Artificial Intelligence Is Here To Stay You Better Learn It Now
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Artificial Intelligence Is Here to Stay, You Better Learn It Now!
Artificial Intelligence (AI) is no longer just a futuristic concept; it is here, and it is transforming the way we live, work, and interact. If you are not actively harnessing the power of AI, you risk being left behind in an increasingly competitive and fast-paced world. Just like the calculator revolutionized the way we perform basic arithmetic, AI is now stepping in to revolutionize a wide array of fields—academic, business, and social. When the calculator was first introduced, many feared it would take away jobs and diminish mental faculties. But over time, we have come to view it as an indispensable tool, one that saves us time and allows us to focus on more complex tasks. In much the same way, AI is poised to become an essential part of our daily lives, yet its potential goes far beyond what a calculator ever offered. From automating mundane tasks to solving complex problems, AI has the power to revolutionize virtually every aspect of our existence. As we move forward, it is crucial to embrace these technological advancements and learn how to effectively incorporate them into our routines. This book is a comprehensive guide to understanding and utilizing some of the most widely used AI tools available today. Across the following chapters, I will take you on a deep dive into AI solutions across various categories, including generative AI, productivity tools, coding assistants, design tools, data analysis, and more. Whether you're a student, a business owner, or simply someone curious about how AI can improve your life, this book is designed to help you navigate and leverage these powerful technologies to meet your academic, professional, and social needs.
The AI Advantage
Cutting through the hype, a practical guide to using artificial intelligence for business benefits and competitive advantage. In The AI Advantage, Thomas Davenport offers a guide to using artificial intelligence in business. He describes what technologies are available and how companies can use them for business benefits and competitive advantage. He cuts through the hype of the AI craze—remember when it seemed plausible that IBM's Watson could cure cancer?—to explain how businesses can put artificial intelligence to work now, in the real world. His key recommendation: don't go for the “moonshot” (curing cancer, or synthesizing all investment knowledge); look for the “low-hanging fruit” to make your company more efficient. Davenport explains that the business value AI offers is solid rather than sexy or splashy. AI will improve products and processes and make decisions better informed—important but largely invisible tasks. AI technologies won't replace human workers but augment their capabilities, with smart machines to work alongside smart people. AI can automate structured and repetitive work; provide extensive analysis of data through machine learning (“analytics on steroids”), and engage with customers and employees via chatbots and intelligent agents. Companies should experiment with these technologies and develop their own expertise. Davenport describes the major AI technologies and explains how they are being used, reports on the AI work done by large commercial enterprises like Amazon and Google, and outlines strategies and steps to becoming a cognitive corporation. This book provides an invaluable guide to the real-world future of business AI. A book in the Management on the Cutting Edge series, published in cooperation with MIT Sloan Management Review.
AI-Assisted Programming for Web and Machine Learning
Author: Christoffer Noring
language: en
Publisher: Packt Publishing Ltd
Release Date: 2024-08-30
Speed up your development processes and improve your productivity by writing practical and relevant prompts to build web applications and Machine Learning (ML) models Purchase of the print or Kindle book includes a free PDF copy Key Features Utilize prompts to enhance frontend and backend web development Develop prompt strategies to build robust machine learning models Use GitHub Copilot for data exploration, maintaining existing code bases, and augmenting ML models into web applications Book DescriptionAI-Assisted Programming for Web and Machine Learning shows you how to build applications and machine learning models and automate repetitive tasks. Part 1 focuses on coding, from building a user interface to the backend. You’ll use prompts to create the appearance of an app using HTML, styling with CSS, adding behavior with JavaScript, and working with multiple viewports. Next, you’ll build a web API with Python and Flask and refactor the code to improve code readability. Part 1 ends with using GitHub Copilot to improve the maintainability and performance of existing code. Part 2 provides a prompting toolkit for data science from data checking (inspecting data and creating distribution graphs and correlation matrices) to building and optimizing a neural network. You’ll use different prompt strategies for data preprocessing, feature engineering, model selection, training, hyperparameter optimization, and model evaluation for various machine learning models and use cases. The book closes with chapters on advanced techniques on GitHub Copilot and software agents. There are tips on code generation, debugging, and troubleshooting code. You’ll see how simpler and AI-powered agents work and discover tool calling.What you will learn Speed up your coding and machine learning workflows with GitHub Copilot and ChatGPT Use an AI-assisted approach across the development lifecycle Implement prompt engineering techniques in the data science lifecycle Develop the frontend and backend of a web application with AI assistance Build machine learning models with GitHub Copilot and ChatGPT Refactor code and fix faults for better efficiency and readability Improve your codebase with rich documentation and enhanced workflows Who this book is for Experienced developers new to GitHub Copilot and ChatGPT can discover the best strategies to improve productivity and deliver projects quicker than traditional methods. This book is ideal for software engineers working on web or machine learning projects. It is also a useful resource for web developers, data scientists, and analysts who want to improve their efficiency with the help of prompting. This book does not teach web development or how different machine learning models work.