Today's AI - Easy, Ethical Artificial Intelligence for All
Today's AI - Easy, Ethical Artificial Intelligence for All
Click to enlarge
Author(s): Flynn, Chris
ISBN No.: 9781070912943
Pages: 110
Year: 201905
Format: Trade Paper
Price: $ 5.00
Dispatch delay: Dispatched between 7 to 15 days
Status: Available (On Demand)

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.


To be able to view the table of contents for this publication then please subscribe by clicking the button below...
To be able to view the full description for this publication then please subscribe by clicking the button below...