The adoption of Reinforcement Learning (RL) in several human-centred applications provides robots with autonomous decision-making capabilities and adaptability based on the observations of the operating environment. In such scenarios, however, the learning process can make robots' behaviours unclear and unpredictable to humans, thus preventing a smooth and effective Human-Robot Interaction (HRI). As a consequence, it becomes crucial to avoid robots performing actions that are unclear to the user. In this work, we investigate whether including human preferences in RL (concerning the actions the robot performs during learning) improves the transparency of a robot's behaviours. For this purpose, a shielding mechanism is included in the RL algorithm to include human preferences and to monitor the learning agent's decisions. We carried out a within-subjects study involving 26 participants to evaluate the robot's transparency in terms of Legibility, Predictability, and Expectability in different settings. Results indicate that considering human preferences during learning improves Legibility with respect to providing only Explanations, and combining human preferences with explanations elucidating the rationale behind the robot's decisions further amplifies transparency. Results also confirm that an increase in transparency leads to an increase in the safety, comfort, and reliability of the robot. These findings show the importance of transparency during learning and suggest a paradigm for robotic applications with human in the loop.

In the post-pandemic era, the widespread adoption of remote work has prompted the educational sector to reassess conventional pedagogical methods. This paper is to scrutinize the underlying psychological principles of ChatGPT, delve into the factors that captivate user attention, and implicate its ramifications on the future of learning. The ultimate objective of this study is to instigate a scholarly discourse on the interplay between technological advancements in education and the evolution of human learning patterns, raising the question of whether technology is driving human evolution or vice versa. Keywords: Artificial intelligence (AI), Human-machine communication, COVID-19, Chat GPT, Learning 3.0, Critical Thinking 1.Introduction of ChatGPT ChatGPT, a chatbot developed by OpenAI, can interpret and respond to natural language input using the GPT-3 language model which has 175 billion parameters (Floridi & Chiriatti, 2020). The utilization of a word-driven dialogue system offers assistance in cross-domain problem resolution and the generation of content to answer users' inquiries.

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This course is the right resource for anyone with some knowledge of Java programming who wants to get started with Data Science and Machine learning as quickly as possible. If you want to gain meaningful insights from big data and develop intelligent applications using Java, this course is also a must-have. Machine Learning is one of the core area of Artificial Intelligence where computers are trained to self-learn, grow, change, and develop on their own without being explicitly programmed. This course demonstrates complex data extraction and statistical analysis techniques supported by Java, applying various machine learning methods, exploring machine learning sub-domains, and exploring real-world use cases such as recommendation systems, fraud detection, natural language processing, and more, using Java programming. The course begins with an introduction to data science and basic data science tasks such as data collection, data cleaning, data analysis, and data visualization.

Artificial Intelligence (AI) represents machine-based intelligence, typically manifest in "cognitive" functions that humans associate with other human minds. There are a range of different technologies involved in AI including Machine Learning, Natural Language Processing, Deep Learning, and more. Cognitive Computing involves self-learning systems that use data mining, pattern recognition and natural language processing to mimic the way the human brain works. Key industry verticals covered include use of AI in Internet related services and products, Financial Services, Medical and Bio-informatics, Manufacturing, and Telecommunications. Some of the key application areas covered include Marketing and Business Decision Making, Workplace Automation, Predictive Analysis and Forecast, Fraud Detection and Classification.