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PwC’s Digital strategy & Innovation
Artificial Intelligence Workshop
October 2018
www.pwc.com/digitalStrictly private and confidential
PwC Digital Services
Confidential information for the sole benefit and use of PwC’s client.
2
PwC Digital Services
What we will talk about
Use casesReal cases where to apply
emerging technologies, so to
have a practical feeling of the
day by day impact
ExplorationHow the future will look like
depends on what we build
along the way
ExperienceDesign and develop with
people’s needs and
characteristics in mind
TechnologyA pervasive stratum that
supports and influences how
we live, work, and think
OrganisationInteractions change,
relationships evolve, a
different way of working is
needed in order to fully
exploit Tech and Data
potential
DataThe power of information can
only be unlocked with the
proper management of the
data lifecycle, from collection
to exploitation
GINNI ROMETTY
CEO IBM
ELIEZER YUDKOWSKY
CO-FOUNDER AND RESEARCH FELLOW AT
THE MACHINE INTELLIGENCE RESEARCH INSTITUTE
Some people call this artificial intelligence, but the reality is this
technology will enhance us. So instead of artificial intelligence, I think we'll
augment our intelligence.
By far, the greatest danger of Artificial Intelligence is that people conclude too
early that they understand it.
“ “
3Confidential information for the sole benefit and use of PwC’s client.
PwC Digital Services
Agenda
PwC Digital Services 4
Demystify AI
Sci-fi DesignWhat could possibly
go wrong?
Brave New WorldAI is everywhere, and the
less you see it, the more it
is working
Planet Earth, 2040
Exercise: create your vision
of the future and the
roadmap to get there
Exercise: in a year from now,
your biggest project is in
trouble. Why? How could
you have done better?
The future of humanity:
exploration of trends and
theories
Exercise: The future wheel,
to discover the impacts of
trends and technologies
Interactive
Presentation
9:30 10:10 14:10 16:30
Wrap up
What we have done
today and what we can
do from now on
17:30
Aaron FrankSingularity University
Speaker
Writer and speaker,
focused on the impact
that emerging
technologies have on
business, society and
culture.
Anita Schjoll Brede
Singularity University
Speaker
CEO and Co-Founder of
Iris.ai; one of the 10
most innovative artificial
intelligence startups in
2017 according to Fast
Company.
Confidential information for the sole benefit and use of PwC’s client.
PwC Digital Services
Meet Artificial Intelligence
5
AI is defined as “the designing and building of intelligent agents that receivepercepts from the environment and take actions that affect that environment.”
Artificial Intelligence: A Modern Approach
Stuart Russell and Peter Norvig (Pearson, 2014)
PwC Digital Services
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PwC’s Digital Services
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6
Artificial General Intelligence (AGI) is the intelligence of amachine that could successfully perform the full range ofhuman cognitive abilities, like attention, the formation ofknowledge, judgment and evaluation, reasoning, problemsolving, decision making, etc.
Artificial General Intelligence
Artificial General Intelligence vs. Artificial Narrow Intelligence
Artificial Narrow Intelligence (ANI) is the intelligence of amachine that allows to study or accomplish specific problemsolving or reasoning tasks.In essence, narrow AI works within a very limited context,and can’t take on tasks beyond its field.
Artificial Narrow Intelligence
The "standard interpretation" of the Turing Test, in
which player C, the interrogator, is given the task
of trying to determine which player – A or B – is a
computer and which is a human. The interrogator
is limited to using the responses to written
questions to make the determination.
Source: Wikipedia
The Turing test
A virtual assistant is a software agent that can
perform tasks or services for an individual. Examples
of Virtual Assistant are Apple's Siri, Google Assistant,
Amazon Alexa, and Microsoft Cortana. Virtual
Assistants can also be accessed via online chat: in
those cases, they are referred to as Chatbots.
Source: Wikipedia
Virtual Assistant
AI is becoming ubiquitous intelligence with the ability to see,
hear, speak, smell, feel, understand gestures, interface with your brain,
and dream
AI is helping us do tasks faster, better and cheaper – Automated
Intelligence; helping us make better decisions – Assisted &
Augmented Intelligence, or even taking over what we do –Autonomous Intelligence
AI is equaling or surpassing humans in a number of other tasks
– playing games, driving cars, recommendations (movies, books,
finance, research) etc.
7
AI is approaching or surpassing human abilities to sense, think, and act in complex business environments
Natural language Knowledge and representation Robotic process automation
SENSE THINK ACT
Audio and speech
Machine vision
Navigation
Visualization
Planning and Reasoning Deep question and answering
Machine Learning Machine translation
Deep Learning Collaborative systems
Simulation and Digital Twins Adaptive systems
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PwC’s Digital Services
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8
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Why do we need to take AI seriously?
9Confidential information for the sole benefit and use of PwC’s client.
First, because AI (finally) works…
10
PwC Digital Services
Near-human level AI – Technical performances show an increasing ability to reach human-like precision and reliability on high complexity tasks
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11
70%
80%
90%
100%
2010 2012 2014 2016
Ac
cu
rac
y
Year
Object Detection, LSVRC Competition
Human Performance Best AI System
Error rates for image labeling have fallen from 28.5% to below 2.5% since 2010
80%
85%
90%
95%
100%
2010 2011 2012 2013 2014 2015 2016 2017
Ac
cu
rac
y
Year
Speech Recognition, Switchboard HUB5’00
Human Performance Best AI System
The performance of AI systems on a task to recognize speech from phone call audio
85%
90%
95%
100%
1995 2000 2005 2010 2015
Constituency Parsing, Penn Treebank WSJ
Sentences less than 40 words Sentences of all lenghts
The performance of AI systems on a task to determine the syntactic structure of sentences
The performance of AI systems on a task to translate news between English and German
0,1
0,2
0,3
0,4
0,5
0,6
2008 2010 2012 2014 2016
BL
EU
Sc
ore
Year
News Translation, WMT Competition
English to German German to English
...so expectations have risen…
12
PwC Digital Services
Bot.me: How artificial intelligence is pushing man and machine closer together
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13
56% of business executives
believe that the benefits of AI to
inform business strategy and
generate growth outweigh the
potential employment concerns
Executives are optimistic about
AI’s potential to increase
efficiencies with automated
communications to enable more
proactive approaches (70%) and
to improve big data analytics
(59%).
Digital assistant are fueling the
first wave of adoption:
42% of consumers and 72% of
business decision makers are
already using digital assistants.
60% of consumers agree that AI
can reduce the time it takes to get
answers while still being highly
tailored to their preferences.
63% of consumers believe AI will
help solve complex problems that
plague modern societies, such as:
Cybersecurity and privacy 68%
Cancer and disease 66%
Climate change 50%
Amplify Society Amplify Services Amplify ManagementHumans 2.0 Cyborg concierge The augmented c-suite
Read more on the research at http://pwcartificialintelligence.com
…and AI is expected to have substantial impact on the economy worldwide
14
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Estimated AI impact on global economyPwC AI Analysis Sizing the prize 2018
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15
Global GDP could be up to 14% larger in
2030 as a result of AI – equivalent of an
additional $15.7 trillion.
The greatest economic gains from AI will be in
China (26% boost to GDP in 2030) and North
America (14.5% boost), equivalent to a total of
$10.7 trillion and accounting for almost 70% of
the global economic impact.
Thus, investments on AI are growing, along with the interest in the skills needed to ride the wave
16
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Global investments in Venture Capital-backed startups
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17
12bn$
in 2017(2x 2016 investments)
Global Venture market investments on AI
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US market funding of AI-focused startups
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18
+463% Growth in total VC funding in
startups in AI from 2012 to 2017
Out of all the innovative technologies, AI is the sub-sector with the highest growth in number startups created, growing at 24.8% year-over-year since 2008.
?
PwC Digital Services
Global Startup Ecosystem Report 2018 - Deep dive on AI
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Exits
Exits Value Growth (2012-2017): 393%
Median Exits Value (2012-2017): $38
million
Funding
Total Funding Value Growth (2012-
2017): 463%
Global Total Funding Value Growth: 377%
Median Seed Deal Value (2012-2017):
$500 thousand
Global Median: $350 thousand
Median Series A Deal Value (2012-2017):
5 million
Global Median: 4.7 million
Startup Output
Global Share of Startup: 5.0%
Global Average: 4.3%
Startup Growth (2008-2016 annual
average): 12.9%
Global Startup Growth: 4.5%
19
Number of Exits and Global Share of Exits
4%
5%
6%
7%
8%
9%
0
100
200
300
400
2012 2013 2014 2015 2016 2017
# of Exits Global Share of Exits
4,5%
6,1% 5,9%
6,6%
6,0%
8,2%
PwC Digital Services
The share of jobs requiring AI skills in the US has grown 4.5x since 2013
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20
The growth of the share of jobs requiring AI skills on the Indeed.com platform, by country
The total number of AI job openings posted on the Monster platform in a given year, broken down by specific required skills
2013 2014 2015 2016 2017
0
5
10
15
Year
Sh
are
of
Jo
b M
ark
et
Share of Jobs Requiring AI Skills
Canada UK US
0k
5k
10k
15k
2015 2016 2017
Jo
b L
isti
ng
s
Year
Job Openings, Skills Breakdown
Machine Learning Deep Learning NLP
Computer Vision Speech Recognition
AI Index – Annual Report 2017
PwC Digital Services
The interest in Artificial Intelligence & Machine Learning courses is growing
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21
Introductory AI class enrollment at Stanford has increased 11 times since 1996 and past enrollment trends at other universities are similar to Stanford’s
We highlight ML courses because of their rapid enrollment growth and because ML techniques are critical to many recent AI achievements
0
500
1000
1995 2000 2005 2010 2015
Stu
de
nts
Academic Year
AI Course Enrollment
Stanford Berkeley GT UIUC UW
0
200
400
600
800
1000
1200
2000 2010
Stu
de
nts
Academic Year
ML Course Enrollment
Stanford Berkeley GT IUIUC UW CMU
AI Index – Annual Report 2017
The italian market is quickly catching up
22
PwC Digital Services
17%
17%
13%10%
10%
33%
Banking, Finance andInsurance Sector
Automotive
Energy
Logistics
Telco
Other
Artificial Intelligence: Italian outlook from research to market - 2018 (1/2)
The research has been published by Osservatorio Artificial Intelligence of Politecnico di Milano, and involved 721 companies and 469 cases of use of Artificial Intelligence, referable to 337 international and Italian companies.
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23
of Italian Companies has started exploring AI with dedicated projects
of AI projects in Italy are focused on areas in which AI is already operational and under continuous improvement
56%
52%
Impacted Industries
Vs.70% In France
and Germany
PwC Digital Services
Artificial Intelligence: Italian outlook from research to market - 2018 (2/2)
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24
35%
25%
10%
8%
7%
7%4%4%
Intelligent Data Processing
Virtual Assistant/Chatbot
Recommendation
Image Processing
Autonomous Vehicle
Intelligent Object
Language Processing
Autonomous Robot
31%
22%18%
15%
14%
Predictive Analysis
Monitoring &Control
Pattern Discovery
Fraud and AnomalyDetection
Others
45%
35%
14%
3%3% Service
Sales & Marketing
Product
HR Management
R&D, IT
Solutions in
detail
Processes
Focus on consolidated technologies
PwC Digital Services
AI & The Future
Many aspects of human life
will be influenced by AI. This
will have an impact on many
areas: environment, politics,
security, economy, education,
healthcare, relational models,
cognitive abilities, etc…
PwC AI Lab | 25
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It’s 2040. You are in the front page of a newspaper. What do you imagine the headline to be?
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26
Ice-breaker
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Sci-Fi Design
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27
Planet Earth, 2040
CREATE YOUR VISION OF THE FUTURE AND THE
ROADMAP TO GET THERE
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Suspend disbelief
IMAGINE IS THE WORD
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28
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Divide into groups
Group 1
Antonio Ballarin
Roberta Lotti
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Group 2
Primo Becuzzi
Davide Filizola
Ciro Gaglione
Group 3
Edoardo Esposito
Lorenzo Montelatici
Giovanni Tagliabue
Davide Tornatore
Marco Turchini
Group 4
Andrea Bordoli
Alessandro Csillaghy
Marco Massimiani
Vito Monopoli
Marco VarottoGroup 5
Ennio Dalpozzo
Cristina Locati
Francesco Stevanato Group 6
Massimo Gumiero
Luca Massaron
Pierluigi Sanna
Group 7
Fabia Ascione
Michele Ferri
Lucia Oteri
Group 8
Claudio La Rocca
Giuseppe Liuni
Lorenzo Pasini
Stefano Rizzi
Group 9
Renato Marchi
Marco Moschini
PwC Digital Services
Sci-Fi Design applied
30
Context
Social
Technological
Environmental
Economics
Political
Worlds of Work
How do you imagine the “World of
Work” in 2040?
How will your company be like?
Which problem would you like to
solve?
Use Cases
Who will your prospect
stakeholders be? What are the
critical needs you want to meet?
What solutions could you create
for them?
Retrocast
Design the roadmap to get there,
to create the solution you
envisioned, leveraging tools and
technologies
40’ 30’ 30’ 20’
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Which of these societal, technological, economic, and political predictions will be true by 2040? And more specifically, what AI elements will influence or be affected by them?
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31
• PRIVACY OBSOLETE: ALL
INTERACTION RECORDED AND
AVAILABLE TO PUBLIC
• THOUSANDS OF REFUGEES DUE TO
CLIMATE CHANGE
• AI ENABLED SMART COMMUTING
FAVOURS DE-URBANIZATION
• MAJORITY OF POPULATION IN
URBAN CENTERS IN 3RD WORLD
• MARS COLONIZATION
• FREE EDUCATION FOR ALL THANKS
TO AR/VR
• DECENTRALIZED HEALTHCARE: TELEMEDICINE IS COMMON
THANKS TO AI ENABLED SELF
DIAGNOSTIC DEVICES
• FEW PEOPLE HAVE ACCESS TO
MEDICAL TREATMENT AND THIS
CAUSES SOCIAL CONFLICTS
• PEOPLE SPEND MORE TIME IN
SOCIAL/PUBLIC SETTINGS WITH AI ENABLED DEVICES THAT MATCH
PEOPLE
• PEOPLE SPEND MOST OF THEIR
TIME IN VIRTUAL WORLDS
• MOST PEOPLE DELEGATE THEIR
PERSONAL ASSISTANTS TO TAKE
DECISIONS FOR THEM (WHICH
SHIRT TO BUY, WHICH FOOD TO
EAT…)
SOCIAL
• NATIONS ARE MOSTLY THE SAME
AS TODAY
• THE WORLD IS MOSTLY PEACEFUL
• SMALL CIVIL WARS
• WAR WEAPONS ARE CHEAP, AI-ENABLED AND AVAILABLE TO THE
PUBLIC
• WAR DUE TO CLIMATE CHANGE
• IDEOLOGIES, RATHER THAN
COUNTRIES
• THE “DEVELOPING WORLD" IS
NOW THE CENTER OF BUSINESS
AND INNOVATION
• DECENTRALIZED GOV'T PREVAILS
• NATIONS DIVIDE INTO CITY-STATES
WITH TRANSNATIONAL CORPORATE
SOVEREIGNTIES
• VOTING AND GOVERNANCE FOR
DEMOCRATIC INSTITUTIONS MOVE
TO THE BLOCKCHAIN
• NO MORE VOTING - GOVERNMENTS
ARE DEFINED BY SUPER-ALGORITHMS
• BIRTH CONTROL BY LAW
• POPULATION DENSITY IS
CONTROLLED
POLITICAL
• ONE UNIVERSAL DIGITAL GLOBAL
CURRENCY
• CRYPTOCURRENCY IS COMMON
• FAVOR ECONOMY PREVAILS
• INCREASED DISPARITY BETWEEN
RICH AND POOR
• DECREASED DISPARITY BETWEEN
RICH AND POOR
• FINANCIAL INDUSTRY IMPLODES
• GOLD STANDARD RETURNS
• DECENTRALIZED INVESTMENT
FUNDS PREVAIL
• UNIVERSAL INSURANCE
• UNIVERSAL BASIC INCOME
• INSURANCE INDUSTRY IMPLODES
• INSURANCE IS PERSONALIZED
BASED ON DATA OF INDIVIDUALS
• MOST PEOPLE DERIVE BASIC
INCOME FROM
BLOCKCHAIN/CRYPTO “MINING” ACTIVITY
ECONOMIC
• UBIQUITOUS AR/VR IS PART OF
DAILY ACTIVITIES AND ENHANCES
PERCEPTION
• AUTONOMOUS VEHICLES ARE
EVERYWHERE
• COMMERCIAL SPACE TRAVEL
• MOST PEOPLE HAVE PERSONAL
GENE SEQUENCE
• MOST PEOPLE HAVE BRAIN
IMPLANTS TO ENHANCE MEMORY
AND OTHER COGNITIVE ABILITIES
• BLOCKCHAIN IS COMMONLY USED
IN SUPPLY CHAIN, LEGAL
DOCUMENTS ETC.
• DECENTRALIZED GOVERNANCE
PREVAILS
• SPECIAL WORKING TOOLS THAT
ENHANCE PEOPLE CAPABILITIES
ARE COMMON (GLASSES TO BOOST
VISION, GLOVES TO ENHANCE
STRENGHT…)
• COMMERCIAL EXO-SUITES
AVAILABLE
• ROBOTS ARE USED IN EVERYDAY
LIFE
• PEOPLE CAN CHOOSE OFFSPRING’SPHENOTYPE
• AI CAN MODIFY GENOTYPE TO
PREVENT CERTAIN GENETIC
DISEASES
TECHNOLOGICAL
• DRINKABLE WATER IS ABUNDANT
• DRINKABLE WATER IS SCARCE
• MINOR CLIMATE CHANGE
• MAJOR CLIMATE CHANGE
• FORMERLY DESERT AREAS ARE
NOW FERTILE
• MICROBIOMES CHANGE FARMING
• RENEWABLE ENERGY IS COMMON
• BIG AGRICULTURE DISRUPTED, SMALL LOCAL FARMING PREVAILS
• SMALL LOCAL FARMING
DISAPPEARS, AI-ENABLED LAB
AGRICULTURE PREVAILS
(CELLULAR AGRICULTURE)
• ULTRA CHEAP BATTERIES
• ENERGY IS ABUNDANT
• ENERGY IS SCARCE
• EUROPE AND US OUTSOURCE
FOOD PRODUCTION
• 3RD WORLD COUNTRIES DISAPPEAR
• WATER DESALINATION
SUCCESSFUL
ENVIRONMENTAL
PwC Digital Services
What will the world look like in 2040?
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32
SOCIAL
• __________
• __________
• __________
WHAT ARE THE IMPLICATIONS FOR SOCIETY AND ORGANISATIONS?
TECHNOLOGICAL
• __________
• __________
• __________
ENVIRONMENTAL
• __________
• __________
• __________
ECONOMIC
• __________
• __________
• __________
POLITICAL
• __________
• __________
• __________
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The Four Worlds of
Work in 2040.
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33
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How will your company be like in 2040?
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34
BUSINESS
WHAT IS YOUR 2040 VALUE PROPOSITION? WHAT IS YOUR CUSTOMER SEGMENT? WHAT PRODUCTS AND SERVICES DO YOU OFFER?
PARTNERSHIP
DO YOU HAVE STRATEGIC PARTNERSHIPS? WITH WHOM? WHAT DO YOU DO TOGETHER?
ORGANIZATIONAL MODEL
WHAT IS YOUR COMPANY ORGANIZATIONAL STRUCTURE? IS IT A VERTICAL OR HORIZONTAL ORGANIZATION? WHAT ABOUT YOUR NETWORK?
PURPOSE
WHAT IS THE MAIN PURPOSE OF YOUR BUSINESS?
IN THE YEAR 2040, I IMAGINE A ___________________ WORLD OF WORK. MY COMPANY WILL HAVE THE FOLLOWING CHARACTERISTICS:CHOOSE ONE SCENARIO
WORKFORCE
DO YOU HAVE MACHINES? DO YOU HAVE EMPLOYEES? WHAT KIND OF WORKFORCE DO YOU HAVE?
WHAT TECHNOLOGIES DOES YOUR BUSINESS NEED?
AI-ENABLED TECHNOLOGIES
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Key Use Case. Write a description of possible future stakeholders
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LIFESTYLE (friends, family, social connective
tissue, free time):..........................................................
............................................................................................
............................................................................................
............................................................................................
............................................................................................
FRUSTRATIONS AND HOPES/DREAMS:………....
………………………………………………………………………………
………………………………………………………………………………
…………………………………………………………………………..…
……………………………………………………………………………...
TECHNOLOGIES USED:………………………………..……
………………………………………………………………………………
………………………………………………………………………………
……………………………………………………………………………...
............................................................................................35
Stakeholder 1
NAME:
AGE:
WHERE DOES HE/SHE LIVE:
LEVEL OF EDUCATION:
OCCUPATION/ROLE:
PURPOSE:
Stakeholder 2
NAME:
AGE:
WHERE DOES HE/SHE LIVE:
LEVEL OF EDUCATION:
OCCUPATION/ROLE:
PURPOSE:
Stakeholder 3
NAME:
AGE:
WHERE DOES HE/SHE LIVE:
LEVEL OF EDUCATION:
OCCUPATION/ROLE:
PURPOSE:
LIFESTYLE (friends, family, social connective
tissue, free time):..........................................................
............................................................................................
............................................................................................
............................................................................................
............................................................................................
FRUSTRATIONS AND HOPES/DREAMS:………....
………………………………………………………………………………
………………………………………………………………………………
…………………………………………………………………………..…
……………………………………………………………………………...
TECHNOLOGIES USED:………………………………..……
………………………………………………………………………………
………………………………………………………………………………
……………………………………………………………………………...
............................................................................................
LIFESTYLE (friends, family, social connective
tissue, free time):..........................................................
............................................................................................
............................................................................................
............................................................................................
............................................................................................
FRUSTRATIONS AND HOPES/DREAMS:………....
………………………………………………………………………………
………………………………………………………………………………
…………………………………………………………………………..…
……………………………………………………………………………...
TECHNOLOGIES USED:………………………………..……
………………………………………………………………………………
………………………………………………………………………………
……………………………………………………………………………...
............................................................................................
PwC Digital Services
What is your Business Idea for 2040?
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36
HOW DOES IT WORK?
NAME OF THE IDEA
WHO IS IT FOR?
WHAT CAPABILITIES DO YOU REQUIRE? WHAT SHOULD YOU PROTOTYPE AND TEST?
WHAT PROBLEM DOES IT SOLVE?
WHAT AI-ENABLERS DO YOU NEED?
WHAT IS THE BIG INNOVATION?
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Retrocast. Work backwards from 2040 to September 2020.
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37
Objective:……………………......................
..................................................................
..................................................................
..................................................................
..................................................................
Resources:……………………..………………
……………………………………………………..…
………………………………………………………..
………………………………………………………..
.……………………………………………………….
Activities:……………………..………………
………………………………………………………
………………………………………………………
………………………………………………………
………………………………………………………
Sept ‘19
Objective:……………………......................
..................................................................
..................................................................
..................................................................
..................................................................
Resources:……………………..………………
……………………………………………………..…
………………………………………………………..
………………………………………………………..
.……………………………………………………….
Activities:……………………..………………
………………………………………………………
………………………………………………………
………………………………………………………
………………………………………………………
2025
Objective:……………………......................
..................................................................
..................................................................
..................................................................
..................................................................
Resources:……………………..………………
……………………………………………………..…
………………………………………………………..
………………………………………………………..
.……………………………………………………….
Activities:……………………..………………
………………………………………………………
………………………………………………………
………………………………………………………
………………………………………………………
2030
Objective:……………………......................
..................................................................
..................................................................
..................................................................
..................................................................
Resources:……………………..………………
……………………………………………………..…
………………………………………………………..
………………………………………………………..
.……………………………………………………….
Activities:……………………..………………
………………………………………………………
………………………………………………………
………………………………………………………
………………………………………………………
2040
2040Sept ‘19
PwC Digital Services
What could possibly go wrong?
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CREATE A ROADMAP TO PREVENT THE FAILURE
OF THE PROJECT
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What could possibly go wrong?
The project is a mess. One year in, we are nowhere near your objective.
I do not really understand what we did wrong, but it seems clear that someone, somewhere, somehow, made a mistake. Or maybe we all made some mistakes.
I will gather the team and we will discuss it together.
HR
Business
Partners
IT External suppliers
Investigate the root causes. Converge on a shared view of the real issues.
Framing (30’)
12 months have passed and the initiative is a failure.Try to find the mistakes in someone else’s field.
Blamestorming (10’)
Elaborate a roadmap of the first 12 months from now to September 2019 to prevent mistakes.
1-year plan (30’)
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Target users
PwC Digital Services
Blamestorming
BUSINESS IT
TARGET USERS SUPPLIERS PARTNERS
HR
OTHER
PwC Digital Services
The 5 Why’s
Issue 1 Issue 2 Issue 3
Why?
Why?
Why?
Why?
Why?
PwC Digital Services
1-year roadmap
42
Month 12
OB
JE
CT
IVE
AC
TIO
NS
CA
PA
BIL
ITIE
S
& P
ER
SO
NN
EL
PA
RT
NE
RS
HIP
S,
RE
GU
LA
TIO
NS
, &
OT
HE
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Month 9Month 6Month 3Month 1Day 15Sept ‘19
Now
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Sharing ideas
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Tips for your presentation
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The presentation should cover these 5 steps. Remember, you only have 5 minutes.
In what world is the solution set? Explain the 2040 scenario
What does your company look like in 2040?
What is your AI-enabled solution?
What problem or need you want to address?
How do you plan on creating it?
1.
2.
3.
4.
5.
When ahead lays opportunity and risk, it is better to walk a careful path through the uncertainty than to wait for the mist to clear, if it ever does.
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Brave New World
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AI Risk #1 Growing reliance on algorithms and automation acts to obfuscate human
accountability in consequential decisions.
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AI Risk #2 Increasingly complex and dynamic systems make it difficult to predict
emergent behavior among various interacting and competing algorithms.
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AI Risk #3 AI algorithms that ingest data on human actions and preferences as input
are prone to also learn our innate biases and prejudices.
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AI Risk #4The widespread adoption of automation across all aspects of the economy
will lead to an unprecedented unemployment crisis.
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AI Risk #5A global AI arms race would encourage speed to market at the expense of
effective control and safety systems.
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AI Risk #6AI powered segmentation and targeting could give unprecedented influence
over a population and even undermine democratic institutions.
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This is why we need AI to be responsible
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System Ethics,
Morality & Legal
Helping clients understand
the systemic and moral
implications of their use of
AI
Bias
Testing for bias in the data,
model, and human use of AI
algorithms to improve
fairness of treatment across
groups
Interpretability
Adding transparency,
explainability and provability
to the modeling process to
improve human
understanding of the model
outputs
Robustness &
Security
Improving security and
robustness of AI through
rigorous validation,
continuous monitoring and
maintenance, verification
and adversarial modeling
Governance
Designing effective AI
operating models and
processes to improve
accountability and quality
Performance Society
Responsible AI Modules
Operations
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The Many Types of BiasBias
There are many types of bias that can apply to Human, Data, Model.For example:
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Selection BiasBiased data resulting from selection of individuals more likely to be included in the sample
Algorithmic biasBias in algorithms representing implicit values of humans due to underlying data, selection and use
Omitted Variable BiasBias caused by omitting important variables, leading to over estimation of the significance of other variables
User Interaction or Feedback BiasBias created by models evolving with human interaction (e.g. search engine bias)
Emergent biasBias in models built resulting from shifts in cultural norms since the model development
Human Data Model
x
x x x
x
x x x
x x x
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XAI has three characteristics: Explainability, Transparency, and Provability
The extent to which each characteristic is used is defined by the rigor required by the use case.
Interpretability
Global understanding of model decision making
Understanding the reasoning behind each individual prediction
Mathematical certainty behind predictions
Provability
Interpretability
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Not all machine learning models are considered “black box”; some algorithms are more inherently interpretable than others
When considering the interpretability of an algorithm, explainability, transparency, and provability should be considered and scored separately.
Regression Decision Tree Support Vector Machine Neural Network
Regression assigns weights to each
parameter, we know which attributes
drive each individual prediction (and
how), as well as how the model
performs overall; therefore, both
explainability, transparency and
provability are high.
SVMs partition data along a decision
plane. While we can visualize the
decision boundary and know it’s
equation, we don’t know which
features are important, resulting in
medium explainability and
transparency; provability is medium
because we require a constrained
input
Neural networks can consist of
hundreds of nodes connected by
hidden layers and different activation
functions. Visualization of feature
spaces for each layer provide some
transparency, but overall NNs are low
in explainability, transparency, and
provability.
A Decision Tree is hierarchical and
organized according to the
information gain, resulting in high
explainability and transparency.
When used in an ensemble Random
Forest, some transparency is lost.
Because value ranges are assigned
to each branch, decision trees are
also highly provable.
Image Source:
Researchgate
Image Source:
Medium
Interpretability
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To determine the rigor of analysis required for the use case, criticality and vulnerability must be assessed and understood
Question Yes/No
Will users be at safety risk? X
Is the safety significant? X
Are there other risks to end users?
Is there financial risk?
Is the financial risk significant? X
Is there a risk to the business?
Criticality AssessmentQuestion Yes/No
Are current users considered experts? X
Is the automated process highly critical?
Does ”traditional” technique require significant
training?X
Will automated process result in job displacement?
Vulnerability Assessment
The rigor required by the use case must also be considered along with other trade-offs and considerations in performance, regulation and security.
Interpretability
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To evaluate the interpretability needs of each use case and the rigor of analysis required, we consider the use case criticality and vulnerability
Customer
Targeting
Criticality
Fraud
Detection
Collision
Avoidance
Comfort with
AI
End User
Vulnerability
Fear of Task
Displacement
Fear of Significant
Injury
Unmanned Control of
Aircraft
Autonomous Vehicle Obstacle
Detection
Manufacturing Automation
Targeted Marketing
Rigor
Low VulnerabilityLow Criticality
Medium VulnerabilityMedium Criticality
High VulnerabilityHigh Criticality
High VulnerabilityHigh Criticality
Rigor Required by Use Case
Interpretability
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Examples of Explainability
For Non-technical Stakeholders / Consumers:
”This person is rejected because their months since most recent inquiry
with value 2, number of trade lines opened in the last 12 months with
value 4, and number of satisfied trade lines with value 2 are similar to
Mike Brown and Alice Wong in the datasets. Wong and Brown could not
payoff.”
For Data Scientist:
“This person is closer to class 3 cluster with a distance of x compared to
y and z to other clusters.”
Example Model:
Home Equity Line of Credit (HELOC) risk prediction model
ML
Model
70% High
Risk
30% Low
Risk
1. Global Explanation: What drives the model’s decision making?
Class 1
Class 2
Class 3Example natural language explanation:
“Overall, External Risk Estimate is the primary determinant in predicting
Good or Bad risk. Customers in Class 1 are categorized by having low
values of Most Recent Inquiry and higher number of Satisfied Trades”
2. Local Explanation: Why did the model predict X for observation Y?
There are different mechanisms for providing explainability, including visualizations, statistical representations and natural language explanations
Interpretability
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Preventing Malicious Attacks
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A malicious actor could inhibit the
performance of a model by targeting
the traditional software components
encapsulating the model.
A malicious actor could reverse
engineer the decision plane of a
classifier in order to alter specific
input data so that it is intentionally
misclassified.
A malicious actor could compromise
a model by introducing fake or
modified data into the models
training set, thereby altering the
models predictions during inference.
Security &Safety
Training Data Manipulation
Adversarial Attacks
Traditional System Security
Mit
iga
tio
ns
Monitor and validate quality and
distribution of underlying data used
for model training.
Develop a model defense layer to
filter adversarial attacks, use
ensemble models to minimize
knowledge gained from models, and
proactively attack models to
understand vulnerabilities.
Implement traditional software
security best practices to mitigate the
risk of common attacks such as
phishing, man in the middle and
denial of service.
Facial Identification
Glasses with a specific pattern
force face classification models
to misclassify researcher as
actress
Spam Detection
Text in spam emails can be
manipulated to be misclassified as
an acceptable email while still
maintaining human readability
Street Sign
Recognition
Simple pattern of black and white
rectangles force self-driving car’s
street sign model to misclassify
stop-sign as 30 mph speed limit
Malware Classification
A malicious PE’s byte-string code
can be manipulated to such that a
malware detection model
misclassifies it as a normal PE
Intrusion Detection
Malicious user can use these
attacks to determine how to
behave on a computer network
to avoid detection
Credit Risk Assessments
A malicious user can determine
what metrics they need to change to
be viewed as credit-worthy by a
specific financial institution
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Types of Adversarial Attacks
A user can trick models into misclassifying inputs to bypass a system or intentionally influence a systems outputs.
‘your application petition has been acceptedrecognized thank you for your loan borrowerrequest petition , which we received yesterday, your refinance subprime application petitionhas been accepted recognized good credit ornot , we are ready to give you a $ oov loan ,after further review , our lenders haveestablished the lowest monthly payments’
Security &Safety
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Adversarial Examples are Easy to Craft
Adversarial examples are carefully tuned perturbations to the original input that are small enough to not be recognized by a human, but large enough that they contribute to activation and classification of a different class
Adversarial Perturbation
The adversarial
perturbation needs to be
large enough for the input
to cross the model’s
decision boundary
Small changes to each pixel
add up to a large change in
the classification step
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Black-Box Models are Not Safe From Adversarial Examples
Even without intimate knowledge of the model architecture and loss function, a substitute model can be trained to learn the decision boundaries of the black-box model to then generate adversarial examples
Adversarial Perturbation
Victim Model
Categorical Cross-Entropy Loss Derivative of loss
with respect to
the inputBlack Box
A small sample of the
training inputs are used
to train the substitute
model
Labels for the training inputs are generated
by the black-box
Images are augmented using perturbations to learn
the decision boundaries of the black-box model
Substitute Model
The substitute model is used to
generate adversarial examples
to trick the substitute model
However, these adversarial
examples used to trick the
substitute model are also very
good at tricking the black-
box model
Security &Safety
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Adversarial Training De-noising Models Security Practices
Data Scientists can actively attack their models
with the newest attacks to collect adversarial
example data. This data is then used in re-training
of the model to be more robust against these
types of attacks
Data pre-processing modules can be added to the pipeline
to predict noise of incoming inputs and subtract the noise
from the image or vector
Data Scientists can implement pre-processing and
threshold markers in their pipeline to make it harder
to for attackers to learn the decision boundaries of
the model
• Use a random subset of ensemble models to
serve API’s or users
• Limit API calls or track distribution of inputs
from users’ API calls
• Set thresholds to minimize number of times an
attacker can learn from the model
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Companies Must Proactively Defend Their Models Against Attacks Security &
Safety
Data Scientists and Engineers must be aware of these attacks and can take steps to ensure that substitute models are harder to train and that adversarial attacks are harder to develop
De-noising-U-Net: used to predict pixel noise in an image
Normal
Training Set
Adversarial
Training Set
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The Future Wheel
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15’ Starting from the opportunity identified in “How will my company be like in 2040?", the groups will explore in brainstorming modethe ethical consequences of first and second order
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Impacts
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20’ Based on what you discussed in the Future Wheel, return to the Business Idea and identify corrective actions
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Wrap Up
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Thank you!
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