artificial intelligence workshop pwc’s digital strategy ... workshopoct... · pwc digital...

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


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.


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.



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)



https://www.pearsonhighered.com/program/Russell-Artificial-Intelligence-A-Modern-Approach-3rd-Edition/PGM156683.html

PwC’s Digital Services


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



PwC’s Digital Services


8


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


Near-human level AI – Technical performances show an increasing ability to reach human-like precision and reliability on high complexity tasks


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


Bot.me: How artificial intelligence is pushing man and machine closer together


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

http://pwcartificialintelligence.com/#amplifying-society

http://pwcartificialintelligence.com/#amplifying-service

http://pwcartificialintelligence.com/#amplifying-management

http://pwcartificialintelligence.com/

…and AI is expected to have substantial impact on the economy worldwide

14


Estimated AI impact on global economyPwC AI Analysis Sizing the prize 2018


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


Global investments in Venture Capital-backed startups


17

12bn$

in 2017(2x 2016 investments)

Global Venture market investments on AI


US market funding of AI-focused startups


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.

?


Global Startup Ecosystem Report 2018 - Deep dive on AI


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%


The share of jobs requiring AI skills in the US has grown 4.5x since 2013


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


The interest in Artificial Intelligence & Machine Learning courses is growing


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


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.


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


Artificial Intelligence: Italian outlook from research to market - 2018 (2/2)


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


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



It’s 2040. You are in the front page of a newspaper. What do you imagine the headline to be?


26

Ice-breaker


Sci-Fi Design


27

Planet Earth, 2040

CREATE YOUR VISION OF THE FUTURE AND THE

ROADMAP TO GET THERE


Suspend disbelief

IMAGINE IS THE WORD


28


Divide into groups

Group 1

Antonio Ballarin

Roberta Lotti

Confidential information for the sole benefit and use of PwC’s client.29

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


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’



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?


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


What will the world look like in 2040?


32

SOCIAL

• __________

• __________

• __________

WHAT ARE THE IMPLICATIONS FOR SOCIETY AND ORGANISATIONS?

TECHNOLOGICAL

• __________

• __________

• __________

ENVIRONMENTAL

• __________

• __________

• __________

ECONOMIC

• __________

• __________

• __________

POLITICAL

• __________

• __________

• __________


The Four Worlds of

Work in 2040.


33


How will your company be like in 2040?


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


Key Use Case. Write a description of possible future stakeholders


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:


LEVEL OF EDUCATION:

OCCUPATION/ROLE:

PURPOSE:

Stakeholder 3

NAME:

AGE:


LEVEL OF EDUCATION:

OCCUPATION/ROLE:

PURPOSE:


tissue, free time):..........................................................

............................................................................................

............................................................................................

............................................................................................

............................................................................................


………………………………………………………………………………

………………………………………………………………………………

…………………………………………………………………………..…

……………………………………………………………………………...


………………………………………………………………………………

………………………………………………………………………………

……………………………………………………………………………...

............................................................................................


tissue, free time):..........................................................

............................................................................................

............................................................................................

............................................................................................

............................................................................................


………………………………………………………………………………

………………………………………………………………………………

…………………………………………………………………………..…

……………………………………………………………………………...


………………………………………………………………………………

………………………………………………………………………………

……………………………………………………………………………...

............................................................................................


What is your Business Idea for 2040?


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?


Retrocast. Work backwards from 2040 to September 2020.


37

Objective:……………………......................

..................................................................

..................................................................

..................................................................

..................................................................

Resources:……………………..………………

……………………………………………………..…

………………………………………………………..

………………………………………………………..

.……………………………………………………….

Activities:……………………..………………

………………………………………………………

………………………………………………………

………………………………………………………

………………………………………………………

Sept ‘19

Objective:……………………......................

..................................................................

..................................................................

..................................................................

..................................................................

Resources:……………………..………………

……………………………………………………..…

………………………………………………………..

………………………………………………………..

.……………………………………………………….

Activities:……………………..………………

………………………………………………………

………………………………………………………

………………………………………………………

………………………………………………………

2025

Objective:……………………......................

..................................................................

..................................................................

..................................................................

..................................................................

Resources:……………………..………………

……………………………………………………..…

………………………………………………………..

………………………………………………………..

.……………………………………………………….

Activities:……………………..………………

………………………………………………………

………………………………………………………

………………………………………………………

………………………………………………………

2030

Objective:……………………......................

..................................................................

..................................................................

..................................................................

..................................................................

Resources:……………………..………………

……………………………………………………..…

………………………………………………………..

………………………………………………………..

.……………………………………………………….

Activities:……………………..………………

………………………………………………………

………………………………………………………

………………………………………………………

………………………………………………………

2040

2040Sept ‘19


What could possibly go wrong?


38

CREATE A ROADMAP TO PREVENT THE FAILURE

OF THE PROJECT


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’)


Target users


Blamestorming

BUSINESS IT

TARGET USERS SUPPLIERS PARTNERS

HR

OTHER


The 5 Why’s

Issue 1 Issue 2 Issue 3

Why?

Why?

Why?

Why?

Why?


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

R

Month 9Month 6Month 3Month 1Day 15Sept ‘19

Now


Sharing ideas

43


Tips for your presentation


44

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.

45

Brave New World

46

AI Risk #1 Growing reliance on algorithms and automation acts to obfuscate human

accountability in consequential decisions.



AI Risk #2 Increasingly complex and dynamic systems make it difficult to predict

emergent behavior among various interacting and competing algorithms.

47



48

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.



AI Risk #4The widespread adoption of automation across all aspects of the economy

will lead to an unprecedented unemployment crisis.

49



50

AI Risk #5A global AI arms race would encourage speed to market at the expense of

effective control and safety systems.



AI Risk #6AI powered segmentation and targeting could give unprecedented influence

over a population and even undermine democratic institutions.

51



This is why we need AI to be responsible

52

PwC Digital Services 53

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


54

The Many Types of BiasBias

There are many types of bias that can apply to Human, Data, Model.For example:



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

55

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



56

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



https://www.researchgate.net/figure/Demonstration-of-finding-a-separating-hyperplane-in-high-dimensional-space-vs-in-low_fig1_42388347

https://towardsdatascience.com/the-mostly-complete-chart-of-neural-networks-explained-3fb6f2367464

57

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



58

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



59

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




Preventing Malicious Attacks


60

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

61

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



62

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

Security &Safety



63

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



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

64

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




The Future Wheel


65

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


Impacts


66

20’ Based on what you discussed in the Future Wheel, return to the Business Idea and identify corrective actions


Wrap Up


67


Thank you!


© 2018 PwC. All rights reserved. Not for further distribution without the permission of PwC. “PwC” refers to the network of member firms of

PricewaterhouseCoopers International Limited (PwCIL), or, as the context requires, individual member firms of the PwC network. Each

member firm is a separate legal entity and does not act as agent of PwCIL or any other member firm. PwCIL does not provide any services

to clients. PwCIL is not responsible or liable for the acts or omissions of any of its member firms nor can it control the exercise of their

professional judgment or bind them in any way. No member firm is responsible or liable for the acts or omissions of any other member firm

nor can it control the exercise of another member firm’s professional judgment or bind another member firm or PwCIL in any way.

68

artificial intelligence workshop pwc’s digital strategy ... workshopoct... · pwc digital...

Documents