Grok is a new AI chatbot created by Elon Musk's xAI, which aims to offer a unique AI experience by providing witty and logical answers to user queries. The name Grok is derived from Robert A. Heinlein's 1961 science fiction novel "Stranger in a Strange Land," where it means "to drink" but is generally understood as having deep empathy or intuition with something. The AI is designed to have a bit of wit, a rebellious streak, and a willingness to tackle controversial or "spicy" questions that other AI systems might avoid. Grok is currently available to select X platform users in the US as part of the early access program. The company claims that Grok has surpassed "all other models in its compute class," including ChatGPT-3.5 and Inflection-1, but it is still outperformed by bots with larger data troves. The goal of xAI is to build artificial intelligence that advances humanity's understanding of the universe. With a team of former employees from OpenAI, DeepMind, and other prominent organizations, xAI is actively hiring for various roles as it continues to develop its technology.

"AI Prompting by ChatGPT & The Art of AI by Grok AI" delves into the rapidly evolving field of AI communication, where artificial intelligence and communication intersect to shape how humans interact with AI systems. "AI Prompting" by ChatGPT, a comprehensive guide, demystifies AI, exploring its capabilities and limitations, and covers fundamental principles, advanced techniques, and ethical considerations in AI communication. Drawing parallels between AI communication and language acquisition, the book progresses to advanced prompting techniques. "The Art of AI" by Grok AI offers an in-depth look at effective AI communication, providing readers with foundational principles for framing questions and prompts clearly, progressing to advanced techniques for problem-solving. The ethical dimension is a focal point, guiding users on responsible interactions with AI, highlighting sensitive topics, and emphasizing the evolving relationship between humans and AI. Practical applications come to life through case studies and success stories, showcasing AI's value across diverse fields. The books also address technical limitations, helping readers navigate potential misinterpretations in AI responses. More than manuals, "AI Prompting by ChatGPT & The Art of AI by Grok AI" are thoughtful explorations designed for a broad audience, from enthusiasts to professionals, educators, and the general public. With interactive exercises, glossaries, and abundant resources, they aim to equip readers with the skills needed for responsible and effective AI communication, preparing them for the evolving role of AI in society.

This book of the bestselling and widely acclaimed Python Machine Learning series is a comprehensive guide to machine and deep learning using PyTorch s simple to code framework. Purchase of the print or Kindle book includes a free eBook in PDF format. Key Features Learn applied machine learning with a solid foundation in theory Clear, intuitive explanations take you deep into the theory and practice of Python machine learning Fully updated and expanded to cover PyTorch, transformers, XGBoost, graph neural networks, and best practices Book DescriptionMachine Learning with PyTorch and Scikit-Learn is a comprehensive guide to machine learning and deep learning with PyTorch. It acts as both a step-by-step tutorial and a reference you'll keep coming back to as you build your machine learning systems. Packed with clear explanations, visualizations, and examples, the book covers all the essential machine learning techniques in depth. While some books teach you only to follow instructions, with this machine learning book, we teach the principles allowing you to build models and applications for yourself. Why PyTorch? PyTorch is the Pythonic way to learn machine learning, making it easier to learn and simpler to code with. This book explains the essential parts of PyTorch and how to create models using popular libraries, such as PyTorch Lightning and PyTorch Geometric. You will also learn about generative adversarial networks (GANs) for generating new data and training intelligent agents with reinforcement learning. Finally, this new edition is expanded to cover the latest trends in deep learning, including graph neural networks and large-scale transformers used for natural language processing (NLP). This PyTorch book is your companion to machine learning with Python, whether you're a Python developer new to machine learning or want to deepen your knowledge of the latest developments.What you will learn Explore frameworks, models, and techniques for machines to learn from data Use scikit-learn for machine learning and PyTorch for deep learning Train machine learning classifiers on images, text, and more Build and train neural networks, transformers, and boosting algorithms Discover best practices for evaluating and tuning models Predict continuous target outcomes using regression analysis Dig deeper into textual and social media data using sentiment analysis Who this book is for If you have a good grasp of Python basics and want to start learning about machine learning and deep learning, then this is the book for you. This is an essential resource written for developers and data scientists who want to create practical machine learning and deep learning applications using scikit-learn and PyTorch. Before you get started with this book, you’ll need a good understanding of calculus, as well as linear algebra.

Winner of the Hugo, Nebula, and Arthur C. Clarke Awards: This record-breaking novel follows a warship trapped in a human body on a quest for revenge. A must read for fans of Ursula K. Le Guin and James S. A. Corey. "There are few who write science fiction like Ann Leckie can. There are few who ever could." -- John Scalzi On a remote, icy planet, the soldier known as Breq is drawing closer to completing her quest. Once, she was the Justice of Toren -- a colossal starship with an artificial intelligence linking thousands of soldiers in the service of the Radch, the empire that conquered the galaxy. Now, an act of treachery has ripped it all away, leaving her with one fragile human body, unanswered questions, and a burning desire for vengeance.

Who benefits from smart technology? Whose interests are served when we trade our personal data for convenience and connectivity? Smart technology is everywhere: smart umbrellas that light up when rain is in the forecast; smart cars that relieve drivers of the drudgery of driving; smart toothbrushes that send your dental hygiene details to the cloud. Nothing is safe from smartification. In Too Smart, Jathan Sadowski looks at the proliferation of smart stuff in our lives and asks whether the tradeoff—exchanging our personal data for convenience and connectivity—is worth it. Who benefits from smart technology? Sadowski explains how data, once the purview of researchers and policy wonks, has become a form of capital. Smart technology, he argues, is driven by the dual imperatives of digital capitalism: extracting data from, and expanding control over, everything and everybody. He looks at three domains colonized by smart technologies' collection and control systems: the smart self, the smart home, and the smart city. The smart self involves more than self-tracking of steps walked and calories burned; it raises questions about what others do with our data and how they direct our behavior—whether or not we want them to. The smart home collects data about our habits that offer business a window into our domestic spaces. And the smart city, where these systems have space to grow, offers military-grade surveillance capabilities to local authorities. Technology gets smart from our data. We may enjoy the conveniences we get in return (the refrigerator says we're out of milk!), but, Sadowski argues, smart technology advances the interests of corporate technocratic power—and will continue to do so unless we demand oversight and ownership of our data.

Summary Grokking Deep Learning teaches you to build deep learning neural networks from scratch! In his engaging style, seasoned deep learning expert Andrew Trask shows you the science under the hood, so you grok for yourself every detail of training neural networks. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the Technology Deep learning, a branch of artificial intelligence, teaches computers to learn by using neural networks, technology inspired by the human brain. Online text translation, self-driving cars, personalized product recommendations, and virtual voice assistants are just a few of the exciting modern advancements possible thanks to deep learning. About the Book Grokking Deep Learning teaches you to build deep learning neural networks from scratch! In his engaging style, seasoned deep learning expert Andrew Trask shows you the science under the hood, so you grok for yourself every detail of training neural networks. Using only Python and its math-supporting library, NumPy, you'll train your own neural networks to see and understand images, translate text into different languages, and even write like Shakespeare! When you're done, you'll be fully prepared to move on to mastering deep learning frameworks. What's inside The science behind deep learning Building and training your own neural networks Privacy concepts, including federated learning Tips for continuing your pursuit of deep learning About the Reader For readers with high school-level math and intermediate programming skills. About the Author Andrew Trask is a PhD student at Oxford University and a research scientist at DeepMind. Previously, Andrew was a researcher and analytics product manager at Digital Reasoning, where he trained the world's largest artificial neural network and helped guide the analytics roadmap for the Synthesys cognitive computing platform. Table of Contents Introducing deep learning: why you should learn it Fundamental concepts: how do machines learn? Introduction to neural prediction: forward propagation Introduction to neural learning: gradient descent Learning multiple weights at a time: generalizing gradient descent Building your first deep neural network: introduction to backpropagation How to picture neural networks: in your head and on paper Learning signal and ignoring noise:introduction to regularization and batching Modeling probabilities and nonlinearities: activation functions Neural learning about edges and corners: intro to convolutional neural networks Neural networks that understand language: king - man + woman == ? Neural networks that write like Shakespeare: recurrent layers for variable-length data Introducing automatic optimization: let's build a deep learning framework Learning to write like Shakespeare: long short-term memory Deep learning on unseen data: introducing federated learning Where to go from here: a brief guide