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Ignorance by proxy
Published in Cathrine Hasse, Posthumanist Learning, 2020
One famous example of how preceding learning influences machine learning differently from humans is the internet “teenage” bot, Tay, that became famous for the way she learned from the humans with whom she entangled. Tay is a so-called chatbot created by Microsoft as a piece of advanced artificial intelligence building on the newest development within machine learning. A chatbot is an application, often found on the internet and typically powered by artificial intelligence, which simulates a conversation with humans. Such bots have developed since engineers first began working on AIML (Artificial Intelligence Mark-up Language) in the mid-1990s and early 2000s. However, recently the field seemed to move away from the basic algorithms that characterised the first chatbots, like Eliza created by Joseph Weizenbaum in 1966 (Weizenbaum 1966). Eliza was built on algorithms that picked up cues from the human speakers and turned them into questions. However, since 2010, the chatbot programmes have become more and more advanced. Microsoft’s Tay from 2016 was supposed to show just how far the machine learning had moved AI towards passing the Turing test.
HealFavor: Machine Translation Enabled Healthcare Chat Based Application
Published in Satya Ranjan Dash, Shantipriya Parida, Esaú Villatoro Tello, Biswaranjan Acharya, Ondřej Bojar, Natural Language Processing in Healthcare, 2022
Sahinur Rahman Laskar, Abdullah Faiz Ur Rahman Khilji, Partha Pakray, Rabiah Abdul Kadir, Maya Silvi Lydia, Sivaji Bandyopadhyay
AI chatbots that use machine learning predict the context and intent of a question before formulating a response. This type of chatbot generates answers to more complicated questions by garnering the context using advanced NLP techniques. Some other advantages of AI chatbots are learning from information gathered, continuously improving as more data comes in, understanding patterns of behaviour, and having a broader range of decision-making skills. Chatbots provide instant customer support at scale. However, the system poses a challenge in the training phase of its development. The system requires understanding the user's intent when user inputs a piece of text to be queried or an answer to the question posed by the system. Since the user may ask the same question differently, it poses a difficult challenge, particularly for the natural language understanding module of the system. There are three main elements of a chatbot application: dialogue system, natural language understanding, and natural language generation. A dialogue system is responsible for taking the user's input and producing output. A chatbot needs a compatible interface for the interaction between the user and the machine. Natural language understanding is the fundamental unit of a chatbot. Since natural languages are complex, the system must understand the language's lexical, synthetic, and semantic levels. After the chatbot successfully parsed and understood the user's input, it must generate an appropriate response and translate it back to natural language. For this purpose, a natural language generation module is used in a chatbot.
Human Resource Management in the Digital Age: Big Data, HR Analytics and Artificial Intelligence
Published in Pedro Novo Melo, Carolina Machado, Management and Technological Challenges in the Digital Age, 2018
Mark L. Lengnick-Hall, Andrea R. Neely, Christopher B. Stone
Technology platforms such as Amazon’s Alexa, Apple’s Siri and Google’s Home have paved the way for the general public to become more comfortable with using voice-activated products such as bots. The open application program interface Slack has formed the basis for many HR bots that rely on an employee typing a command such as requesting time off. So-called HR Slackbots can perform functions formerly handled by clerical HR personnel, such as asking employees questions to determine whether they can take time off. More sophisticated chatbots use natural language processing and machine learning to carry on sustained interactions with employees and learn from processing more interactions over time.
Chatbots or Humans? Effects of Agent Identity and Information Sensitivity on Users’ Privacy Management and Behavioral Intentions: A Comparative Experimental Study between China and the United States
Published in International Journal of Human–Computer Interaction, 2023
Yu-li Liu, Wenjia Yan, Bo Hu, Zhi Lin, Yunya Song
Information sensitivity can be viewed as an indicator of control rules because it implies the degree of risk that addresses the sense of control for privacy management. This concept reflects the perceived discomfort that people feel when they disclose personal information (Dinev et al., 2013) to customer service chatbots or human agents, which largely relies on the types of information requested by the channel or platform (Malhotra et al., 2004). Chatbots are already being applied in a large number of areas where sensitive information is needed, such as financial care and healthcare (Przegalinska et al., 2019). Users may be asked to provide medical records, personally identifiable information or financial information during the conversation, which may cause physical, financial, or psychological harm (Bickmore & Cassell, 2001). Since the context criteria play a vital role in formulating individuals’ privacy rules, we contend that when chatting with a customer agent, the classification of sensitive information by individuals reflects a clear identification of the ownership rule.
ChatGPT versus engineering education assessment: a multidisciplinary and multi-institutional benchmarking and analysis of this generative artificial intelligence tool to investigate assessment integrity
Published in European Journal of Engineering Education, 2023
Sasha Nikolic, Scott Daniel, Rezwanul Haque, Marina Belkina, Ghulam M. Hassan, Sarah Grundy, Sarah Lyden, Peter Neal, Caz Sandison
A chatbot is a computer program designed to engage in conversations with humans and provide solutions to their questions (Dahiya 2017). Chatbot technology is not new, with the first known program called ELIZA being developed in 1966, using simple pattern-matching techniques together with a template-based response mechanism (Adamopoulou and Moussiades 2020). Chatbot technology evolved, and a major breakthrough in the technology was made in 1995 with the award-winning program ALICE, combining pattern-matching with artificial intelligence (the ability of machines or computer systems to perform tasks that normally require human intelligence) to provide a natural language user experience (Bani and Singh 2017). The more natural the communication experience, the more likely the user would feel that they are having a real conversation and not a simulated one. Natural Language Processing (NLP) is a subfield of artificial intelligence providing the scaffold that allows chatbot programs to understand one or more human languages (Khanna et al. 2015). One of the next major advancements in the field came through virtual personal assistants like Apple’s Siri in 2011 and Amazon’s Alexa in 2014 (Adamopoulou and Moussiades 2020). These are technologies known by name across much of the world.
“I Am Here to Assist Your Tourism”: Predicting Continuance Intention to Use AI-based Chatbots for Tourism. Does Gender Really Matter?
Published in International Journal of Human–Computer Interaction, 2023
Banghui Zhang, Yonghan Zhu, Jie Deng, Weiwei Zheng, Yang Liu, Chunshun Wang, Rongcan Zeng
AI will be at the heart of HCI technology and industry in the near future (Harper, 2019). Recently, an alternative form of HCI has gained traction known as CUI. It allows users to interact with computer systems via spoken dialogue or text for information access (McDonnell & Baxter, 2019). Along with the emergence of CUI, considerable attention has been given to making HCI more natural and humanlike (Lee et al., 2020). As a type of CUI, AI-based chatbots become increasingly popular in a variety of domains, such as task management (Aoki, 2020), mental healthcare (Zhu, Janssen, et al., 2022), and banking (Hari et al., 2022). An AI-based chatbot usually uses natural language processing, machine learning, and other AI technologies. Commonly, the interaction between users and chatbots is triggered by users’ inputs, such as wake-up call and inquiry (McTear et al., 2016). The contextual awareness technologies allow chatbots to wait until the systems receive a message before taking the turn (Pearl, 2016).