This is a practical, non-technical guide for business leaders, entrepreneurs, and curious minds. This comprehensive guide on agentic AI cuts through the hype and offers a clear, jargon-free strategic roadmap to understanding and applying this technology. The authors bring a rare perspective, having implemented agentic AI across diverse organizations—from global enterprises to agile startups—witnessing both remarkable successes and sobering failures.

In The Optimist, the Wall Street Journal reporter Keach Hagey presents the most detailed account yet of Altman’s rise, from his precocious childhood in St. Louis to his first, failed startup experience; his time as legendary entrepreneur Paul Graham’s protégé and successor as head of Y Combinator, the start-up accelerator where Altman became the premier power broker in Silicon Valley; the founding of OpenAI and his recruitment of a small yet superior team; and his struggle to keep his company at the cutting edge while fending off determined rivals, including Elon Musk.

When AI expert and investigative journalist Karen Hao first began covering OpenAI in 2019, she thought they were the good guys. Founded as a nonprofit with safety enshrined as its core mission, the organization was meant, its leader Sam Altman told us, to act as a check against more purely mercantile, and potentially dangerous, forces. What could go wrong?

Is artificial intelligence going to take over the world? Have big tech scientists created an artificial lifeform that can think on its own? Is it going to put authors, artists, and others out of business? Are we about to enter an age where computers are better than humans at everything? The answer to these questions, linguist Emily M. Bender and sociologist Alex Hanna make clear, is “no,” “they wish,” “LOL,” and “definitely not.” This kind of thinking is a symptom of a phenomenon known as “AI hype.”

The difference between growing your business and going out of business is your ability to think strategically. The problem is, most leaders are stuck in the operational weeds, struggling to find the time to make better strategic decisions. This challenge is only heightened by the rise of AI. While you know AI is the future, current demands leave you with no time to explore its benefits. Meanwhile, your competition is gaining an edge by integrating AI. The time to act is now.

In June of 2024, thirty-one years after its founding in a Denny’s restaurant, Nvidia became the most valuable corporation on Earth. The Thinking Machine is the astonishing story of how a designer of video game equipment conquered the market for AI hardware, and in the process re-invented the computer. Essential to Nvidia’s meteoric success is its visionary CEO Jensen Huang, who more than a decade ago, on the basis of a few promising scientific results, bet his entire company on AI.

Confused about AI and worried about what it means for your future and the future of the world? You’re not alone. AI is everywhere—and few things are surrounded by so much hype, misinformation, and misunderstanding. In AI Snake Oil, computer scientists Arvind Narayanan and Sayash Kapoor cut through the confusion to give you an essential understanding of how AI works, why it often doesn’t, where it might be useful or harmful, and when you should suspect that companies are using AI hype to sell AI snake oil—products that don’t work, and probably never will.

The breakthrough concepts of Pattern Breakers come from the observations of Mike Maples Jr., a seasoned venture capitalist, who noticed something strange. Start-ups like Twitter, Twitch, and Lyft had achieved extraordinary success despite their disregard for “best practices.” In contrast, other startups deemed highly promising often failed, even when they seemed to do everything right. Seeking answers, Maples and coauthor Peter Ziebelman set out to discover the hidden forces that drive extraordinary start-up success.

Every big project—whether it’s creating new technology, developing a fresh line of sneakers, or opening a neighborhood café—requires a major investment to get off the ground. Unfortunately, most big projects flop with customers. Too many teams waste time, money, and opportunity because they follow the wrong strategy and lose sight of what really matters: Do people want what you’re making? Does your solution click with customers?

Nvidia is the darling of the age of artificial intelligence: the company’s chips are powering the generative-AI revolution, and demand is insatiable. For all the current interest and attention, however, Nvidia is not of our time. Founded more than three decades ago in a Denny’s in East San Jose, for years it was known primarily in the then-niche world of computer gaming. In fact, the company’s leather-jacketed leader, Jensen Huang, is the longest-serving CEO in an industry marked by near constant turmoil and failure.

We see notebooks everywhere we go. But where did these indispensable implements come from? How did they revolutionize our lives? And how can using a notebook help change the way you think? In this wide-ranging history, Roland Allen reveals how the notebook became our most dependable and versatile tool for creative thinking.

In this accessible, up-to-date, and authoritative examination of the world’s most radical technology, neuroscientist and AI researcher Christopher Summerfield explores what it really takes to build a brain from scratch. We have entered a world in which disarmingly human-like chatbots, such as ChatGPT, Claude and Bard, appear to be able to talk and reason like us—and are beginning to transform everything we do. But can AI ‘think’, 'know' and ‘understand’? What are its values? Whose biases is it perpetuating? Can it lie and if so, could we tell? Does their arrival threaten our very existence?

Great minds have long sought to understand the relationship between music and mathematics. Both involve patterns, structures, and relationships. Both generate ideas of great beauty and elegance. Music is a fertile testing ground for mathematical principles, while mathematics explains the sounds instruments make and how composers put those sounds together. Understanding the connections between music and mathematics helps you appreciate both, even if you have no special ability in either field....

Large language models (LLMs) and diffusion models such as ChatGPT and Stable Diffusion have unprecedented potential. Because they have been trained on all the public text and images on the internet, they can make useful contributions to a wide variety of tasks. And with the barrier to entry greatly reduced today, practically any developer can harness LLMs and diffusion models to tackle problems previously unsuitable for automation. With this book, you'll gain a solid foundation in generative AI, including how to apply these models in practice.

In this updated and expanded edition of Human + Machine—including a new chapter on gen AI—Daugherty and Wilson show that the essence of the AI paradigm shift is the transformation of all business processes within an organization, whether related to breakthrough innovation, everyday customer service, or personal productivity habits. As humans and smart machines collaborate ever more closely, work processes become more fluid and adaptive, enabling companies to change them on the fly—or completely reimagine them.

This book predicts the decline of today's professions and describes the people and systems that will replace them. In an Internet society, according to Richard Susskind and Daniel Susskind, we will neither need nor want doctors, teachers, accountants, architects, the clergy, consultants, lawyers, and many others to work as they did in the 20th century.

Is artificial intelligence going to take over the world? Have big tech scientists created an artificial lifeform that can think on its own? Is it going to put authors, artists, and others out of business? Are we about to enter an age where computers are better than humans at everything? The answer to these questions, linguist Emily M. Bender and sociologist Alex Hanna make clear, is “no,” “they wish,” “LOL,” and “definitely not.” This kind of thinking is a symptom of a phenomenon known as “AI hype.”

Written by seasoned tech executive Sandy Carter, AI First, Human Always is your guidebook to the seven hottest trends in AI. This book will recalibrate your approach to the exponential curve of emerging AI solutions for business. It will help you transform today's unstoppable currents of change into tailwinds that propel your organization to great heights.

When AI expert and investigative journalist Karen Hao first began covering OpenAI in 2019, she thought they were the good guys. Founded as a nonprofit with safety enshrined as its core mission, the organization was meant, its leader Sam Altman told us, to act as a check against more purely mercantile, and potentially dangerous, forces. What could go wrong?

This is a practical, non-technical guide for business leaders, entrepreneurs, and curious minds. This comprehensive guide on agentic AI cuts through the hype and offers a clear, jargon-free strategic roadmap to understanding and applying this technology. The authors bring a rare perspective, having implemented agentic AI across diverse organizations—from global enterprises to agile startups—witnessing both remarkable successes and sobering failures.

From mechanical looms to the combustion engine to the first computers, new technologies have always provoked panic about workers being replaced by machines. For centuries, such fears have been misplaced, and many economists maintain that they remain so today. But as Daniel Susskind demonstrates, this time really is different. Breakthroughs in artificial intelligence mean that all kinds of jobs are increasingly at risk.

The difference between growing your business and going out of business is your ability to think strategically. The problem is, most leaders are stuck in the operational weeds, struggling to find the time to make better strategic decisions. This challenge is only heightened by the rise of AI. While you know AI is the future, current demands leave you with no time to explore its benefits. Meanwhile, your competition is gaining an edge by integrating AI. The time to act is now.

Author Brendan Burns demonstrates how you can adapt existing software design patterns for designing and building reliable distributed applications. Systems engineers and application developers will learn how these long-established patterns provide a common language and framework for dramatically increasing the quality of your system. This fully updated second edition includes new chapters on AI inference, AI training, and building robust systems for the real world.

Welcome to AI Riches: Make Money with ChatGPT – How I Made $10K a Month Talking to a Robot (And How You Can Too) — your hilariously honest, eye-opening, and ridiculously useful guide to turning conversations with AI into cold, hard cash. This isn't one of those dry, dusty tech books written in a language that only PhDs and space engineers understand. Nope. This is the blueprint for normal humans—with zero tech skills—who want to build side hustles, launch digital empires, or just finally afford guac at Chipotle without flinching.

This is the first reference book on intelligent automation (IA). Also called hyperautomation, it is one of the most recent trends in the field of artificial intelligence. IA is a cutting-edge combination of methods and technologies involving people, organizations, machine learning, low-code platforms, robotic process automation (RPA), and more. This book is for everyone - whether you are an experienced practitioner, new to the topic, or simply interested in what the future holds for enterprises, work, life, and society as a whole.

"You look like a thing and I love you" is one of the best pickup lines ever...according to an artificial intelligence trained by scientist Janelle Shane, creator of the popular blog AI Weirdness. She creates silly AIs that learn how to name paint colors, create the best recipes, and even flirt (badly) with humans — all to understand the technology that governs so much of our daily lives.

Today's "machine-learning" systems, trained by data, are so effective that we've invited them to see and hear for us - and to make decisions on our behalf. But alarm bells are ringing. Systems cull résumés until, years later, we discover that they have inherent gender biases. Algorithms decide bail and parole - and appear to assess black and white defendants differently. We can no longer assume that our mortgage application, or even our medical tests, will be seen by human eyes. And autonomous vehicles on our streets can injure or kill.

An award-winning mathematician shows how we prove what’s true, and what to do when we can’t.

We are used to thinking of computers as being a step up from calculators - very good at storing information, and maybe even at playing a logical game like chess. But up to now they haven't been able to think in ways that are intuitive, or respond to questions as a human might. All that has changed, dramatically, in the past few years. Our search engines are becoming answer engines. Artificial intelligence is already revolutionising sectors from education to healthcare to the creative arts. But how does an AI understand sentiment or context?

Within the next five years, Jeremy Kahn predicts, AI will disrupt almost every industry and enterprise, with vastly increased efficiency and productivity. It will restructure the workforce, making AI copilots a must for every knowledge worker. It will compel us to reimagine how we make art, compose music, and write and publish books. The potential of generative AI to extend our skills, talents, and creativity as humans is undeniably exciting and promising. But while this new technology has a bright future, it also casts a dark and fearful shadow.