Why Are All the Black Kids Sitting Together in the Cafeteria?

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Why Are All the Black Kids Sitting Together in the Cafeteria?

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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.

Proofreading- Basics to Artificial Intelligence Integration - Presentation:Sl...

David Celestin

Introduction to Artificial intelligence.

thamaeteboho94

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Mahamudul Hasan

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.

Unlocking Exploration: Self-Motivated Agents Thrive on Memory-Driven Curiosity

Hung Le

Dreaming Music Video Treatment _ Project & Portfolio III

NhPhngng3

Why Are All the Black Kids Sitting Together in the Cafeteria?

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