検索キーワード： 機械理論・人工知能

Artificial intelligence surrounds us. More and more of the systems and services you interact with every day are based on AI technology. Most AI is narrowly specific; that is, it can only do a single thing, in a single context. Human intelligence is different. We can solve a variety of tasks, including those we have not seen before. In Artificial General Intelligence, Julian Togelius explores technical approaches to developing more general artificial intelligence and asks what general AI would mean for human civilization.

Artificial intelligence does the seemingly impossible - driving cars, trading stocks, and teaching children. But facing the sea change that AI will bring can be paralyzing. How should companies set strategies, governments design policies, and people plan their lives for a world so different from what we know? In Prediction Machines, three eminent economists recast the rise of AI as a drop in the cost of prediction. With this single, masterful stroke, they lift the curtain on the AI-is-magic hype and show how basic tools from economics provide clarity about the AI revolution and a basis for action by CEOs, managers, policy makers, investors, and entrepreneurs.

In this absorbing and frequently contentious book, Roger Penrose puts forward his view that there are some facets of human thinking that can never be emulated by a machine. The book's central concern is what philosophers call the "mind-body problem". Penrose examines what physics and mathematics can tell us about how the mind works, what they can't, and what we need to know to understand the physical processes of consciousness.

Veteran software developer and GitHub community hero Kurt Dowswell delivers an insightful and hands-on exploration of GitHub's powerful, new AI coding assistant, Copilot. In the book, you'll discover how to use the tool's capabilities to push the boundaries of what you thought was possible in programming. Even if you've used autocomplete tools-like VS Code's TabNine extension—before, you'll be floored by GitHub Copilot's potential to transform the way you code.

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.

How will artificial intelligence affect crime, war, justice, jobs, society, and our very sense of being human? The rise of AI has the potential to transform our future more than any other technology - and there's nobody better qualified or situated to explore that future than Max Tegmark, an MIT professor who's helped mainstream research on how to keep AI beneficial.

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.

In Generative AI: Navigating the Course to the Artificial General Intelligence Future, Martin Musiol delivers an incisive and one-of-a-kind discussion of the current capabilities, future potential, and inner workings of generative artificial intelligence. In the book, you'll explore the short but eventful history of generative artificial intelligence, what it's achieved so far, and how it's likely to evolve in the future. You'll also get a peek at how emerging technologies are converging to create exciting new possibilities in the GenAI space.

今、何が起きているのか？これからどうなるのか？人工知能とそれを支えるクラウド技術などの進化を長年追い続けてきた著者による、今後10年の社会変革を理解するためのベーシック・レポート

Generative AI is rewriting the rulebook with its seemingly endless capabilities, from crafting intricate industrial designs, writing computer code, and producing mesmerizing synthetic voices to composing enchanting music and innovating genetic breakthroughs. In Generative AI in Practice, renowned futurist Bernard Marr offers listeners a deep dive into the captivating universe of GenAI. This comprehensive guide introduces you to the basics of this groundbreaking technology and outlines the profound impact that GenAI will have on business and society.

Unlock the potential of Generative AI with this Large Language Model production-ready playbook for seamless deployment, optimization, and scaling. This hands-on guide takes you beyond theory, offering expert strategies for integrating LLMs into real-world applications using retrieval-augmented generation (RAG), vector databases, PEFT, LoRA, and scalable inference architectures. Whether you're an ML engineer, data scientist, or MLOps practitioner, you’ll gain the technical know-how to operationalize LLMs efficiently, reduce compute costs, and ensure rock-solid reliability in production.

In this audiobook, you'll get a clear overview of artificial intelligence technology, the common misconceptions surrounding it, and a fascinating look at its applications in everything from self-driving cars and drones to its contributions in the medical field.

Since it was first published in 2005, Ray Kurzweil's The Singularity Is Near and its vision of the future have been influential in spawning a worldwide movement with millions of followers, hundreds of books, major films, and thousands of articles. During the succeeding decade, many of Kurzweil's predictions about technological advancements have been borne out, and their viability has become familiar to the public through such now commonplace concepts. In this entirely new book Ray Kurzweil brings a fresh perspective to advances in the singularity.

In Trustworthy AI, award-winning executive Beena Ammanath offers a practical approach for enterprise leaders to manage business risk in a world where AI is everywhere by understanding the qualities of trustworthy AI and the essential considerations for its ethical use within the organization and in the marketplace. The author draws from her extensive experience across different industries and sectors in data, analytics and AI, the latest research and case studies, and the pressing questions and concerns business leaders have about the ethics of AI.

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.

■因果を求めない人工知能の世界 先日、人工知能に関する事業をしている人と合う機会がありました。 僕の会社が展開している採用系のサービスでも、人工知能などを用いることで、 マッチング部分でもっと手間を省いた方が絶対にいいと思っている。会社側がどういった人を求めているのか、応募者がどういった会社を望んでいるのかっていうことは、履歴書にたくさん文章を書けば伝わる問題でもないから。 もちろん最後は人間が面談することが必要なんだけど、その手前で機械ができる ことの精度を高められれば、可能性が広がるんじゃないかな。 例えばビッグデータやディープラーニング。これらは、今のトレンドといっちゃ あトレンドです。大量のデータを処理することで、あらゆることの確率が表で表 せるようになる。」(本文より) 家入一真　「家入学級」はこちらから→ http://yakan-hiko.com/ieiri.html

For over three decades, Ray Kurzweil has been one of the most respected and provocative advocates of the role of technology in our future. In his classic The Age of Spiritual Machines, he argued that computers would soon rival the full range of human intelligence at its best. Now he examines the next step in this inexorable evolutionary process: The union of human and machine, in which the knowledge and skills embedded in our brains will be combined with the vastly greater capacity, speed, and knowledge-sharing ability of our creations.

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.

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.

Unlock the power of AI with the beginner’s handbook to AI prompt engineering: master ChatGPT, boost productivity, and turn your skills into profits! In today’s fast-paced digital age, mastering artificial intelligence (AI) isn’t a luxury—it’s a necessity. Whether you’re curious about AI, looking to maximize productivity, or eager to tap into income-generating opportunities, this book is your ultimate guide to mastering prompt engineering and transforming your life with tools like ChatGPT.