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He said to them, “Go into all the world and preach the gospel to all creation. Whoever believes and is baptized will be saved, but whoever does not believe will be condemned. And these signs will accompany those who believe: In my name they will drive out demons; they will speak in new tongues; they will pick up snakes with their hands; and when they drink deadly poison, it will not hurt them at all; they will place their hands on sick people, and they will get well.” After the Lord Jesus had spoken to them, he was taken up into heaven and he sat at the right hand of God. Then the disciples went out and preached everywhere, and the Lord worked with them and confirmed his word by the signs that accompanied it. Mark 16:15-20

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User research literature widely assumes that researchers should build empathy for their users, and that this will enable companies to design better experiences. Businesses point to UX research as the empathy in their company ethos. But when we talk about empathy in a business context, what do we really mean? Is it reasonable to expect researchers to empathize with every person they interview? Is it even productive? In this presentation, I will discuss why empathy is not what drives business and makes researchers good advocates for our users. We will first look at the literal and theoretical definitions of empathy in user experience research, then draw from that definition what businesses are ultimately in search of—rightly or wrongly—when they bring UX research into the process. We will talk about how it may not only be impossible to achieve true empathy, but also that “being empathetic” may not make researchers better advocates for the user. I will argue that we should reframe our goal as compassion, an action-driven, thoughtful, and achievable north star that leverages core researcher talents and better communicates user needs.

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Despite remarkable successes in various domains such as robotics and games, Reinforcement Learning (RL) still struggles with exploration inefficiency. For example, in hard Atari games, state-of-the-art agents often require billions of trial actions, equivalent to years of practice, while a moderately skilled human player can achieve the same score in just a few hours of play. This contrast emerges from the difference in exploration strategies between humans, leveraging memory, intuition and experience, and current RL agents, primarily relying on random trials and errors. This tutorial reviews recent advances in enhancing RL exploration efficiency through intrinsic motivation or curiosity, allowing agents to navigate environments without external rewards. Unlike previous surveys, we analyze intrinsic motivation through a memory-centric perspective, drawing parallels between human and agent curiosity, and providing a memory-driven taxonomy of intrinsic motivation approaches. The talk consists of three main parts. Part A provides a brief introduction to RL basics, delves into the historical context of the explore-exploit dilemma, and raises the challenge of exploration inefficiency. In Part B, we present a taxonomy of self-motivated agents leveraging deliberate, RAM-like, and replay memory models to compute surprise, novelty, and goal, respectively. Part C explores advanced topics, presenting recent methods using language models and causality for exploration. Whenever possible, case studies and hands-on coding demonstrations. will be presented.

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Today, I had the opportunity to review Professor Sylvester Monye's latest book, "The Concession of Asaba International Airport: Balancing Politics and Policy Execution.” The review was attended by former President Goodluck Ebele Jonathan, GGFR, Governor Sheriff F. Oborevwori of Delta State, former Senate President, H.E Dr Abubakar Bukola Saraki CON, former SGF Boss Mustapha, CFR, and former President Chief Olusegun Obasanjo, GCFR, who chaired the event. The book offers a master class in policy management in a complex political environment and sheds light on the challenges and intricacies involved in such a monumental project. It was a privilege to gain insight into the experiences and perspectives shared in the book, and I believe it offers valuable lessons for future policymakers and governments undertaking similar endeavours. Congratulations to Professor Monye on this insightful and thought-provoking work.

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Proofreading: Basics to Artificial Intelligence Integration. Become a Professional Proofreader and Editor Leveraging Human Expertise and Artificial Intelligence (AI) Technology and Tools. Table of Contents MODULE 1: INTRODUCTION TO PROOFREADING What is proofreading, and how is it different from editing? The importance of proofreading in publishing and communication Types of materials that require proofreading (books, articles, websites, legal documents, etc.) Proofreading marks and symbols MODULE 2: DEVELOPING PROOFREADING SKILLS MODULE 3: PROOFREADING TECHNIQUES Preparing for the proofreading process (setting up the workspace, tools, etc.) Reading techniques (line-by-line, word-by-word, backwards reading) Using proofreading checklists and guidelines Cross-checking against style guides and reference materials Marking up corrections and queries MODULE 4: PROOFREADING DIFFERENT TYPES OF CONTENT Proofreading fiction and non-fiction books Fiction Books: Non-Fiction Books: Proofreading academic and scientific papers Proofreading legal and business documents Proofreading websites and digital content Proofreading marketing and advertising materials MODULE 5: PROOFREADING TOOLS AND RESOURCES MODULE 6: PROOFREADING WORKFLOWS AND BEST PRACTICES MODULE 7: BUILDING A SUCCESSFUL PROOFREADING CAREER MODULE 8: AI TEXT EDITING AND PROOFREADING Benefits and limitations of AI in this field Benefits of AI in Proofreading: Limitations of AI in Proofreading: Examples of AI-powered text editing and proofreading tools Combining AI with Human Expertise AI Ethics and Bias in Text Editing Future Trends and Developments in AI Text Editing This cutting-edge course equips you with the essential skills and tools to become a proofreading pro in the age of AI technology. Designed for writers, editors, content creators, and anyone seeking to elevate their proofreading game, this masterclass combines time-tested proofreading techniques with the latest AI-powered text editing solutions. Through in-depth modules, you will master the art of proofreading, from developing razor-sharp attention to detail to mastering grammar, punctuation, and style guidelines. This masterclass also dives deep into the world of AI text editing. You’ll discover how AI-powered tools can aid in grammar and style checking, context-aware spell checking, consistency management, content optimization, and more. You will learn to seamlessly integrate AI tools into your proofreading workflows, combining the best of human expertise and AI technology for unparalleled results. Finally, this course will help you discover how to harness the power of AI to streamline your proofreading workflows, enhance accuracy, and deliver impeccable content every time, thanks to advanced proofreading techniques, such as line-by-line reading, backward reading, and cross-checking against reference materials, ensuring no error goes unnoticed.

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