This is the deck for my workshop at DTEC, the technological incubator of the Government of Dubai, during Entrepreneur Day 2017. #DTEC #AI #ED17

1. source: thenextweb.com (2017)
SMART BUSINESS IN AN
AGE OF INTELLIGENT
MACHINES
Dr. Alessandro Lanteri
Entrepreneur Day
DTEC, Nov 2017
Dubai (UAE)

2. Alessandro Lanteri
Founded (2009), advised business incubators
TEDx speaker (2017)
Advisor, Consultant, Coach
PhD Philosophy & Economics, Erasmus (NL)
MA Economics, Bocconi (IT)
Exec.Ed. Said Oxford (UK), MIT (USA)
Academics
Abu Dhabi University (UAE)
Hult International Business School (UK)
Professor of Entrepreneurship
Ran a marathon | Lived in 15+ global cities
Other Stuff
https://www.youtube.com/watch?v=HAfLCTRuh7U

3. >> universities where I worked <<
UCLA
NYU
HULT
AUB
ADU
ERASMUS
BOCCONI
HELSINKI

4. Open innovation partnership
to propose solutions for the
future of banking and private
banking.
Virgin Money (2015/16)
Portfolio selected recent projects
Open innovation partnership
on the future of mobility. Ran
multiple design sprints.
Ford Motors (2016)
Open innovation partnership
to define business model and
go-to-market strategy for a
smart tag.
ABB (2016)
Open innovation partnership
to design new products and a
new social business model.
Unilever (2017, 2015)
Member of the advisory board
of PACI. Led workshops in
Geneva, Mexico City, London.
Contributed to Davos reports.
World Economic Forum(2016/17)
Open innovation partnership
to design a new service and a
new business model.
www.studentlifestart.com
Virgin Money (2016/17)

5. Exponential Technologies
The Future (and Present) of Work
Exponential Leadership
AI will Save the World. Or Destroy It
Overview
Collective Intelligence
AI & Machine Learning
Autonomous Vehicles
AI Strategy for Disruption
Trends in AI Startups
The Future of AI
Natural Language Processing
Robots and other Machines

6. AI & Machine Learning
Collective Intelligence
AI Strategy for Disruption
Trends in AI Startups
Content for today
Exponential Technologies

7. AI & Machine Learning
Collective Intelligence
AI Strategy for Disruption
Trends in AI Startups
Content
Exponential Technologies

9. Machine Learning (1)
tree/not
tree type
training set features classifica/on value
tree/not
tree type
features + classifica/on valuetraining set

10. Machine Learning (2)
neural network
(deep, convolu/onal, recurrent…)
input algorithm output
[]
1
0
0
1
… []
1
0
0
1
…

11. source: Lee et al. (2011)

13. What do you see?
A group of young people playing
a game of Frisbee.
Computer caption
A group of men playing Frisbee in
the park.
Human caption
source: Google (2016)

14. source: Google (2016)

15. What do you see? http://places2.csail.mit.edu/demo.html
A group of young people playing
a game of Frisbee.
Computer caption
A group of men playing Frisbee in
the park.
Human caption

16. Machine Learning (3)
source: MGI (2017)

17. “Almost all of AI’s recent progress is
through one type, in which some input
data (A) is used to quickly generate
some simple response (B).”
“
source: Ng (2016)
- Andrew Ng -

18. “Can a problem be solved with ML?”
“Are there enough data to train ML?”
“Is the solution predictable enough
for patterns to be reliable?”
?

19. AI & Machine Learning
Collective Intelligence
AI Strategy for Disruption
Trends in AI Startups
Exponential Technologies
Content

20. A Cambrian Explosion in AI
NetworksData Algorithms
Exponential TechsCloud
source: McAfee & Brynjolfsson (2017)

21. source: Kurzweil (2001), Singularity University (2017)

22. Progress
Time
30
1 billion
linear
exponential
source: Kurzweil (2001), Singularity University (2017)
linear: 1, 2, 3, 4… 30
exponential: 1, 2, 4, 8… 109

23. Progress
Time
30
1 billion
linear
exponential
source: Kurzweil (2001), Singularity University (2017)
linear: 1, 2, 3, 4… 30
exponential: 1, 2, 4, 8… 109
😕
😍
😮
DECEPTIVE
GROWTH
DISRUPTIVE
GROWTH

24. “The Law of Accelerating Returns:
Price, performance, and capacity of
information technology progress at a
predictable, exponential rate”
“
source: Kurzweil (2001)
- Ray Kurzweil -

25. source: Diamandis & Kotler (2016), Singularity University (2017)
The 6 D’s of exponential techs
3 EFFECTS
3 PHASES

26. “What amazing ‘next big thing’ does not
seem to be giving any results?”
“What physical product or service can be
digitized?”
“What expensive product will change the
world when it becomes widely available?”
?

27. AI & Machine Learning
Collective Intelligence
AI Strategy for Disruption
Trends in AI Startups
Exponential Technologies
Content

28. The role(s) of humans
Creating Software Selecting Applications Performing Human-­‐only TasksFixing Problems
source: Malone (2017)
Providing Examples Providing Feedback

29. The role(s) of technology
Tool
Tech performs the task
Humans monitor tech
Assistant
Peer
spreadsheets, cruise control
Tech with some supervision
Tech takes some initiative
KLM’s social media bots, StichFix
IBM’s Watson
Tech/Humans perform similar tasks
Humans solve complex cases
Lemonade insurance
Amazon package control
Manager
Tech assigns tasks, evaluates, trains humans
CrowdForge, Cogito
source: Malone (2017)

30. Collective Intelligence
source: Malone (2017)
How can humans and machines be connected so that collec%vely they act
more intelligently than any person, group, or machine can?
Input Output
Machine Humans
Learning Loop
e.g.

31. Collective Intelligence cases
?
https://www.youtube.com/watch?v=fIw7BwIoGus
taking the robot out of the human
https://www.youtube.com/watch?v=6KRjuuEVEZs
making the human into a robot?
Assistant
Peer

32. “What jobs entail many repetitive and
predictable tasks?”
“Can they be automated?”
“Will a computer take these job?”
?

33. AI & Machine Learning
Collective Intelligence
AI Strategy for Disruption
Trends in AI Startups
Exponential Technologies
Content

34. Tailoring products to
narrow market segments.
Niche
Serving customers at a
lower cost than
competitors.
Cost Leadership
Providing customers with
unique value.
Differentiation
AI & Business Strategy
source: Porter (1979), Martin (2015)
Robotic Process Automation
Loan default
Axa “large loss”
Google search algorithms
IBM Watson
TellusLab
Clustering to identify niches
Netflix & Amazon
recommendation systems

35. source: MGI (2017)

36. “What industry has the indicators of
potential for disruption?”
“What archetype of disruption would work
in that industry?”
“What disruption will ensue?”
?

37. AI & Machine Learning
Collective Intelligence
AI Strategy for Disruption
Trends in AI Startups
Exponential Technologies
Content

38. source: Corea (2017)
Trends in AI startups

39. source: Corea (2017)
Trends in AI startups

40. Trends in AI startups
source: Corea (2017)
open source (academics, raise bar for
competition, lower bar for adoption, multiple
crowd/platform effects)
Unprofitable
similar to pharma (long-term, uncertain
returns to expensive R&D)
data is the new oil
Pharma-like + Oil
exit in 1-3 years
poaching of scarce AI talent
Early acqui-hire

41. source: CB Insights, in: Corea (2017)
Trends in AI startups

42. source: Corea (2017)
Trends in AI startups

43. thank you
source: www.slidemash.com
Alessandro Lanteri
alelanteri@gmail.com

44. References
Corea, F. (2017). Artificial Intelligence and Exponential Technologies: Business Models Evolution and New Investment
Opportunities. Springer.
Diamandis, P. & S. Kotler (2016). Bold. How to Go Big, Create Wealth and Impact the World. Simon & Schuster.
Kurzweil, R. (2001). “The law of accelerating returns”, www.kurzweilai.net/the-­‐law-­‐of-­‐accelerating-­‐returns
Lee, H., Grosse, R., Ranganath, R. & A. Ng (2011). “Unsupervised Learning of Hierarchical Representations with
Convolutional Deep Belief Networks”. Comm. ACM 2011.
Malone, T. (2017). MIT Sloan & MIT CSAIL Artificial Intelligence: Implications for business strategy program 2017-­‐10-­‐30. MIT.
Martin, R. (2015). “There Are Still Only Two Ways to Compete”, Harvard Business Review, April.
McAfee, A. & Brynjolfsson, E. (2017). Machine, Platform, Crowd: Harnessing Our Digital Future. Norton & Company.
McKinsey Global Institute (2016). The age of analytics: Competing in a data-­‐driven world. www.mckinsey.com/business-­‐
functions/mckinsey-­‐analytics/our-­‐insights/the-­‐age-­‐of-­‐analytics-­‐competing-­‐in-­‐a-­‐data-­‐driven-­‐world
Ng, A. (2016). What AI Can and Can't Do. https://hbr.org/2016/11/what-­‐artificial-­‐intelligence-­‐can-­‐and-­‐cant-­‐do-­‐right-­‐now
Porter, M. (1979). “How Competitive Forces Shape Strategy”, Harvard Business Review, 57.
Singularity University (2017). Understanding Exponentials. https://beta.su.org/courses/understanding-­‐exponentials