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Artificial Intelligence
From humble evolutions in research papers and labs, artificial intelligence (AI)--which encompasses Machine Learning (ML) and Deep Learning (DL)--has matured in its many forms, infused in applications that can learn on their own and become progressively smarter with each interaction and transaction. Coupled with immense stores of data, maturity in CPU and GPU hardware, the invention of new, open source deep learning algorithms, and cloud technologies, operational AI has become available to the masses, setting the wheels in motion for a worldwide AI revolution that has never been seen before. This book attempts to help the reader on their AI journey by covering the concepts of AI, Machine Learning, and Deep Learning in its many forms; key ML and DL algorithms data scientists should learn; ethical challenges for the use of AI; how AI is being used across industries; possible future outlook for AI, and an AI Ladder to help accelerate the AI journey.
Data Fabric
Many organizations recognize the value and benefits Artificial Intelligence (AI) can bring if implemented correctly. This topic is outlined in the authors' previous book, Artificial Intelligence: Evolution and Revolution. A long-standing challenge that many organizations continue to face is preparing for AI and making sure that their data and assets are accessible, manageable, and governed and are of the right quality so that they can be consumed by new and existing AI applications in order to infuse AI across the enterprise to help drive smarter business outcomes. Over the years, numerous paradigms and efforts have attempted to address the complexities of managing sprawling and disparate data silos, but all seemed to have fallen short of their promises and expectations. Organizations need the flexibility to put their data and assets where it makes most business sense, whether that's on premises or in the private or public cloud. This book attempts to explain the concepts and values that a data fabric approach can deliver to both technical and business communities.
Artificial Intelligence Technology in Healthcare
Artificial Intelligence Technology in Healthcare: Security and Privacy Issues focuses on current issues with patients’ privacy and data security including data breaches in healthcare organizations, unauthorized access to patients’ information, and medical identity theft. It explains recent breakthroughs and problems in deep learning security and privacy issues, emphasizing current state-of-the-art methods, methodologies, implementation, attacks, and countermeasures. It examines the issues related to developing artifiicial intelligence (AI)-based security mechanisms which can gather or share data across several healthcare applications securely and privately. Features: Combines multiple te...
The Lakehouse Effect
Data warehouses and data lakes each evolved to meet a set of specific technology and business needs and values. As organizations often need both, there has been increasing demand for convergence of both technologies. Thus, the lakehouse was born. A lakehouse couples the cost benefits and versatility of data lakes with the data structure and high-performance data management capabilities of data warehouses into a single unified data store that can be consistently and efficiently accessed, governed, analyzed and consumed by AI applications. Lakehouses are designed to help organizations get more from their existing investment in data warehouses and data lakes. It supports the existence of both through access to and management of a larger variety of combined data for increased flexibility, enhancing business intelligence and AI initiatives by revealing deeper insights into an organization' s data estates. This book is intended for technical communities, such as developers, data scientists, and C-level IT executives, as well as business communities, such as business managers requiring self-service analytics / AI, and C-level business executives.
Understanding Foreign Policy Decision Making
Understanding Foreign Policy Decision Making presents a psychological approach to foreign policy decision making. This approach focuses on the decision process, dynamics, and outcome. The book includes a wealth of extended real-world case studies and examples that are woven into the text. The cases and examples, which are written in an accessible style, include decisions made by leaders of the United States, Israel, New Zealand, Cuba, Iceland, United Kingdom, and others. In addition to coverage of the rational model of decision making, levels of analysis of foreign policy decision making, and types of decisions, the book includes extensive material on alternatives to the rational choice model, the marketing and framing of decisions, cognitive biases, and domestic, cultural, and international influences on decision making in international affairs. Existing textbooks do not present such an approach to foreign policy decision making, international relations, American foreign policy, and comparative foreign policy.
The Descendants of Adam and Isabel (Thurston) Patterson of Pelham, Massachusetts
Adam and Isabel Thurston Patterson lived during the early 1700's.
