The field of artificial intelligence is currently the most sought-after. As a result, many scientists and engineers are interested in working in artificial intelligence, data science, and analytics.

The most significant way to study is from the best resources, so here is a list of interesting AI books published in 2022.

Patterns, Predictions, and Actions: Foundations of Machine Learning

The book teaches the reader the fundamentals of machine learning while providing historical and social context. Beginning with the fundamentals of decision-making, the authors describe representation, optimization, and generalization as the components of supervised learning.

They then address causality, causal inference practice, sequential decision-making, and reinforcement learning.

Reinforcement Learning: An Introduction by Andrew Barto and Richard Sutton

One of the most active study fields in artificial intelligence, reinforcement learning, is a computational approach to learning. 

In Reinforcement Learning, the authors explain the fundamental concepts and techniques of reinforcement learning clearly and concisely. Their discussion covers the conceptual roots of the field's history to its most recent advancements and uses. The only required mathematical background is a fundamental understanding of probability.

Designing Human-Centric AI Experiences: Applied UX Design for Artificial Intelligence (Design Thinking) (1st Edition)

Due to the growing integration of AL/ML in more and more software products, user experience (UX) design methodologies have undergone a fundamental change. This book explores the role that UX design plays in enabling user participation with AI and making technologies inclusive.

It also discusses best practices for managers, designers, and product developers and explains how people without technical backgrounds can work well with AI/ML teams.

Deep Learning (Adaptive Computation and Machine Learning series) by Ian Goodfellow

The text covers relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning and provides a mathematical and conceptual foundation. It describes deep learning techniques employed by industry practitioners, such as:

• deep feedforward networks, 
• regularisation, 
• optimization algorithms, 
• convolutional networks, 
• sequence modelling, and 
• practical methodology, 

In addition, it surveys AI applications and video games. Finally, the book concludes with research perspectives on theoretical issues like:

• linear factor models, 
• autoencoders, 
• representation learning, 
• structured probabilistic models, 
• Monte Carlo methods, 
• the partition function, 
• approximation inference, and 
• deep generative models.

Artificial Intelligence: What Everyone Needs to Know by Jerry Kaplan

The rise of systems capable of independent reasoning and action raises severe problems concerning whose interests they are allowed to serve and what restrictions our society should impose on their production and usage. On the steps of our courthouses, complex ethical issues that have perplexed philosophers for years will inevitably surface. Can a machine be made to answer for its deeds? Are intelligent systems simply property, or should they have their rights and obligations? When a self-driving vehicle kills a pedestrian, who should be held accountable? Can you force your robot to testify against you or hold your position in line? Is it still you if it turns out to be able to upload your mind into a machine? The solutions might surprise you.

The Sentient Machine: The Coming Age of Artificial Intelligence by Amir Husain

Husain "prepares us for a better future in The Sentient Machine, not with hysteria about good and wrong, but with serious arguments about risk and promise" (Dr Greg Hyslop, Chief Technology Officer, The Boeing Company). He discusses the big existential questions surrounding the development of AI, such as: 

• Why are we important? 
• What kind of world can we build here? 
• How did humans become so smart? 
• What does progress mean to us? 
• And how could it be that we don't advance?

Husain uses various cultural and historical allusions to explain his views and simplifies complicated computer science and AI principles into straightforward language. In the end, Husain questions several societal standards and challenges our presumptions about "the good life."