Muutke küpsiste eelistusi

Beyond Algorithms: Delivering AI for Business [Pehme köide]

, (IBM T. J. Watson Research Center, New York, USA),
  • Formaat: Paperback / softback, 286 pages, kõrgus x laius: 254x178 mm, kaal: 500 g, 24 Tables, black and white; 62 Line drawings, color; 1 Line drawings, black and white; 62 Illustrations, color; 1 Illustrations, black and white
  • Ilmumisaeg: 30-May-2022
  • Kirjastus: Chapman & Hall/CRC
  • ISBN-10: 0367613263
  • ISBN-13: 9780367613266
Teised raamatud teemal:
Beyond Algorithms: Delivering AI for BusinessSuurem pilt
  • Formaat: Paperback / softback, 286 pages, kõrgus x laius: 254x178 mm, kaal: 500 g, 24 Tables, black and white; 62 Line drawings, color; 1 Line drawings, black and white; 62 Illustrations, color; 1 Illustrations, black and white
  • Ilmumisaeg: 30-May-2022
  • Kirjastus: Chapman & Hall/CRC
  • ISBN-10: 0367613263
  • ISBN-13: 9780367613266
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9780367613266.html
Märksõnad:
With so much artificial intelligence (AI) in the headlines, it is no surprise that businesses are scrambling to exploit this exciting and transformative technology. Clearly, those who are the first to deliver business-relevant AI will gain significant advantage.

However, there is a problem! Our perception of AI success in society is primarily based on our experiences with consumer applications from the big web companies. The adoption of AI in the enterprise has been slow due to various challenges. Business applications address far more complex problems and the data needed to address them is less plentiful. There is also the critical need for alignment of AI with relevant business processes. In addition, the use of AI requires new engineering practices for application maintenance and trust.

So, how do you deliver working AI applications in the enterprise?

Beyond Algorithms: Delivering AI for Business answers this question. Written by three engineers with decades of experience in AI (and all the scars that come with that), this book explains what it takes to define, manage, engineer, and deliver end-to-end AI applications that work. This book presents:





Core conceptual differences between AI and traditional business applications A new methodology that helps to prioritise AI projects and manage risks Practical case studies and examples with a focus on business impact and solution delivery Technical Deep Dives and Thought Experiments designed to challenge your brain and destroy your weekends

Arvustused

"Much has been written about the mechanics of modern AI - what it is, what it isn't, and how to build and train one - but this fascinating book is among the few that explain what it means for the enterprise - where it fit in to business needs, how it can be integrated into existing systems, and how it can create opportunities for new value." -- Grady Booch, IBM Fellow, Chief Scientist for Software Engineering

"I recommend Beyond Algorithms - Delivering AI for Business to anyone that is responsible for leading and transitioning AI to achieve and maintain a competitive advantage. The book addresses many of the important issues faced in the application of AI to businesses. It is written in an easy to read style, while providing enough technical content that many people working in the field can capture the main messages." -- David R. Martinez, MIT Instructor and Laboratory Fellow

"Books about AI too often tend towards the extremes -- either praising or condemning unconditionally. Beyond Algorithms offers a more nuanced, nontechnical introduction from experienced practitioners. Filled with guiding principles and real-world examples, this book offers a balanced view of AI's strengths and limitations and provides practical advice for problem selection, project management, and expectation setting for AI initiatives." -- Emily Riederer, Senior Analytics Manager at Capital One

"Beyond Algorithmsis one of those wonderful story-driven IT books that distils decades worth of real world experience into intoxicating wisdom that is refreshingly easy to consume. The book plows through and around the hype of the 'all powerful' deep neural nets to provide an engineering approach for the field. It wont tell you how to establish the Singularity, but its A to I checklist, accuracy and monitoring advice, and doability method could make you an AI delivery hero." -- Richard Hopkins, FREng FIET CEng, former President of IBMs Academy of Technology

Important and timely. A practical guide for realizing business value through AI capabilities! Rather than focusing on hard to access details of the mathematics and algorithms behind modern AI, this book provides a roadmap for a diversity of stakeholders to realize AI business solutions that are reliable, responsible, and sustainable. -- Matthew Gaston, AI Engineering Evangelist

Authors xi
Acknowledgements xiii
Prologue 1(2)
Chapter 1 Why This Book?
3(8)
AI is Everywhere
3(1)
Enterprise Applications
4(1)
AI Winters
5(2)
What is Different Now?
7(1)
Proceed With Caution!
8(1)
Delivering AI Solutions
8(1)
Better Understanding of AI is Critical For Society
8(1)
Target Audience For the Book
9(1)
An Outline of the Book
9(1)
References
10(1)
Chapter 2 Building Applications
11(44)
What's Different About AI When Building An Application?
11(4)
Prominent AI Applications of the Last Seven Decades
15(8)
Ai Or No AI?
23(1)
The Present -- The Dominance of the Web
24(3)
The Future -- The Enterprise Strikes Back
27(4)
Examples of Real Enterprise Applications
31(7)
Where Do You Introduce AI?
38(1)
Activities in Creating An AI Application
39(3)
Complexity of AI Applications
42(2)
Architectural and Engineering Considerations
44(2)
Three Stages of An Enterprise AI Application
46(2)
Enabling Enterprise Solutions At Scale
48(3)
In Summary -- Are You Ready To Start Building Applications?
51(1)
References
52(3)
Chapter 3 It's Not Just the Algorithms, Really!
55(40)
Introducing Algorithms
56(1)
Algorithms in AI
57(2)
Algorithm Addiction
59(1)
Applications Versus The Underlying Technology
60(1)
Algorithms and Models
60(3)
Object Dropping Problem
63(2)
Understanding the Object Dropping Data
65(2)
Four Models To Predict Object Breakage
67(8)
Comparing the Two ML Approaches
75(3)
Comparing Physics Model With Ml
78(1)
What Are the ML Algorithms Actually Learning?
79(2)
Feature Definition and Extraction
81(2)
Revenge of the Artificial Neural Networks
83(1)
Human Interpretation of Artificial Neural Networks
84(1)
So Which Algorithm is Best?
85(1)
Transfer Learning
86(2)
Reinforcement Learning
88(1)
Brain Versus Artificial Neural Networks
88(2)
Fundamental Principles and Fundamental Mistakes
90(1)
So It Really Isn't All About the Algorithm
91(1)
In Summary -- There Really is So Much More To AI Than the Algorithms
92(1)
References
93(2)
Chapter 4 Know Where to Start - Select the Right Project
95(14)
The Doability Method
96(1)
Innovation and Emerging Technologies
96(1)
A Portfolio-Based Approach
97(1)
Doability Method Step 1 -- To AI Or Not AI
97(4)
Three Recommendations From Doability Method Step 1
101(1)
Do Ability Method Step 1 Worked Examples
102(4)
Do Ability Method Step 2 Prioritising AI Projects in the Portfolio
106(1)
In Summary -- Success Or Failure Will Depend On Selecting the Right Project
107(1)
References
108(1)
Chapter 5 Business Value and Impact
109(28)
What is Different About AI Applications?
110(1)
Building Business Cases
110(2)
Stakeholders
112(5)
Measurability and Understandability
117(2)
Importance of Ethics in AI Development
119(3)
Delivering Trustworthy AI
122(1)
Fairness and Bias
123(5)
Explainability
128(4)
Transparency
132(1)
Tackling the Weakness of ML Systems
133(1)
In Summary -- There's More To Value Than Monetary Return
134(1)
References
135(2)
Chapter 6 Ensuring It Works -- How Do You Know?
137(26)
Managing Quality of Traditional Software
137(2)
Managing Quality of AI Applications
139(1)
Statistical Accuracy
140(6)
Cost Functions
146(1)
Multiple Outcomes
147(1)
Quality Metrics For Natural Language Understanding
147(3)
What Does This Mean in Practice?
150(4)
How Accurate Does It Need To Be?
154(2)
Where Do You Assess Accuracy and Business Impact?
156(1)
Operating Within Limits
156(3)
Quality Attributes of Trustworthy AI Systems
159(1)
In Summary -- If the AI Isn't Trustworthy, People Won't Trust It
160(1)
References
161(2)
Chapter 7 It's All about the Data
163(38)
Data Tsunami
164(2)
Datatypes
166(1)
Data Sources For AI
167(1)
Data For the Enterprise
168(1)
Enterprise Reality
168(1)
Humans Versus AI -- Learning and Decision-Making
169(1)
Data Wrangling
170(1)
How Much Data Do We Need?
171(8)
So, What Features Do We Need?
179(4)
Enabling Expanding Feature Spaces
183(1)
What Happens in the Real World?
183(4)
Coping With Missing Data
187(2)
Use of Synthetic Data
189(4)
Managing the Data Workflow
193(5)
Improving Data Quality
198(1)
In Summary -- It Really is All About the Data!
199(1)
References
199(2)
Chapter 8 How Hard Can It Be?
201(18)
Demonstrations Versus Business Applications
201(2)
Setting Expectations Yours and Others!
203(1)
Do We Need An Invention?
203(1)
Current State of AI
204(4)
The Importance of Domain Specialists
208(1)
Business Change and AI
208(2)
AI is Software
210(1)
The Great Reuse Challenge
210(4)
The AI Factory
214(2)
In Summary -- It Can Be As Hard As You Make It
216(1)
References
217(2)
Chapter 9 Getting Your Priorities Right
219(12)
AI Project Assessment Checklist
220(4)
Using the Doability Matrix
224(6)
In Summary - Never Take Off Without Completing Your Checklist
230(1)
Reference
230(1)
Chapter 10 Some (Not So) Boring Stuff
231(26)
Traditional Engineering
231(3)
Why is Engineering AI Different?
234(4)
Four Phases of an AI Project
238(1)
Developing an Enterprise AI Application
239(1)
AI Model Lifecycle
240(2)
Application Lifecycle
242(2)
Application Integration and Deployment
244(7)
Project Management
251(1)
Auditability and Explainability
252(1)
Security
253(1)
In Summary -- The Boring Stuff Isn't Really Boring
254(1)
References
254(3)
Chapter 11 The Future
257(18)
It's All About The Data -- Trends in the Enterprise
259(1)
Efficient Computing For AI Workloads -- New Paradigms
260(3)
Advances in Algorithms -- Targeting Data Challenges and Neuro-Symbolic AI
263(6)
Al Engineering -- Emergence of A New Discipline
269(2)
Human--Machine Teaming
271(3)
In Summary -- Some Final Thoughts
274(1)
References 275(4)
Epilogue 279(2)
Index 281
James Luke, is an Engineer with over 25 years experience delivering real AI solutions that solve real world problems. James is the Innovation Director at Roke, a leading UK technology company, having previously worked as an IBM Distinguished Engineer and Master Inventor. James has multiple US patents in subjects relating to AI and, for his PhD, researched the application of AI in detecting previously unseen computer viruses. James is an experienced conference speaker and has given evidence on the development of AI to both the European Commission and the House of Lords Select Committee. In 2018, James delivered a TEDx talk entitled, How To Survive An AI Winter ( https://www.youtube.com/watch?v=MWOkEVdITIg ). James started his career failing to deliver an AI solution for a leading Formula 1 team. This experience changed Jamess understanding and perspective on what it takes to actually deliver a working AI solution. James responded to his early failure by developing new methods for the definition, design and delivery of AI solutions. He has delivered projects in multiple industries from Public Sector to Insurance and Retail. Prior to joining Roke, James held a number of key positions in IBM including Chief Architect for Watson Tools, CTO of the Cognitive Practice in Europe and Leader of the Academy of Technology core team on AI.

Dr. Padmanabhan Santhanam is currently a Principal Research Staff Member at the IBM T. J. Watson Research Center in New York, working to enable AI systems in government and public sector. His personal research interest is both in the use of AI for engineering traditional software systems and the emerging field of AI Engineering (i.e. how to engineer trust-worthy AI systems). Prior to that, Dr. Santhanam worked on several aspects of AI strategy and execution in IBM Research. He holds a Ph.D. in Applied Physics from Yale University. Dr. Santhanam worked in software engineering research for two decades, having to do with the creation of tools and methodology to improve commercial software development. His interests included software quality metrics, automation of software test generation, realistic modeling of software development processes, etc. He has more than fifty published research papers in peer-reviewed journals and conferences in a variety of topics. He is a member of the ACM & AAAI and a Senior Member of the IEEE. He is also a Member of the IBM Academy of Technology.

David Porter is currently an Associate Partner at IBM Consulting. He graduated in 1995 from the University of Greenwich with a degree in Information Systems Engineering. He has worked in AI and Data Science ever since, with consultancy roles at SAS Software, Detica/BAE Systems and now IBM. Early on in his career he chose to focus on counter-fraud and law enforcement systems. This specialisation has allowed him to work with governments and organisations all over the world. Achievements in this field include the co-invention of the graph analytics software NetReveal and leading the design teams for both the UK's Insurance Fraud Bureau and the original Connect system at Her Majesty's Revenue and Customs (HMRC). He joined IBM in 2016, enticed by the Watson story; could AI be used to catch crooks? He has been putting Natural Language Processing to good use ever since.