Publisher's Synopsis
NB All & any profits from this book will be given to charity to help promote learning about AI & how to use it ethically for the widest good of man/woman kind. We are pricing this book as low as is allowed via this platform so that as many people as possible can share in this advice. This is a book for beginners and non experts. There is no confusing code in this book and jargon is minimised where possible. Please review us fairly on that basis.: ) TESTIMONIALS: "We want to use this approach." Top 10 UK University - "Feeling more confident about AI now." WPP MD - "Clever yet easily understood." Indy Agency MD - "Extremely relevant & timely." Top 3 Bank MD - "Important for all our members." RSA Director - "Even I get it now!" Google AI StartUp CFO - CONTENTS: 1. Intro: What's Important in AI Today?What Does AI Mean? What's Changed in AI? What's AI Good For? Who Needs Today's AI? So What's Ethical AI? Case Study: Microsoft & LinkedIn Aim to Democratise AI - Case Study: PWC and L'Oreal Find New Ways to Identify Successful Candidates - Conclusions 2. Today's AI For All 2.1 How to Prepare? The Big Issue? New Tools! 2.2 How to Prototype & Improve? Data Gathering Choosing AI Tools & Training Self Monitoring Case Study: Today's Simple AI(TM) Tools Case Study: Unilever HR Tests New Tools Next Steps Case Study: Adobe adds AI to Design 2.3 How to Roll Out? Big Changes - Complex AI - Simple AI 2.4 Our Conclusions: Let's Play & Learn! 2.5 What's the Near Future? Appendix 1 - Our Reviews of AI Toolsets 1.1 Wipro Holmes 1.2 Apache PredictionIO 1.3 IBM Watson 1.4 Google Cloud Machine Learning Engine 1.5 Azure Machine Learning Studio 1.6 Google Tensor Flow 1.7 Ayasdi 1.8 Infosys Nia 1.9 Meya 1.10 Nvidia Deep Learning 1.11 Rainbird 1.12 Receptiviti 1.13 Salesforce Einstein 1.14 Today's Simple AI(TM) Appendix 2 - AI Training Courses Reviewed 2.1 Today's Simple AI(TM) Training 2.2 Udacity Machine Learning Engineer Nanodegree 2.3 Artificial Intelligence MicroMasters 2.4 Google's ML Crash Course 2.5 IBM Open Badge Programme 2.6 MIT's Deep Learning for Cars 2.7 NVIDIA Deep Learning Specialization 2.8 Stanford University Machine Learning 2.9 Elements of AI 2.10 Fundamentals of Deep Learning for Computer Vision 2.11 Learning from Data 2.12 Grokking Deep Learning in Motion 2.13 CS188.1x: Artificial Intelligence
