1. The document discusses the future of artificial intelligence and its interaction with humans. It proposes a vision of a "Human AI" where humans and machines cooperate through a system of open algorithms and governance. 2. It provides background on AI, discussing how machine learning works and addressing concerns about job losses. It advocates a strategy where humans direct strategy and oversight while machines handle tactics. 3. The Open Algorithms project aims to test this approach through a public-private partnership accessing private data to power algorithms that benefit public policy, while ensuring ethics, relevance and user capacity building. It seeks to move from data/algorithm tyranny to democratic governance.

2. Humans and Machines:
A Future with Artificial Intelligence?
Emmanuel Letouzé, PhD
Visiting Scholar, MIT Media Lab
Connection Science Fellow, MIT
Director & co-Founder, Data-Pop Alliance
Program Director, OPAL Project
3rd Encuentro Mundial Big Data
Bogotá, April 25th 2017

3. Outline
1. A short introduction and 2 big questions
2. Concepts and context: AI 101 in 2018
3. AI, creative destruction, and destructive creation
4. A BrAIgther vision: A human strategy for a “Human
AI”
5. Short-term milestone: Open Algorithms

4. Outline
1. A short introduction and 2 big questions
2. Concepts and context: AI 101 in 2018
3. AI, creative destruction, and destructive creation
4. A BrAIgther vision: A human strategy for a “Human
AI”
5. Short-term milestone: Open Algorithms

6. 1. Are we ever going to be enslaved by robots?
Lose our jobs? Have a machine-driven
nuclear war? Or get all very rich? Get paid to
do no work? Hopefully and probably none of
the above. But…
2. Can we envision and build a better world
where humans and machines cooperate – a
”Human AI” or “human-machine ecology”?
What would it feel like, look like, and take?

7. Outline
1. A short introduction and 2 big questions
2. Concepts and context: AI 101 in 2018
3. AI, creative destruction, and destructive creation
4. A BrAIgther vision: A human strategy for a “Human
AI”
5. Short-term milestone: Open Algorithms

8. What is “Artificial
Intelligence” ?Artificial intelligence—broadly-- is
the simulation of human
intelligence processes by computer
systems, especially artificial neural
networks (ANNs) inspired by
the biological neural networks that
constitute animal brains, which can
"learn" (i.e. progressively improve
performance on) through iterations
and feedback. Basically it’s
algorithms that learn to automate
parts or all of tasks, and the
machines they power. (It’s also what
has not been invented yet)
Input(s)
Hidden layer(s)
Output(s)

9. The basics of AI is learning through many
feedbacks
1. Try to guess / recognize. Right or Wrong?
2. Correct: +1. Reward!
3. Incorrect: -1. Penalty.
4. Repeat and learn through a feedback
loop.
 (The) machine (is) learning!

11. Is AI some new (black) magic? No…but…
No…
1.It is at least 60+ years old.
2.The big difference is data.
More on that later.
3.It still generalizes poorly. It
has no sense of context. It is
still pretty stupid.
4.We are far from general AI.
5.Humans are still in control
(for better or worse)
…but…
1. The (good) magic / core of the current AI is
the credit assignment function to encourage
and reinforce neurons / functions that help
the most achieve the goal (and reverse if
not)

12. Ian Morris, chart of all human
history
Reflecting on and learning from
history

13. Outline
1. A short introduction and 2 big questions
2. Concept and context: AI 101 in 2018
3. AI, creative destruction, and destructive creation
4. A BrAIgther vision: A human strategy for a “Human
AI”
5. Short-term milestone: Open Algorithms

16. “I’m not too worried about machines replacing cartoonists,”
the artist R. Kikuo Johnson says, about his cover for the
Money Issue. Johnson may have switched from drawing with
ink, brushes, and paper to using a stylus and a digital tablet, but
he isn’t worried that computers will take over the rest of his
cartooning process. “When robots are advanced enough to be
neurotic, then maybe I’ll be concerned,” he said, “though I don’t
think too many of us choose this field for job security, anyway.”

19. Outline
1. A short introduction and 2 big questions
2. Concept and context: AI 101 in 2018
3. AI, creative destruction, and destructive creation
4. A BrAIgther vision: A human strategy for a “Human
AI”
5. Short-term milestone: Open Algorithms

20. [How] can we build a Human AI?
Alex ‘Sandy’ Pentland, MIT:
“The big question that I'm asking myself these days is how
can we make a human artificial intelligence? Something
that is not a machine, but rather a cyber culture that we
can all live in as humans, with a human feel to it. I don't
want to think small—people talk about robots and stuff—I
want this to be global. What would happen if you had a
network of people where you could reinforce the ones that
were helping and maybe discourage the ones that weren't?
That begins to sound like a society or a company”.
The Human Strategy. www.thehumanstrategy.mit.edu

21. 1. Key principle 2. Key features 3. Key requirements
Taking the key insights of AI
especially
•role of data
•credit assignment function
reinforcing “neurons” that
work (teams, groups, policies)
apply this general framework
to entire societies
Leveraging human-machine
complementarities through
cooperation:
• humans do the strategy
and oversight and machines
do the tactics and
bookkeeping.
• Humans + Machines >>
Humans or Machines. (E.g.
chess competition)
• New jobs will be created
(e.g. machine prison guards
• Good data on the system’s
functioning and performance
• Good feedback and
response systems (i.e.
“human or society in the
loop”)
• Some general agreement on
inputs (facts) and outputs
(goals)
• Sufficient human skills to
adapt, implement and
oversee
Vision of a “Human AI”

22. Key contemporary challenges for a Human
AI1. Some powerful agents have strong incentives
for this not to work (e.g. economic and
political monopolies benefit from status quo)
2. Most societies / countries currently lack the
appropriate data sources, capacities and
culture for this
3. There is widespread (and growing?) digital
and analog segregation that feed on and fuel
distrust, disdain, echo chambers, alternative
facts narratives, etc., hampering cooperation
and consensus building
4. We know AI can and has been used to nurture
3. (cf Facebook newsfeed; Amazon Prime..)

23. Humans are and should remain meddling socio-
political animals experimenting and working with
machines
“Automation may ultimately not take our jobs by being better than us at performing
them, but by us being worse and worse at effecting even the most simple tasks, such
a reading a document for ten minutes. Robots may also not replace our lovers and
friends but not by being as good as them and giving warmth and companionship, but
by technology making us worse at dealing with real people. Losing to automation may
be a much more banal process that we conjecture it to be. Machines may not need to
catch up, if we drop the ball as fast as we seem to be dropping it. ”
Manuel Cebrian and Iyad Rahwan, MIT

24. Outline
1. A short introduction and 2 big questions
2. Concept and context: AI 101 in 2018
3. AI, creative destruction, and destructive creation
4. A BrAIgther vision: A human strategy for a “Human
AI”
5. Short-term milestone: Open Algorithms

25. “….move from the feared tyranny of data and algorithms to a data-enabled model
of democratic governance running against tyrants and autocrats, and for the
people.”
Pillars of positive data-enabled positive
disruption

26. “Open Algorithms”: a bold new
paradigm
-and project

27. 2010
2011
2012 2013 2014 2015
Origin of OPAL: the (Big) Data (& AI)
Revolution(s),experimentations, expectations, …and
questions
2016 2017
How can
societies
‘leverage’
human
behavioural
data collected
by private
companies
for public
good in a
sustainable,
safe, ethical
?
2018
Data Challenges arranged by private
companies like Orange, Telefónica and
BBVA that gave teams of researchers
access to aggregated anonymized data,
revealing and spurring a thirst for these
kinds of insights about human
societies

28. Building a 1.5 M Euros Minimum Viable Product
with 2 tracks, 2 phases, in Senegal and
ColombiaMinimum Viable Technology
1
Minimum Viable Technology
2
Minimum Viable Governance 1 Minimum Viable Governance 2
Q4 2017 Q1 2018 Q2 2018 Q3 2018 Q4 2018
A technology track
with an open platform
and open algorithms
running on telecom
operators’ data that
never leave the
companies’ servers
A governance track,
with a participatory
design (FUTs), an
orientation and
ethics committee
(CODE), and capacity
building activities

29. OPAL Consortium
Founding organizations
Local partners Donors
OPAL is a unique Public-Private-People-
Partnership aiming to build the foundations of a
Human AI

30. OPAL’s future human-AI ecosystem /
ecology Certified open algorithms developed by
developers are sent and run on the
servers of partner private companies,
behind their firewalls
Partner private
companies (here a
telecom operator) allow
OPAL to access its
servers through a
secured platform
A governance system including a Council for
the Orientations of Development and Ethics
(CODE) ensures that the algorithms and use
cases are ethically sound, context relevant,
etc.; users benefit from capacity building
activities
Key indicators derived from
private sector data such as
population density, poverty
levels, or mobility patterns,
feed back into use cases in
various public policy and
economic domains

31. Thank you
eletouze@mit.edu
3rd Encuentro Mundial Big Data Bogotá,
April 25th 2017