Must Read AI Books Released in 2022
Artificial intelligence is the top in-demand field today. Most engineers want to make a career in AI, data science and analytics. Going through the best and most reliable resources is the best way to learn, so here is the list of the 10 best AI books launched in 2022! Architects of Intelligence Author: Martin Ford Ford interviews 23 of the most experienced AI and robotics researchers in the world about the current state of AI, how it can solve practical problems, and what the future of robotics and computing holds. The book is technical enough for the readers to know the whole story from each interviewee. However, the conversations still flow in a pleasant way for the reader, and just about everything is easy to understand even without a technical background. Designing Human-Centric AI Experiences: Applied UX Design for Artificial Intelligence (Design Thinking) (1st Edition) Author: Akshay Kore User experience (UX) design practices have seen a fundamental shift owing to an increased integration of AL/ML in more and more software products. This book probes into UX design’s role in making technologies inclusive and enabling user collaboration with AI. Moreover, it considers best practices for designers, managers, and product creators and elaborates how individuals from a non-technical background can collaborate effectively with AI/ML teams. Unity Artificial Intelligence Programming (5th Edition) Author: Dr Davide Aversa This book aims to teach the basics of AI programming for video games using one of the most popular commercial game engines available: Unity3D. The author writes in great detail about the ways to implement behaviour trees and finite state machines. The initial chapters provide background on AI in the context of its broader academic and traditional domains. Probabilistic Machine Learning: An Introduction Author: Kevin P. Murphy The author regularly criticises non-Bayesian statisticians. However, many of the methods described in the book are non-Bayesian—all in all, a very comprehensive read on the topic. Interestingly, it is also evident that the author has substantial practical experience gained through working at Google—which shows in the book. Green Internet of Things and Machine Learning: Towards a Smart Sustainable World (1st Edition) Authors: Roshani Raut, Sandeep Kautish, Zdzislaw Polkowski, Anil Kumar, Chuan-Ming Liu This book lays the foundation of an in-depth analysis of Green-Internet of Things (G-IoT) principles using machine learning. It outlines various green ICT technologies and explores the potential towards diverse real-time areas. Furthermore, it highlights various challenges and obstacles towards implementing G-IoT in the real world. In addition, this work also provides insights into how machine learning and green IOT will impact various applications. Designing Machine Learning Systems: An Iterative Process for Production-Ready Applications Author: Chip Huyen The book is a deep dive into designing reliable, maintainable ML systems adaptable to changing environments and business requirements. The author considers each design decision—processing and creating training data, features to use, and what to monitor. The iterative framework in this book uses actual case studies supported by references. Patterns, Predictions, and Actions: Foundations of Machine Learning Authors: Moritz Hardt and Benjamin Recht The book introduces the reader to the essentials of machine learning while offering perspectives on its history and social implications. Beginning with the foundations of decision-making, the authors explain representation, optimisation, and generalisation as the constituents of supervised learning. They then go on to discuss causality, the practice of causal inference, sequential decision-making, and reinforcement learning. The Equality Machine: Harnessing Digital Technology for a Brighter, More Inclusive Future Author: Orly Lobel In the book, the author argues that while we cannot stop technological development, we can direct its course according to our fundamental values. With provocative insights in every chapter, Lobel shows that digital technology frequently has a comparative advantage over humans in addressing the world’s thorniest problems—from poverty to health issues. Micro Prediction: Building an Open AI Network Author: Peter Cotton In the book, the author talks about the ways in which a web-scale network of micromanagers challenge the AI revolution and fight against costly quantitative business optimisation. He further explains how the AI revolution is leaving behind small businesses and organisations that cannot afford in-house teams of data scientists. Machine Learning and Optimization Models for Optimization in Cloud Authors: Punit Gupta, Mayank K Goyal, Sudeshna Chakraborty, Ahmed A Elngar With an increase in services migrating over cloud providers, the load over the cloud increases—resulting in faults and various security failures. The book talks about how the cloud system uses a prediction algorithm to manage the system’s performance and plan for upcoming requests.
“Different people have different levels of understanding of the many things around them, and science is about trying to deepen our understanding of those many things.” — Martin Ford, Author of Architects of Intelligence
“Different people have different levels of understanding of the many things around them, and science is about trying to deepen our understanding of those many things.” — Martin Ford, Author of Architects of Intelligence
["AI Books"]
["data science", "Go", "artificial intelligence", "machine learning", "AI Books", "AI", "ML", "Git", "Aim", "analytics", "R"]
["AI", "artificial intelligence", "machine learning", "ML", "data science", "analytics", "Aim", "R", "Go", "Git"]
Tasmia is a tech journalist at AIM, looking to bring a fresh perspective to emerging technologies and trends in data science, analytics, and artificial intelligence.