List: Top Books for Learning About Generative Artificial Intelligence
For years, VCs have promised to upend books and the structures around their creation and consumption. Some came from within the publishing industry, but like their counterparts “disrupting” other sectors, including film and TV, many more did not. And for the most part, despite tech’s sometimes drastic (and often negative) effects on other industries, book- and reading-related startups failed to alter much at all. As those who shared the tweet observed, it seems like a lot of book industry “disruptors” just don’t like reading. One of the greatest applications of generative AI—and the one we are going to cover the most throughout this book—is its capability to produce new content in natural language.
- Generative AI in Teaching and Learning delves into the revolutionary field of generative artificial intelligence and its impact on education.
- According to Reisner, while some training text comes from sources like Wikipedia and online articles, the high-quality input needed to produce sophisticated AI responses often comes from books.
- PyTorch is the natural programming language for machine learning, making it easier to learn and to code with.
- The world took notice in August 2022 when Stability AI released their model, data, and code as open source, allowing anyone to use the technology and build new experiences.
The history of generative AI can be traced back to the early days of artificial intelligence research in the 1950s and 1960s, when computer scientists first began exploring the idea of using machines to generate new content. Early generative AI systems focused primarily on simple tasks such as pattern recognition and rule-based decision-making. In addition to these uses, ChatGPT can also be used to build chatbots for marketing and sales. For example, a chatbot could be used to provide information about a company’s products or services, or to assist with lead generation and qualification.
My profession is online marketing and development (10+ years experience), check my latest mobile app called Upcoming or my Chrome extensions for ChatGPT. If I read a book I always want to find the best part of it, every book has it’s Yakov Livshits unique value. GANs in Action guides you through building and training your own Generative Adversarial Networks. You’ll start by building simple generator and discriminator networks, which are the foundation of GAN architecture.
When it comes to artificial intelligence, we either hear of a paradise on earth or of our imminent extinction. It’s time we stand face-to-digital-face with the true powers and limitations of the algorithms that already automate important decisions in healthcare, transportation, crime, and commerce. Hello World is indispensable preparation for the moral quandaries of a world run by code, and with the unfailingly entertaining Hannah Fry as our guide, we’ll be discussing these issues long after the last page is turned.
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The concepts covered include subjects such as search algorithms, game theory, multi-agent systems, statistical Natural Language Processing, local search planning methods, etc. Bishop, the director of Microsoft Research AI4Science, details the growth of Bayesian methods in this textbook while also offering up an introduction to pattern recognition and machine Yakov Livshits learning. In this serious dive into deep reinforcement learning, Morales provides an overview of the approach, complete with illustrations, exercises, and real-world applications. Here scholar Baum proposes a computational explanation of thought and explores what computer scientists can learn from understanding the evolution of human intelligence.
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A prolific businessman and investor, and the founder of several large companies in Israel, the USA and the UAE, Yakov’s corporation comprises over 2,000 employees all over the world. He graduated from the University of Oxford in the UK and Technion in Israel, before moving on to study complex systems science at NECSI in the USA. Yakov has a Masters in Software Development.
Its meanings float with a fluidity and indeterminacy analogous to the networked world of contemporary culture. Another great milestone was achieved in 2017 when a new architecture, called Transformer, was introduced by Google researchers in the paper, – Attention Is All You Need, was introduced in a paper by Google researchers. In 2013, Kingma and Welling introduced a new model architecture in their paper Auto-Encoding Variational Bayes, called Variational Autoencoders (VAEs).
The debate surrounding the fair use of copyrighted material for training AI models is complex. While some argue that AI-generated content constitutes transformative works that enrich culture without harming the market for original works, others stress the need for authors to retain control over their creations. The blurred line between transformative works and unauthorised use complicates legal arguments, as AI companies maintain their proprietary stance, insisting on control over their models’ outputs and usage. The lawsuit filed by writers Sarah Silverman, Richard Kadrey, and Christopher Golden against Meta has brought to light the issue of AI companies using copyrighted books to train large language models. The book also covers important topics such as data preprocessing, model evaluation, and ethical considerations in AI.
In addition, researchers from Open AI recently released a paper showcasing an entirely new technique for generating 3d models. Larger improvements are probably pending the cessation of reliance on 2D generators to render 3D models. Witnessing how the quality of 2D image generators improved from rudimentary to astonishing over the past few years, the same thing happening to 3D model generation can happen very quickly.
In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the key ideas and algorithms of reinforcement learning. Their discussion ranges from the history of the field’s intellectual foundations to the most recent developments and applications. The only necessary mathematical background is familiarity with elementary concepts of probability.
Fundamental AI algorithms such as linear regression, clustering, dimensionality, and distance metrics are covered in depth. The algorithms are explained using numeric calculations, which the readers can perform themselves and through interesting examples and use cases. Greg Bensinger joined Reuters as a technology correspondent in 2022 focusing on the world’s largest technology companies. He was previously a member of The New York Times editorial board and a technology beat reporter for The Washington Post and The Wall Street Journal. He also worked for Bloomberg News writing about the auto and telecommunications industries.