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Tag: Approaches

Generative Artificial Intelligence (AI) Approaches for Industrial Applications (Information Systems Engineering and Management, 24)

Price: ~~$199.99~~ – $189.99
(as of Dec 25,2024 11:42:33 UTC – Details)

Publisher ‏ : ‎ Springer; 2025th edition (February 20, 2025)
Language ‏ : ‎ English
Hardcover ‏ : ‎ 356 pages
ISBN-10 ‏ : ‎ 3031767098
ISBN-13 ‏ : ‎ 978-3031767098
Item Weight ‏ : ‎ 1.74 pounds

Generative Artificial Intelligence (AI) Approaches for Industrial Applications (Information Systems Engineering and Management, 24)

In today’s rapidly evolving technological landscape, the integration of artificial intelligence (AI) into industrial applications has become increasingly prevalent. One area of AI that is gaining significant attention is generative AI, which involves the use of machine learning algorithms to generate new and unique content based on existing data.

In the field of Information Systems Engineering and Management, the adoption of generative AI approaches holds great promise for enhancing efficiency, productivity, and innovation. By leveraging advanced algorithms and deep learning techniques, organizations can harness the power of AI to automate tasks, optimize processes, and drive business growth.

From predictive maintenance and quality control to product design and optimization, generative AI offers a wide range of applications that can revolutionize the industrial landscape. By analyzing vast amounts of data and identifying patterns, AI algorithms can generate valuable insights and recommendations that can help organizations make informed decisions and stay ahead of the competition.

As the demand for intelligent automation continues to grow, it is crucial for industrial professionals to stay informed about the latest developments in generative AI and how they can be applied to their specific domain. By staying ahead of the curve and embracing cutting-edge technologies, businesses can unlock new opportunities for growth and success in the digital era.

In conclusion, generative AI approaches have the potential to transform industrial applications and drive innovation in Information Systems Engineering and Management. By harnessing the power of AI, organizations can unlock new possibilities, optimize operations, and achieve sustainable competitive advantage in today’s dynamic marketplace.
#Generative #Artificial #Intelligence #Approaches #Industrial #Applications #Information #Systems #Engineering #Management

December 25, 2024
Artificial Intelligence Ethics and International Law: Practical approaches to AI governance – 2nd Edition

Price: $29.95
(as of Dec 24,2024 22:58:42 UTC – Details)

Artificial Intelligence Ethics and International Law: Practical approaches to AI governance – 2nd Edition

As the use of artificial intelligence (AI) continues to grow, it is essential to address the ethical and legal implications of this powerful technology. The second edition of “Artificial Intelligence Ethics and International Law” offers practical approaches to AI governance, providing policymakers, industry professionals, and academics with the tools they need to ensure that AI is developed and deployed in a responsible and ethical manner.

This updated edition explores the latest developments in AI ethics and international law, including new regulations, guidelines, and best practices. It examines the ethical issues surrounding AI, such as bias, privacy, and accountability, and provides practical strategies for addressing these challenges. The book also explores the legal framework for AI governance, including international treaties, national laws, and industry standards.

With contributions from leading experts in the field, “Artificial Intelligence Ethics and International Law” offers a comprehensive overview of the key issues in AI governance and provides practical guidance for policymakers, industry professionals, and researchers. Whether you are a policymaker looking to develop regulations for AI, an industry professional seeking to ensure ethical AI practices, or an academic studying the intersection of AI and law, this book is an essential resource for understanding and navigating the complex landscape of AI governance.
#Artificial #Intelligence #Ethics #International #Law #Practical #approaches #governance #2nd #Edition

December 24, 2024
Modern Deep Learning for Tabular Data: Novel Approaches to Common Modeling Problems

Price: ~~$59.99~~ – $38.27
(as of Dec 24,2024 19:22:01 UTC – Details)

Publisher ‏ : ‎ Apress; 1st ed. edition (December 30, 2022)
Language ‏ : ‎ English
Paperback ‏ : ‎ 870 pages
ISBN-10 ‏ : ‎ 148428691X
ISBN-13 ‏ : ‎ 978-1484286913
Item Weight ‏ : ‎ 3.59 pounds
Dimensions ‏ : ‎ 7 x 2 x 10 inches

In the world of data science, deep learning has revolutionized the way we approach modeling and analysis. Traditionally, deep learning has been used primarily for image and text data, where its ability to learn complex patterns and relationships in data sets with large numbers of features has proven to be incredibly powerful. However, in recent years, deep learning has also been applied to tabular data, which presents its own unique challenges and opportunities.

In this post, we will explore some novel approaches to common modeling problems in tabular data using modern deep learning techniques. We will discuss how deep learning can be used to tackle issues such as feature engineering, handling missing data, and dealing with imbalanced classes. We will also explore some cutting-edge research in the field, including the use of attention mechanisms and graph neural networks for tabular data.

Overall, we will show how modern deep learning techniques can be applied to tabular data to achieve state-of-the-art performance on a wide range of modeling tasks. Whether you are a seasoned data scientist looking to expand your toolkit or a newcomer to the field looking to learn more about the latest advancements in deep learning, this post will provide valuable insights and practical tips for working with tabular data in the modern age.
#Modern #Deep #Learning #Tabular #Data #Approaches #Common #Modeling #Problems

December 24, 2024
Flight Safety Management: Quantitative and AI Approaches (Studies in Systems, Decision and Control, 228)

Price: ~~$169.99~~ – $161.49
(as of Dec 24,2024 16:13:46 UTC – Details)

Publisher ‏ : ‎ Springer (February 22, 2025)
Language ‏ : ‎ English
Hardcover ‏ : ‎ 250 pages
ISBN-10 ‏ : ‎ 9819612349
ISBN-13 ‏ : ‎ 978-9819612345
Item Weight ‏ : ‎ 1.74 pounds

Flight Safety Management: Quantitative and AI Approaches (Studies in Systems, Decision and Control, 228)

In the ever-evolving field of aviation, ensuring flight safety is of utmost importance. With advancements in technology, the use of quantitative and AI approaches has become increasingly prevalent in the management of flight safety.

This book, part of the Studies in Systems, Decision and Control series, delves into the various quantitative and AI approaches that can be utilized to enhance flight safety management. From predictive analytics to machine learning algorithms, the authors explore how these tools can be applied to analyze data, identify potential risks, and ultimately improve safety protocols.

With contributions from experts in the field, this book provides a comprehensive overview of the latest methodologies and technologies being used to ensure the safety of passengers and crew members. Whether you are a seasoned aviation professional or a newcomer to the industry, Flight Safety Management: Quantitative and AI Approaches is a valuable resource for understanding the complexities of flight safety management in today’s fast-paced aviation landscape.
#Flight #Safety #Management #Quantitative #Approaches #Studies #Systems #Decision #Control

December 24, 2024
Ethics in Information Technology: A Practical Guide (Cognitive Approaches in Cloud and Edge Computing.)

Price: $31.81
(as of Dec 24,2024 07:46:31 UTC – Details)

ASIN ‏ : ‎ B09VPTC1G8
Publisher ‏ : ‎ CRC Press; 1st edition (May 15, 2022)
Publication date ‏ : ‎ May 15, 2022
Language ‏ : ‎ English
File size ‏ : ‎ 7437 KB
Simultaneous device usage ‏ : ‎ Up to 4 simultaneous devices, per publisher limits
Text-to-Speech ‏ : ‎ Enabled
Screen Reader ‏ : ‎ Supported
Enhanced typesetting ‏ : ‎ Enabled
X-Ray ‏ : ‎ Not Enabled
Word Wise ‏ : ‎ Not Enabled
Print length ‏ : ‎ 412 pages

Ethics in Information Technology: A Practical Guide (Cognitive Approaches in Cloud and Edge Computing)

In today’s fast-paced digital world, the use of cloud and edge computing has become increasingly prevalent. These technologies offer immense benefits in terms of storage, processing power, and scalability, but they also raise important ethical considerations that must be addressed.

As we rely more and more on cloud and edge computing for our daily tasks and activities, it is crucial to consider the ethical implications of these technologies. From data privacy and security concerns to the potential for bias in artificial intelligence algorithms, there are a myriad of ethical issues that must be navigated in the realm of information technology.

This practical guide aims to provide a framework for approaching these ethical challenges in cloud and edge computing. By adopting cognitive approaches to ethics, we can better understand and address the complex ethical dilemmas that arise in the use of these technologies.

Through case studies, best practices, and real-world examples, this guide will help IT professionals and decision-makers navigate the ethical terrain of cloud and edge computing. By incorporating ethical considerations into their decision-making processes, organizations can ensure that their use of these technologies is responsible, transparent, and fair.

Ethics in information technology is not just a theoretical concept – it is a practical necessity in today’s digital landscape. By embracing cognitive approaches to ethics in cloud and edge computing, we can create a more ethical and sustainable future for technology.
#Ethics #Information #Technology #Practical #Guide #Cognitive #Approaches #Cloud #Edge #Computing

December 24, 2024
Machine Learning for High-Risk Applications: Approaches to Responsible AI

Price: ~~$79.99~~ – $56.64
(as of Dec 24,2024 02:43:11 UTC – Details)

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Publisher ‏ : ‎ O’Reilly Media; 1st edition (May 23, 2023)
Language ‏ : ‎ English
Paperback ‏ : ‎ 466 pages
ISBN-10 ‏ : ‎ 1098102436
ISBN-13 ‏ : ‎ 978-1098102432
Item Weight ‏ : ‎ 1.7 pounds
Dimensions ‏ : ‎ 6.93 x 1.1 x 9.13 inches

Machine learning has revolutionized the way we approach data analysis and decision-making processes, but when it comes to high-risk applications, such as healthcare, finance, and autonomous vehicles, the stakes are much higher. In these critical scenarios, ensuring that machine learning algorithms are reliable, fair, and ethical is paramount.

One approach to ensuring responsible AI in high-risk applications is to prioritize transparency and explainability. By understanding how a machine learning model arrives at its decisions, stakeholders can have more confidence in its outputs and identify and address any biases or errors.

Additionally, incorporating diverse and representative data sets is crucial for developing fair and unbiased machine learning models. By training algorithms on data that accurately reflects the real-world population, we can reduce the risk of perpetuating discriminatory practices or making flawed predictions.

Another key aspect of responsible AI in high-risk applications is continuous monitoring and evaluation. Machine learning models should be regularly tested and updated to ensure they remain accurate, reliable, and ethical over time.

Ultimately, the goal of machine learning for high-risk applications should be to empower decision-makers with valuable insights while minimizing potential harm. By implementing these approaches to responsible AI, we can harness the power of machine learning to drive innovation and progress in a safe and ethical manner.
#Machine #Learning #HighRisk #Applications #Approaches #Responsible

December 24, 2024
Data Fabric and Data Mesh Approaches With Ai : A Guide to Ai-based Data Catal…

Data Fabric and Data Mesh Approaches With Ai : A Guide to Ai-based Data Catal…

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Data Fabric and Data Mesh Approaches With AI: A Guide to AI-based Data Catalysis

In the ever-evolving world of data management, organizations are constantly seeking new ways to harness the power of data for better decision-making and business outcomes. Two emerging approaches, Data Fabric and Data Mesh, are gaining traction as innovative methods to manage and analyze data at scale.

Data Fabric is a unified architecture that enables organizations to seamlessly integrate and connect data from various sources, regardless of its location or format. This approach aims to create a unified view of data across the organization, providing a holistic view of information that can be accessed and analyzed in real-time.

On the other hand, Data Mesh is a decentralized approach to data management that distributes data processing and analysis tasks to individual teams or departments. This approach aims to empower teams to take ownership of their data and drive insights and decisions independently, without relying on central data teams.

Both Data Fabric and Data Mesh approaches can benefit greatly from AI technologies, such as machine learning and natural language processing, to automate data processing, analysis, and insights generation. AI can help organizations extract meaningful patterns and insights from vast amounts of data, enabling them to make faster and more informed decisions.

In this guide, we will explore how organizations can leverage AI to catalyze their Data Fabric and Data Mesh approaches, enabling them to unlock the full potential of their data assets. We will delve into the key AI technologies that can enhance data management and analysis, as well as best practices for implementing AI in Data Fabric and Data Mesh architectures.

Stay tuned for our upcoming posts, where we will deep dive into the specific ways AI can revolutionize data management and analysis in the context of Data Fabric and Data Mesh approaches. Get ready to supercharge your data strategy with AI-based data catalysis!
#Data #Fabric #Data #Mesh #Approaches #Guide #Aibased #Data #Catal.., Data Fabric

December 24, 2024
Multi-Agent Reinforcement Learning: Foundations and Modern Approaches

Price: $70.00
(as of Dec 24,2024 01:34:05 UTC – Details)

Publisher ‏ : ‎ The MIT Press (December 17, 2024)
Language ‏ : ‎ English
Hardcover ‏ : ‎ 396 pages
ISBN-10 ‏ : ‎ 0262049376
ISBN-13 ‏ : ‎ 978-0262049375
Item Weight ‏ : ‎ 1.9 pounds
Dimensions ‏ : ‎ 6.37 x 1.09 x 9.3 inches

Multi-Agent Reinforcement Learning: Foundations and Modern Approaches

Reinforcement learning has gained significant attention in recent years for its ability to enable agents to learn and adapt to complex environments through trial and error. When it comes to multi-agent systems, where multiple agents must interact and collaborate to achieve a common goal, the challenges become even more complex.

In this post, we will delve into the foundations of multi-agent reinforcement learning (MARL) and explore some of the modern approaches that researchers are using to tackle these challenges.

Foundations of MARL:
Multi-agent reinforcement learning involves multiple agents that interact with each other and the environment to learn optimal strategies for achieving a common goal. Unlike single-agent reinforcement learning, in which the agent is the sole decision-maker, MARL requires agents to consider the actions of other agents in their decision-making process. This introduces challenges such as coordination, communication, and competition among agents.

Modern Approaches:
Researchers have developed various approaches to address the challenges of MARL, including:
1. Decentralized training with centralized learning: In this approach, each agent learns its own policy using decentralized training, but a centralized entity is responsible for coordinating and sharing information among agents to improve overall performance.
2. Communication and coordination: Some approaches focus on enabling agents to communicate and coordinate with each other to achieve better outcomes. This can involve sharing information about the environment, coordinating actions, or even negotiating with other agents.
3. Game-theoretic approaches: Game theory is often used to model the interactions between agents and predict their behaviors. By considering the strategic interactions between agents, researchers can design algorithms that lead to more stable and optimal outcomes.
4. Self-play and population-based training: Some approaches leverage self-play or population-based training to enable agents to learn from each other and improve their strategies over time. This can lead to emergent behaviors and more robust solutions.

Overall, multi-agent reinforcement learning is a rapidly evolving field with many challenges and opportunities for innovation. By exploring the foundations of MARL and the modern approaches being developed, researchers can continue to push the boundaries of what is possible in collaborative and competitive multi-agent systems.
#MultiAgent #Reinforcement #Learning #Foundations #Modern #Approaches

December 24, 2024
Data Fabric and Data Mesh Approaches with AI: A Guide to AI-based Data Catalogin

Data Fabric and Data Mesh Approaches with AI: A Guide to AI-based Data Catalogin

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Data Fabric and Data Mesh Approaches with AI: A Guide to AI-based Data Cataloging

In today’s rapidly evolving digital landscape, organizations are inundated with vast amounts of data from various sources. Traditional data management approaches are no longer sufficient to handle the complexities and volumes of data being generated. This is where Data Fabric and Data Mesh approaches come into play, offering innovative solutions to effectively manage and utilize data assets.

Data Fabric is a unified architecture that enables organizations to seamlessly integrate, analyze, and share data across different systems and platforms. It provides a centralized view of data assets, making it easier for users to access and utilize data in a consistent and efficient manner. With the help of artificial intelligence (AI) technologies, Data Fabric can automate data cataloging processes, making it easier to discover, understand, and govern data assets.

On the other hand, Data Mesh is a decentralized approach to data management that aims to distribute data ownership and governance responsibilities across different teams and departments. This approach promotes data democratization, allowing organizations to leverage data expertise from various sources. AI plays a crucial role in Data Mesh by providing intelligent data cataloging capabilities that enable users to easily find and utilize data assets across the organization.

By combining Data Fabric and Data Mesh approaches with AI-based data cataloging, organizations can effectively manage, analyze, and derive valuable insights from their data assets. AI technologies can automate data discovery, classification, and metadata tagging processes, making it easier for users to search, access, and utilize data assets. Additionally, AI can enhance data governance and compliance efforts by providing real-time insights into data usage and access patterns.

In conclusion, Data Fabric and Data Mesh approaches with AI-based data cataloging offer innovative solutions to modern data management challenges. By leveraging these technologies, organizations can unlock the full potential of their data assets and drive business growth and innovation.
#Data #Fabric #Data #Mesh #Approaches #Guide #AIbased #Data #Catalogin, Data Fabric

December 23, 2024
Proactive Approaches to Data Center Change Management

In today’s fast-paced business environment, data centers are constantly evolving to meet the demands of increasing data storage and processing needs. As a result, change management in data centers has become a critical aspect of ensuring smooth operations and minimizing downtime. Proactive approaches to data center change management are essential to effectively manage changes and mitigate risks associated with them.

One of the key proactive approaches to data center change management is implementing a comprehensive change management process. This process should include clear guidelines and procedures for requesting, approving, and implementing changes in the data center environment. By establishing a formal change management process, organizations can ensure that all changes are properly documented, tested, and approved before being implemented, reducing the risk of disruptions to critical systems and applications.

Another proactive approach to data center change management is conducting regular assessments and reviews of the data center environment. By regularly assessing the current state of the data center, organizations can identify potential areas for improvement and proactively address any issues before they escalate into larger problems. This can help organizations stay ahead of potential risks and ensure that the data center is operating at peak performance.

Additionally, organizations can proactively manage changes in the data center by utilizing automation and orchestration tools. These tools can help streamline the change management process by automating routine tasks, such as configuration updates and software deployments, and ensuring that changes are implemented consistently and efficiently. By leveraging automation and orchestration tools, organizations can reduce the risk of human error and accelerate the pace of change implementation in the data center.

Furthermore, proactive monitoring and alerting systems can help organizations detect and respond to changes in the data center environment in real-time. By monitoring key performance metrics and setting up alerts for abnormal behavior, organizations can quickly identify potential issues and take corrective action before they impact operations. Proactive monitoring can help organizations proactively manage changes and minimize the impact of disruptions on critical systems and applications.

In conclusion, proactive approaches to data center change management are essential for ensuring the stability and reliability of data center operations. By implementing a comprehensive change management process, conducting regular assessments, utilizing automation and orchestration tools, and implementing proactive monitoring and alerting systems, organizations can effectively manage changes in the data center environment and mitigate risks associated with them. By taking a proactive approach to data center change management, organizations can ensure that their data center operations remain resilient and responsive to the evolving needs of the business.

December 22, 2024

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