Books like Competing in the Age of AI by Marco Iansiti

Daniel H. Pink explores how intrinsic motivation—not external rewards—drives better performance, creativity, and well‑being, drawing on three decades of research and practical examples for individuals and organizations.

Roger Penrose argues that human thought is rooted in deep, non-computable aspects of physics, suggesting true intelligence cannot be fully replicated by any machine.

Deep Learning is a comprehensive textbook that equips students and practitioners with the foundational knowledge and practical skills needed to enter the fields of machine learning and deep learning.

James F. Moore argues that modern enterprises succeed not by competing on product alone but by mastering total system leadership, integrating technologies, and building resilient communities within dynamic business networks.

Author Klaus Schwab explores how the fourth industrial revolution—a convergence of AI, biotechnology, nanotechnology and digital networks—will reshape society, economy and identity, urging a future where technology serves humanity within ethical bounds.

From Robocop to the Terminator to Eve 8, no image better captures our deepest fears about technology than the cyborg, the person who is both flesh and metal, brain and electronics. But philosopher and cognitive scientist Andy Clark sees it differently. Cyborgs, he writes, are not something tobe feared--we already are cyborgs. In Natural-Born Cyborgs, Clark argues that what makes humans so different from other species is our capacity to fully incorporate tools and supporting cultural practices into our existence. Technology as simple as writing on a sketchpad, as familiar as Google or a cellular phone, and aspotentially revolutionary as mind-extending neural implants--all exploit our brains' astonishingly plastic nature. Our minds are primed to seek out and incorporate non-biological resources, so that we actually think and feel through our best technologies...

The idea of artificial intelligence--job-killing robots, self-driving cars, and self-managing organizations--captures the imagination, evoking a combination of wonder and dread for those of us who will have to deal with the consequences. But what if it's not quite so complicated? The real job of artificial intelligence, argue these three eminent economists, is to lower the cost of prediction. And once you start talking about costs, you can use some well-established economics to cut through the hype. The constant challenge for all managers is to make decisions under uncertainty. And AI contributes by making knowing what's coming in the future cheaper and more certain. But decision making has another component: judgment, which is firmly in the realm of humans, not machines. Making prediction cheaper means that we can make more predictions more accurately and assess them with our better (human) judgment. Once managers can separate tasks into components of prediction and judgment, we can begin to understand how to optimize the interface between humans and machines. More than just an account of AI's powerful capabilities, Prediction Machines shows managers how they can most effectively leverage AI, disrupting business as usual only where required, and provides businesses with a toolkit to navigate the coming wave of challenges and opportunities.--