Real World AI Paperback – 16 March 2021

If you are a business leader wondering how to use AI at your company, I highly recommend reading this book before embarking into your first AI project or making your first Ai expert hire. The book isn't filled with jargon or daunting technical details. It explains in plain English what AI is good for or not and how to get started. It emphasizes leveraging your current roles in the business (product managers, designers, user researchers) to ensure the proper integration of AI in your workflows and only using AI if there's no simpler solution to the problem you're trying to address. The book also clearly outlines data handling and monitoring needs (ML is not a "set it and forget it" solution). I wish it would have been more in depth regarding all the responsible aspects of AI, especially as it is using this word in its title. So the reader will need to dig deeper to ensure they fully understand what "responsible AI" means and act accordingly when building up their AI practice.

Responsible AI, as the name aptly describes is a thought-provoking book about the importance of applying AI in a responsible way. This book takes the reader through many real life examples of how well meaning intent of people and companies failed to deliver results through AI. The author, Alyssa’s, willingness to be candid and vulnerable about her own 1st time experience where she created unintended bias shows that she deeply cares about the topic. It was eye opening to read how AI is all around us but as users of products and services you often don’t think of the technology that powers them. This book motivated me to learn more about AI (I did have to Google some of the technical terminology used in this book). My professional takeaway was that I don’t have to be a data scientist to be in the AI space, instead I can use my existing skills as a product leader to leverage this technology:- aligning a cross-functional team to a common business goal, outcome and measurement- continue with an Agile mindset of delivering, reviewing feedback and iterating. The iterating is especially important when it comes to training the data. Given that our world is not static, it’s imperative that the AI data be retrained to an evolving world- Garbage in, garbage out still holds true so focus on the quality and diversity of the training dataAs AI continues to get deeply ingrained in our day to day, it is our moral responsibility to understand and start conversation about developing this technology ethically and mindfully so we can leave a better world to future generations

This book is a good introduction to AI product management. Thankfully, it doesn't delve into technical aspects. If you are a non-technical person, this book is for you.

As someone tech savvy but very much at "entry level" when it comes to AI, this book made for an incredibly compelling read. Often books addressing technical subject matter rapidly alienate me as a reader as they are dense, overly complex and (occasionally) reek of self importance. This book is NOT that.Accessible, easy to read and intensely practical, 'Real World AI' provides a fantastic guide to the world of machine learning and how to leverage it for meaningful application, rather than using it as a shiny new thing to show off, without having any real substantial use in your business.Alyssa and Wilson bring their wealth of experience to this book in a generous, thoughtful and compelling way. I'd highly recommend this to anyone who wants to burst the bubble on AI as a buzzword and learn more about how it can impact our businesses and our world in a myriad of exciting new ways.

Full disclosure - I'm an AI novice. However, after reading Real World AI, it's abundantly clear that the responsible use of AI is a complex and delicate process. Despite that fact, the real-world stories (from many titans in the tech industry) that the authors expertly weave throughout this book are captivating, and help to present and illustrate the important concepts in a way that is both approachable and easily understood.A major compelling point that the authors drive home is that if the technology is not implemented correctly by people, the mistakes and biases in our past can very easily shape our future in unintended or unanticipated negative ways. With that being said, this guide is a must read for data scientists and business folks alike - to ensure that these mistakes can be avoided from the start, and to understand the impact that these critical decisions have on shaping the final outcome of your project or product.