Margaret Mark & Carol Pearson
Matthew B. Crawford
Scale: The Universal Laws of Growth, Innovation, Sustainability, and the Pace of Life in Organisms, Cities, Economies, and Companies
What To Do When Machines Do Everything: How to Get Ahead in a World of AI, Algorithms, Bots, and Big Data
Malcolm Frank, Paul Roehrig and Ben Pring
As previous versions of these readings list would prove I am inclined for abstract (and at times philosophical) ideas around AI. This book very clearly brushes off the need to address thinking machines, robot takeover and the super intelligence. Instead it gives real, tangible and easy to cite stats and ideas surrounding automation, innovation and emerging business models. If you're struggling to share notes on AI with business people this book will likely untangle your points and help you to get your points across.
According to wikipedia Sensemaking is the process by which people give meaning to their collective experiences (Laura McNamara gives a more thoughtful definition). This book by Madsbjerg is a great compass for human first thinking in linear, high speed environments. It touches on data (thick, thin), human decision making, priority mechanisms (sprints et al) and leaves us with actionable frames to think differently. Highly recommended.
I often find myself in a room, being briefed on a client problem (either directly or from an agency). 9 times out of 10 the answer, symptom and cause to the problem is silo-ed innovation. This book by Gillian Tett is wonderful articulation of that grade of problems, with plenty of examples.
Prof Klaus Schwab
What to Think About Machines That Think: Today's Leading Thinkers on the Age of Machine Intelligence
John Brockman is an author, literally agent and the founder of Edge FoundationThrough Edge's Annual Question Edge manages to bring together some of the brightest minds in the worlds to answer some of the most complex questions we're struggling with. This response to the 2015 question is a well balanced, and thought–provoking. It will leave you with as many answers as new questions. Recommended for anyone who does not buy ideas whole sale, and wants to look at the problem of AI from a range of different perspectives.
Joi Ito and Jeff Howe
Michael Lewis is an author and a journalist, mostly known for his book (and later movie) Moneyball. In this title Lewis covers the work of Daniel Kahneman and Amos Tversky. Friends and collaborators who together wrote the field of behavioral economics. Their respective essays, books and engagements heavily promoted the field of cognitive biases, decision making systems and risk analysis.I highly recommend this book to anyone curious about the role of cognition in the interaction with AI systems — or if you want to unpack the term human–based computation.
Excellent book by Todd Rose, of The Mind, Brain, & Education Program at Harvard. Rose's hypothesis is that average has moved from simply being a mathematical tool, to now acting as a social, economic and educational framework. Highly recommended for who questions a singular way of looking at things.
Alec Ross is a technology thinker, writer and an accomplished policy expert. Through his work in government he travelled around the world, often being asked how can other countries replicate Silicon Valley's success. All the time gaining perspectives and learning new models of incorporating technology at scale. Things like Estonia's e–residency to decentralized crypto–driven businesses and sharing economies. It is a great view into the future, but also very much anchored in the present, trying not to leave anyone behind. Recommended for anyone interested in a global perceptive on innovation, automation and forecasting.
John Markoff is a journalist and an author, mostly known for his work at the New York Times. In this title he explores the difference between the AI school of thought, and the IA (intelligence augmentation) one. He highlights interesting distinctions between the two approached, without being categorical.Recommended for anyone who is designing AI system and curious if it's an 'it' or a 'who'. Are we building tools, or intelligent beings.
Kurzweil is a successful entrepreneur, inventor and author. He is also the co–founder and chair of the Singularity university and currently director of engineering at Google. This book is the most succinct view you can find on building an artificial brain. It is in the core of the brain as a computer problem point of view, which is responsible for questionable predictions about machines taking over the human race. Nonetheless, this is an important read for a balanced point of view.
Alan Turing's contributions to science and computers are beyond doubt. His work on information technology, cryptography, and logic is still echoing today (especially around the Turing Machine and intelligent agents). Dyson does more than just tell the story of Turing. The book shares the eco–system Turing lived and worked within. It reinforces the value in polymathic thinking, and tells the story of other statisticians, biologists and logicians. Those insights are surprisingly valuable in today's context, as a lot of those problems dealt with in the early days of computing are still unsolved.
Jeff Hawkins is another successful entrepreneur. Jeff is the founder of Palm Pilot, and in recent years has moved his interest to neuroscience. He established the Redwood Center for Theoretical Neuroscience and has an interesting point of view on smart machines, sensory augmentation and the roles of machines in this collaboration. Hawkins' views will seem refreshing following Kurzweil's cumbersome predictions of chips in brains and downloading life onto a server.