CHALLENGES AND LIMITATIONS OF DATA IN AI

In the vast universe of technology, Artificial Intelligence (AI) stands out as one of the most promising and, at the same time, challenging areas. But behind every advancement in AI is a fundamental issue: data. They are the foundation on which AI algorithms are built, and understanding their challenges is essential for anyone who wants to successfully navigate this ever-evolving field.

In Challenges and Limitations of Data in AI, you will be taken on an in-depth exploration of the most crucial aspects that affect the efficiency and applicability of AI in various areas. This book is part of Prof. Marcão's renowned "Artificial Intelligence" collection and offers a complete overview of how data can both promote and limit the advancement of this technology.

Aimed at data scientists, engineers, developers, and IT managers, this volume is also an indispensable resource for academics and policymakers who need a solid understanding of the implications of data on AI performance. If you're a professional interested in transforming your understanding of AI, this book is an essential tool for mastering the concepts shaping the future of this technology.

Main topics covered:

- Data fundamentals in AI, including common types and sources
- Data lifecycle in AI projects
- Challenges of data quality, consistency, and impact in AI models
- Practical data cleansing and validation techniques
- The ethical and technical impact of biases on data
- Methods for detecting and correcting biases, such as algorithmic audits and fairness constraints
- Data security and privacy, including regulatory compliance and protection of sensitive data
- Scalability and management challenges of large volumes of data with the use of Big Data and cloud computing
- Tools for explainability and transparency in AI results
- Practical case studies in areas such as health, finance, education and public safety
- Current limitations of data in AI and predictions for the future

Results that the reader will obtain from studying this book:

- In-depth understanding of the types and quality of data that feed AI models
- Ability to detect and mitigate bias in data, ensuring fairer and more accurate decisions
- Knowledge of data security and privacy best practices in AI systems
- Mastery of scalability techniques and management of large volumes of data
- Tools to increase trust in AI results, with a focus on transparency and explainability
- Practical application of concepts through case studies in critical sectors
- Clear insight into emerging limitations and opportunities in using data for AI
- Ethical reflections on the responsible use of data in AI positively impacting society

Challenges and Limitations of Data in AI is not just informative reading; is a practical guide that reveals how AI, powered by data, can be both a powerful tool and a source of challenges. By studying this book, you will be prepared to face the obstacles that come with the use of big data and, most importantly, to ensure that your solutions are ethical, secure, and scalable.

If you're looking for a deep and critical understanding of how data shapes the future of AI, this book is for you. Get it now on Amazon and transform the way you interact with Artificial Intelligence. Get ready to navigate the challenges and realize the full potential of data in AI!

Shop today and take your knowledge to the next level!

About the author:
Marcus Pinto – Prof. Marcão – has a master's degree in Information Technology and works in the field of information architecture. IT professional with decades of experience in data intelligence has been working in the area of information technology since the mid-1980s, especially in the area of information architecture/attribute engineering, systems development and data warehouse.