are not very efficient (Brambilla & Molinelli, n.d.; Guzman, 2019). However, with the use of Artificial Intelligence (AI) complex queries can be resolved more quickly. A Virtual Assistant (VA) can automatically assist customers by solving frequently asked questions and customer problems according to available scenarios. In such a scenario, a VA can easily access base warehouse information and internal data to find answers for customers without consulting multiple sources.
Symmetric and Asymmetric Modeling to Understand Drivers and Consequences of Hotel Chatbot Engagement
Published in International Journal of Human–Computer Interaction, 2022
Sandra Maria Correia Loureiro, Faizan Ali, Murad Ali
One of the tools used in AI is the intelligent virtual assistant—chatbot—that can answer the questions posed by guests through information extracted from several sources (Osei et al., 2020). Tourism and hospitality industries are among the industries worldwide that are highly interested in investing in chatbots for general inquiries, bookings, or similar services (Statista, 2021). Hotel chatbots can offer suggestions, tips, ideas, and alternative plans while scheduling, checking, and providing information about the service status (Revfine, 2021). The market’s growing interest in AI paves the way for future investigations of the human-chatbot relationship, boosting research on the topic (Ivanov et al., 2019; Loureiro et al., 2021).
Exploring the Privacy Concerns in Using Intelligent Virtual Assistants under Perspectives of Information Sensitivity and Anthropomorphism
Published in International Journal of Human–Computer Interaction, 2021
Quang-An Ha, Jengchung Victor Chen, Ha Uy Uy, Erik Paolo Capistrano
An Intelligent Virtual Assistant (IVA), such as Apple Siri, Google Assistant, Amazon Alexa, Microsoft Cortana, and Samsung Bixby, is a software agent that helps users, especially mobile users, to perform tasks and services using voice or text commands (Cooper et al., 2008; Naone, 2009). Via a series of conversations, an IVA on a mobile device can help a user perform routine tasks, such as fixing personal schedules, plotting commute routes, and even ordering food (Chung et al., 2017). Past and present iterations of these technologies have proven to be capable of autonomous actions (Gao et al., 2007; Xu & Wang, 2006) and have helped to effectively reduce the load on the user and increase task completion efficiency (Liang & Huang, 2000). With the popularity of smartphones, smart homes, smart cars, and the Internet of Things, the IVA market is expected to grow from USD2.48 billion in 2017 to USD17.72 billion by 2023, representing a CAGR of 38.82% (Research and Markets, 2017). IVA is designed and developed based on advanced artificial intelligence, machine learning, and natural language analysis techniques, resulting in accurate captures and natural responses to user requirements. However, despite the usefulness of IVAs, the extensive collection and storage of user information intended to improve their performance over time raise serious privacy-related issues. Some researchers have even expressed doubt about the security of the conversations occurring between the device and the user (Chung et al., 2017; Chung & Lee, 2018). These privacy issues are not to be taken lightly, as they are also present in the use of mobile apps, services and IoT as well (Hsu & Lin, 2018; Wottrich et al., 2018). These agents, in many cases, require internal knowledge to diligently perform a task (Liang & Huang, 2000). Also, the latest iterations of IVAs have collected more detailed user information, even down to the daily routines of the mobile device’s user (Chung & Lee, 2018). This problem is exacerbated by IVA developers anthropomorphizing IVA interfaces, incorporating human voices and natural conversations to make them sound “smart,” witty, friendly, and to communicate like a human being to encourage more user interaction. This is because people exhibit more complex feelings and behaviors when they interact with another human being as compared with objects and things. Therefore, because of these new technologies, Eastin et al. (2016) pointed out that the nature of digital information exchanges have also changed, emphasizing the need to rethink present perceptions and definitions involving privacy concerns. For the case of IVAs, any information shared with a device carrying an IVA can be transmitted and stored to a storage area, which may be a physical drive or the cloud (Chung et al., 2017). Thus the privacy and trust to the IVA is a primary reason to adopt this technology (Liao et al., 2019).