The Future of Finance 2016

Documents from The Future of Financial Services 2016
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T h e F u t u r e o f
F i n a n c e | 2 0 1 6
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Index
2
Understanding Exponentials /p.4
Will Weisman. Executive Director, Conferences, Singularity University.
What Does Money Want /p.8
Bob Pisani, CNBC “On-Air Stocks” Editor.
Machine Learning, AI and Big Data /p.12
Neil Jacobstein, AI & Robotics Co-Chair at SU, Former CEO of Teknowledge
Corporation.
Quantum Computing /p.17
Marcos López de Prado, Senior Managing Director, Guggenheim Partners.
Energy and Smart Networks /p.21
Ramez Naam, Energy & Environmental Systems Faculty, Singularity University.
Robotics /p.28
Rob Nail, Associate Founder & CEO, Singularity University.
Uber Yourself Before You Get Kodaked
/p.31
An Interview with Ray Kurzweil /p.37
Ray Kurzwei, Director of Engineering at Google, Co-founder & Chancellor of SU.
Exponential Computing /p.41
Brad Templeton, Director, the EFF & Founder, ClariNet
The Death of Products, When Everything
Becomes a Service /p.45
Marco Annunziata, Chief Economist & Executive Director of Global Market Insight,
General Electric Company.
Business Decentralized /p.50
Brian Forde, Director of Digital Currency, MIT Media Lab.
Index
The Future of Finance

3
The Blockchain Panel /p.53
Bill Barhydt, Founder/CEO, Abra; Chairman, Boom Financial.
Realizing The Value Of FinTech Panel
/p.56
Amelia Dunlop, Principal, Deloitte Doblin.
Investing the Uninvested /p.58
Jane Barratt, Founder & CEO, GoldBean.
Future of Financial Services /p.60
Jesse McWaters, Project Lead, Disruptive Innovation in Financial Services, World
Economic Forum.
Becoming an Exponential Financial
Services Organization /p.64
Salim Ismail, Global Ambassador, Singularity University
Managing Exponential Risk, Rethinking
Insurance /p.68
Daniel Schreiber, CEO & Co-Founder, Lemonade

Understanding Exponentials
4
Will Weisman. Executive Director, Conferences, Singularity
University.
Will Weisman begin his
speech by welcoming everyone
to the third annual Exponential
Finance.
He also welcomes everyone
to NY city, and states that
the city has obviously played
an incredibly important role
in the history of the country,
and in the history of financial
services.
Willthenproceedstoexpand
on the history of Singularity
University. The foundation’s
mission is to educate, inspire
and empower leaders
to apply exponentially
accelerating technologies to
address the world’s largest
problems. Will says you can
think of them as part think
tank, part educator and part
new company accelerator.
They focus on eight primary
accelerating technologies, all
areas that have crossed over
to be information technologies
and are growing exponentially,
and obviously, they believe
they are going to be the
biggest drivers of disruption
going forward. Will then states
that it’s not just the technology
individually, but it’s all about
convergence. it’s how do these
disparate technologies come
together and then cause the
creation of new products and
services.
Continuing on his speech,
Will then explains why
Singularity University is also
about “Impact”, and names
what they call global grand
challenges, integrated by
eleven areas that they have
chosen to focus on. These are
areas where they see some of
the largest problems in the
world and where they think
technology can really have the
biggest impact.
ABOUT SU’S FOUNDING
HISTORY: founded in 2008
Ray Kurzweil and Peter
Diamandis came together at
a TED conference, where they
both started talking about
exponential technologies, the
changes that were happening,
and that we were about to
enter this period of rapid and
transformative growth. They

5
eventually came together to talk about
how they needed to create an organization
where people, individuals, companies and
governments could come to learn about
these technology areas. Peter Diamandis is
chairman of SU, and an executive chairman
of the XPRIZE, of planetary resources, of
human longevity and bold capital partners.
Will then talks a little about abundance,
and tells the audience that in the next few
days of the conference they’ll hear a lot of
talk about that theme, a way of looking at
the world and thinking about it in terms
of going from a scarcity mindset to really
abundance mindset.
Ray Kurzweil, the other part of the
founding equation is a brilliant thinker. He
is one of the most accurate Futurists and
predictors that this world has ever known.
Adventurer, entrepreneur and a director of
engineering at Google.
Will the proceeds to state several
headlines that have been resonating in the
last few days:
1. Clean energy jobs for the first-
time surpassing oil jobs
2. The UK is thinking about a
universal basic income
3. In May of this year Portugal ran
their entire country off renewable
energy for four years
4. Block chain based distributed
autonomous organization that
was just launched. Raised a
hundred million dollars to invest
in early-stage capital
To really understand some of these
things though you need to understand about
Exponentials, and you need to understand
about Ray’s laws of accelerating returns,
according to Will.
What does it mean when something is
exponential? All basically means is that
something is doubling in a finite period of
time. We’ve heard of Moore’s Law is being
one of the paradigms.

6
It’s a difficult concept for us to grasp.
If you think about when our brains went
through their major evolutionary changes,
it was hundreds of thousands of years
ago, in a very different world. The world
was very much linear and it was very much
local. The world was changing very slowly
and we focused on just the things that
were immediately around us. Fast forward
to today this is very much a global and
exponential world, so something happens
on one side of the globe and we hear about
it in minutes or seconds. Computers hear
about it in milliseconds. One of the key
things from this conference is hopefully
to help you think about and understand
exponential thinking so that you can start
to look at problems and start to think about
what the world might look like in the future
using this exponential thinking.
Will then proceeds to give examples
of exponential behavior on current
technologies, and talks about Ray’s law
of accelerating returns. Will says that
there have been examples since as early
as 1890’s, and we all know each of these
technologies have finite lifespan. What
basically happens is in the earliest days
things start off very slow, they reach a
point where the growth becomes visible
through rapid acceleration, and they reach
some finite maxim. What’s interesting is
that another technology that appears and
so it’s a city series of these nested s-curves
you can imagine it’s one technology
basically sitting on the shoulders of another
technology and together create that very
smooth curve. Will says that in 20-30
years, he’s going to have a thousand-dollar
computer that’s going to be as powerful as
the human brain, and by 2050, he may have
a thousand-dollar computer that’s going
to be as powerful as all human brains for
thousand dollars.
Will then continues with examples
to show how exponential growth has a
affected us. Starting in 1956 five megabytes
of storage cost you $120.000 dollars;
fast forward to 2014 and 28 megabytes
of storage costs you $99 dollars and
fast forward to today 120 gigabytes now
cost you $30 dollars on amazon. That’s
a 3000x performance improvement and
storage capabilities and 11 years and over
90 million since 1956. Will also mentions
GPUs technology, as well as the impact
in the advances of the optical camera has
made through the years.
“If you were lucky enough to be a
successful company and make it to the S&P
500 back in the early nineteen twenties,
you know you had a 67-year run […] the top
of the heap today that’s down to 15 years
and that’s shrinking dramatically. If you’re
lucky enough to make it to the 500 list,
that’s a very short life span. It’s predicted
that forty percent of those companies
might not even exist after 10 years so that
were entering…” states Will, and continues:
“One of my hopes from this conference is
that you’re going to come out of here with
a not being pessimistic not being feeling
kind of beat down, but feeling like this
is really an exciting time and what could
be disrupted stress is really an incredible
opportunity so part of that is embracing

7
this abundant mindset I mentioned”
Will then mentions the framework they
use that helps to understand the path of
exponential technologies and abundance:
1. First, what happens is something
becomes digitized, becomes an
information technology.
2. Then it starts growing very slowly,
it’s very deceptive, it doesn’t
seem like much is happening.
3. Then all the sudden disruptive
things start to happen. People
are surprised, and all the sudden
these new companies, new
technologies are coming on that
to potentially create stress and
challenge them.
4. That’s when we start to see these
companies’ products and services
start to dematerialize and to
monetize things moved to board
to becoming almost or 100% free.
5. Then ultimately, we see them get
democratize and readily available
to everyone.
Will completes the idea by showing
statistics about how the world is becoming
a safer place, and it’s according to him “the
best time to be alive”, and adds the fact
that we’re going to 3 billion new people
coming online in the next few years, so
almost a doubling of the number of brains
that are out there that are harnessing the
internet will be contributing to the solution
of world’s problems creating companies.
It’s really going to be an extraordinary
time. All of a sudden, you’ll start to see lots
of new customers for new companies.
Will finishes his speech by thanking
everyone who has attended, and
encouraging everyone to seize the
opportunity to be there.
Para profundizar,
recomendamos:
VIDEO CHARLA
https://www.youtube.com/
watch?v=2n6JO7KvHvM
HERRAMIENTAS
http://www.kurzweilai.net/the-law-of-
accelerating-returns

What Does Money Want
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Bob Pisani, CNBC “On-Air Stocks” Editor.
Bob Pisani starts his speech
by saying how excited he is
for living in times like these,
and makes a special remark
about Artificial Intelligence. A
particularly profound moment
occurred during the GO battle
with Google’s Deepmind, the
GO champion said that the
machine made a move that did
not make sense to him, that was
not in any way comprehensible
at all, and only later when the
game was at its end did the
player realize that and move
was very profound that it threw
him off, and that it was largely
responsible for winning the
game.
Bob says he wanted to
highlight where we are and
maybe a little bit about
where we’re going right now,
commenting on the advances
Fintech has had in the last few
years. Regarding investment
in private FinTech, 1.8 billion
dollars in 2010, 19 billion dollars
in 2015. Bob then says that
what’s driving Fintech is very
simple: maintaining control
of the customer relationship,
making customer experience
as smooth and effortless as
possible, and cost cutting,
critical in a world of low
growth, low rates and low
profitability.
Then Bob goes on talking
about where is the Fintech
money going: “Mobile money
(money transfer, mobile
banking), consumer lending
(prosper, lending club), Small
business lending (cabbage,
circle up) and personal finance
management (robo-advisors
like Betterment or Wealthfront)
We are now into this in tech
revolution. It’s more difficult
than a lot of people thought
to build brands, build trust
and build relationship with
our customers. “The personal
finance management area, it’s
amazing what the potential
is for them; we haven’t even
scratched the surface in
this…” Bob continues. The
global mutual fund business
is about 30 trillion dollars the
global ETF industry is about
2.6 trillion in the US, and the
Robo-advisors business is 20
billion dollars right now, “that
under management and this

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is less than one-tenth one percent of the
current market, this just gives you an idea”.
Peer-to-peer (p2p) lending: it’s small
so far 2015. It’s only 7% of the cumulative
lendingvolumesin2018.Anotherexampleis
Alibaba, the largest e-commerce company,
having more users than the population in
all of US. Their Gross Merchandise Value
is 500 billion dollars, “that’s the GDP of
Norway right now”, says Bob, commenting
on the characteristics Alipay brings to the
Chinese Market.
“Where’s the tipping point, where these
technologies start to really dominate
digital disruption?”
We know what digital disruption has
done to other businesses, you know what
did the music’s sales and video rentals,
but could this happen in FinTech where
you get these massive disruptions? Well,
the answer is yes, and the question is how
do we know what’s happening? “There are
very different ways of looking at this”, says
Bob, “but one thing that I’ve noticed is once
a technology starts to get somewhere in
the mid-teens in terms of its penetration,
when that happens in the digitally it
accelerates very quickly because that’s
enough for first early adopters, and then
you get widespread discussion from
people and it starts moving quicker from
there. This happened in all these industries.
That’s a tipping point.“
So, where’s the Banks “Uber moment”?
(A shift for mobile distribution). Bob
emphasizes on a specific area in banking
industry: “I have been hearing for 20 years
that the bank branches are going to go
away, this was one of the earliest things
you could say in 1996 to sound smart. Once
teller machines came in everybody said
nobody’s going to bank branches. You

10
know what happened actually? We have
seen modest declines in bank branches,
bank branches actually peaked in 2007.
There’s been some modest decline since
then. Now the question is will these
new technologies, once you get the
adoption a little heavier, will the bank
branches started coming down? Citigroup
estimated that if you started getting
massive adoption bank branches could
come down anywhere from twenty to
fifty percent. Nobody really knows, but
my thought is these banks are not going
to let those branches go that easy. If they
can cost justify them by turning their staff
into advisory based roles where you come
into the banks and they’re now selling you
a whole suite of services that conclude
not just mortgages but personal wealth
management”. And then reinforces the
idea: “For them to remain competitive,
banks need to get innovation before the
FinTech companies get scale”, quoting
Citigroup, but makes clear that the banks
are not rolling over. In the last few years
the big banks have been working on the
digital disruption area, and are not rolling
over. The good news, according to Bob,
is that disruptors are causing innovation
inside the banks. “They’re going to buy
a model as well, and you’re going to see
some consolidation going on in this area”.
BIG DATA: CHANGING THE WAY RISK
IS PRICED. There’s now new methods
of gathering data on consumers. Loyalty
cards, social networks, purchase data or
even browsing habits, and this eventually
supports faster, more accurate decisions.
“This is absolutely invaluable to a bank.”
BLOCKCHAINS. How do I know I got
something if it’s not in my hand? How
do I know I sent that money to Thailand?
How do I know that I truly sold that stock
to somebody? How do I know I own that
piece of real estate? Well we have an
elaborate system in the stock market call
clearing to tell that you did buy that stock,
and we have elaborate system regarding
real estate insurance mechanisms that
says that guarantees transactions, but the
blockchain can bypass all that. It is truly
revolutionary.
What’s coming according to Bob?
1. P2P, small businesses.
2. B2B will be the next wave.
3. Industrial Fintech, financial
transactions incorporated into the
IOT. Smart meters at home, that
could even negotiate the rates of,
for instance, energy.
“Cross Fintech with the IOT, and
you’ve got a lot of great potential”. Bob
continues… “When I talk at CNBC, when
we talk to people who are CEOs, who
run companies in the United States they
say regulation and compliance is a really
big headache and increasingly they’re
citing cybersecurity what there be some
application to take big data and apply it to
regulatory issues to find a better way to do
regulation and compliance…”
Bob then advances in his speech talking
about possible negative outcomes (at least
temporarily) for some of the disruptive

11
tendencies, understanding that there may
be things that could go wrong in Fintech.
“You are just moving money around, and
when money moves around stuff can go
wrong, models can collapse, stuff can get
stolen”, and continues “remember, the
FinTech business outside of the bank’s, is
remarkably lightly regulated… and you’re
going to see much more press pressure for
regulatory scrutiny, and that could change
things as well…”
Bob then talks about what happened to
Lending Club, just a couple months ago, the
CEO abruptly resigned. There were debts
that were misstated regarding its price, the
CEO properly disclosed investment, and
the buyers of the loans started to refuse, so
Lending Club had funding problems, and
the stock dropped almost 50% in a week.
“There’s nothing completely revolutionary
here, you’ve got people who want loans
and then you’ve got people who want to
buy the loans, if you don’t have somebody
that wants to buy the loans you’ve got a
funding problem here, so again nobody’s
repealing the laws of gravity here”.
Bob ends his speech by talking about the
tech-pessimists, focusing on the country’s
mood, and discouraging the pessimism
that’s being seen around, and encouraging
to believe in innovation. “I can’t help but to
be excited about the future, people need
to have more imagination, and the thought
leaders have to do something to counteract
the wave of pessimism...”
Para profundizar,
recomendamos:
VIDEO CHARLA
watch?v=SyGQhzjxg7
HERRAMIENTAS
https://deepmind.com/
http://icg.citi.com/icg/citi_research/index.
jsp
https://www.lendingclub.com/

Machine Learning, AI and Big Data
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Machine Learning, AI and Big
Data
Neil Jacobstein, AI & Robotics Co-Chair at SU, Former CEO of
Teknowledge Corporation.
AI spent the first six
decades of its history flying
below the radar and now
it has suddenly exploded
into public consciousness.
The MIT press published a
seminal work and artificial
intelligence applications and
manufacturing 14 years ago.
Manufacturing is a data and
knowledge driven process,
it’s all about manipulating
molecules and we still
can’t do atomically precise
manufacturing at scale in spite
of that historical trends and
materials and manufacturing
or clear increasing precision.
Decreasingcosts,increasing
flexibility, increasing speed,
in fact accelerating speed and
complexity is now free.
Thanks to the advances
in hardware, in algorithms,
in cheap sensors and then
access to data, things have
really changed so AI can now
contribute to manufacturing
faster actions and decisions:
1. Product and material
innovations
2. Improved efficiency
3. Higher accuracy
4. Lower costs
5. Increasing scale
It’s not just about improving
things on the manufacturing
floor but improving things
throughout the manufacturing
enterprise from design to
customer service, sales and
administration and quality
control.
General Electric has been
using AI in its manufacturing
process for three decades.
They’ve been using big data
to make smarter turbines, to
diagnose problems in their
train engines for decades.
Business Wire just published
a report a few weeks ago,
on a collaboration between
robot manufacturers FANUC,
Rockwell Automation, Cisco
and Preferred Networks, an
AI company. They’re all about
creating an advanced analytics
platform that improves overall
equipment efficiency on the
manufacturing floor.
Yale University has

13
published a series of articles on using
machine learning to provide integrated
optimization of semiconductor
manufacturing.
General Electric is all also providing
a software fabric to go over traditional
manufacturing floors to provide increases in
prediction responsiveness and connectivity
to take a standard manufacturing floor
of old-style equipment and turn it into
a software configurable manufacturing
engine.
IBM has graduated Watson from playing
jeopardy games and working in medicine to
Watson explorer for manufacturing a single
portal for integrating all of the information
from the manufacturing enterprise.
Search IBM Watson blog just a few
weeks ago, looking for the article on how
content analytics, text analytics and NLP
(natural language processing) has helped
auto manufacturers identify potential
product liability just by looking at Twitter
feeds and customer feedback.
There’s been an increasing pace of
AI investment and acquisition from 2011
through 2015, over three billion dollars
and VC funds were invested in cognitive
technology. During the same period over
a hundred of AI related companies either
merged or were acquired by the typical
players: Alphabet, IBM, Facebook, Amazon,
Apple
People often described AI as a game-
changing technology but it’s actually much
more than that. AI is disrupting the entire
field that we are operating on!
The achievement where ALPHA GO
won against the World Champion on Go
four games to one is a led the Korean
government to promise three billion
dollars for an AI R&D program aimed at
manufacturing.
The underlying technology behind

14
that Alpha GO player was a pioneering
technology that combined convolutional
neural networks (you can think of that as a
fancy word for pattern recognition agent)
with reinforcement learning that reinforces
that agent for a high score in an arbitrary
Atari game.
These systems were developed by
DeepMind Technologies in the UK,
demonstrated for the first time in 2013.
The system started from zero knowledge
of any of these Atari games, says Neil
showing images of well-known games in
the history of gaming consoles, and they
outperformed all previous approaches
machine learning approaches and did
amazingly well they discovered strategies
on their own. After a hundred and twenty
minutes of training DeepMind Tech
machine learning system begins playing at
a kind of mediocre human level. But then
after 240 minutes of training the system
tunnels up the left-hand side of the screen
and plays from the back court where
the game has no defenses that is a real
breakthrough in AI.
Just a month after that 2013 demo,
Google acquired Deepmind Technologies
for more than 500 million dollars.
What is AI? People often want
definitions… It’s pattern recognition
techniques to solve practical business
and technical problems. Software agents
that can utilize resources efficiently.
Most of the work that goes on in AI and
robotics is not going to result in the sort
of science fiction scenarios but if anybody
tells you that the long-term consequences
of the AI will be all good or all bad, they
are cherry picking the data…
AI comes with trade-offs, yes!
Faster, cheaper, better problem
solving but also job disruption, human
identity change and risk amplification…
and sometimes risk reduction.
The AI community is taking risks
seriously; “I was recently invited to a
Conference on the future of AI in Puerto
Rico where I joined with 50 AI researchers.
We sat around for 3 days and talked about
the research agenda needed over the next
few decades to keep AI doing beneficial
things for us”.
Elon Musk worked with people from Y
Combinator to create a new organization
called Open AI to pursue advanced AI in
the open so that everybody has access,
not just big companies. They’re investing 1
billion dollars and some great researchers
in that endeavor. They’ve already put out
their first product: the Open AI Gym to
evaluate different reinforcement learning
algorithms
The human brain evolved under very
different circumstances and the ones
that we have today it hasn’t had a major
upgrade and over 50,000 years. Compare
to your laptop or your cell phone upgrade
in last five years…
We’re going to have to augment our
cognition both in everyday life and on the
factory floor. We’re going to use statistical
and deep machine learning task and
domain-specific knowledge engineering
marshalling the data and biologically
inspired computing architectures like deep

15
learning.
Deep learning is the champion
algorithm in machine learning has been
winning all the contests and one of the
things it does is hierarchical pattern
recognition. It can start with pixels and
then go to edges and then it begins to
recognize object parts at the next layer of
the hierarchy and finally objects and you
can add arbitrary numbers of layers, 152
or 300 or 1.000 and get amazing feature
discrimination.
DARPA (Defense Advanced Research
Projects Agency) has a new program called
probabilistic programming for advancing
machine learning and what that’s about
is opening up the black box of machine
learning algorithms and getting them to
play well with others particularly relevant
for the manufacturing floor where we
want to be able to use simulation and
optimization and get all these algorithms
to play together.
AI it’s not just better, faster, cheaper…
it’s different. AI allows us to expand the
range of the possible in the form of
practical business and manufacturing
problem solving.
Create an application oriented
AI toolbox! Download the Machine
Intelligence Landscape infographic, is
just lots of different examples of machine
learning companies and techniques, all
classified into different bins.
There’s a company called Nutonian
that you might want to track, they have a
machine learning product called Eureqa.
A company called Río Tinto was working
on powdered metal steel and they were
having trouble in their quality control. They
used Eureqa to analyze many different
variables involved in the powdered metal
manufacturing process and it turned out
that one of the variables that they just
threw in there for kicks turned out to be the
really important one causing their quality
control problems.
If you don’t have the bench strength in
your own company, you can extend your
bench strength by putting out your data in
the public and putting up some prizemoney
at Kaggle and the world’s greatest data
science teams will compete for the solution
to your problem.
Another way to increase your bench
strength is to use a company called x / 5
that curates a marketplace of qualified data
scientists they came out of the Harvard
Innovation lab and they allow you to just
put out your problem without revealing
your data and then pick a qualified data
scientist to work with you.
Let’s talk about some work implications
Oxford Business School had a study in 2013
of the next 10 to 20 years of the American
job market and they can be noted that 47%
of the most routine jobs in America would
be vulnerable to automation over the next
few decades. They updated their work over
the last few weeks. They put out a new
report called Technology at Work where
they looked at the entire world and they
concluded that developing countries have
an even bigger problem, they have more
people involved in routine work both in
factories and in administrative jobs, their

16
risk is even higher!
That suggests we’re going to need to
team with AI strength that way we’ll get
best-in-class performance at least for some
deaths pure biases and decision-making
superfast operational velocity will be able
to team with a partner that works seven
days a week 24 hours a day, no vacation
or sick leave required, no healthcare
insurance but limited empathy, language
understanding and social grace.
The winning formula for working with AI
and robots for that matter humans plus a
is plus good business process that’s more
powerful than AI alone. We’re going to be
able to combine that power to increase our
productivity remarkably. We’re going to
build very smart systems.
“We’re going to reverse engineer the
human neocortex, make vast artificial
neocortex and make our factory floor
super smart.”
How to think outside the box? There
is no box, remember that AI is going to
change the balance of power between
big companies and small startups.
Some operational recommendations
to transform manufacturing products and
services:
1. To team humans plus AI best-
in-class business process is a
winning formula.
2. Utilize the power of machine and
deep learning, try all the free
tools that are out there, they’re
surprisingly powerful.
3. Leverage AI platforms and your
manufacturing operations, not
just in your customer facing
products and services.
4. Outsourced and increase your
bench strength with Kaggle and
other crowd services and be
proactive about security ethics
and product liability.
Para profundizar,
recomendamos:
VIDEO CHARLA
watch?v=0lH706jQ8GQ
ARTÍCULOS
Manufacturing Automation Leaders
Collaborate: Optimizing Industrial
Production Through Analytics
http://www.businesswire.com/news/
home/20160418006725/en/Manufacturing-
Automation-Leaders-Collaborate-
Optimizing-Industrial-Production
How content analytics helps manufacturers
improve product safety and save lives
https://www.ibm.com/blogs/
watson/2016/04/content-analytics-helps-
manufacturers-improve-product-safety-
save-lives/
The Current State of Machine Intelligence
http://www.bloomberg.com/company/
announcements/current-state-machine-
intelligence/
HERRAMIENTAS
www.kaggle.com
https://deepmind.com/
http://www.nutonian.com/products/
eureqa/

17
Quantum Computing
Marcos López de Prado, Senior Managing Director, Guggenheim
Partners.
Marcos begins his speech
by asking: “How many of you
have heard about quantum
computing? and how many
of you have actually use a
quantum computer?”
The motivation for why
quantum computing is
important when you think
about the challenges that
financial research faces today.
Most of the financial research
that you’ll find are publishing
papers in journals, it’s a
research that is not based most
ofitonexperiencemeaningthat
most academics do not have
access to the only laboratory
that exists for finance. Think
of a physicist who has never
been able to drop the ball and
experience how gravity works,
or physicists who is thinking
about particles but has never
had the chance to experience
in an accelerator, so that is the
situation in finance.
The second problem,
according to Marcos, is models
tend to be very simplistic.
When you think about it,
financial markets are very
complex networks, much more
complex than for instance
climate or weather prediction,
because when you’re trying to
predict the weather you have to
model all these variables, but
the variables are not reacting
against you, they are not trying
to learn what you are learning
and acting in a different way to
counter to produce a counter
effect for countermeasures,
and that’s the second problem:
typical financial research
models are way too simple
for the kind of problems that
we have at hand.
What is the implication?
Most research that appears
in journals tends to be very
simplistic toy models that
are over-feed on data, they
are not experimental, they
are empirical. They measure
things but they don’t observe
reactions. In the end, the
president of the American
Finance Association has
acknowledged most research
findings in financial economics
are just false.
So, what can we do about it?

Quantum Computing
18
The biggest problem is computational
power. The models are simple because
people don’t have access to the
computational power needed to solve
difficult problems.
What finance needs to advance and to
solve meaningful problems in a reliable way
is a kind of machinery that other fields of
science used to solve their problems. There
is no Large Hadron Collider equivalent in
finance. Quantum Computing offers this
possibility.
What is Quantum Computing? It is a field
of research that studies the algorithms
and systems to the solution of complex
mathematical problems. It was founded
in early 1980s by Feynman Benioff, who
noted that Deterministic Computers could
not simulate efficiently a probabilistic
problem. He proposed to develop the new
kind of computer device, centering on the
probabilistic system usage. It was proven
in theory that it was possible to have a
computer that could solve an encryption in
a matter of seconds, where deterministic
computers needed years.
The holy grail of Super Computers is
Parallelism. You can split the problem
in various pieces, and solve them in
parallel. In Quantum Computer, this occurs
in a circuit level. Quantum Computers don’t
work in bits. A bit is a memory item that
can have a state of 0 or 1, so 2 bits could
be in 00 01 10 or 11, but two qubits (unit in
Quantum Computing) hold four units of
information; Why? because the four states
00 01 10 and 11 are in a linear superposition,
this means that there are four coefficients
that characterize the state of the system
for variables to represent the information
packed into qubits. If you have “n” qubits
this means that you can hold two to the

19
power of “n” units of information, and
there is word “exponential finance”, the
exponential power of Quantum Computing.
There is a second very important fact
about quantum computers and it is the way
they solve problems. Marcos proceeds to
use a videogame called Angry birds as an
example for comparing how a computer
works, and show the difference with
quantum computing: “You see, when we
take a traditional computer let’s say that
we are playing a video game like angry
birds and you’re going to shoot that
bird over those pigs, right? What is the
computer doing? Is its solving a partial
differential equations system? Is it solving
a mathematical problem that represents
a physical phenomenon? What quantum
computers do is, they work the other way
around. They use physics to solve a math
problem, it is the anti-angry birds game, is
the game where you code a microchip to
behave like gravity and then you go and
throw the bird and see where it falls…”
When you think about it what is
essentially powerful bit about this paradigm
is that it replicates how the universe solves
mathematical problems. There are plenty of
very hard mathematical problems that the
universe and nature solves all the time, for
free, instantaneously. Quantum computer
is just a way of changing the problem, it is
asking nature to solve this problem for us.
“How do they look?” says Marcos,
showing a picture of a tiny microchip,
basically inside a fridge. That fridge
is for isolating the microchip from
electromagnetic fields, and cooling it to
a temperature of 15 micro Kelvin, which is
180 times colder than interstellar space.
Until five years ago, many people in
the physics community were very skeptic
about the power of computing and even
the possibility of one appearing which now
is kind of undeniable, says Marcos. In fact,
there are multiple commercial quantum
computers available.
“In the end, you have to think of
Quantum computing as a paradigm
breaker”, continues Marcos.
How can it be used for optimizing a
portfolio is trivial, but when you want to
compute the optimal portfolio, the optimal
trajectory of the portfolio over multiple
rebalances, that turns into a very hard
problem to solve for traditional computing.
Once we get larger supercomputers in a
few years we will be able to solve very large
scale portfolio optimization problems.
A second use case is a scenario analysis,
how many times you hear in the board
meeting the CIO asking for scenario
analysis? How many scenarios can you think
of? 10? 15? What if we allowed a quantum
computer to use its power to simulate the
portfolio under billions of scenarios?
The third use case could be Option
pricing according to Marcos. Pricing
becomes problematic when you end up in
path dependent processes, but this sort
of path dependency is not a problem to
one Quantum Computer because they
will evaluate a tremendous number of
alternative outcomes and derive the
price based on those. Fourth and last, is
Clustering. A classic problem where you are

Quantum Computing
20
trying to put together things that are similar,
and when you go into the dimensions of
one thousand instruments, the correlation
matrix is a heat map of the correlation
matrix with, which is essentially is going to
be garbage, but what if we use a classroom
algorithm to reduce the dimension of the
problem to a manageable size and take
these thousands of instruments and tells:
“You have 10 different instruments” after
contemplating all the data set. This could
have a huge impact on risk management,
for example.
Why is this relevant? There are many
reasons why quantum computing is
relevant and is going to change all of our
lives:
1. Feynman’s observation: Quantum
Computers are better suited than
deterministic computers to solve
Financial problems.
2. The end of Moore’s Law: The
size of transistors is reaching
its physical limit. We have been
living for the past 30 years an
incredible era, where computers
became more powerful. In 2012
we experienced the peak for
Transistors per dollar.
“The Digital computers era is about to
end, and we need something to replace
it, and the best candidate is Quantum
Computing. “Marcos then goes on
commenting on Artificial Intelligence
and Machine Learning, stating that
Quantum Computing could provide the
computational power we don’t currently
have to boost those technologies.
“Nobody really knows how a quantum
computer works because this quantum
mechanics, right? As soon as you try
to look into what is going to happen,
what is happening in the system, you are
perturbing the system and the system
changes its state, so I think it’s going to be
very interesting when we recognize that in
order to develop artificial intelligence we
need this kind of machinery that we don’t
truly understand…” Marcos reinforces.
PARA PROFUNDIZAR
RECOMENDAMOS
VIDEO CHARLA
watch?v=kU7vk9jmQC8
CONTENIDO ADICIONAL DE MARCOS
http://www.quantresearch.info/Lectures.
htm
HERRAMIENTAS
www.Quantumforquants.org

21
Energy and Smart Networks
Ramez Naam, Energy & Environmental Systems Faculty,
Singularity University.
Ramez spent 13 years at
Microsoft, where he led teams
developing early versions of
Microsoft Outlook, Internet
Explorer, and the Bing
search engine. His career has
focused on bringing advanced
collaboration, communication,
and information retrieval
capabilities to roughly one
billion people around the
world, and took him to the
role of Partner and Director
of Program Management
within Microsoft, with deep
experience leading teams
working on cutting edge
technologies such as machine
learning, search, massive
scale services, and artificial
intelligence.
Ramez starts the speech
talking about the disruption of
energy, because energy despite
being a fairly static industry
for decades, it is now an
exponential industry with rapid
changes in technology. Ramez
says a few years ago, he wrote
a book about the challenges
of Natural Resources and
environment and the question:
“Can we innovate fast enough
to overcome them?”, and
what he found in doing the
research for that book is that
the technology progress in
the energy space is incredibly
rapid and far faster than most
people take for granted, and
if we play out those trends
and take the math seriously it
has staggering consequences.
Ramez also says he is a clean
tech investor, so he comes at
this from a perspective looking
at where they’re good deals,
where there are opportunities
to disrupt markets and also
build large new markets and
large new companies.
Wind power might look
like a stagnant 19th century
technology but it’s actually one
thathaschangedtremendously.
Over the last dozen years,
the amount of wind power
we have deployed to buy a
thousand percent (x10). That’s
not normal in the energy field,
and that happen for a number
of reasons: Policy pushing it
was a big one, but that policy
would not have been effective

Energyand Smart Networks
22
if it were not for an exponential decline
in the cost of wind power. Whole power,
electricity prices in the US are around seven
cents. Let’s say in the nineteen eighties
wind power costs nearly 10 times that
much per kilowatt hour, now, last year, the
average price of a new wind power long-
term contract in the US was actually 2.3
cents per kilowatt hour and the cheapest
were below 2 cents. That is a staggering
drop in the price of wind energy and it’s
been driven by a huge amount of innovation
in the sector. A basic thing is that we have
learned to make these wind turbines bigger.
Why does that matter? Well higher up the
wind blows more steadily and it blows
faster, so that gives you an advantage. And
secondly, the amount of power you get
from a wind turbine is equal to the area the
blade sweeps through, so if you can double
the blade length you can quadruple the
amount of power you’re capturing. You can
also capture lower speed winds.
So, as we’re learning more and more
about manufacturing techniques, about
new materials, we’re able to tap into
these, and we can see how the price of
wind power has plunged as the scale of
these turbines has grown, and this leads
not just to more power at a lower cost, it
leads to steadier power. Today the wind
fleet operates at a thirty percent capacity
factor. That means that a wind turbine
produces about thirty percent of the max
that is expected for, but as we get towards
taller and taller turbines the Department of
Energy expects that within a few years we’ll
be able to get sixty percent up time of these
wind turbines, and now it no longer looks
like an intermittent platform for energy but
more like a steady one. GE is one of the
leaders worldwide in the development of

23
these new wind turbines and innovating in
new ways like the GE space frame to build
them taller while transporting them.
Big data, machine learning, and so
on, that is also vital here because these
individual turbines are intermittent. What
was found is in cases like Colorado with
the excel utility there, using sensors on
the wind turbines collecting that data and
putting it into algorithms to do predictions
of which turbine was going to spin at
which speed, a few minutes from now, a
few hours from now, on a day from now,
allowed excel to triple the amount of wind
power they could put on their grid and that
saved them billions of dollars, because it is
the cheapest power they could buy in their
state.
As we mentioned before, higher up
the wind blows more steadily. Marc
Andreessen says software is eating the
world, and this is an example of that: A
prototype that’s not in production yet about
a blimp that has inside of it a small wind
turbine, and it is actually a drone, it flies
under computer power and it can hover
about 500 meters above the ground (three
times taller than the tallest wind turbines)
and tap into those high-speed winds and
then drop down in the case where the wind
is too high for it to be safe. Or as another
example there’s another drone from a
company called Makhani Power in the Bay
Area. It tilts back, takes off under computer
control, flies up to as high as a kilometer
up tapping into those high-speed steady
wins; You could never pay the capital
cost for a kilometer-tall tower but you
can pay the software costs to self-steer
this drone and then act as a giant wind
turbine in the sky. This company was
acquired by google two years ago, who
wants to bring it to production.
Another point to highlight is Solar
energy. There has been an incredible pace
of innovation in solar panels. They are
made of a chip and if we wanted to plaster
thousands of square miles with intel chips,
the cost for that would be Quadrillions
of dollars, it would be impossible. But,
like silicon wafers solar power has had a
ferocious and exponential cost decline
over the course of the last 30 years (it
has plunged by 200 times), that means
we’re now seeing solar energy winning
deals without subsidies in various parts of
the world. It happens especially in places
where the 1.3 billion people that don’t
have electricity today live and about three-
quarters of the world’s growth in energy
consumption over the next few decades
will be which is a relatively sunny area.
Regarding Cross-over, one physical case
worth mentioning is a natural gas plant in
the US the EIA estimates this cost seven and
a half cents per kilowatt hour if you build a
new one. In Chile we’ve had about a dozen
deals and an average price about six cents
per kilowatt hour without subsidies, China
(the Gobi Desert) also got six cents, India
an Ambar ultra-plant 4 gigawatt plant, an
enormous plant the size of four large coal
powered plants and capacity at about six
cents per kilowatt hour, and in the US six
months ago First Solar sold to Berkshire
Hathaway a 3.9 cents kw/h central in our

24
deal and then last month the city of Palo
Alto bought solar from a company in LA at
3.6 cents kw/h. Now this is a subsidized
price, but back out all the subsidies about
5 cents per k/h and it’s still about a third
lower the price of new natural gas and
its producing at peak demand time. And
around the world is even better than that,
Mexico: the average price in their solar
auction last month was 5.1 cents, the
lowest price was 3.5 cents unsubsidized.
The price in the US in the last eight years
has plunged about 80%, just a phenomenal
pace of change.
In Dubai, one of the oil capitals of the
world, this 800 megawatt plant a giant
plant being built by Aqua Power (a Saudi
firm) and this price did with no subsidies
for this plant for the next tranche was 2.99
cents, about half the price of natural gas. In
the last three years, we’ve gone from solar
being completely uncompetitive to solar
in sunny parts of the world, to crushing
all other competitors as far as price goes
up and that has helped drive an enormous
explosion.
Wind power scaled by a thousand
percent 10x in a dozen years, Solar has
left that in the dust a 100 times growth in
13 years. For now, this explosion is unlike
anything that we’ve seen in energy.
This happens for a lot of reasons.
Manufacturing scale, one of the first
Exponential’s we ever saw is that the
price drops along the learning curve, and
this curve is quite ferocious, 20 to 25%
reduction in cost per doubling of scale,
and that’s going to keep on going for
quite some time. That allows the industry
to reinvest revenue in R&D to make more
and more efficient cells that capture more
of the sunlight that hits them, so the prices
are going to keep dropping. We are very
far from done yet and the prices that you
see in Dubai or Mexico will one day be
the prices in California, and then after that
they’ll be the prices in Middle America and
we will have the ability to extend the grid
to spread out but, what do you do if the Sun
isn’t shining or if the wind isn’t blowing?
These are still intermittent resources, no
matter how high their capacity factors are.
With good integration, you have a far
steadier output. Diversity, you can put
solar and wind together. We think now
that about 80% of electricity meet needs
in California can be met with no storage
just putting together solar and wind and
large-scale grid connections. There’s also
another kind of challenge that happens
because solar is getting so cheap so fast.
For a long time, the power prices that you
all a had been paying are highest in the
middle of the day, because that’s when
peak demand is (Supply and demand:
there’s more demand the middle of the day
in the late afternoon so prices are higher),
but as solar comes online or perhaps a
decade away from a point where the
middle of the day power prices in much
in America are at the lowest prices because
it’s a surplus of power right then and there.
INTERNET OF THINGS. You hear a lot
about smart power, and that’s what this
means is being clever about how and

25
when to use energy to match the prices
you want. Nest company bought by Google.
Why does google want a thermostat?
Because this is part of the smart grid. This
thermostat knows if someone is home
and it has a connection to the utility, and
if the utility sees that a very expensive
peak of power demand is coming late that
afternoon because it’s a hot day in Austin,
then they can reach out to these nests and
say “hey we want to avoid that peak run
the air conditioner a little harder a couple
hours early and then we won’t have to have
such a high peak demand”, and spin up
new power plants and the person at home
never notices this is happening and they
get a reward. They had a kickback from the
utility because this is a savings of tens of
billions of dollars potentially across the
country.
In Europe, it’s hot water heaters, so
these hot water heaters increasingly are
getting smart. They know when the power
cost is low; in Europe, it’s when wind power
peaks sometimes it might the price drops
to zero or negative, utilities will pay you to
take energy, and so these connected smart
water heaters know that and say we’ll take
that will save it up for the morning. Or data
centers, the world’s data centers use now
about 12% of electricity and growing; all
that IOT and cloud stuff it’s not for free,
it’s real and it exists in places like this, and
they’re becoming increasingly smart being
able to absorb load. Another example are
electric vehicles; if you have a Tesla you
already know that has programming to
go for low power prices or during the day
they sit at work and whether its own solar
panels at the workplace or just the solar
utility-scale attention to the grid, when
that price drops during the peak hours of
the day that car is sitting there and ready
to be charged and provide services of the
grid by being able to soak up the extra.
WATER. In West coast California
when there is a massive drought, we can
desalinate water. Desalinated acceleration
is expensive but about half the cost is
energy costs, so this is something you can
do when the energy gets the cheapest;
a desalination plant in Dubai consumes
12 gigawatts of power and desalination
about 500 million barrels of water gallons
of water per day. Mapping that the lowest
energy prices suddenly allows you the
flexibility didn’t have.
Ultimately though you need energy
storage. We all know who Elon Musk is.
He’s announcing the tesla Powerwall
battery, and the funny thing is Panasonic
makes that battery and it’s got a Tesla
casing on the outside; but it’s not some
technical breakthrough that got them
there it’s a long-term exponential trend.
A tripling in the amount of power you can
store program and a 10x reduction in the
cost of batteries over the course of the last
20 years and that keeps on going.
In Germany now it looks like with a small
battery, about half the size of the Powerwall,
and a small solar panel a German household
can provide about seventy percent of its
own energy in summer months. So, if your
utility and your business model is charging

26
by volume, what happens to you in this
world? Businesses models are going to
have to change and now we start to see
utilities wanting to own that solar panel
so they can get it on both sides, and the
ones that flourish will do that but Tesla got
a billion dollars in pre-orders the first
week they announced the battery most
of them did not go to homes, 90% were for
this size battery which went to businesses,
commercial spaces, factories and utilities
and now every manufacturer in the world
that does solar is moving into the same
thing. Trina solar battery, the largest solar
company in the world, about a 1 megawatt
hour battery, and they go after some very
simple scenarios, even if you don’t care
about solar that’s probably the case that
you pay one price for energy at night that’s
very cheap and another price during the
day; in California that Delta is about 20
cents per kw/h. Guess what? A battery
is cheaper than that now, so you can fill
it up at night with cheap power and then
discharged it during the day instead of
using that expensive power.
Battery prices are not done coming
down. There is a whole range of different
forecasts about where battery prices would
go and these analysts (including the EIA)
said that there would be huge drops over
a 10 years’ period. Batteries also follow
the exponential learning curve, as they get
higher scale they drop in price and they do
so it basically the exact same pace as solar.
This leads to a crazy idea being that energy
going clean might actually be cheaper
than dirty energy, we’ve always assumed

27
that going clean meant higher prices,
but now we’re starting to see even very
conservative organizations say that it
might actually be cheaper.
The IEA says solar will be the dominant
form of electricity in midcentury and the
cost will be unbeatable for UBS. They said
renewables are now deflationary to energy
prices. We’ve coupled the cost of energy
to the ever-declining cost of technology.
It’s like your Kodak: you think these digital
cameras will never catch up. The world is
now to using more clean energy per year
than dirty energy, and we will never
look back.
The former Saudi oil minister once
said: “The stone age didn’t and for a
lack of stone, and the oil age will end
long before we run out of oil”. He’s
warning us that the world is going to
produce a technology replacement for
oil. Oil fluctuates by a two percent,
two million barrels per day difference
in supply demand has caused this huge
oil fluctuation that nobody predicted
it, but we are headed there and electric
vehicles (EVs) are going to get us there.
The IEA forecasted that we’d be selling
1,000 vehicles a year with a 200-mile
range by 2040, so you just can’t trust the
experts in this, trust the technology and
the innovators. And as the battery prices
come down we’ll sell more EVs which will
bring down the cost of batteries, which
will make EVs cheaper, and we’ll some
more EVs as a perfect virtuous cycle.
By 2030 they’ll be cheaper than the
cheapest car sold in the US. The long-
term price of oil is very cheap as our
demand drops.
PARA PROFUNDIZAR
RECOMENDAMOS
VIDEO CHARLA
watch?v=O9FR5-aElWA
HERRAMIENTAS
https://www.iea.org/
http://www.iea.org/newsroom/
news/2016/november/world-energy-
outlook-2016.html
https://www.tesla.com/powerwall
https://nest.com/

Robotics
28
Robotics
Rob begins his speech by
doing a quick overview of
the robotics space: “Robotics
as you can imagine doesn’t
have a lot of direct impact
on the finance space, but a
lot of indirect implications,
if you think about insurance
or maybe interesting retail
opportunities”.
Rob then continues by
announcing SU opened its
first innovation hub. A regional
innovation hub in Netherlands,
as part university’s strategy
to bring awareness about
exponential technologies
around the world. They’re
empowering their alumni base
to take some of this thinking
to develop their own content
and programming, and the
first manifestation of that is
in the Netherlands because of
their alumni base and some
interesting partnerships. Rob
then tells about an example
in the opening celebration
involving the archaic
regulations that limited the
Queen’s (who attended the
event) interaction with robots,
using said example to highlight
how the advances in technology
are quickly forcing a lot of
work to upgrade our thinking,
and our systems in general.
After that, Rob mentions three
key things that drive the pace
of robotics today: Moore’s law,
the sensors, and the way we
teach robots: “20 years ago,
to program a robot to do the
simplest of moves you would
have to write assembly code in
machine language and it would
take you weeks and months
to program a complicated
system. Today we have open
source libraries of code, we
have drag-and-drop software
tools a robotic operating
system where eight year
old kids can program pretty
complicated robots to chase
the cat and throw a ball at it
when you say a magic word. If
you get the Lego’s Mindstorm
Kit you can do exactly that this
weekend, different paradigm,
right?... “Anything that we do
physically that it has to be
safe, accurate, fast and cheap
is perfect for consideration
of Robotics”. But leading edge
robotics in terms of volume

29
and efficiency is not all. Right now, the
new face of manufacturing robots is Baxter
(rethink robotics), a robot that is aware
and interactive with the environment,
and allows you to walk it through the
steps to perform a task, learning from
what you taught it.
DARPA ROBOTICS CHALLENGE. The
Defense Advanced Research Projects
Agency was pretty scared of the what
happened with the Fukushima nuclear
power plant disaster several years ago. If
ever there was a situation where you have
to go in and solve the problem but you
really don’t want to send humans in its a
nuclear fallout situation, right? So they put
up a two-million-dollar prize to develop
robots that could do a disaster recovery and
they had eight discrete tasks to complete,
and about 20 teams that competed. Each
team had an hour to complete every one
of these tasks. Two and a half years ago,
no single team completed every task. This
year, the challenge gave the teams one
hour to complete 10 challenges, and three
teams completed the whole challenge.
Where it does get interesting mostly
for commercial applications is when you
simplify it. Rob then talks about Panasonic’s
exoskeleton that was just launched last
year in inventory management to heighten
human’s capabilities, and states that more
technologies are coming online every
day, for example, concierge or security
bots: Relay (Savioke), Navii (Fellow
Robots), K5 (Knighscope) or HOPI-R
(Panasonic). Imagine if you overlayed
this with some other technology around
facial expression or verbal analysis. Now
robots can understand emotional state
and act accordingly, and a whole new line
of interesting consumer-level products are
out on the market or being introduced this
year. Rob says his favorite is Pepper, which
is a robot introduced in Japan, marketed
specifically for the elderly, functioning as
a companion.
Some researches has come out over the
last couple years (specifically targeting
elderly) that suggests if your behavior
changes dramatically, it’s a very clear sign
of a declination of health or a change in
health status. You wouldn’t recognize if that
was you, but a robot would be tracking it
and noticing patterns. If all the devices
around us were information platforms
aware of their environment, what kind of
new correlations and interesting valuable
propositions can they provide to us?
Rob then goes on talking about how
there’s been a lot of research and concern
about the future of Jobs, largely because of
robots. There’s a research from Oxford on
2013 that says 47% of US jobs face a “high”
risk of being replaced by automation. The
rest of the researchers come online are a
little bit more conflicted: Millions of jobs
are going away, but new jobs are coming
online or transforming, because of
automation and robotics. If you see how
this plays out on an exponential curve,
that’s just going to keep happening more
and more, what’s the inevitable conclusion?
Technology, robotics and automation
will provide all the basics for us so that every

Robotics
30
physical task and activity will be provided
through them (Rob suggests 20 years in
a timeline for this to start happening).
Every physical task worldwide will be
an opportunity for robot usage. “Are we
prepared for that? No, we are not”. We still
live in a world where our economic system
is very much dependent on everyone
having a job. Rob ran some forums where
he invited some administrative positions in
the government and when this topic was
mentioned in the invitation, everyone’s
response was that it would be political
suicide to show up on an event where they
about how “everyone in the future does
not have a job”. If our politicians can’t even
entertain a different version of the future,
how are we going to get there?
Rob then comments on the voted
and rejected Universal Basic Income
experiment in Switzerland, highlighting
his interest in that kind of examples, in a
future where technology is going to play
a much bigger role. Rob says we need to
be running thousands and thousands of
experiments around the world to figure out
a new economic system, but he thinks most
importantly is how the mindset is required
to change. The future of abundance where
technology can provide the basics, and our
systems need to adapt fast and radically.
PARA PROFUNDIZAR
RECOMENDAMOS
VIDEO CHARLA
watch?v=Mnc9F_bF6dM
UNIVERSAL BASIC INCOME
http://www.bbc.com/news/world-
europe-36454060
HERRAMIENTAS
http://www.savioke.com/
http://www.darpa.mil/program/darpa-
robotics-challenge
http://www.fellowrobots.com/
http://knightscope.com/
http://www.panasonic.com/global/
corporate/technology-design/r-and-d.html

31
Uber Yourself Before You Get
Kodaked
Deloitte set up a program
called the innovation
partnership program with
Singularity University XPrize.
They established about three
years ago, and the reason he
brings this up is because over
those three years they had
30 of the largest companies
in the world, all fortune
500 corporations mostly
fortune 200 corporations, get
together in a membership
model where they bring them
up to singularity university
or X PRIZE headquarters and
spend three or four days with
executive teams literally that
the senior leaders of these
major organizations. There’s
no competitors in the same
room there, where companies
really started to realize that
their next 10 years, the level
of disruption that could take
place as well as a level of
opportunity that they could
capture was significant, and
you could almost see which
industries are moving faster
than others. More importantly
within each industry, you could
see certain companies that
were moving forward well
everybody else wasn’t paying
attention, so whether you
know it or not there is a level
of disruptive advantage that’s
starting to take place right
now.
Disrupt yourself before you
get disrupted. Uber yourself
before you get Kodak’d. The
notion is disruptive advantage
instead of competitive
advantage, so instead of trying
to do 10% improvement go for
ten times improvement. If you
look at certain organizations
like Google with Astro teller
who runs the Google X
program, he comes and talks
at the innovation partnership
program and he discusses this
notion of legitimately creating
a culture where you are trying
to go 10 times better not ten
percent because to go 10 times
better doesn’t take ten times
smarter people, it doesn’t take
ten times as more people, it
doesn’t take ten times the

32
amount of investment, but it’s ten times
better so the economics really pay off. The
other notion is the pace and magnitude of
change, and who can create that change.
The fact that we are surrounded by a world
of entrepreneurs that are really creating
new ecosystems, that are creating a level
of innovation we just haven’t seen before.
Marcus then says he’s going to talk
about “The future of the firm”. Particularly
in this time with everyone being connected
to the internet, with super computers in the
pockets, you have this crowd phenomenon
that’s happening. You have a labor force
that goes way beyond your four walls
as an organization. If you haven’t been
paying attention to the crowd movement,
you’re not designed as an exponential
organization.
In terms of industrialized crowdsourcing
think about this: what if you could get
access to anyone, anywhere in the world,
any time a day and at the scale that you
need. If any of you are in the HR side and
you’re thinking about the labor that your
organization needs, how are you going
to start to staff up around some of these
technologies? Having scale on demand is
very important in that model, to be able to
scale the expertise when you need it. If it
has a long shelf life great, but it has a short
shelf life you don’t have to let everybody
off, you can scale back and in scale up,
and that’s very important from having that
adaptable and agile framework.
And this is not just simple task, but

33
you can crowdsource people around the
world to do very complex things and very
open ended things, and the reason why
we’re seeing this happen now versus we
weren’t talking about this even 5 years ago
(or 10 years ago) is because it’s this trend
that’s happening where you’ve got cloud
internet computing, everyone you know
in the world is walking around with super
computers in their pockets, and then also
keep in mind the social aspect people,
especially Millennials. “My kids there are
more used to collaborating with people
that they don’t work in the same four walls
with, their growing up this way…” says
highlights Marcus. The Millennials are the
largest portion of the workforce in the US.
Other trend that’s happening which is
the delineation between current economic
conditions is creating an interesting
scenario where you have one group of
people worldwide that have too much
money and not enough time, and you
have another group that has not enough
money in too much time. It relates a little
bit to the inequality and income discussion.
This groups are connecting with each
other. The notion of using brains outside
of your departments, and the wisdom
of the world of the genius of the crowd
worldwide. You have got to keep in mind
that right now they have a lot of projects
that are happening that are underway,
like Google Loon, or the Facebook Drone
project that Mark Zuckerberg is working on,
you have all these efforts that are underway
to give everybody on the planet access to
the Internet, in many cases for free, so
it’s very realistic to think that in the next
five to ten years the experts are saying
we will have free Wi-Fi for everybody
on the planet or everybody will have
access. That’s going from 3 billion people
of the planet to 7 billion people that have
access to the internet; think about all these
minds that are now connected. If you don’t
have a strategy to tap into that wisdom, to
leverage all those minds across the world,
you’re going to lose against those that
have that strategy.
Who are the crowds? It could be external
crowds (these could be your consumers,
communities of interest, affinity groups,
scientific communities) or internal crowds,
have hundreds of thousands of people in
your own organization. When you hear
about these Crowds strategies, which is
a way to capture excess capacity, you
can use it without going outside your
organization, you can just harness the
wisdom of your entire crowd within your
own organization. And these are one in
the same, make no mistake about it that
you have people in your organization today
that a nights and weekends or perhaps even
in during the day when they’re working
for you there and on the web starting to
play in these crowd models. Especially
the younger generation that understands
how these models work. Whatever they’re
doing they can participate in these models,
and start making income outside of your
organization.Thesearerathersophisticated
groups, sometimes people think that these
crowd models are all the people the world
that don’t have regular jobs or can’t get jobs,

34
and that’s actually not the case. They’re
just competing on these crowd platforms
on nights and weekends. The interesting
thing is that some of the more prominent
crowdsourcing sites where you can crowd
source data scientists from around the
world, or developers from around the
world to make your next commercial. We’re
finding out that if you’re really talented, so
you graduated top of your class and you’re
just the best developer in your class, out
of school you can start to play in these
crowd models. They don’t go searching for
any job, they get into these models and
they’re making seven figures out of school
because they can just quickly win (a lot of
them are competition-based).
So, you’re finding that some of the
best people in the world are going into
these crowd models, which you can
take advantage of, and it’s important to
understand why this is actually a superior
model, the reason being is that the
smartest people in the world don’t work
in our companies, because there’s seven
billion people on the planet, the smartest
people world have to be somewhere out
there in the crowd. So, if you start to
compete against a company that’s using
the genius of the crowd or the wisdom of
the world, you’re going to put yourself in
a competition and you’re going to lose.
Breaking down these models you start
to see the type of incentive structure in
which these crowd platforms work:
1. Compensation. You can pay
people and they’ll work for you,
regardless of where they are in
the world, regardless of what time
of day it is, right compensation is

35
a big incentive.
2. Convenience. You also can make
things very convenient. Gig Walk,
for example, an app that when
they walk into a grocery store it
prompts and offers to pay for
information.
3. Gamification. Making a
competition in the game out of the
exercise to where people actually
fulfilled your need because
they actually thought they were
partaking in a game.
4. Credibility/Ego. A lot of these
models are competition based
models where the winner gets
recognized, or the group of
winners get recognized
5. Passion.
6. Competitive spirit. You’ll see a
lot of crowd sourcing sites use
competition to do it. It’s not
always about the money.
XPRIZE is now trying to use all of these
elements. It’s a non-profit focused on
taking on some of the world’s greatest
challenges. The world needs it solved, so
they basically designed an instrument that
they put out to the crowd. Marcus then
highlights the Qualcomm Tricorder XPRIZE,
a competition that offers 15 million dollars
to whoever can create a handheld device
that can give you the same diagnostics of
54 health markers, essentially having a
board-certified physician in your pocket.
According to Marcus, there’s a trifecta
that rules the models: Crowdsourcing,
sharing economy and excess capacity.
1. Excess capacity is alluding to
physical things, but can also
be interpreted as intellectual
capacity. A couch that you have
in your house, a bedroom, the
automobile, etc. AirBnb, Uber,
Couchsourfing.
2. Sharing economy. Everyone is
more inclined to share with each
other.
3. Crowdsourcing, as a way to
connect supply and demand.
If you go out into the ecosystems of
entrepreneurs and innovation ecosystems
around the world, this is what everybody
playbook. Who capitalize on all the excess
date of coming off when you’re in traffic?
Waze used that. That was their model,
excess capacity data that just wasn’t being
used, it was considered not even valuable.
What you should start asking yourself is
where do you have where is your consumer’s
product excess capacity, because it’s
going to be put into this model It’s absurd
idea to think that the spare bedroom in
your house is going to be used, and shared
in your clothing there is technology that’s
going to make it friendly to scan yourself
to understand your dimensions, that to be
able to use artificial intelligence machine
vision to figure out just by taking a picture
of your closet to start to inventory your
closet, and then get an Uber car to bring it
from your house to another house.
Someone’s is going to make an app for
people to take all the assets they don’t use,
or sub-use, and start sharing them in the
sharing economy. So, if you manufacture

36
anything or manufacture parts that go
into any of these things, you should
assume that you’re going to be out of the
mix.
Intellectual capital is being shared too.
Crowds sites using all that intellect to be
part of the crowd model, so you see sites
like Topcoder to developing and coding,
you have Medcast for doctors, you have
InCloudCounsel for legal, and even Tongal
for crowd commercials. Everything is
going to this model. Colgate-palmolive
did this in about three years ago. They
spent seventeen thousand dollars as the
prize money, put it out to 70,000 creative
people. They only had to pay if they like the
winner, so they picked a winner, gave him
$17.000 for the video, and it was so good it
became their 2012 Super Bowl ad.
PARA PROFUNDIZAR
RECOMENDAMOS
VIDEO CHARLA
watch?v=ivZIt7BMnnM
HERRAMIENTAS
https://tongal.com/
https://www.airbnb.com
https://www.incloudcounsel.com/
https://www.couchsurfing.com/
http://www.mdb.gov.my/medcast/login/
https://www.topcoder.com/

37
An Interview with Ray
Kurzweil
Ray Kurzweil, Director of Engineering at Google, Co-founder &
Chancellor of SU.
Ray Kurzweil is director of
engineering at Google and
the co-founder and Chancellor
of Singularity University. He
is one of the world’s leading
inventors, thinkers and futurists
with a 30-year track record of
accurate predictions called the
Restless genius by the wall
street journal and the ultimate
thinking machine by Forbes
magazine, and was selected
one of the top entrepreneurs by
ink magazine which described
him as the rightful heir to
Thomas Edison. Bob Pisani
then continues to introduce
him until Ray himself joins the
conversation through a Beam,
a robot that contains a screen
which he can remotely operate.
Ray then goes on talking
about the law of accelerating
returns, mentioning a lot of
important events that have
been recently happening (such
as the GO championship for
AI, self-driving cars, image
recognition, and comments on
how he is currently writing a
new book, called “Singularity
is nearer”.
Ray continues talking about
AI: “There’s been tremendous
confidence in AI recently, so
much that people are now
concerned with the downsides.
We’ve gone from AI will never
work to “oh my god is going to
work and what does that mean
for Humanity””. Ray then goes
on talking about how in the last
few years the mathematical
advances allowed to create
more complex neural networks
regarding AI, and computation
in general. Ray states that is
amazing how the first chart
of the law of accelerating
returns (dated 1981) can now
reflect on how predictable
it was, and mentions that
they are effectively updating
the current chart. “Not only
predictable, but exponential”,
says Ray, highlighting the
fact that exponentials tend to
be very seductive in the way
that, at first, nothing seems
to be happening but half way

An Interview with Ray Kurzweil
38
through the project the growth turns truly
visible.
On the other hand, Ray mentions that
despite all evidence to the contrary,
people tend to be pessimistic towards
exponentials. Many people even think
things are getting worse. According to
Ray, what’s happening is that the advances
in communication technologies, allow
everyone to know everything with
little time apart, and no geographical
boundaries (For example violence or
environmental degradation). This is the
most peaceful time in human history,
says Ray, your chance of being killed is
dramatically less that it was centuries ago
when it was dire scarcity of resources. He
believes that the rise of democratization
has to do with the rise of communication
technologies, and so the world is getting
“dramatically better”. Life expectancy
in 1848 was 37 years, and with the gran
transformation in Health and Medical
Centers, we are now living that numbers
in the dust.
Bob and Ray then focus on the subject
of AI. They mention the GO Championship
withheld during that year, and the
breakthrough that’s been impacting public
consciousness about said subject. “The
machine had made a move that the player
did not understand but later described it
as beautiful; I don’t know how to describe
that but the Machine did something human
players did not think was going to happen
and later realized there was a beautiful
move, something happened there…”,
Bob says. Ray then boards the subject:

39
“Well it’s interesting, in the early eighties
I predicted the computer take the world
chess championship by 98, and it happened
in 97. That was really a victory for the
symbolic school of AI that at as many of
you realize it’s been these two competing
schools of artificial intelligence being able
to think things through logically, the so-
called symbolic school and the connection
in the school exemplified by neural Nets,
where computers basically emulate human
abilities at recognizing patterns... […] “In
my recent book I talked about how I believe
the neocortex works which is a connection/
system that is somewhat different
regarding the deep neural Networks, it’s
a hierarchy of modules that can recognize
a pattern. I estimate we have about 300
million of those modules and recreate the
hierarchy that recognizes with our own
thinking” Ray then continues talking about
the GO championship Bob had mentioned,
saying that in GO you would actually
have to deal with a sequence of moves
that expands radically, having hundreds
of moves available at each time, so the
levels of pattern recognition have to be
really deep to assess the board and know
logically if you are winning or losing, and
play accordingly. Recent breakthroughs
in AI technology give the computers the
ability to assess those situations. The
Google GO playing program perfected
itself by playing with itself to generate
the training data and then it could assess
the moves based on the outcome of these
simulated games.
Bob then addresses what he calls “the
new wave of the techno pessimists”.
People out there, saying “where’s my
jetpack”, coming out and arguing that
things haven’t changed that much. Ray
says that there has always been people
that can make arguments in the face of all
reason. We’ve seen the rise of internet,
computers, AI, robotics, and we are seeing
machines do things like driving cars, that
would make absurd to say that this have
not been transformative changes. “[…]
We have always used our machines to
extend our own reach and leverage our
muscles. Unaided human muscles couldn’t
create skyscrapers. We can now access
all of human knowledge with a few
keystrokes, how many people can do
their work without the brain extenders
we already have? These will become
more and more profound and more and
more intimately connected to us, so we’re
becoming smarter…”
65% of the jobs today in America didn’t
exist 25 years ago, let alone a hundred
years ago, and we keep increasing the
sophistication of jobs. We enhance our
intelligence not only with education, but
by merging with intelligent machines.
“Ultimately, we will connect our brains to
all this technology and extend our physical
and mental reach…”
Bob then mentions Genomics, the
potential to cure long-standing diseases
like cancer or diabetes, and continues
expressing his feelings about the potential
of all new technologies like quantum
computing or robotics, and questions Ray
about the best way to make people aware

An Interview with Ray Kurzweil
40
of all these positive changes. Ray then
goes on talking about the consequences of
the readiness that we have of information
around the world, as communications get
exponentially better, every time we plug
into the news you get to know what’s
negative or what disaster is happening right
now, and that influences our perception
vastly. Ray affirms that that’s one of the
things that keep the whole pessimistic
wave alive. “15 years ago, search engines
were just getting started, and know
thinking of a world without them sounds
like ancient history”, says Ray.
“We very quickly get used to the positive
things, looking at the future and change
is always threatening because we don’t
immediately have an answer…”
We’re moving up Maslow’s hierarchy, you
know people were just involved with basic
elements of survival centuries go. Because
we are geared towards survival, we tend
to be very alert to potential threats, to not
just existential threats but even threats to
our well-being, so for example it’s very
easy for the media to report how many jobs
have been lost. Bob mentions that since
the 2008 financial crisis, we’ve all become
behavioral economists essentially. Not just
life expectancy, but there are a lot of good
things happening. There are dozens of
new jobs that didn’t exist only a couple of
years ago, so humans keep going forward,
concludes Bob.
PARA PROFUNDIZAR
RECOMENDAMOS
VIDEO CHARLA
https://www.youtube.com/watch?v=-
lvAxU2PdpA
MEDIA ACERCA DE DEEPMIND Y EL
CAMPEONATO DE GO
https://www.youtube.com/channel/
UCP7jMXSY2xbc3KCAE0MHQ-A
HERRAMIENTAS
http://www.singularity.com/
http://howtocreateamind.com/

41
Exponential Computing
Brad Templeton, Director, the EFF & Founder, ClariNet
Brad begins his speech
saying he wants to talk about
how Moore’s law is digitizing
everything, and what’s
happening in computing right
now.
Brad start talking about
early adopters, which is the
first component he states
is necessary for innovation.
Early adopters are the first
consumers a certain new
line of technology have, and
they become the “fuel” for
innovation. Other necessary
component for innovation are
Open platforms, according
to Brad. “No matter what
company you are, the
smartest people in the world
don’t work for you”, and Open
platforms come here to break
through that wall by allowing
people to contribute what they
learned back to the world,
and even beat out Microsoft
spending billions of dollars
trying to do the same thing.
Brad then goes on talking
about how the internet was not
an invention but an accident;
the internet gives people the
illusion that it’s free, but it’s
nowhere near that, everyone
pays a part, and this allows
every idea to lift from the
ground and become a reality.
In the days of Netscape, they
put up a camera on their fish
tank, and around the world
everyone thought it was very
amusing to go and see fish on
the other side of the planet.
But had that been done on the
old ways the network where
you paid by the kilobyte for
everything you used, they’ve
gotten a big bill at the end
of the month and those guys
would have been called in to
their bosses office and say why
did I get this big bill so people
can look at our fish?
“All the intelligence of the
internet breaks the idea of
having intelligence in things,
and give the network the
intelligence it needs to be
smart.” That smart network
then allows anything, for
instance smartphones, to
innovate, to amplify the needs
technology covers. Brad then
goes forward stating that “If
your business, your city is
not software-based you may

42
not be a company soon. You now need
information of 2023 to make a plan for
2025, because the world is changing fast.”
NASA’s probe that have been sent to Pluto
arrived last year, and by the time it took to
get there Pluto was no longer a planet, but
when it arrived, new software was already
running in the probe that allowed it for
new types of image and signal processing,
things that weren’t even conceived by the
people who launched the probe into space.
That’s the value of software right there.
Brad then talks about how bandwidth
is widening its range and in the next few
years, the 4 billion people that are now
“disconnected” will be coming online,
and that certainly is a number that
can change how things are done. For
instance, virtual/augmented reality and
robotics may impact and even replace
travel, likewise Ray’s speech before Brad’s.
Facebook spent 2 billion dollars to acquire
the Oculus Rift, company which was
only 18 months old and it never ship the
product, and so you’ve also may have seen
some of the hype now about going beyond
this - what’s called augmented or mixed
reality where we actually bring things into
the world that you’re in as though they’re
real, and the most exciting company in this
space is one called Magic Leap. Brad then
talks about the advances this company
is accomplishing, and how soon this
technology is coming.
Magic Leap raised 1.4 billion dollars
to build this technology, the largest

43
private investment drowned in history.
Microsoft will be shipping to developers
their augmented reality glasses called
Holo-Lens sometime around 2016.
THE INTERNET OF THINGS (IOT).
Brad says the “internet” is the wrong
metaphor, this is more a convergence of
technologies making it happen. The three
technologies are sensors, computers and
networking, and are getting cheaper and
smaller every year. They’re getting to use
less power, which is highly important in a
world switching to renewable energy as
we move forward. If you bought a phone
recently, you’ve seen what happened to
sensors because every company advertise
it. Advances in computers have enable us to
prototype cheaply; Raspberry Pi, not only
really small, but it’s also $5 dollars. How will
the world change when supercomputing
shows up in that price tag? Regarding
networking, it’s interesting how many
formats and standards are out there. Very
long distance low power communication,
or Bluetooth energy format, which has
been in our phones for a couple of years
now. There’s a lot of hype around IOT
according to Brad, and highlights what are
the things that will become important in
the next couple of years, and for instance,
industrial application will give the ability
to gain value with just a really low cost of
investment and implementation of sensors
and networking. Supply chain companies
are another area where we should focus.
Brad also talks about wearables, and
makes the comparison on how on healthy
people, there’s still low value on most of
wearables or IOT related stuff, but there’s
a lot of potential when you have already
a condition, such as pill bottle caps that
know when you have taken the pills, and
can help someone track their medications,
or even wearables designed for their
condition.
“One thing I think we’re going to be
able to do with all these cheap sensors is
copy our friends here at Google…” Google
came up with a very interesting philosophy
of running a big server and instead of
buying enterprise-grade hard drives and
enterprise-grade everything as everyone
else was told to do they bought the cheapest
they could find in the marketplace and they
expected it to fail, and they designed it so
that it just failed and it made a notification,
and the drives had to be changes, but
costumers didn’t notice, because of how
the system was designed. They made the
cheap perform better that the best, and
allowed them to survive failure. “This is
one of the great lessons that Google has
taught the world…” says Brad.
Brad then goes on his speech to talk
about “Frictionless”. Making things so
that you don’t observe the user interface
anymore. Regarding Moore’s law, there’s
certain types of problems. If you have to
dig a ditch, and can hire 9 people, the
ditch should be faster, but what about
when you hire 1000 people? There’s a part
where we can’t make the ditch faster, and
that’s what happening with computers
right now. Brad says that we have to start
advancing towards quantum computing

44
can solve this type of problems, obeying
the laws of quantum mechanics we are all
familiar with. Problems like factoring
large numbers in a short time can really
change how things are viewed and done.
For instance, all of our internet security
is built around how it’s really hard for us
right now to do that, including financial
security.
The energy industry will change because
you’re not going to care about the power
in your vehicle anymore and you’re not
going to buy a car, you’re going to buy a
ride. And when you do that suddenly all
the energy equations change. A lot of cars
are going to move to being electric and
that means that the United States will stop
importing oil.
The value of real estate is driven by
location, and the meaning of distance and
location are going to change in a world
where self-driving rides are available for
twenty cents a kilometer less than a bus
ticket, that’s what transportation is going
to cost in the future.
Moore’s Law is going to come to
transportation. Retailers will change,
Northern Europe is building delivery robots
that will bring you anything in 30 minutes,
not just a pizza, for less than a dollar. What
if you can get anything in 30 minutes or
less than a dollar? Do you even need to
own things? What will this mean for the
retailers of the world?
PARA PROFUNDIZAR
RECOMENDAMOS
VIDEO CHARLA
watch?v=G9gfNR0Eiyo
HERRAMIENTAS
https://www.raspberrypi.org/
https://www.microsoft.com/microsoft-
hololens/en-us
https://www.oculus.com/

45
The Death of Products,
When Everything Becomes a
Service
Marco Annunziata, Chief Economist & Executive Director of
Global Market Insight, General Electric Company.
Marco begins his speech
reinforcing what other
speakers haven been talking
about the negativity subject,
and he says he used to be
like that, so he understands
why people tend to be in that
posture. He joined General
Electric and started seeing
more of what is happening in
technology and that’s what he
says he wants to talk about:
How exponential change is
beginning to permeate in the
industrial industry, or what
he calls “The next industrial
revolution”. “This is going
to be as big as the previous
industrial revolution”, says
Marco, mentioning the impact
on the economy and jobs it is
going to have.
DIGITAL INDUSTRIAL
TRANSFORMATION. The
convergence of physical
and analytical technologies.
According to Marco, the
most important in defining
aspects are industrial assets
becoming intelligent inter-
connected devices, equipped
with electronic sensors,
resulting in insights of
enormous amounts of data
and allowing to understand
and operate with greater
efficiency. “You can now
understand a lot better how
a jet engine performs in
different flight conditions so
is it flying over the desert? Or
is it flying over the oceans?
What are the implications for
taking different flight paths?
Different strategies of taking
off and landing so as to save
fuel but also to improve the
maintenance, and here is one
thing which really changes:
you start understanding these
machines a lot better and move
from reactive maintenance
to preventive maintenance,
fixing things before they break
because you know what is
going to go wrong 80/90%
percent probability over the

The Death of Products, When Everything Becomes a Service
46
next two weeks…” This really gives you the
sense of how once an industrial asset that
becomes an intelligent input connected
device, the functionality changes and it
becomes something completely different.
The smartphone we carry around today is
completely different from the old phone,
or think of what we’ve seen with a Tesla:
you have a car that you drive back one
evening from the office to home, you park
it in your garage, and the morning after
the car drives you to work because at night
through software upgrade it’s become
self-driving. This is what it means to have
machines that through changes in software
changing integrated functionality.
Marco says his favorite example
regarding renewable energy are Wind
Turbines. There’s new intelligent and
interconnected wind turbines can react to
changes in weather. They can change the
inclination of its blades so it catches the
wind in a better way, but that’s only the
first step. The second step is to realize
that the way in which the first line of wind
turbines intercept the wind impacts not
just how much power they can produce, but
also how the wind then passes through the
second, and third line of turbines. What
happens with intelligent wind turbines
as the wind changes is that they literally
talk to each other, and they react in a way
which is coordinated so as to maximize the
power output for the wind farm as a whole;

47
here you have team playing by machines.
According to Marco, there is a reason
why economies have been so pessimistic
over the last few years even as digital
innovation has accelerated. Today industry
has been left behind; industrial productivity
used to be 4%, over the last five years it’s
going down to just 1%, and in the last few
quarters it’s been even lower. Until now,
the industrial world that has really missed
the train of visual innovation because it
needed more time for a critical mass of
these innovations to find their way into
industrial assets are now doing so. They
are starting to build digital twins, which are
essentially a software module of a generic
machine. “Think of what’s happening in
the consumer space, where services like
Amazon, Netflix or Facebook increasingly
know us, so they can recommend to us
what we would probably like to read or
to watch...” In the machine world that
means knowing exactly every specific
piece functioning and knowing in
advance how it will perform and adapt
in different circumstances, which means
you can optimize the performance and
reduce the risk of failure. “The important
implication of this is if you are a company
like General Electric, it shifts the focus
from assets and products to services in
outcomes because suddenly you’re no
longer thinking in terms of producing
a best-in-class machine and selling it
to a customer and then the customer
will do whatever they want, you’re
focusing on the idea that thanks to
these technologies what you’re really
selling to your customers is outcomes,
services and solutions…” It changes the
game to a significant extent and as this
concept starts to take hold across the
industrial world. In the consumer space,
we have companies like Uber and Airbnb
that have enormous valuation even though
they don’t really own any of the underlying
assets which are being used to deliver the
services they provide. Similarly, we have a
company like Apple which makes a large
amount of revenue out of its app services
even though it’s just creating a few apps,
so you’re moving to a world where even
though the physical assets that have always
been a vehicle for providing services, but
now the focus really shifts to the services
themselves.
Marco says that it’s going to be
interesting how this plays out, as
companies will no longer sell the machines
to the industry, but keep the ownership
and allow the use of the assets to the
customers, shifting the balance of risk.
What happens when you have a third
party that says “I can do this better”, and
proceeds to buy the industrial assets and
sell the use of them?
Marco then reinforces that if the world is
changing so much, maybe being big is not
enough. “You have to start investing more
in software, take digital technologies more
seriously… The first question you must ask
yourself when evaluating an investment
is: How seriously are we taking the digital
revolution? Are they investing in software?”
Resuming the topic about energy
discussed in previous conversation, Marco

The Death of Products, When Everything Becomes a Service
48
highlights how the digital and physical
technologies help the consumer role evolve
from one that just buys and consumes
energy, to someone that not only buys
and consumes, but can generate, store
and sell back to the grid energy, and this
puts in perspective the question: “How do
I help different players in the energy game
generate more value?”
Marco continues by saying
technological progress is getting the
traditional lighting business out of
business, and that they had to start
thinking differently and combining years of
research and knowledge to transcend said
traditional industry. What happens when
you can use the lighting system not only
to transmit electricity, but also data and
information. Lighting becomes the neural
network. Intelligent lighting by GE helps
each lightbulb become part of an enormous
system that can help on numerous tasks
across the globe, such as enhancing
safety along the streets, monitor weather
conditions or even pollution levels.
If you are entering this digital industrial
revolution where everything becomes his
service, what kind of play can you run?
1. Figure out what your set of
customers will be
2. Focus on what you are actually
selling
And how do you monetize this? With
a new generation of customer service
agreements where the value that you
bring to the customer is generated by a
combination of hardware and software.
Marco also highlights the importance of
maintaining the relationship with start-
ups, because big companies have to
start learning from companies that are
nimble and flexible in a rapid changing
environment.
BRILLIANT FACTORY. Evolving the
intelligent factory by applying sensors
and interconnect all the factory floor by
a smart network. Therefore, the factory
floor is connected in smarter ways to
the supply chain to distribution channels
in a way that allows you to recalculate
almost instantaneously, when something
happens, how you should we optimize the
flow of your supplies but also how you can
reorganize the work on your factory floor
to do this. It also has implications for the
way we work: “We’re seeing that a lot of
these technologies actually augmented
the ability of workers at different levels of
this distribution, so you have a specialized
recognition on a factory floor or going out
in the field to repair that machine, and this
person is now carrying around a portable
device (the equivalent of an iPad or a
wearable device) that aids in training and
day-to-day manual tasks.” The implication
of this is that the workers who are not a
computer scientist or engineers, will have
ever disposal match greater abilities which
will make them more productive and
greater productivity means the ability to
support the higher wages.
Marco continues: “I do feel that we are
at the point of the elbow of the exponential
growth curve were over the next five to
ten years you will see a larger number

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