More Related Content Similar to Ai, business, organizations, society T. evgeniou (20) Ai, business, organizations, society T. evgeniou1. AI, Business, Organizations, Society:
Holistic Approach to the Humans+Machines Loop
Theos Evgeniou
Professor Decision Sciences and Technology Management,
INSEAD
theodoros.evgeniou@insead.edu
[Presentation Available on LinkedIn]
2. ▪ Early/Mid 1990s: Enterprise Resource Planning (e.g. SAP, Oracle, etc.) –
Business Process Re-engineering, process automation and support
▪ Mid/Late 1990s: Internet – networks, global connectivity, information
capturing/sharing, increased “richness and reach”, virtual teams, etc.
▪ Early 2000s: Knowledge Management – capture, share, reuse, connect
internal knowledge, experts, best practices, etc.
▪ Early/Mid 2000s: Customer/Supplier/etc. Relationship Management –
acquire, manage, understand, cross/up-sell, retain customers/suppliers/etc.
▪ Late 2000s/Early 2010s: Big Data + Cloud – the last step before…
© T. Evgeniou, INSEAD
Technological Innovations for Business: 1990s to Today
3. All previous technologies were at best
decision support tools
AI can
take increasingly complex decisions
© T. Evgeniou, INSEAD
4. Automation (of “simple” – often repetitive – decisions and
processes) has been happening for centuries [certain
mechanization in earlier centuries, autopilots, etc.]
However, with AI we will be able for the first time ever to
“automate” decisions not imaginable today to be done by
anyone other than humans
© T. Evgeniou, INSEAD
History: from past to future
6. Experience + Learning à (Human) Intelligence
Experience for Humans = (Big) Data for Machines
(Big) Data + Machine Learning à Artificial Intelligence
© T. Evgeniou, INSEAD
Learning is at the Core of Intelligence
Big Data vs Machine Learning vs AI [watch video]
7. (Big) Data
+
Computation (and Cloud)
+
Mathematics of Machine Learning (Statistical Learning Theory, etc)
à Artificial Intelligence
© T. Evgeniou, INSEAD
AI in Business: Why Now?
8. AI, more than any other technology, requires a more
Holistic Approach
© T. Evgeniou, INSEAD
[Watch Video]
9. Data Engineering and Quality Management
Machine Learning and Analytics
Project Management
IT infrastructure and Management
Society
Organizational Change
Industry Transformation
Regulation
Principles and Philosophy (e.g., Ethics)
Key Message:
AI requires a Holistic Approach
© T. Evgeniou, INSEAD
10. Data Engineering and Quality Management
Machine Learning and Analytics
Project Management
IT infrastructure and Management
Society
Organizational Change
Industry Transformation
Regulation
Principles and Philosophy (e.g., Ethics)
Key Message:
AI requires a Holistic Approach
© T. Evgeniou, INSEAD
11. Example Use Cases
Credit Scoring
(since the 80s – HNC’s “Database Mining Workstation”)
Recommender Systems and cross-selling
(Amazon/Netflix/etc. since the 90s)
Churn Management
(with ML/AI since the early 2000s)
Targeted advertisement and campaigns
(since early 2000s)
… other Customer Relationship Management (Marketing) decisions...
The Early Days...
22. “We can’t rely on machine learning to stop another financial crisis - In fact, overreliance on AI and big data could lead to the
next one”
© T. Evgeniou, INSEAD
28. How is this all (technically) feasible?
Demystifying AI and Machine Learning…
© T. Evgeniou, INSEAD
29.
Conceptual Model of Technology
(“the rough knowledge of a technology we need to have
in order to use it effectively”)
… and some Historical Perspectives
© T. Evgeniou, INSEAD
30. Two types of AI
Symbolic AI Statistical AI
✓ Explicit “hand written rules” [human designs]
✓ Relatively slower update of rules
✓ Learning mainly from human experience
✓ “Limited” data usage
✓ Rules + Models from data [not only from human]
✓ Rules can evolve fast, e.g. with environment
✓ Learning mainly from data
✓ “Unlimited” data usage
© T. Evgeniou, INSEAD
[Article]
31. Two types of AI
Symbolic AI Statistical AI = Machine Learning
© T. Evgeniou, INSEAD
32. Rules... Is this how Humans (often) decide?
• (Light AND Lasting Battery) OR (Big Screen AND Good Camera ) OR …
• (Floats AND High Resolution) OR (Floats AND Small Size) OR (Floats AND Light
Weight) OR …
• (Announced Buyback AND Small Cap AND High Idiosyncratic Risk) OR (Small Cap
AND Value Stock) OR (Mid Cap AND High Momentum Stock) OR …
• (M&A Live Target AND Same Industry as Acquirer) OR (M&A Live Target AND Friendly
Acquisition) OR (M&A Live Target AND Cash Deal AND Friendly) OR …
© T. Evgeniou, INSEAD
[Article link]
[Article link]
[Article link]
34. Polanyi’s Paradox
“We know more than we can tell”
Michael Polanyi, 1958
The “earlier way of thinking about AI and Computers”:
“For a computer to accomplish a task, a programmer must first fully understand the sequence
of steps required to perform that task, and then must write a program that, in effect, causes the
machine to precisely simulate these steps.”
David Autor, MIT, 2014
© T. Evgeniou, INSEAD
35. Symbolic AI: User Writes the Rules/Program
Computer
Data
Program
Output
Computer
Data
Output Program
(Adopted from Domingos, 2017)
Statistical AI: Machine Learns/Writes the Rules/Program
© T. Evgeniou, INSEAD
36. Demystifying AI and Machine Learning:
Example from “Learning to Recognize People”
1. What is an “image” (for a machine)?
2. How does the machine “represent what it learned”?
© T. Evgeniou, INSEAD
37. Everything for a Computer is Data/Numbers
(12, 92, 74, 0, 12, …., 124)
Data REPRESENTATION:
• Pixel Values
• Projections on filters
• PCA
Feature Learning
© T. Evgeniou, INSEAD
(Article link)
38. How does the machine “represent what it learned”?
(1,13,…)
(92,10,…)
(41,11,…)
(19,3,…)
(4,24,…) (7,33,…)
(4,71,…)
A Function
© T. Evgeniou, INSEAD
39. “Why is it that the simple, abstract language of mathematics can accurately capture so much
of our infinitely complex world [human intelligence]?”
The “New Physics of Intelligence”?
Eugene Wigner, Physics Nobel laureate, 1959
“The Unreasonable Effectiveness of Mathematics in the Natural Sciences”
© T. Evgeniou, INSEAD
40. It is mainly (only?) about Functions...
© T. Evgeniou, INSEAD
Rules are also Functions
41. From “Rules” Functions to “Black Box” Functions
Most Machine Learning methods “learn” (find) functions that cannot be
expressed, and explained, with simple rules.
[the learned functions are often kind of “infinite rules”, with “infinite
number of parameters” – some may say like the “infinite” connections
between neurons developed/learned due e.g. to neural plasticity]
The “AI Polanyi’s Paradox”?
Machines, like humans, know more than they can tell
© T. Evgeniou, INSEAD
43. Data Engineering and Quality Management
Machine Learning and Analytics
Project Management
IT infrastructure and Management
Society
Organizational Change
Industry Transformation
Regulation
Principles and Philosophy (e.g., Ethics)
Key Message:
AI requires a Holistic Approach
© T. Evgeniou, INSEAD
48. Data Engineering and Quality Management
Machine Learning and Analytics
Project Management
IT infrastructure and Management
Society
Organizational Change
Industry Transformation
Regulation
Principles and Philosophy (e.g., Ethics)
Key Message:
AI requires a Holistic Approach
© T. Evgeniou, INSEAD
49. “[This] isn’t a software package; it’s a way of doing business”
Quote from an executive
“The dominant issue in computer technology will be the ability to implement human
behavior change.”
Warren McFarlan, 1968
© T. Evgeniou, INSEAD
50. Data Engineering and Quality Management
Machine Learning and Analytics
Project Management
IT infrastructure and Management
Society
Organizational Change
Industry Transformation
Regulation
Principles and Philosophy (e.g., Ethics)
Key Message:
AI requires a Holistic Approach
© T. Evgeniou, INSEAD
[Article]
52. Data Engineering and Quality Management
Machine Learning and Analytics
Project Management
IT infrastructure and Management
Society
Organizational Change
Industry Transformation
Regulation
Principles and Philosophy (e.g., Ethics)
Key Message:
AI requires a Holistic Approach
© T. Evgeniou, INSEAD
54. Deep Learning and Business
© T. Evgeniou, INSEAD
New Business/Industry
No New Business/
Industry
[INSEAD
MBA course
on Data
Science and
AI]
57. Kasparov’s Laws (The “Missing Middle”)
[watch video]
© T. Evgeniou, INSEAD
Weak Human + Machine + Strong Process >
Strong Human
Weak Human + Machine + Strong Process >
Strong Machine
Weak Human + Machine + Strong Process >
Strong Human + Machine + Inferior Process
58. The Pivotal Management Challenge of the AI Era will be…
(INSEAD Knowledge, April 8, 2019) [watch video]
© T. Evgeniou, INSEAD
“How can we get Humans and
Machines to Best Work Together”
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Theos Evgeniou
Professor Decision Sciences and Technology Management,
INSEAD
theodoros.evgeniou@insead.edu