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Discover the advanced capabilities of the APC Symmetra RM Main Intelligence Module, a high-tech solution for power protection, environmental control, and site monitoring. This intelligent module is meticulously designed to proactively identify and rectify issues, preventing costly downtime before it happens. As the central "brain" of the Symmetra RM system, the Main Intelligence Module (MIM) is a testament to the reliability and durability synonymous with the American Power Conversion (APC) brand. It seamlessly communicates with the external environment and synchronizes all modules within the system, ensuring optimal performance at all times. Beyond its primary function, the Symmetra RM Main Intelligence Module is also a data powerhouse. It diligently gathers and processes critical data, including the real-time condition of each module within the system. This feature makes it an invaluable asset in commercial or industrial settings such as data centers or server rooms, where uninterrupted power supply and system intelligence are paramount. Experience the peace of mind that comes with the APC Symmetra RM Main Intelligence Module, a reliable and efficient solution for your power protection needs.

Markov Decision Processes (MDPs) are widely popular in Artificial Intelligence for modeling sequential decision-making scenarios with probabilistic dynamics. They are the framework of choice when designing an intelligent agent that needs to act for long periods of time in an environment where its actions could have uncertain outcomes. MDPs are actively researched in two related subareas of AI, probabilistic planning and reinforcement learning. Probabilistic planning assumes known models for the agent's goals and domain dynamics, and focuses on determining how the agent should behave to achieve its objectives. On the other hand, reinforcement learning additionally learns these models based on the feedback the agent gets from the environment. This book provides a concise introduction to the use of MDPs for solving probabilistic planning problems, with an emphasis on the algorithmic perspective. It covers the whole spectrum of the field, from the basics to state-of-the-art optimal and approximation algorithms. We first describe the theoretical foundations of MDPs and the fundamental solution techniques for them. We then discuss modern optimal algorithms based on heuristic search and the use of structured representations. A major focus of the book is on the numerous approximation schemes for MDPs that have been developed in the AI literature. These include determinization-based approaches, sampling techniques, heuristic functions, dimensionality reduction, and hierarchical representations. Finally, we briefly introduce several extensions of the standard MDP classes that model and solve even more complex planning problems. Table of Contents: Introduction / MDPs / Fundamental Algorithms / Heuristic Search Algorithms / Symbolic Algorithms / Approximation Algorithms / Advanced Notes

The Intelligence Trap: Why smart people do stupid things and how to make wiser decisions by David Robson (Signed Hardback)'As a rule, I have found that the greater brain a man has, and the better he is educated, the easier it has been to mystify him,' - Houdini to Arthur Conan DoyleSmart people are not only just as prone to making mistakes as everyone else-they may be even more susceptible to them. This is the 'intelligence trap' the subject of David Robson's fascinating and provocative debut.Packed with cutting-edge research, historical case studies, entertaining stories, and practical advice, The Intelligence Trap explores the flaws in our understanding of intelligence and expertise, and reveals the ways that even the brightest minds and talented organizations can backfire - from some of Thomas Edison's worst ideas to failures at NASA, Nokia, and the FBI.With a knack for explaining complex ideas and featuring timeless lessons from Socrates to Benjamin Franklin to Richard Feynman and the latest behavioral science, Robson shows how to build a cognitive toolkit to avoid mistakes and protect ourselves from misinformation and fake news.More details:Publisher: Hodder & Stoughton General DivisionISBN: 9781473669833Number of pages: 352Weight: 576 g

Markov Decision Processes (MDPs) are widely popular in Artificial Intelligence for modeling sequential decision-making scenarios with probabilistic dynamics. They are the framework of choice when designing an intelligent agent that needs to act for long periods of time in an environment where its actions could have uncertain outcomes. MDPs are actively researched in two related subareas of AI, probabilistic planning and reinforcement learning. Probabilistic planning assumes known models for the agent's goals and domain dynamics, and focuses on determining how the agent should behave to achieve its objectives. On the other hand, reinforcement learning additionally learns these models based on the feedback the agent gets from the environment. This book provides a concise introduction to the use of MDPs for solving probabilistic planning problems, with an emphasis on the algorithmic perspective. It covers the whole spectrum of the field, from the basics to state-of-the-art optimal and approximation algorithms. We first describe the theoretical foundations of MDPs and the fundamental solution techniques for them. We then discuss modern optimal algorithms based on heuristic search and the use of structured representations. A major focus of the book is on the numerous approximation schemes for MDPs that have been developed in the AI literature. These include determinization-based approaches, sampling techniques, heuristic functions, dimensionality reduction, and hierarchical representations. Finally, we briefly introduce several extensions of the standard MDP classes that model and solve even more complex planning problems. Table of Contents: Introduction / MDPs / Fundamental Algorithms / Heuristic Search Algorithms / Symbolic Algorithms / Approximation Algorithms / Advanced Notes

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Personal motivation. The dream of creating artificial devices that reach or outperform human inteUigence is an old one. It is also one of the dreams of my youth, which have never left me. What makes this challenge so interesting? A solution would have enormous implications on our society, and there are reasons to believe that the AI problem can be solved in my expected lifetime. So, it's worth sticking to it for a lifetime, even if it takes 30 years or so to reap the benefits. The AI problem. The science of artificial intelligence (AI) may be defined as the construction of intelligent systems and their analysis. A natural definition of a system is anything that has an input and an output stream. Intelligence is more complicated. It can have many faces like creativity, solving prob­ lems, pattern recognition, classification, learning, induction, deduction, build­ ing analogies, optimization, surviving in an environment, language processing, and knowledge. A formal definition incorporating every aspect of intelligence, however, seems difficult. Most, if not all known facets of intelligence can be formulated as goal driven or, more precisely, as maximizing some utility func­ tion. It is, therefore, sufficient to study goal-driven AI; e. g. the (biological) goal of animals and humans is to survive and spread. The goal of AI systems should be to be useful to humans.

Can you envision a healthy financial life but have old, hard to break habits that prevent it from taking root? You are not alone. It turns out our brains are hard-wired to make poor financial and life decisions. Financial Intelligence helps you break the cycle of habitual and emotional decision-making that hard-wiring automatically sparks when faced with emotionally charged events. Beginning with your principles, values, and beliefs, Financial Intelligence offers a concrete guide to help you achieve better results. Doug Lennick provides a simple and clear four-step method, known as the 4Rs, for making wise financial and life decisions based on your core values. When you find yourself in a highly charged event, such as the Dow Jones plummeting, rash decisions often follow like selling out your entire stock portfolio at record lows! Recognizing your automatic mental and physical responses is half the battle toward taking control of the situation. After reflecting on your values and your circumstances (while accounting for your inherent biases), you can reframe a more realistic interpretation of what is happening and what lies ahead. This allows you to respond to a critical situation with an understanding of it and foresight about which current actions will best serve your future goals. Accepting the certainty of uncertainty means you must prepare for the ups and downs of life so when you are thrown a curve ball you are confident you have the foundation in place to act wisely. Financial Intelligence gives you the tools to build this foundation to be able to step back from a tense situation, return to your core values, and transition to an emotional state where decisions are based on thoughtful analysis of what will bring you the greatest long-term success and fulfillment in life.

Medical practices are swimming in data. Data flows into medical practices daily from practice management (PM) systems, electronic medical record (EMR) systems, accounting systems and many other sources. Too many practices extract only the bare minimum of data required to file claims and meet reporting obligations, without recognizing the value in the flood of data that passes through the practice. The book Better Data, Better Decisions: Using Business Intelligence in the Medical Practice demonstrates creative uses of data and reveals inexpensive data extrapolation tools, more efficient uses of data and inexpensive tools that will reduce operating costs. Medical practice managers at all levels can benefit from a greater understanding of the data management tools available. In fact, many of these tools are already present in your computer systems. Give your practice a competitive advantage by learning what data to use, how to mine it and how to organize and report it in order to make better business decisions.

Despite a clear and compelling need for an intelligence-led approach to security, operational, and reputational risks, the subject of corporate security intelligence remains poorly understood. An effective intelligence process can directly support and positively impact operational activity and associated decision-making and can even be used to drive the firm’s business in key markets. Corporate Security Intelligence and Strategic Decision-Making outlines the basic theory and supplies practical solutions for implementing an effective intelligence process in any commercial organization. The main areas covered include how intelligence in the corporate security environment relates to strategic decision-making; the factors that drive the requirement for corporate security intelligence, as well as the main legislative and ethical imperatives; and how intelligence-led processes can not only prevent loss but also support business growth and revenue generation. Detailed topics include: Fundamental and theoretical ideas underlying intelligence work in the public and private sector The collection, validation, collation, and analysis of intelligence The effective and safe dissemination of intelligence material and the different reporting formats available The use of operational models to help guide structures, processes, and the deployment of resources How to implement an effective intelligence function in a corporate environment The topics include real-life examples of where intelligence has been used to support corporate operations and demonstrate how the theory applies to these practical examples, based on years of experience. While corporate security is the natural home of intelligence, the tools and techniques outlined are of course equally applicable for any decision-support process―making this book valuable reading for any leader.

The debate over cyber technology has resulted in new considerations for national security operations. States find themselves in an increasingly interconnected world with a diverse threat spectrum and little understanding of how decisions are made within this amorphous domain.With The Decision to Attack, Aaron Franklin Brantly investigates how states decide to employ cyber in military and intelligence operations against other states and how rational those decisions are. In his examination, Brantly contextualizes broader cyber decision-making processes into a systematic expected utility–rational choice approach to provide a mathematical understanding of the use of cyber weapons at the state level.Discussed: The Key Concepts of Cyber The Motivation and Utility for Covert Action Digital Power Anonymity and Attribution in Cyberspace Cyber and Conventional Operations: The Dynamics of Conflict Defining the Role of Intelligence in Cyberspace How Actors Decide to Use Cyber―a Rational Choice Approach Cognitive Processes and Decision-Making in Cyberspace Finding Meaning in the Expected Utility of International Cyber Conflict

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Appropriate for all courses in Decision Support Systems (DSS), computerized decision making tools, and management support systems. Decision Support and Business Intelligence Systems 8e provides the only comprehensive, up-to-date guide to today's revolutionary management support system technologies, and showcases how they can be used for better decision-making. This completely revised and re-titled edition incorporates the expanded coverage of Business Intelligence and reflects the emphasis that most decision support courses are now taking.

The Final Months of the War With Japan: Signals Intelligence, U.S. Invasion Planning, and the A-Bomb Decision.

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The Final Months of the War With Japan: Signals Intelligence, U.S. Invasion Planning, and the A-Bomb Decision.

For courses in decision support systems, computerized decision-making tools, and management support systems. Market-leading guide to modern analytics, for better business decisions Analytics, Data Science, & Artificial Intelligence: Systems for Decision Support is the most comprehensive introduction to technologies collectively called analytics (or business analytics) and the fundamental methods, techniques, and software used to design and develop these systems. Students gain inspiration from examples of organizations that have employed analytics to make decisions, while leveraging the resources of a companion website. With six new chapters, the 11th edition marks a major reorganization reflecting a new focus – analytics and its enabling technologies, including AI, machine-learning, robotics, chatbots, and IoT.

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