#1 NEW YORK TIMES BESTSELLER - From the author of Sapiens comes the groundbreaking story of how information networks have made, and unmade, our world.

Striking original . . . A historian whose arguments operate on the scale of millennia has managed to capture the zeitgeist perfectly.--The Economist

This deeply important book comes at a critical time as we all think through the implications of AI and automated content production. . . . Masterful and provocative.--Mustafa Suleyman, author of The Coming Wave

For the last 100,000 years, we Sapiens have accumulated enormous power. But despite all our discoveries, inventions, and conquests, we now find ourselves in an existential crisis. The world is on the verge of ecological collapse. Misinformation abounds. And we are rushing headlong into the age of AI--a new information network that threatens to annihilate us. For all that we have accomplished, why are we so self-destructive?

Nexus looks through the long lens of human history to consider how the flow of information has shaped us, and our world. Taking us from the Stone Age, through the canonization of the Bible, early modern witch-hunts, Stalinism, Nazism, and the resurgence of populism today, Yuval Noah Harari asks us to consider the complex relationship between information and truth, bureaucracy and mythology, wisdom and power. He explores how different societies and political systems throughout history have wielded information to achieve their goals, for good and ill. And he addresses the urgent choices we face as non-human intelligence threatens our very existence.

Information is not the raw material of truth; neither is it a mere weapon. Nexus explores the hopeful middle ground between these extremes, and in doing so, rediscovers our shared humanity.

A rich, narrative explanation of the mathematics that has brought us machine learning and the ongoing explosion of artificial intelligence

Machine learning systems are making life-altering decisions for us: approving mortgage loans, determining whether a tumor is cancerous, or deciding if someone gets bail. They now influence developments and discoveries in chemistry, biology, and physics--the study of genomes, extrasolar planets, even the intricacies of quantum systems. And all this before large language models such as ChatGPT came on the scene.

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, while suggesting intriguing links between artificial and natural intelligence. Might the same math underpin them both?

As Ananthaswamy resonantly concludes, to make safe and effective use of artificial intelligence, we need to understand its profound capabilities and limitations, the clues to which lie in the math that makes machine learning possible.

El esperado nuevo libro de Yuval Noah Harari, uno de los pensadores más innovadores, interesantes y clarividentes de la actualidad, y autor de Sapiens, el fenómeno literario global que ha cautivado a millones de lectores.

En Nexus, Harari contempla a la humanidad desde la amplia perspectiva de la historia para analizar cómo las redes de información han hecho y deshecho nuestro mundo. Durante los últimos 100.000 años, los sapiens hemos acumulado un enorme poder. Pero, a pesar de todos los descubrimientos, inventos y conquistas, ahora nos enfrentamos a una crisis existencial: el mundo está al borde del colapso ecológico, abunda la desinformación y nos precipitamos hacia la era de la I.A. Con todo el camino andando, por qué somos una especie autodestructiva?

A partir de una fascinante variedad de ejemplos históricos, desde la Edad de Piedra, pasando por la Biblia, la caza de brujas de principios de la Edad Moderna, el estalinismo y el nazismo, hasta el resurgimiento del populismo en nuestros días, Harari nos ofrece un marco revelador para indagar en las complejas relaciones que existen entre información y verdad, burocracia y mitología, y sabiduría y poder.

Examina cómo diferentes sociedades y sistemas políticos han utilizado la información para lograr sus objetivos e imponer el orden, para bien y para mal. Y plantea las opciones urgentes a las que nos enfrentamos hoy en día, cuando la inteligencia no humana amenaza nuestra propia existencia.

La información no es el principio activo de la verdad; tampoco una simple arma. Nexus explora el esperanzador término medio entre estos extremos.

ENGLISH DESCRIPTION

From the #1 New York Times bestselling author of Sapiens comes the groundbreaking story of how information networks have made, and unmade, our world.

For the last 100,000 years, we Sapiens have accumulated enormous power. But despite allour discoveries, inventions, and conquests, we now find ourselves in an existential crisis. The world is on the verge of ecological collapse. Misinformation abounds. And we are rushing headlong into the age of AI--a new information network that threatens to annihilate us. For all that we have accomplished, why are we so self-destructive?

Nexus looks through the long lens of human history to consider how the flow of information has shaped us, and our world. Taking us from the Stone Age, through the canonization of the Bible, early modern witch-hunts, Stalinism, Nazism, and the resurgence of populism today, Yuval Noah Harari asks us to consider the complex relationship between information and truth, bureaucracy and mythology, wisdom and power. He explores how different societies and political systems throughout history have wielded information to achieve their goals, for good and ill. And he addresses the urgent choices we face as non-human intelligence threatens our very existence.

Information is not the raw material of truth; neither is it a mere weapon. Nexus explores the hopeful middle ground between these extremes, and in doing so, rediscovers our shared humanity.

The fundamental mathematical tools needed to understand machine learning include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability and statistics. These topics are traditionally taught in disparate courses, making it hard for data science or computer science students, or professionals, to efficiently learn the mathematics. This self-contained textbook bridges the gap between mathematical and machine learning texts, introducing the mathematical concepts with a minimum of prerequisites. It uses these concepts to derive four central machine learning methods: linear regression, principal component analysis, Gaussian mixture models and support vector machines. For students and others with a mathematical background, these derivations provide a starting point to machine learning texts. For those learning the mathematics for the first time, the methods help build intuition and practical experience with applying mathematical concepts. Every chapter includes worked examples and exercises to test understanding. Programming tutorials are offered on the book's web site.

Generative AI is the hottest topic in tech. This practical book teaches machine learning engineers and data scientists how to use TensorFlow and Keras to create impressive generative deep learning models from scratch, including variational autoencoders (VAEs), generative adversarial networks (GANs), Transformers, normalizing flows, energy-based models, and denoising diffusion models.

The book starts with the basics of deep learning and progresses to cutting-edge architectures. Through tips and tricks, you'll understand how to make your models learn more efficiently and become more creative.

• Discover how VAEs can change facial expressions in photos
• Train GANs to generate images based on your own dataset
• Build diffusion models to produce new varieties of flowers
• Train your own GPT for text generation
• Learn how large language models like ChatGPT are trained
• Explore state-of-the-art architectures such as StyleGAN2 and ViT-VQGAN
• Compose polyphonic music using Transformers and MuseGAN
• Understand how generative world models can solve reinforcement learning tasks
• Dive into multimodal models such as DALL.E 2, Imagen, and Stable Diffusion

This book also explores the future of generative AI and how individuals and companies can proactively begin to leverage this remarkable new technology to create competitive advantage.

The definitive guide to LLMs, from architectures, pretraining, and fine-tuning to Retrieval Augmented Generation (RAG), multimodal Generative AI, risks, and implementations with ChatGPT Plus with GPT-4, Hugging Face, and Vertex AI

Key Features

- Compare and contrast 20+ models (including GPT-4, BERT, and Llama 2) and multiple platforms and libraries to find the right solution for your project

- Apply RAG with LLMs using customized texts and embeddings

- Mitigate LLM risks, such as hallucinations, using moderation models and knowledge bases

- Purchase of the print or Kindle book includes a free eBook in PDF format

Book Description

Transformers for Natural Language Processing and Computer Vision, Third Edition, explores Large Language Model (LLM) architectures, applications, and various platforms (Hugging Face, OpenAI, and Google Vertex AI) used for Natural Language Processing (NLP) and Computer Vision (CV).

The book guides you through different transformer architectures to the latest Foundation Models and Generative AI. You'll pretrain and fine-tune LLMs and work through different use cases, from summarization to implementing question-answering systems with embedding-based search techniques. You will also learn the risks of LLMs, from hallucinations and memorization to privacy, and how to mitigate such risks using moderation models with rule and knowledge bases. You'll implement Retrieval Augmented Generation (RAG) with LLMs to improve the accuracy of your models and gain greater control over LLM outputs.

Dive into generative vision transformers and multimodal model architectures and build applications, such as image and video-to-text classifiers. Go further by combining different models and platforms and learning about AI agent replication.

This book provides you with an understanding of transformer architectures, pretraining, fine-tuning, LLM use cases, and best practices.

What you will learn

- Breakdown and understand the architectures of the Original Transformer, BERT, GPT models, T5, PaLM, ViT, CLIP, and DALL-E

- Fine-tune BERT, GPT, and PaLM 2 models

- Learn about different tokenizers and the best practices for preprocessing language data

- Pretrain a RoBERTa model from scratch

- Implement retrieval augmented generation and rules bases to mitigate hallucinations

- Visualize transformer model activity for deeper insights using BertViz, LIME, and SHAP

- Go in-depth into vision transformers with CLIP, DALL-E 2, DALL-E 3, and GPT-4V

Who this book is for

This book is ideal for NLP and CV engineers, software developers, data scientists, machine learning engineers, and technical leaders looking to advance their LLMs and generative AI skills or explore the latest trends in the field.

Knowledge of Python and machine learning concepts is required to fully understand the use cases and code examples. However, with examples using LLM user interfaces, prompt engineering, and no-code model building, this book is great for anyone curious about the AI revolution.

Table of Contents

- What are Transformers?

- Getting Started with the Architecture of the Transformer Model

- Emergent vs Downstream Tasks: The Unseen Depths of Transformers

- Advancements in Translations with Google Trax, Google Translate, and Gemini

- Diving into Fine-Tuning through BERT

- Pretraining a Transformer from Scratch through RoBERTa

- The Generative AI Revolution with ChatGPT

- Fine-Tuning OpenAI GPT Models

- Shattering the Black Box with Interpretable Tools

- Investigating the Role of Tokenizers in Shaping Transformer Models

(N.B. Please use the Read Sample option to see further chapters)

The Practical, Step-by-Step Guide to Using LLMs at Scale in Projects and Products

Large Language Models (LLMs) like Llama 3, Claude 3, and the GPT family are demonstrating breathtaking capabilities, but their size and complexity have deterred many practitioners from applying them. In Quick Start Guide to Large Language Models, Second Edition, pioneering data scientist and AI entrepreneur Sinan Ozdemir clears away those obstacles and provides a guide to working with, integrating, and deploying LLMs to solve practical problems.

Ozdemir brings together all you need to get started, even if you have no direct experience with LLMs: step-by-step instructions, best practices, real-world case studies, and hands-on exercises. Along the way, he shares insights into LLMs' inner workings to help you optimize model choice, data formats, prompting, fine-tuning, performance, and much more. The resources on the companion website include sample datasets and up-to-date code for working with open- and closed-source LLMs such as those from OpenAI (GPT-4 and GPT-3.5), Google (BERT, T5, and Gemini), X (Grok), Anthropic (the Claude family), Cohere (the Command family), and Meta (BART and the LLaMA family).

• Learn key concepts: pre-training, transfer learning, fine-tuning, attention, embeddings, tokenization, and more
• Use APIs and Python to fine-tune and customize LLMs for your requirements
• Build a complete neural/semantic information retrieval system and attach to conversational LLMs for building retrieval-augmented generation (RAG) chatbots and AI Agents
• Master advanced prompt engineering techniques like output structuring, chain-of-thought prompting, and semantic few-shot prompting
• Customize LLM embeddings to build a complete recommendation engine from scratch with user data that outperforms out-of-the-box embeddings from OpenAI
• Construct and fine-tune multimodal Transformer architectures from scratch using open-source LLMs and large visual datasets
• Align LLMs using Reinforcement Learning from Human and AI Feedback (RLHF/RLAIF) to build conversational agents from open models like Llama 3 and FLAN-T5
• Deploy prompts and custom fine-tuned LLMs to the cloud with scalability and evaluation pipelines in mind
• Diagnose and optimize LLMs for speed, memory, and performance with quantization, probing, benchmarking, and evaluation frameworks

A refreshing and inspiring resource. Jam-packed with practical guidance and clear explanations that leave you smarter about this incredible new field.
--Pete Huang, author of The Neuron

Register your book for convenient access to downloads, updates, and/or corrections as they become available. See inside book for details.

This is the first text on pattern recognition to present the Bayesian viewpoint, one that has become increasing popular in the last five years. It presents approximate inference algorithms that permit fast approximate answers in situations where exact answers are not feasible. It provides the first text to use graphical models to describe probability distributions when there are no other books that apply graphical models to machine learning. It is also the first four-color book on pattern recognition. The book is suitable for courses on machine learning, statistics, computer science, signal processing, computer vision, data mining, and bioinformatics. Extensive support is provided for course instructors, including more than 400 exercises, graded according to difficulty. Example solutions for a subset of the exercises are available from the book web site, while solutions for the remainder can be obtained by instructors from the publisher.

THE #2 TIMES BESTSELLER

'Artificial intelligence is going to have a massive impact on everyone's lives... an accessible and sensible read that helps demystify AI' Deborah Meaden, entrepreneur and star of Dragon's Den

'Nigel Toon is a visionary leader in the field of artificial intelligence... a must-read' Marc Tremblay, Distinguished Engineer, Microsoft

Those who understand how AI thinks are about to win big.

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? How does it play and win games that have an almost infinite number of moves? And how can we work with AI to produce insights and innovations that are beyond human capacity, from writing code in an instant to unfolding the elaborate 3D puzzles of proteins?

We stand at the brink of a historic change that will disrupt society and at the same time create enormous opportunities for those who understand how AI thinks. Nigel Toon shows how we train AI to train itself, so that it can paint images that have never existed before or converse in any language. In doing so he reveals the strange and fascinating ways that humans think, too, as we learn how to live in a world shared by machine intelligences of our own creation.

The definitive computer vision book is back, featuring the latest neural network architectures and an exploration of foundation and diffusion models

Purchase of the print or Kindle book includes a free eBook in PDF format

Key Features

- Understand the inner workings of various neural network architectures and their implementation, including image classification, object detection, segmentation, generative adversarial networks, transformers, and diffusion models

- Build solutions for real-world computer vision problems using PyTorch

- All the code files are available on GitHub and can be run on Google Colab

Book Description

Whether you are a beginner or are looking to progress in your computer vision career, this book guides you through the fundamentals of neural networks (NNs) and PyTorch and how to implement state-of-the-art architectures for real-world tasks.

The second edition of Modern Computer Vision with PyTorch is fully updated to explain and provide practical examples of the latest multimodal models, CLIP, and Stable Diffusion.

You'll discover best practices for working with images, tweaking hyperparameters, and moving models into production. As you progress, you'll implement various use cases for facial keypoint recognition, multi-object detection, segmentation, and human pose detection. This book provides a solid foundation in image generation as you explore different GAN architectures. You'll leverage transformer-based architectures like ViT, TrOCR, BLIP2, and LayoutLM to perform various real-world tasks and build a diffusion model from scratch. Additionally, you'll utilize foundation models' capabilities to perform zero-shot object detection and image segmentation. Finally, you'll learn best practices for deploying a model to production.

By the end of this deep learning book, you'll confidently leverage modern NN architectures to solve real-world computer vision problems.

What you will learn

- Get to grips with various transformer-based architectures for computer vision, CLIP, Segment-Anything, and Stable Diffusion, and test their applications, such as in-painting and pose transfer

- Combine CV with NLP to perform OCR, key-value extraction from document images, visual question-answering, and generative AI tasks

- Implement multi-object detection and segmentation

- Leverage foundation models to perform object detection and segmentation without any training data points

- Learn best practices for moving a model to production

Who this book is for

This book is for beginners to PyTorch and intermediate-level machine learning practitioners who want to learn computer vision techniques using deep learning and PyTorch. It's useful for those just getting started with neural networks, as it will enable readers to learn from real-world use cases accompanied by notebooks on GitHub. Basic knowledge of the Python programming language and ML is all you need to get started with this book. For more experienced computer vision scientists, this book takes you through more advanced models in the latter part of the book.

Table of Contents

- Artificial Neural Network Fundamentals

- PyTorch Fundamentals

- Building a Deep Neural Network with PyTorch

- Introducing Convolutional Neural Networks

- Transfer Learning for Image Classification

- Practical Aspects of Image Classification

- Basics of Object Detection

- Advanced Object Detection

- Image Segmentation

- Applications of Object Detection and Segmentation

- Autoencoders and Image Manipulation

- Image Generation Using GANs

(N.B. Please use the Read Sample option to see further chapters)

Need information on the New Windows 11 Operating System from Microsoft? Then read on...
Microsoft has released the future of the Windows operating system with a user interface completely different from the last Windows 10. There are new settings and designs intended to improve the fluidity of Windows and give users a new look. This book has been written to give you a first taste at what Windows 11 looks like, especially for those who have updated to the beta version. The book talks about what has changed in Windows, and how you can find your ways around the new operating system. Use this guide for Windows 11 only, as most of the settings are not applicable for Windows 10.
Click on the BUY NOW WITH 1-CLICK to get started

Learn the skills necessary to design, build, and deploy applications powered by machine learning (ML). Through the course of this hands-on book, you'll build an example ML-driven application from initial idea to deployed product. Data scientists, software engineers, and product managers--including experienced practitioners and novices alike--will learn the tools, best practices, and challenges involved in building a real-world ML application step by step.

Author Emmanuel Ameisen, an experienced data scientist who led an AI education program, demonstrates practical ML concepts using code snippets, illustrations, screenshots, and interviews with industry leaders. Part I teaches you how to plan an ML application and measure success. Part II explains how to build a working ML model. Part III demonstrates ways to improve the model until it fulfills your original vision. Part IV covers deployment and monitoring strategies.

This book will help you:

• Define your product goal and set up a machine learning problem
• Build your first end-to-end pipeline quickly and acquire an initial dataset
• Train and evaluate your ML models and address performance bottlenecks
• Deploy and monitor your models in a production environment

As machine learning applications gain widespread adoption and integration in a variety of applications, including safety and mission-critical systems, the need for robust evaluation methods grows more urgent. This book compiles scattered information on the topic from research papers and blogs to provide a centralized resource that is accessible to students, practitioners, and researchers across the sciences. The book examines meaningful metrics for diverse types of learning paradigms and applications, unbiased estimation methods, rigorous statistical analysis, fair training sets, and meaningful explainability, all of which are essential to building robust and reliable machine learning products. In addition to standard classification, the book discusses unsupervised learning, regression, image segmentation, and anomaly detection. The book also covers topics such as industry-strength evaluation, fairness, and responsible AI. Implementations using Python and scikit-learn are available on the book's website.

3D deep learning is a rapidly evolving field that has the potential to transform various industries. This book provides a comprehensive overview of the current state-of-the-art in 3D deep learning, covering a wide range of research topics and applications. It collates the most recent research advances in 3D deep learning, including algorithms and applications, with a focus on efficient methods to tackle the key technical challenges in current 3D deep learning research and adoption, therefore making 3D deep learning more practical and feasible for real-world applications.This book is organized into five sections, each of which addresses different aspects of 3D deep learning. Section I: Sample Efficient 3D Deep Learning, focuses on developing efficient algorithms to build accurate 3D models with limited annotated samples. Section II: Representation Efficient 3D Deep Learning, deals with the challenge of developing efficient representations for dynamic 3D scenes and multiple 3D modalities. Section III: Robust 3D Deep Learning, presents methods for improving the robustness and reliability of deep learning models in real-world applications. Section IV: Resource Efficient 3D Deep Learning, explores ways to reduce the computation cost of 3D models and improve their efficiency in resource-limited environments. Section V: Emerging 3D Deep Learning Applications, showcases how 3D deep learning is transforming industries and enabling new applications for healthcare and manufacturing.This collection is a valuable resource for researchers and practitioners interested in exploring the potential of 3D deep learning.

Get well versed with state-of-the-art techniques to tailor training processes and boost the performance of computer vision models using machine learning and deep learning techniques

Key Features:

• Develop, train, and use deep learning algorithms for computer vision tasks using TensorFlow 2.x
• Discover practical recipes to overcome various challenges faced while building computer vision models
• Enable machines to gain a human level understanding to recognize and analyze digital images and videos

Book Description:

Computer vision is a scientific field that enables machines to identify and process digital images and videos. This book focuses on independent recipes to help you perform various computer vision tasks using TensorFlow.

The book begins by taking you through the basics of deep learning for computer vision, along with covering TensorFlow 2.x's key features, such as the Keras and tf.data.Dataset APIs. You'll then learn about the ins and outs of common computer vision tasks, such as image classification, transfer learning, image enhancing and styling, and object detection. The book also covers autoencoders in domains such as inverse image search indexes and image denoising, while offering insights into various architectures used in the recipes, such as convolutional neural networks (CNNs), region-based CNNs (R-CNNs), VGGNet, and You Only Look Once (YOLO).

Moving on, you'll discover tips and tricks to solve any problems faced while building various computer vision applications. Finally, you'll delve into more advanced topics such as Generative Adversarial Networks (GANs), video processing, and AutoML, concluding with a section focused on techniques to help you boost the performance of your networks.

By the end of this TensorFlow book, you'll be able to confidently tackle a wide range of computer vision problems using TensorFlow 2.x.

What You Will Learn:

• Understand how to detect objects using state-of-the-art models such as YOLOv3
• Use AutoML to predict gender and age from images
• Segment images using different approaches such as FCNs and generative models
• Learn how to improve your network's performance using rank-N accuracy, label smoothing, and test time augmentation
• Enable machines to recognize people's emotions in videos and real-time streams
• Access and reuse advanced TensorFlow Hub models to perform image classification and object detection
• Generate captions for images using CNNs and RNNs

Who this book is for:

This book is for computer vision developers and engineers, as well as deep learning practitioners looking for go-to solutions to various problems that commonly arise in computer vision. You will discover how to employ modern machine learning (ML) techniques and deep learning architectures to perform a plethora of computer vision tasks. Basic knowledge of Python programming and computer vision is required.

Machine learning is one of the fastest growing areas of computer science, with far-reaching applications. The aim of this textbook is to introduce machine learning, and the algorithmic paradigms it offers, in a principled way. The book provides an extensive theoretical account of the fundamental ideas underlying machine learning and the mathematical derivations that transform these principles into practical algorithms. Following a presentation of the basics of the field, the book covers a wide array of central topics that have not been addressed by previous textbooks. These include a discussion of the computational complexity of learning and the concepts of convexity and stability; important algorithmic paradigms including stochastic gradient descent, neural networks, and structured output learning; and emerging theoretical concepts such as the PAC-Bayes approach and compression-based bounds. Designed for an advanced undergraduate or beginning graduate course, the text makes the fundamentals and algorithms of machine learning accessible to students and non-expert readers in statistics, computer science, mathematics, and engineering.

Demystifying Prompt Engineering: AI Prompts At Your Fingertips.

AI is poised to create a whole new way of working. Are you ready for the pivotal moment?

Generative AI tools are rapidly transforming businesses and redefining career roles. And, Prompt Engineering-the ability to communicate with AI models like ChatGPT is fast emerging as an essential skill. This book will help you master the new skills for the coming decade.

What you will learn

• Five foundation attributes of writing effective prompts.
• Eight advanced techniques of Prompt Engineering.
• Best Practices from the trenches.
• Hone your prompt writing skills with hands-on lab exercises.
• Hit the ground running with ready-to-use Prompt Cookbooks

__________________________________________________________

Riveting and captivating, this book is a must-read for every professional seeking to create an edge within their respective domain. - Nikhil Parva-Former Managing Consulting Advisor, KPMG.

__________________________________________________________

This step-by-step is your on-ramp to the AI Enabled Workforce. Learn how to use AI as your co-pilot with effective prompts that unleash your creativity, turbocharge your productivity and enhance operational efficiency. Each chapter features curated prompts that illustrate key concepts with simplicity and clarity.

And as an added bonus, supplement your learning with our companion book website at getAIready.com for continuous updates, tips & insights, engaging blogs and content downloads.

Book is authored by Harish Bhat, a seasoned tech industry professional based in Silicon Valley, California. During his various consulting engagements, he has seen at close quarters the technology disruption that is transforming businesses and creating new business models.

Give your career an AI Boost by ordering a copy of this book now!

In today's rapidly changing digital landscape, understanding and leveraging artificial intelligence (AI) is not just beneficial-it's essential. Raising AI-Smart Kids: A Practical Guide for Parents to Navigate the Future of Artificial Intelligence by Stephanie Worrell, MA, is your essential companion on this journey.

What You Will Find Inside

Raising AI-Smart Kids equips you with the knowledge, tools, and strategies to confidently navigate the AI landscape. Inside, discover:

Easy-to-understand explanations of AI basicsPractical applications of AI in your homeTools to enhance your child's learning and creativityTips for managing digital safety and privacyStrategies for fostering critical thinking and ethical AI useGuidance on addressing AI-generated content and misinformationInsights on preparing your child for a future with AI

Why You Need This BookAI is everywhere-whether it's a smart speaker recommending songs, an app assisting with homework, or a security system keeping your home safe. Raising AI-Smart Kids equips you with the knowledge and tools to navigate AI's complexities confidently.

Real-world Benefits

Say goodbye to the days of being confused by your child's homework. With an AI assistant, even the dreaded new math becomes less intimidating. Whether you're assisting your child in building a science fair volcano or deciphering Shakespeare's sonnets, AI is your go-to helper, armed with quick facts, useful diagrams, and a knack for explaining complex concepts in simple terms.

Empowerment through Knowledge

Understanding AI can be challenging. This book simplifies complex concepts, helping you grasp what AI is, what it isn't, and how it works. Learn the fundamentals of AI, including data, pattern recognition, and prediction.

Practical Tools and Tips

Raising AI-Smart Kids provides a curated list of AI tools to enhance learning, creativity, and productivity. Discover educational apps like Khan Academy Kids and Photomath, and creative tools like Craiyon and Soundtrap for Education.

Safety and Privacy Guidelines

Protecting your family's privacy is essential. This book offers practical advice for managing digital footprints, implementing privacy controls, and teaching your children the value of data security.

Ethical and Critical ThinkingAI is not perfect and reflects the biases of its creators. Raising AI-Smart Kids teaches you how to instill a questioning mindset in your children, encouraging them to think critically about the information and technology they encounter.Future-Proof Skills

As AI evolves, so must our skills. This book focuses on timeless human abilities that complement AI, such as creativity, communication, critical thinking, and adaptability.

Family-Centered Approach

Raising AI-Smart Kids promotes open communication between parents and children, allowing them to navigate the digital world together. By sharing values and establishing clear boundaries, you ensure AI is a positive force in your home.