The most in-demand career path right now is in the field of artificial intelligence (AI). As a direct consequence of this trend, a significant number of scientists and engineers are considering careers in areas such as artificial intelligence, data science, and analytics.
Studying from the most credible sources is the most effective method, therefore in that spirit, the following is a selection of fascinating AI books that were published in 2022.
The fundamentals of machine learning can be broken down into three categories: patterns, predictions, and actions.
The reader is taught the principles of machine learning while also being provided with historical and social background across the course of the book. The authors begin by discussing the principles of decision-making, and then go on to outline the components of supervised learning, which are representation, optimization, and generalisation.
After that, they discuss the concept of causality, as well as the practise of causal inference, sequential decision-making, and reinforcement learning.
The book “An Introduction to Reinforcement Learning” was written by Andrew Barto and Richard Sutton.
The computational method of learning known as reinforcement learning is now one of the most active research subjects in the field of artificial intelligence.
In their book titled “Reinforcement Learning,” the authors provide a clear and succinct explanation of the core concepts and techniques of reinforcement learning. Their conversation ranges from the theoretical underpinnings of the field’s history to its most recent technological developments and practical applications. One and only a fundamental comprehension of probability is required to have a background in mathematics.
The application of user experience design principles to the development of artificial intelligence (design thinking) is referred to as “designing human-centric AI experiences” (1st Edition)
User experience (UX) design techniques have been subjected to a fundamental shift as a direct result of the rising incorporation of AL and ML into an increasing number of software products. This book investigates the function that user experience design (UX design) plays in enabling user interaction with artificial intelligence (AI) and in ensuring that technologies are inclusive.
Additionally, it illustrates how those who lack a technical expertise may successfully collaborate with AI and machine learning teams and provides best practises for managers, designers, and product developers.
Deep Learning is a book written by Ian Goodfellow and is part of the Adaptive Computation and Machine Learning series.
The text provides a mathematical and conceptual foundation, and it covers key concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It discusses the methods of deep learning that are used by professionals in many industries, such as:
convolutional networks, deep feedforward networks, regularisation, algorithms for optimization, sequence modelling, and practical approach,
In addition to that, it analyses other uses of AI as well as video games. Last but not least, the book comes to a close with some research perspectives on theoretical concerns such as:
Models with linear factors, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximation inference, and deep generative models are some examples of these types of models.
What Everyone Needs to Know About Artificial Intelligence is a book written by Jerry Kaplan.
The development of systems that are capable of independent reasoning and action has given rise to serious questions regarding whose interests these systems are permitted to serve and what limitations our society ought to place on the production and utilisation of these systems. Complex ethical problems, the likes of which have baffled philosophers for years, will invariably rear their heads on the steps of our nation’s courthouses. Is it possible for a machine to be held accountable for the things it does? Should intelligent systems just be treated as property, or should they have rights and responsibilities of their own? Who is to blame when a pedestrian is killed by a vehicle that is operating on its own discretion? Is it possible for you to coerce your robot into giving evidence against you or to maintain your position in line? If it is possible to upload your mind into a machine, does it mean you will still be the same person? It’s possible that the answers will shock you.
Written by Amir Husain, “The Coming Age of Artificial Intelligence” is titled “The Sentient Machine.”
“The Sentient Machine” by Husain “prepares us for a better future, not with hysteria about good and bad, but with serious arguments about risk and potential” (Dr Greg Hyslop, Chief Technology Officer, The Boeing Company). He addresses some of the most fundamental existential concerns raised by the advancement of AI, including the following:
Why should anyone care about us?
What kind of a world are we capable of creating here?
How did humans evolve to become so intelligent?
What exactly do we understand by the term “progress”?
And what might possibly be the cause of our lack of progress?
In order to convey his points of view, Husain makes a number of allusions to different aspects of culture and history, and he also simplifies complex ideas related to computer science and artificial intelligence. At the end of the book, Husain challenges numerous of our preconceived notions about “the good life” and criticises a number of society norms.
