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E-raamat: AI Ladder: Accelerate Your Journey to AI

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  • Formaat: 226 pages
  • Ilmumisaeg: 30-Apr-2020
  • Kirjastus: O'Reilly Media
  • Keel: eng
  • ISBN-13: 9781492073383
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AI Ladder: Accelerate Your Journey to AISuurem pilt
  • Formaat: 226 pages
  • Ilmumisaeg: 30-Apr-2020
  • Kirjastus: O'Reilly Media
  • Keel: eng
  • ISBN-13: 9781492073383
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AI may be the greatest opportunity of our time, with the potential to add nearly $16 trillion to the global economy over the next decade. But so far, adoption has been much slower than anticipated, or so headlines may lead you to believe. With this practical guide, business leaders will discover where they are in their AI journey and learn the steps necessary to successfully scale AI throughout their organization.

Authors Rob Thomas and Paul Zikopoulos from IBM introduce C-suite executives and business professionals to the AI Ladder&;a unified, prescriptive approach to help them understand and accelerate the AI journey. Complete with real-world examples and real-life experiences, this book explores AI drivers, value, and opportunity, as well as the adoption challenges organizations face.

  • Understand why you can&;t have AI without an information architecture (IA)
  • Appreciate how AI is as much a cultural change as it is a technological one
  • Collect data and make it simple and accessible, regardless of where it lives
  • Organize data to create a business-ready analytics foundation
  • Analyze data, and build and scale AI with trust and transparency
  • Infuse AI throughout your entire business and create intelligent workflows
Preface ix
1 What in the AI? How Did We Get Here?
1(14)
Collecting Data in Real Time, but Understanding It in Stale Time
3(3)
The Modality of Everything and the Data Collection Curve
6(1)
Even Steeper: The Future of the Data Collection Curve
7(1)
Where We Are Now---Haystacks, Needles, and More Data
8(2)
How to Displace Today's Disrupters
10(1)
Let's Get Ready for a Climb!
11(4)
2 The Journey to AI
15(26)
What Is Artificial Intelligence, Anyway?
19(8)
Types of AI
22(1)
Data
23(2)
Models
25(2)
Where AI Has Been
27(3)
What Does AI Mean for Business?
30(1)
The Journey to AI
31(4)
All Radically New Technologies Face Resistance
35(1)
Where Are We Now? And Where Are We Going?
36(2)
Moving Forward
38(3)
3 How to Overcome AI Failures and Challenges
41(20)
AI's Emergence in Business Today
41(5)
Data
42(2)
Computing Power
44(1)
Investment
45(1)
Early Examples of AI Success
46(2)
Example: Vodafone's TOBi Transforms the Customer Experience
46(1)
Example: How a French Bank Built on Its Strength of Quality Customer Service
47(1)
Early AI Failures
48(1)
AI Challenges: Data, Talent, Trust
49(10)
AI Challenge: Data
49(2)
AI Challenge: Talent
51(5)
AI Challenge: Trust
56(3)
Overcoming Challenges with Advanced Research and Products
59(1)
Overcoming Challenges with the Right Partner
60(1)
4 The AI Ladder: A Path to Organizational Transformation
61(12)
Suitability of AI
62(1)
Determining the Right Business Problems to Solve with AI
63(1)
Building a Data Team
64(1)
Putting the Budget in Place
64(1)
Developing an Approach
65(1)
There Is No AI Without IA
66(2)
The AI Ladder
68(4)
Collect
69(1)
Organize
70(1)
Analyze
71(1)
Infuse
71(1)
Simplify, Automate, and Transform
72(1)
5 Modernize Your Information Architecture
73(28)
A Modern Infrastructure for AI
76(4)
All Parts Are Visible
76(1)
Legacy Systems Are Made Accessible or Eliminated
77(2)
All Parts of the System Are Continuously Monitored
79(1)
Inefficiencies Are Identified and Removed
79(1)
New Architectures for IT
79(1)
Data: The Fuel; Cloud: The Means
80(3)
To the Cloud, and Beyond: Cloud as Capability
80(2)
Fuel for the Fire
82(1)
From Databases to Data Warehouses, Data Marts, and Data Lakes
83(4)
Example: Wireless Carrier Architects a Solution Using Both a Data Lake and a Data Warehouse
86(1)
Data Virtualization
87(7)
Unifying Access to Data Through Big SQL
89(1)
Object Storage as the Preferred Fabric
90(2)
Open Data Stores and Open Data Formats
92(1)
Next-Generation Databases
93(1)
The Power of an AI Database
94(1)
Streaming Data
95(1)
Get the Right Tools
95(1)
The Importance of Open Source Technologies
96(2)
Community Thinking and Culture
96(1)
High Code and Component Quality
97(1)
Real Examples of Modernizing IT Infrastructure
98(1)
Example: Siemens Looks to the Cloud to Unify Its Data Processes
98(1)
Example: Fannie Mae Transforms with a Governed and Centralized Data Environment
98(1)
Don't Neglect the Foundation!
99(2)
6 Collect Your Data
101(14)
What Needs to Happen on the Collect Rung
102(2)
Example: EMC Develops a Data Collection Strategy
103(1)
Start with a Data Census: Learn What's Out There
104(2)
Understand Data in a Business Context, and Partner with SMEs
106(1)
Getting Beyond Transactional Data
107(1)
The Challenges of Collecting New Sources of High-Volume Unstructured Data
108(1)
Organizational Aspects of Data Access
108(3)
Example: Procter & Gamble Avoid Data Silos Using a Central Data Warehouse
109(1)
Example: eBay Eliminates Data Silos by Publishing Business Processes as APIs
110(1)
Ownership, Stewardship, Regulatory Compliance, and Discipline
111(1)
Example: Owens-Illinois
112(1)
Collecting Data: You Can Win This Battle!
112(3)
7 Organize Your Data
115(20)
Poor Data Leads to Poor AI
116(1)
Regulation Demands Quality Data
117(1)
What Needs to Happen on the Organize Rung
118(1)
Cleaning Data
118(4)
Documenting and Cataloging Data
122(2)
Understanding Data: The "Seller" Gong Show
124(1)
Metadata for Models
125(1)
Maintaining the Catalog
126(1)
Governing Data
126(6)
Enterprise Performance Management
130(1)
Example: ANZ Banking Group Embeds Sound Data Management and Governance Policies
131(1)
DataOps
132(1)
Now That Your Data Is Trustworthy, on to Analysis!
133(2)
8 Analyze Your Data
135(20)
Why Organizations Need an End-to-End AI Lifecycle
136(1)
Build
136(1)
Example: Using Machine Learning, an Insurer Cuts Costs and Boosts Productivity
137(1)
Run
137(1)
Manage
138(3)
Aligning Model Output with Business Metrics
138(1)
Learning, Iterating, Learning
139(1)
Example: Risk Management Company Gets Creative to Offset the Expense of Training Models
140(1)
Automating the AI Lifecycle
141(4)
AutoAI
142(2)
NeuNetS
144(1)
Incorporating AI into DevOps Processes
145(3)
Emphasizing Trust and Transparency
148(5)
Example: By Shining Light on Data Attributes, a Bank's AI System Demonstrates Integrity, Fairness, Explainability, and Resiliency
150(1)
Example: Avoiding the "Black Box" Dilemma
151(1)
Avoiding the Piecemeal Approach
152(1)
Example: SaaS Company Gleans New Insights by Applying AI to Historical Data
152(1)
Ready to Infuse...
153(2)
9 Infuse AI Throughout the Business
155(10)
Customer Service
156(2)
Financial Operations
158(1)
Risk and Compliance
159(1)
IT Operations
160(1)
Business Operations
160(2)
Themes Across All Intelligent Workflows
162(1)
Building the Next-Generation C-Suite
163(2)
10 Tips and Best Practices on How to Get Started
165(16)
Manage Organization-Wide Change
165(4)
Change in Daily Tasks
166(1)
Change in Overall Business Processes
166(2)
Change in Thinking About Data
168(1)
Make Data a Team Sport (And Some Cool History About Car Racing)
169(4)
Subject Matter Experts
170(1)
Data Scientists
170(1)
Data Operations (DataOps) Specialists
171(1)
Data Engineers
172(1)
Training for Career Development
172(1)
Embrace AI Centers of Excellence
173(1)
Example: Honda Sets Up Knowledge Hubs to Build Minimum Viable Products, Organize Training, Share Data
173(1)
Build Ethics Into Your Process
174(3)
Privacy
175(1)
Safety
175(1)
Fairness
176(1)
Building Trust in AI
177(1)
Choose Projects Selectively, and Embrace Failure
177(2)
Example: Insurer Tracks Metrics to Communicate Success of Its Model
178(1)
Beware of False Negatives
179(2)
11 The Future of AI
181(22)
AI Themes to Take Us Through the Next Five Years
182(6)
Theme #1 AI Is Not a Fad
182(1)
Theme #2 Data-Generating Sensors Will Proliferate
183(1)
Theme #3 Data Will Be Processed at the Edge
184(1)
Theme #4 AI Will Spread Everywhere
185(3)
Theme #5 AI Will Disappear into the Background and Become Boring
188(1)
Future AI Use Cases for Business
188(7)
Cybersecurity
190(1)
Autonomous Driving, Autonomous Everything
190(1)
Conversational Digital Agents and Personal Assistants
191(1)
Real Estate
191(1)
Retail
192(1)
Insurance
193(1)
Customer Service
194(1)
The Future of Work in an AI-Driven World
195(1)
A Deeper Dive into AI and Edge Computing
196(5)
Using the Edge and AI for Good
199(2)
Conclusion
201(2)
Index 203
Robert D. Thomas is the general manager of IBM Data and ArtificialIntelligence. He directs IBM's product investment strategy, salesand marketing, expert labs, and global software product development.

Major product brands under Rob's leadership include Watson, Db2, Netezza, Cognos, SPSS, and InfoSphere. Since joining IBM's softwareunit, Mr. Thomas has held roles of increasing responsibilityincluding business development, product engineering, sales andmarketing, and general management. He has overseen four acquisitions by the firm representing more than $2.5 billion in transaction value.

Mr. Thomas trained in economics at Vanderbilt University, earninghis BA in Economics. During his MBA from the University of Florida, Mr. Thomas worked in equity research, learning applied economics, finance, and financial analysis.

Mr. Thomas has published two books: Big Data Revolution: WhatFarmers, Doctors, and Insurance Agents Can Teach Us About Patterns in Big Data (John Wiley & Sons, 2015), and The End of Tech Companies (self-published, 2016), educating business leaders on how to navigate digital disruption in every industry. Today, he writes extensively on his blog robdthomas.com.

Paul C. Zikopoulos is the Vice President of Cognitive BigData Systems at IBM. Paul has more than 23 years of experience in data, and is seen as a global expert in Big Data and Analytic technologies. Independent groups often recognize Paul as a thought leader with nominations to SAP's"Top 50 Big Data Twitter Influencers", Maptive's "Top 100 Big Data Experts to Follow in 2016", "Big Data Republic's "Most Influential", Onalytica's "Big Data Top 100 Influencers and Brands", and Analytics Week "Thought Leaders in Big Data and Analytics" lists. Big Data Made Simple noted him as one of the "Top 200 Big Data Thought Leaders on Twitter", Technopedia listed him one of its "Big Data Experts to Follow", and GreyCampus included him on their list of "150 Most Influential People in Big Data & Hadoop". He has also been featured on "60 Minutes", speaking on the topic of Big Data, and advises various universities on their graduate-level analytics programs.

Paul has written hundreds of magazine articles and 19 books, including "Big Data: Beyond the Hype", "Hadoop for Dummies", "Harness the Power of Big Data", "Understanding Big Data", "New Dynamic InMemory Analytics for the Era of Big Data", "Risk Free Agile Scaling", "DB2 Certification for Dummies", and "DB2 for Dummies. In his spare time, Paul is active in bolstering Women in Technology and is an advisory board member for Women 2.0, which works to close the gender gaps in tech companies. Paul enjoys all sorts of sporting activities (including the futile resistance and transformation into a "Horse Dad"). Ultimately, Paul is trying to figure out the world according to Chloe--his daughter, whom he notes didn't come with a handbook and is more complex than the topic of BigData, but more fun too.
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