10. First Principle Thinking
Reasoning from first principles, to break
down complicated problems into basic
elements and then reassemble them from
the ground up.
First introduced by Aristotle more than
2,000 years ago
Tesla Modal 3 is a rapid (modular/system)
innovation but not a disruptive innovation?!
How about SpaceX? AI…??
12. Knowledge Building & Decision Making:
Know-what, Know-why, Know-how
Objective? Scientific? Feasible?
Know-how
(Feasible?)
Prescriptive
Know-Why
(Scientific?)
Normative
Know-What
(Objective?)
Descriptive
Prescriptive D-M:
How decisions could be made better?
Descriptive D-M:
How decisions are made?
Normative D-M:
How decisions should be made?
Entrepreneurship
Harvard DBA
Harvard Business Review
BFCGED
23. Matrix = Associations
Rose Navy Olive
Alice 0 +4 0
Bob 0 0 +2
Carol -1 0 -2
Dave +3 0 0
Things are associated
Like people to colors
Associations have strengths
Like preferences and
dislikes
Can quantify associations
Alice loves navy = +4,
Carol dislikes olive = -2
We don’t know all
associations
Many implicit zeroes
Source: Sean Owen(2012), Cloudera
24. In Terms of Few Features
Can explain associations by appealing to underlying
features in common (e.g. “blue-ness”)
Relatively few (one “blue-ness”, but many shades)
(Alice)
(Blue)
(Navy)
Source: Sean Owen(2012), Cloudera
25. Losing Information is Helpful
When k (= features) is small, information is lost
Factorization is approximate
(Alice appears to like blue-ish periwinkle too)
(Alice)
(Blue)
(Navy)
(Periwinkle)
Source: Sean Owen(2012), Cloudera
26. Eigen value, Eigen Vector & Factor Analysis
eigen- is adopted from the German word eigen for
"proper", "characteristic".
In the 18th century Euler studied the rotational motion
of a rigid body and discovered the importance of
the principal axes.
Lagrange realized that the principal axes are the
eigenvectors of the inertia matrix.
square matrix: A
column vector: v
28. ALS Algorithm
• Optimizing X, Y simultaneously is non-convex,hard
• If X or Y are fixed, system of linear equations:convex,easy
• Initialize Y with random values
• Solve for X
• Fix X, solve for Y
• Repeat (“Alternating”)
X
YT
38. World, Model & Theory
Credit: John F. Sowa
generalized statements,
proven scientifically with evidence
Simplified representation, helpful tool to
understand specific phenomena
NormativePrescriptiveDescriptive
46. Knowledge Building & Decision Making:
Know-what, Know-why, Know-how
Objective? Scientific? Feasible?
Know-how
(Feasible?)
Prescriptive
Know-Why
(Scientific?)
Normative
Know-What
(Objective?)
Descriptive
Prescriptive D-M:
How decisions could be made better?
Descriptive D-M:
How decisions are made?
Normative D-M:
How decisions should be made?
Entrepreneurship
Harvard DBA
Harvard Business Review
BFCGED
47. Data Science is the ART tuning data into Action
Segments Reports
For Human
(Explanatory)
Models Data-driven
Actions
Intelligence Effectiveness
51. A new, louder Echo Dot $49.99 Echo Auto $24.99 Echo Sub $129.99
Fire TV Recast $229.99 A slicker Echo Show $229 A new Ring security camera $179
The speaker-less Echo Input $34.99 Alexa microwave $59.99 An Alexa Clock that visualizes your timers
$29.99
56. Paradigm Shift(Reverse)
Move
u Data à program
Value
u Things à Product Service à Personal Service
u Value/revenue shift
u What if phone price is near its cost or free?
72. Amazon vs. XiaoMi
Competitive pricing hardwares
Mi store vs Amazon market
Mi UI vs Alexa
1000- products
1500+ products
73.
74. Knowledge Building & Decision Making:
Know-what, Know-why, Know-how
Objective? Scientific? Feasible?
Know-how
(Feasible?)
Prescriptive
Know-Why
(Scientific?)
Normative
Know-What
(Objective?)
Descriptive
Prescriptive D-M:
How decisions could be made better?
Descriptive D-M:
How decisions are made?
Normative D-M:
How decisions should be made?
Entrepreneurship
Harvard DBA
Harvard Business Review
BFCGED
- A
-.