Ebenezer Esenogho – författare

This book explores the transformative intersection of artificial intelligence (AI) and microbiology, introducing the concept of microbial artificial intelligence (MAI). Designed to bridge the gap between microbiology and AI, it provides a comprehensive overview of fundamental AI concepts, practical applications, limitations, ethical considerations, and future opportunities in this emerging field.

Featuring contributions from a global team of experts, the book is structured into two distinct sections for a deeper exploration of MAI. The first section introduces the fundamentals of MAI, offering microbiologists a clear understanding of AI principles and their relevance to microbiology. The second section highlights real-world applications of MAI across diverse industries, including healthcare, genomics, metagenomics, drug discovery, antimicrobial resistance, biofuel production, food quality and safety, astrobiology, and forensic science. This section addresses critical issues such as bias in MAI, ethical challenges, and the impact of data collection and interpretation on research outcomes, while proposing strategies to mitigate these challenges.

This essential resource is ideal for students and researchers eager to explore the potential of MAI and understand AI’s growing influence on microbiology. It also serves as valuable supplementary reading for AI professionals seeking insights into broader applications of AI across scientific disciplines.

This book explores the transformative intersection of artificial intelligence (AI) and microbiology, introducing the concept of microbial artificial intelligence (MAI). Designed to bridge the gap between microbiology and AI, it provides a comprehensive overview of fundamental AI concepts, practical applications, limitations, ethical considerations, and future opportunities in this emerging field.

Featuring contributions from a global team of experts, the book is structured into two distinct sections for a deeper exploration of MAI. The first section introduces the fundamentals of MAI, offering microbiologists a clear understanding of AI principles and their relevance to microbiology. The second section highlights real-world applications of MAI across diverse industries, including healthcare, genomics, metagenomics, drug discovery, antimicrobial resistance, biofuel production, food quality and safety, astrobiology, and forensic science. This section addresses critical issues such as bias in MAI, ethical challenges, and the impact of data collection and interpretation on research outcomes, while proposing strategies to mitigate these challenges.

This essential resource is ideal for students and researchers eager to explore the potential of MAI and understand AI’s growing influence on microbiology. It also serves as valuable supplementary reading for AI professionals seeking insights into broader applications of AI across scientific disciplines.