The impact of artificial intelligence on the current and future practice of clinical cancer genomics.

Artificial intelligence (AI) is one of the most significant fields of development in the current digital age. Rapid advancements have raised speculation as to its potential benefits in a wide range of fields, with healthcare often at the forefront. However, amidst this optimism, apprehension and opposition continue to strongly persist. Oft-cited concerns include the threat of unemployment, harm to the doctor-patient relationship and questions of safety and accuracy. In this article, we review both the current and future medical applications of AI within the sub-speciality of cancer genomics

Do Chatbots Dream of Androids? Prospects for the Technological Development of Artificial Intelligence and Robotics

The article discusses the main trends in the development of artificial intelligence systems and robotics (AI&R). The main question that is considered in this context is whether artificial systems are going to become more and more anthropomorphic, both intellectually and physically. In the current article, the author analyzes the current state and prospects of technological development of artificial intelligence and robotics, and also determines the main aspects of the impact of these technologies on society and economy, indicating the geopolitical strategic nature of this influence. The author considers various approaches to the definition of artificial intelligence and robotics, focusing on the subject-oriented and functional ones. It also compares AI&R abilities and human abilities in areas such as categorization, pattern recognition, planning and decision making, etc. Based on this comparison, we investigate in which areas AI&R’s performance is inferior to a human, and in which cases it is superior to one. The modern achievements in the field of robotics and artificial intelligence create the necessary basis for further discussion of the applicability of goal setting in engineering, in the form of a Turing test. It is shown that development of AI&R is associated with certain contradictions that impede the application of Turing’s methodology in its usual format. The basic contradictions in the development of AI&R technologies imply that there is to be a transition to a post-Turing methodology for assessing engineering implementations of artificial intelligence and robotics. In such implementations, on the one hand, the ‘Turing wall’ is removed, and on the other hand, artificial intelligence gets its physical implementation

Motivations, Classification and Model Trial of Conversational Agents for Insurance Companies

Advances in artificial intelligence have renewed interest in conversational agents. So-called chatbots have reached maturity for industrial applications. German insurance companies are interested in improving their customer service and digitizing their business processes. In this work we investigate the potential use of conversational agents in insurance companies by determining which classes of agents are of interest to insurance companies, finding relevant use cases and requirements, and developing a prototype for an exemplary insurance scenario. Based on this approach, we derive key findings for conversational agent implementation in insurance companies.Comment: 12 pages, 6 figure, accepted for presentation at The International Conference on Agents and Artificial Intelligence 2019 (ICAART 2019

Chatbot Quality Assurance Using RPA

Chatbots are becoming mainstream consumer engagement tools, and well-developed chatbots are already transforming user experience and personalization. Chatbot Quality Assurance (QA) is an essential part of the development and deployment process, regardless of whether it’s conducted by one entity (business) or two (developers and business), to ensure ideal results. Robotic Process Automation (RPA) can be explored as a potential facilitator to improve, augment, streamline, or optimize chatbot QA. RPA is ideally suited for tasks that can be clearly defined (rule-based) and are repeating in nature. This limits its ability to become an all-encompassing technology for chatbot QA testing, but it can still be useful in replacing part of the manual QA testing of chatbots. Chatbot QA is a complex domain in its own right and has its own challenges, including the lack of streamlined/standardized testing protocols and quality measures, though traits like intent recognition, responsiveness, conversational flow, etc., are usually tested, especially at the end-user testing phase. RPA can be useful in certain areas of chatbot QA, including its ability to increase the sample size for training and testing datasets, generating input variations, splitting testing/conversation data sets, testing for typo resiliency, etc. The general rule is that the easier a testing process is to clearly define and set rules for, the better it's a candidate for RPA-based testing. This naturally increases the lean towards technical testing and makes it moderately unfeasible as an end-user testing alternative. It has the potential to optimize chatbot QA in conjunction with AI and ML testing tools

Evaluating Chatbots to Promote Users' Trust -- Practices and Open Problems

Chatbots, the common moniker for collaborative assistants, are Artificial Intelligence (AI) software that enables people to naturally interact with them to get tasks done. Although chatbots have been studied since the dawn of AI, they have particularly caught the imagination of the public and businesses since the launch of easy-to-use and general-purpose Large Language Model-based chatbots like ChatGPT. As businesses look towards chatbots as a potential technology to engage users, who may be end customers, suppliers, or even their own employees, proper testing of chatbots is important to address and mitigate issues of trust related to service or product performance, user satisfaction and long-term unintended consequences for society. This paper reviews current practices for chatbot testing, identifies gaps as open problems in pursuit of user trust, and outlines a path forward

Institutional Research-Focused Conversational Artificial Intelligence Agent

A capstone submitted in partial fulfillment of the requirements for the degree of Doctor of Education in the Ernst and Sara Lane Volgenau College of Education at Morehead State University by Joshua C. Frisby on March 8, 202

Chatbot Introduction and Operation in Enterprises – A Design Science Research-based Structured Procedure Model for Chatbot Projects

Chatbot research has become an emerging research area. Researchers survey the technology behind and the whole ecosystem from different perspectives, e.g., human-computer interaction, design research, or anthropomorphism. To foster the transfer from research to practice, a comprehensive structured procedure model is missing yet. Due to this, the transfer of the research results into real-world settings in enterprises is often complicated. Hereto, we propose a comprehensive structured procedure model to guide practitioners in chatbot projects based on a Design Science Research study. In doing so, necessary project steps are pointed out and corresponding research results are highlighted to make them reusable for practice in a targeted manner. Thus, we provide structured support for chatbot projects in enterprises

Online Student Performance System integrating Multidimensional Data Visualization and Chatbot for Primary School

Today's technology has improved to the point that it can be utilized to execute many activities in daily life with minimum effort, and the world has acknowledged the worth of education in one's life. The schools have to analyze student performance manually, which requires a lot of time and effort from teachers to work on. However, the increasing amount of student data becomes difficult to analyze using traditional statistical techniques and database data management tools. The objective of this project is to study the current problems in the online student performance system. A preliminary survey of 30 respondents was conducted in order to gather information based on previous user experiences with the online student performance system. The next objective is to develop an Online Student Performance System integrating Multidimensional Data Visualization and Chatbot for Primary School using Web Development Life Cycle that can visualize student performance systems to assist teachers and parents. Following that, this project employed a tool based on Multidimensional Data Visualization techniques. Google Charts and Dialogflow were used in this project to visualize the dashboard and construct a chatbot for the system. The last objective is to evaluate the usability of the system. There are three experts to test the project usability using the Post-Study System Usability Questionnaire (PSSUQ). The findings of the project can be used as a guideline to improve the system in the future. Overall, this project will assist teachers and parents in obtaining information about their students’ academic performance. The data about the students' performance can be displayed in the dashboard as a chart, graph, or diagram, and they can also communicate with the chatbot if they require assistance or guidance in using the system and obtaining their students' performance

The Development of Chatbot Provided Registration Information Services for Students in Distance Learning

In recent years, chatbots have become crucial, particularly for assisting students with real-time registration information. This research focused on 1) synthesizing registry works related to information provided for students, 2) designing chatbots and conversation structures in the form of interactive conversations between students and robots for answering questions and providing information tailored to their needs, and 3) examining and evaluating the use of chatbots in providing information services to students, while analyzing the accuracy and suitability of the developed chatbot. This study, based on research and development, utilized a sample consisting of 16 staff directly involved in the provision of registration information to students and 255 undergraduate students from Sukhothai Thammathirat Open University, with respondents being selected through a simple random sampling technique. The synthesis of the research results revealed the following findings: 1) A qualitative study revealed that the registration information related to students, called STOU Journey, consisted of 10 issues, and was required for the whole learning period. 2) The result of the design and development of the chatbot revealed that the developer chatbot could be used on both the website and the LINE application. It was also found that the chatbot could answer most questions correctly and completely. The chatbot responded quickly and was easy to use. The chatbot used language that was easy to understand and natural, while 3) satisfactory evaluation results from 255 undergraduate students showed that overall, students who had used the completed version of the chatbot were satisfied with the use of the chatbot at a high level (Mean = 4.19, SD = 0.98) while they also felt that the chatbot was easy to use (Mean = 4.33, SD = 0.95) and the using the chatbot felt like a natural conversation (Mean = 4.22, SD = 0.99)

KAI: An AI-powered Chatbot To Support Therapy

Aquest projecte intenta reduir l'enorme bretxa existent entre les persones que pateixen problemes de salut mental i les que realment reben tractament amb el disseny i la implementació de Kai, un xatbot impulsat per IA que dóna suport a la teràpia cognitivoconductual (TCC). La TCC es basa en la identificació de les distorsions cognitives (pensaments negatius) per qüestionar-les amb l'objectiu de millorar l'estat d'ànim i la salut mental. Per això es va utilitzar la Classificació de Text, una tècnica de Processament del Llenguatge Natural (PLN) per identificar potencials distorsions cognitives en el text. Durant el projecte es va fer una experimentació de models per comparar diferents models supervisats d'aprenentatge automàtic per triar el millor per a la classificació de textos. El conjunt de dades necessari per entrenar els models es va generar proporcionant manualment exemples etiquetats de les 15 distorsions cognitives principals. L'experimentació del model es va fer íntegrament en python. A més, el disseny del flux de diàleg del chatbot es va fer seguint les directrius del TCC i la implementació del chatbot es va fer a Python utilitzant TKinter, un Framework per a la interfície. Finalment, es van realitzar dos tests per comprovar el funcionament correcte del chatbot.This project attempts to bridge the huge gap between people who struggle with mental health and people who actually get treated with the design and implementation of Kai, an AI-powered Chatbot that supports Cognitive Behavioral Therapy (CBT). CBT is based on identifying cognitive distortions (negative thoughts) and challeng- ing them to improve mood and overall mental health. This was done by using Text Classification, a Natural Language Processing (NLP) technique to identify potential cognitive distortions in text. During the project, a model experimentation was done to compare different super- vised machine learning models in order to choose the best one for the text classification. The dataset needed to train the models was generated by manually giving labelled ex- amples of the 15 major cognitive distortions. The model experimentation was entirely done in python. Furthermore, the design of the dialogue flow of the chatbot was done following the CBT's guidelines and the implementation of the chatbot was done in Python using the TKinter in Framework for the interface. Finally, two test were made to check for the correct functioning of the chatbot