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- Herausgeber: John Wiley & Sons
- Kategorie: Wissenschaft und neue Technologien
- Sprache: Englisch
With the idea of "deep learning" having now become the key to this new generation of solutions, major technological players in the business intelligence sector have taken an interest in the application of Big Data. In this book, the author explores the recent technological advances associated with digitized data flows, which have recently opened up new horizons for AI. The reader will gain insight into some of the areas of application of Big Data in AI, including robotics, home automation, health, security, image recognition and natural language processing.
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Veröffentlichungsjahr: 2018
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Table of Contents
Cover
Title
Copyright
List of Figures
Preface
Introduction
1 What is Intelligence?
1.1. Intelligence
1.2. Business Intelligence
1.3. Artificial Intelligence
1.4. How BI has developed
2 Digital Learning
2.1. What is learning?
2.2. Digital learning
2.3. The Internet has changed the game
2.4. Big Data and the Internet of Things will reshuffle the cards
2.5. Artificial Intelligence linked to Big Data will undoubtedly be the keystone of digital learning
2.6. Supervised learning
2.7. Enhanced supervised learning
2.8. Unsupervised learning
3 The Reign of Algorithms
3.1. What is an algorithm?
3.2. A brief history of AI
3.3. Algorithms are based on neural networks, but what does this mean?
3.4. Why do Big Data and AI work so well together?
4 Uses for Artificial Intelligence
4.1. Customer experience management
4.2. The transport industry
4.3. The medical industry
4.4. “Smart” personal assistant (or agent)
4.5. Image and sound recognition
4.6. Recommendation tools
Conclusion
APPENDICES
Appendix 1: Big Data
Appendix 2: Smart Data
Appendix 3: Data Lakes
Appendix 4: Some Vocabulary Relevant to
Appendix 5: Comparison Between Machine Learning and Traditional Business Intelligence
Appendix 6: Conceptual Outline of the Steps Required to Implement a Customization Solution based on Machine Learning
Bibliography
Glossary
Index
End User License Agreement
List of Illustrations
Preface
Figure 1. Identity resolution
Introduction
Figure I.1. “Digital assimilation”
Figure I.2. The traces we leave on the Internet (whether voluntarily or not) form our Digital Identity
Figure I.3. Number of connected devices per person by 2020
1 What is Intelligence?
Figure 1.1. Diagram showing the transformation of information into knowledge
Figure 1.2. Business Intelligence evolution cycle
Figure 1.3. The Hadoop MapReduce process
2 Digital Learning
Figure 2.1. Volume of activity per minute on the Internet
Figure 2.2. Some key figures concerning connected devices
Figure 2.3. Supervised learning
Figure 2.4. Supervised learning
Figure 2.5. Enhanced supervised learning
Figure 2.6. Unsupervised learning
Figure 2.7. Neural networks
Figure 2.8. Example of facial recognition
3 The Reign of Algorithms
Figure 3.1. The artificial neuron and the mathematical model of a biological neuron
Figure 3.2. X1 and X2 are the input data, W1 and W2 are the relative weights (which will be used as weighting) for the confidence (performance) of these inputs, allowing the output to choose between the X1 or X2 data. It is very clear that W (the weight) will be the determining element of the decision. Being able to adapt it in retro-propagation will make the system self-learning
Figure 3.3. Example of facial recognition
Figure 3.4. Big Data and variety of data
4 Uses for Artificial Intelligence
Figure 4.1. Markess 2016 public study
Figure 4.2. What is CXM?
Figure 4.3. How does the autonomous car work?
Figure 4.4. Connected medicine
Figure 4.5. A smart assistant in a smart home
Figure 4.6. In this example of facial recognition, the layers are hierarchized. They start at the top layer and the tasks get increasingly complex
Figure 4.7. The same technique can be used for augmented reality (perception of the environment), placing it on-board a self-driving vehicle to provide information to the automatic control of the vehicle
Figure 4.8. Recommendations are integrated into the customer path through the right channel. Customer contact channels tend to multiply rather than replace each other, forcing companies to adapt their communications to each channel (content format, interaction, language, etc.). The customer wishes to choose their channel and be able to change it depending on the circumstances (time of day, location, subject of interest, expected results, etc.)
Figure 4.9. Collaborative filtering, step by step. In this example, we can see that the closest “neighbor” in terms of preferences is not interested in videos, which will inform the recommendation engine about the (possible) preferences of the Internet user (in this case, do not recommend videos). If the user is interested in video products, models (based on self-learning) will take this into account when browsing, and their profile will be “boosted” by this information
Figure 4.10. Mapping of start-ups in the world of Artificial Intelligence
Guide
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Table of Contents
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Advances in Information Systems Set
coordinated byCamille Rosenthal-Sabroux
Volume 8
Artificial Intelligence and Big Data
The Birth of a New Intelligence
Fernando Iafrate
First published 2018 in Great Britain and the United States by ISTE Ltd and John Wiley & Sons, Inc.
Apart from any fair dealing for the purposes of research or private study, or criticism or review, as permitted under the Copyright, Designs and Patents Act 1988, this publication may only be reproduced, stored or transmitted, in any form or by any means, with the prior permission in writing of the publishers, or in the case of reprographic reproduction in accordance with the terms and licenses issued by the CLA. Enquiries concerning reproduction outside these terms should be sent to the publishers at the undermentioned address:
ISTE Ltd
27-37 St George’s Road
London SW19 4EU
UK
www.iste.co.uk
John Wiley & Sons, Inc.
111 River Street
Hoboken, NJ 07030
USA
www.wiley.com
© ISTE Ltd 2018
The rights of Fernando Iafrate to be identified as the authors of this work have been asserted by him in accordance with the Copyright, Designs and Patents Act 1988.
Library of Congress Control Number: 2017961949
British Library Cataloguing-in-Publication Data
A CIP record for this book is available from the British Library
ISBN 978-1-78630-083-6
List of Figures
Figure 1.
Identity resolution
Figure I.1.
“Digital assimilation”
Figure I.2.
The traces we leave on the Internet (whether voluntarily or not) form our Digital Identity
Figure I.3.
Number of connected devices per person by 2020
Figure 1.1.
Diagram showing the transformation of information into knowledge
Figure 1.2.
Business Intelligence evolution cycle
Figure 1.3.
The Hadoop MapReduce process
Figure 2.1.
Volume of activity per minute on the Internet
Figure 2.2.
Some key figures concerning connected devices
Figure 2.3.
Supervised learning
Figure 2.4.
Supervised learning
Figure 2.5.
Enhanced supervised learning
Figure 2.6.
Unsupervised learning
Figure 2.7.
Neural networks
Figure 2.8.
Example of facial recognition
Figure 3.1.
The artificial neuron and the mathematical model of a biological neuron
Figure 3.2.
X1 and X2 are the input data, W1 and W2 are the relative weights (which will be used as weighting) for the confidence (performance) of these inputs, allowing the output to choose between the X1 or X2 data. It is very clear that W (the weight) will be the determining element of the decision. Being able to adapt it in retro-propagation will make the system self-learning
Figure 3.3.
Example of facial recognition
Figure 3.4.
Big Data and variety of data
Figure 4.1.
Markess 2016 public study
Figure 4.2.
What is CXM?
Figure 4.3.
How does the autonomous car work?
Figure 4.4.
Connected medicine
Figure 4.5.
A smart assistant in a smart home
Figure 4.6.
In this example of facial recognition, the layers are hierarchized. They start at the top layer and the tasks get increasingly complex
Figure 4.7.
The same technique can be used for augmented reality (perception of the environment), placing it on-board a self-driving vehicle to provide information to the automatic control of the vehicle
Figure 4.8.
Recommendations are integrated into the customer path through the right channel. Customer contact channels tend to multiply rather than replace each other, forcing companies to adapt their communications to each channel (content format, interaction, language, etc.). The customer wishes to choose their channel and be able to change it depending on the circumstances (time of day, location, subject of interest, expected results, etc.)
Figure 4.9.
Collaborative filtering, step by step. In this example, we can see that the closest “neighbor” in terms of preferences is not interested in videos, which will inform the recommendation engine about the (possible) preferences of the Internet user (in this case, do not recommend videos). If the user is interested in video products, models (based on self-learning) will take this into account when browsing, and their profile will be “boosted” by this information
Figure 4.10.
Mapping of start-ups in the world of Artificial Intelligence
Preface
This book follows on from a previous book, From Big Data to Smart Data [IAF 15], for which the original French title contained a subtitle: “For a connected world”. Today, we could add “without latency” to this title, as time has become the key word; it all revolves around acting faster and better than competitors in the digital environment, where information travels through the Internet at light speed.
Today more than ever before, time represents an “immaterial asset” with such a high added value (high-frequency trading operated by banks is an obvious example, I invite you to read Michael Lewisʼ book, Flash Boys: A Wall Street Revolt1 [LEW 14]). It seems obvious that a large part of our decisions and subsequent actions (personal or professional) are dependent on the digital world (which mixes information and algorithms for processing this information); imagine spending a day without your laptop, smartphone or tablet, and you will see the extent to which we have organized our lives around this “Digital Intelligence”. Although it does render us many services and increases our autonomy, it also accentuates our dependence and even addiction to these technologies (what a paradox!). This “new” world is structured around the Internet and requires companies to make decisions and act in a highly competitive environment, managing complex data in a matter of milliseconds (or less).
We live in a world where “customer experience” has become the key and our demand as consumers (for all types of goods, services or content: messaging, products, offers, information) is only growing. We demand to be “processed” in a relevant way, even as we navigate in this digital world “anonymously” (without formerly having used a personal authenticated account), which implies that other mechanisms must be in place to allow this “traceability”. Who was it who said that “the habit does not make the monk”? I fear that in this digitized world, our clothes in the Internet are the traces we leave (navigation, cookies, IP address, download history, etc.), voluntarily or not, allowing a digital identity to be built without our knowledge and therefore being one that we barely or do not have any control over!
All this information is interconnected, joined together as they are being generated, following the “keyring” principle (see Figure 1). They are then exploited by targeting, segmenting and through recommendation engine solutions, which have been implemented over the last decade or so and are based on software agents backed by rule engines (recommendation engines). In order to meet a contactʼs expectation of “relevance”, “a company does not own a customer but merely the time that he chooses to devote”. During this time, which becomes the “grail” for companies to unveil vaults of imaginative ideas (but also much spending in terms of finances) to attract customers to their channels (website, call center, shops, etc.), they must be as “relevant” as possible.
The solutions currently in place (rule/recommendation engine) are not very interactive with their environment (they are predefined models based on a limited number of descriptive variables for the situation), they do not exhibit much self-learning (updating of models after analytical processing, which is often very arduous) and the result is that the same causes (identified by a few variables) trigger the same effects. These solutions do not or take very little account of context variations in real time (how a user arrived on a web page, what content they saw just before, what the nature of their search is, etc.), or do they consider results from previous decisions and actions. Last but not least, they barely or do not allow all contextual data to be exploited (navigation behavior, what was previously proposed in terms of content, the resulting actions, etc.).
Figure 1.Identity resolution
This need to act and react in real time in a complex environment has been the case for years, and the advent of Big Data and connected devices has only increased the complexity of processing this information; solutions and organizations (statisticians, decision analysts, etc.) are overwhelmed by this continuous flow of data (the Internet never sleeps). No or few solutions have been proposed through processes and historical analysis tools in companies, which tend to be too cumbersome and complex to develop, and require resources to be implemented despite that these resources are becoming increasingly scarce (it is likely to be one of the major problems over the next few years in this field – the lack of Business Intelligence experts and statisticians will be very much highlighted). Consumer purchasing behaviors are constantly changing (collaborative platforms such as Uber and Airbnb have “invented” this new business model), which will ultimately create new risks (for those who cannot adapt to this ever-changing world) and opportunities (for those who will be able to exploit this new “Eldorado” that is “Big Data”).
Artificial Intelligence (AI) is one of the most promising solutions to the massive, self-learning, autonomous exploitation of “Big Data”. More precisely, “Deep Learning”, which emerged in the 1980s with the advent of neural networks, is now becoming the keystone to this new generation of solutions. Advances in technology with digitized data flows have opened up new horizons in this field and anything that has not misled the major technological players in Business Intelligence has been swallowed up as the logical sequel to Big Data.
There are many possible fields of application for AI, such as robotics (connected and autonomous cars), home automation (smart home), health (assistance in medical diagnosis), security, personal assistants (which will become essential tools in our daily lives), expert systems, image, sound and facial recognition (and why not analyze emotions too), natural language processing… But also, customer relations management (to anticipate or even exceed our expectations). All these systems will be self-learning, their knowledge will only grow with time and they will be able to exchange knowledge between each other.
Certain people like Bill Gates, the founder of Microsoft, or serial entrepreneur Elon Musk, or Steve Wozniak, cofounder of Apple, or scientist Stephen Hawking were deeply moved by the thought that AI could change within our society, at the risk that humanity could one day be controlled by machines (somewhat reminiscent of the film “The Matrix”). The purpose of this book is not to be philosophical or ethical (although this would be an interesting – and necessary – debate, as the questions it raises are relevant). What can be seen throughout human history is that technological development has always occurred alongside evolution, for the “better” and for the “worse”. I will therefore focus on the role (current and in the near future) of AI in the world of Business Intelligence, how AI could replace (supplement) Business Intelligence as we know it today now companies are beginning to adopt solutions built around AI platforms, and how these solutions will help create bridges between “traditional” and Big Data Business Intelligence.
There are two types of AI: strong AI and weak AI.
Strong AI refers to a machine that can produce intelligent behavior2 and maintain an impression of real selfconsciousness, true emotion. In this world, the machine can understand what it does (and therefore the consequences of its actions). Intelligence arises from the biology of the brain based on a process of learning and reasoning (thus it is material and follows an “algorithmic” logic). In this regard,
