Pearson [FTSE: PSON.L], the world’s leading learning company, today launched an AI Summer Reading List, a collection of titles selected to encourage the exploration of artificial intelligence. Pearson is a leader in AI publishing, helping students and professionals understand and apply generative AI to learn and advance their careers.  These titles, written by leading AI experts, have been selected to spark curiosity, generate forward-thinking and unlock new potential.
“AI is the most talked about technology that is revolutionizing how we live and interact with each other. To harness its power responsibly, leaders and learners must have the resources to understand its potential and associated challenges. In order to truly understand its far-reaching social impact, these titles will help people understand its strength and apply AI responsibly in their careers and our society.” said Andy Bird, CEO of Pearson.
Pearson’s 2023 Summer Reading list includes:
The AI Revolution in Medicine: GPT-4 and Beyond by Peter Lee, Carey Goldberg, and Isaac Kohane
Three insiders who’ve had months of early access to GPT-4 reveal its momentous potential — to improve diagnoses, summarize patient visits, streamline processes, accelerate research, and much more. There has never been technology like this. Whether you’re a physician, patient, healthcare leader, payer, policymaker, or investor, artificial intelligence will profoundly impact you — and it might make the difference between life or death.
Artificial Intelligence: A Modern Approach by Stuart Russell and Peter Norvig
This is a guide to the theory and practice of modern AI, written by renowned AI experts, Stuart Russell, Professor of Computer Science at UC Berkely and Peter Norvig, Director of Research at Google. It introduces major concepts using intuitive explanations and nontechnical language, before going into mathematical or algorithmic details. In-depth coverage of both basic and advanced topics provides you with a solid understanding of the frontiers of AI without compromising complexity and depth.
Deep Learning Illustrated: A Visual, Interactive Guide to Artificial Intelligence by Jon Krohn with Grant Beyleveld and Aglaé Bassens
Three world-class instructors and practitioners present a uniquely visual, intuitive, and accessible high-level introduction to the techniques and applications of deep learning. Packed with vibrant, full-color illustrations, it abstracts away much of the complexity of building deep learning models, making the field more fun to learn and accessible to a far wider audience.
Learning Deep Learning: Theory and Practice of Neural Networks, Computer Vision, Natural Language Processing, and Transformers Using TensorFlow by Magnus Ekman
This is a complete guide to Deep Learning. It illuminates both the core concepts and the hands-on programming techniques needed to succeed. Ekman demonstrates how to use the essential building blocks of deep neural networks to build advanced architectures, and how these concepts are used to build modern networks for computer vision and natural language processing (NLP), including Mask R-CNN, GPT, and BERT.
Foundations of Deep Reinforcement Learning: Theory and Practice in Python by Laura Graesser and Wah Loon Keng
This title is an introduction to deep Reinforcement Learning, uniquely combining both theory and implementation. It starts with intuition, then carefully explains the theory of deep RL algorithms, discusses implementations in its companion software library SLM Lab, and finishes with the practical details of getting deep RL to work.
