検索キーワード： 機械理論・人工知能

"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.

AI is coming fast and will affect every part of a business, including the role of the leader. And up until now, leaders have largely ceded their role in the transformation-pushing determination of strategy out to tech teams and leaving investment decisions with groups that don't have a full view of the organization. Just when responsible leadership is more imperative than ever, leaders are not stepping up to understand and execute in the new world of human-machine collaboration. A generation of AI transformation failures awaits if leaders don't connect their use of AI to their strategies.

Machine Learning for Absolute Beginners Third Edition has been written and designed for absolute beginners. This means plain-English explanations and no coding experience required.

In this book, Amy Webb reveals the pervasive, invisible ways in which the foundations of AI - the people working on the system, their motivations, the technology itself - is broken. Within our lifetimes, AI will, by design, begin to behave unpredictably, thinking and acting in ways which defy human logic. The big nine corporations may be inadvertently building and enabling vast arrays of intelligent systems that don't share our motivations, desires, or hopes for the future of humanity.

We are about to cross a critical threshold in the history of our species. Everything is about to change. Soon we will live surrounded by AIs. They will carry out complex tasks-operating businesses, producing unlimited digital content, running core government services and maintaining infrastructure. This will be a world of DNA printers and quantum computers, engineered pathogens and autonomous weapons, robot assistants and abundant energy. It represents nothing less than a step change in human capability. We are not prepared.

In today's technological society, with an unprecedented amount of information at our fingertips, learning plays a more central role than ever. In How We Learn, Stanislas Dehaene decodes its biological mechanisms, delving into the neuronal, synaptic and molecular processes taking place in the brain. He explains why youth is such a sensitive period, during which brain plasticity is maximal, but also assures us that our abilities continue into adulthood, and that we can enhance our learning and memory at any age.

What does it mean to be smart? To be human? What do we really want from life and the intelligence we have, or might create? With deep and exclusive reporting, across hundreds of interviews, New York Times Silicon Valley journalist Cade Metz brings you into the rooms where these questions are being answered. Where an extraordinarily powerful new artificial intelligence has been built into our biggest companies, our social discourse, and our daily lives, with few of us even noticing.

Under the aegis of machine learning in our data-driven machine age, computers are programming themselves and learning about - and solving - an extraordinary range of problems, from the mundane to the most daunting. Today it is machine learning programs that enable Amazon and Netflix to predict what users will like, Apple to power Siri's ability to understand voices, and Google to pilot cars.

The Age of Surveillance Capitalism is neither a hand-wringing narrative of danger and decline nor a digital fairy tale. Rather, it offers a deeply reasoned and evocative examination of the contests over the next chapter of capitalism that will decide the meaning of information civilization in the 21st century. The stark issue at hand is whether we will be the masters of information and machines or its slaves.

How will AI evolve and what major innovations are on the horizon? What will its impact be on the job market, economy, and society? What is the path toward human-level machine intelligence? What should we be concerned about as artificial intelligence advances? Architects of Intelligence contains a series of in-depth, one-to-one interviews where New York Times best-selling author Martin Ford uncovers the truth behind these questions from some of the brightest minds in the artificial intelligence community.

We are living through a revolution in machine learning-powered AI that shows no signs of slowing down. This technology is based on relatively simple mathematical ideas, some of which go back centuries, including linear algebra and calculus, the stuff of seventeenth- and eighteenth-century mathematics. It took the birth and advancement of computer science and the kindling of 1990s computer chips designed for video games to ignite the explosion of AI that we see today. In this enlightening book, Anil Ananthaswamy explains the fundamental math behind machine learning.

Easy-to-use AI tools, contactless commerce, crypto for business, the mature metaverse—new technologies like these are reshaping organizations at the hybrid office, on factory floors, and in the C-suite. What should you and your company be doing now to take advantage of the new opportunities these technologies are creating—and avoid falling victim to disruption? The Year in Tech 2023: The Insights You Need from Harvard Business Review will help you understand what the latest and most important tech innovations mean for your organization.

Silicon Valley has sold us the illusion that artificial intelligence is a frictionless technology that will bring wealth and prosperity to humanity. But hidden beneath this smooth surface lies the grim reality of a precarious global workforce of millions laboring under often appalling conditions to make A.I. possible. This book presents an urgent, riveting investigation of the intricate network that maintains this exploitative system, revealing the untold truth of A.I.

This study guide follows the syllabus for the Microsoft AI Foundations exam (AI-900). More importantly, this audiobook will help you gain the foundational knowledge needed to become an AI practitioner. You do not need a mathematical or programming background to understand the concepts in this audiobook or to pass the AI-900 exam. This study guide does not contain any material that compromises the integrity of any Microsoft exam. All materials, including practice questions, were developed using the syllabus for the exam and thorough research of published articles.

Psychologist Gloria Mark began researching how technology affects human attention when offices were first getting computers. Over the last 30 years, she has tracked changes in our attention spans and stress levels, and in the fundamental way our brains process information. Now in Attention Span, Dr Mark shows how much of what we think we know about attention is wrong. She explores the current crisis of focus and productivity that is so deeply entwined with rising rates of anxiety and depression, and investigates what we might be able to do about it.

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.

Every day, we use our computers to perform remarkable feats. A simple web search picks out a handful of relevant needles from the world's biggest haystack. Uploading a photo to Facebook transmits millions of pieces of information over numerous error-prone network links, yet somehow a perfect copy of the photo arrives intact. Without even knowing it, we use public-key cryptography to transmit secret information like credit card numbers, and we use digital signatures to verify the identity of the websites we visit.

Machine learning (ML) is changing virtually every aspect of our lives. Today ML algorithms accomplish tasks that until recently only expert humans could perform. As it relates to finance, this is the most exciting time to adopt a disruptive technology that will transform how everyone invests for generations. The book addresses real-life problems faced by practitioners on a daily basis and explains scientifically sound solutions using math, supported by code and examples. 

In their bestselling first book, Prediction Machines, eminent economists Ajay Agrawal, Joshua Gans, and Avi Goldfarb explained the simple yet game-changing economics of AI. Now, in Power and Prediction, they go deeper, examining the most basic unit of analysis: the decision. The authors explain that the two key decision-making ingredients are prediction and judgment, and we perform both together in our minds, often without realizing it.

A new field of collective intelligence has emerged in the last few years, prompted by a wave of digital technologies that make it possible for organizations and societies to think at large scale. This "bigger mind" - human and machine capabilities working together - has the potential to solve the great challenges of our time. So why do smart technologies not automatically lead to smart results?