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 main way to study is with the best sources, so here is a list of interesting AI books published in 2022.
Patterns, Predictions, and Actions: Basics of Machine Learning
The book teaches the reader the fundamentals of machine learning while providing historical and social context. Starting with the basic principles of decision making, the authors describe representation, optimization and generalization as the components of supervised learning.
They then cover causality, practice of causal inference, sequential decision making, and reinforcement learning.
Reinforcement Learning: An Introduction by Andrew Barto and Richard Sutton
One of the most active areas of study in artificial intelligence, reinforcement learning, is a computational approach to learning.
In Reinforcement Learning, the authors clearly and concisely explain the fundamental concepts and techniques of Reinforcement Learning. Their discussion covers the conceptual roots of the field’s history to its most recent advancements and applications. The only mathematical background required is a fundamental understanding of probability.
Designing Human-Centered AI Experiences: Applied UX Design for Artificial Intelligence (Design Thinking) (1st Edition)
With the increasing integration of AL/ML into more and more software products, user experience (UX) design methodologies have undergone a fundamental change. This book explores the role 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 non-technical people 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 and information theory, numerical computation and machine learning and provides a mathematical and conceptual foundation. It describes deep learning techniques used by industry professionals such as:
- deep feedforward networks,
- optimization algorithms,
- convolutional networks,
- sequence modeling, and
- practical methodology,
In addition, it researches AI applications and video games. Finally, the book concludes with research perspectives on theoretical issues such as:
- linear factor models,
- automatic encoders,
- learning representation,
- structured probabilistic models,
- monte carlo methods,
- the partition function,
- approximation inference, and
- deep generative models.
Artificial Intelligence: What Everyone Should Know by Jerry Kaplan
The emergence of systems that can reason and act independently raises serious issues about whose interests they should serve and what restrictions our society should place on their production and use. On the steps of our courthouses, complex ethical issues will inevitably surface that have baffled philosophers for years. Can a machine be made to answer for its actions? 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 keep your position in line? Is it still you when it turns out you can upload your mind into a machine? The solutions may surprise you.
The Sentient Machine: The Coming Age of Artificial Intelligence by Amir Husain
Husain “prepares us for a brighter future in The Sentient Machine, not with hysteria about right and wrong, but with earnest 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 people get so smart?
- What does progress mean to us?
- And why are we not moving forward?
Husain uses a variety of cultural and historical allusions to explain his views and simplifies complex computer science and AI principles into plain language. Ultimately, Husain questions several societal norms and challenges our presumptions about “the good life.”