emerging trends and exponential forces - ffcoi · 2019. 3. 23. · 23 c opy righ t© 2017 d elott...

Emerging trends and exponential forcesBill Eggers, Executive Director, Deloitte Center for Government Insights

2 | Copyright © 2017 Deloitte Development LLC. All rights reserved.

The S-Curve

Sources: The Big Shift by John Hagel, John Seely Brown and Lang Davison, Deloitte Center for the Edge, Carlota Perez, “Technological Revolutions and Financial Capital”, Intel,

U.S. Bureau of Labor Statistics, FCC, CNet, Skype, Hartford Courier, Brad M. Barber & Terrance Odean, Clayton Christensen

Burst of technology innovation

Past Technological Revolutions

Time

Pe

rfo

rma

nce

Stabilization

Stabilization

Stabilization

2000sConnecting

2015Rapid Change

2020 and beyondEXPONENTIAL

Artificial Intelligence

Genomics

Connected Homes

Internet of Things

Crypto-Currencies

Robotics

3D Printing

Drones

Personalized Services

Oculus Rift

Electric and

Connected Cars

Mobile

Analytics

Social

Cloud

Sharing Economy

Content

Customer EngagementDisruptive Innovations

Digital

4 | Copyright © 2017 Deloitte Development LLC. All rights reserved.

Digital technologies: Progressing at an exponential rate

4

DECEPTIVE DISRUPTIVE DEMATERIALIZE DEMONETIZE DEMOCRATIZE

Taxi Fleets Book Stores Long Distance

Hotel ChainsClassifiedsResearch / Libraries

Source: Peter Diamandis, “6 D’s of Exponentials”

Dropping costs fuel the digitization cycle

AI: The Next Frontier

Computer algorithms

that ‘learn’ based on trial

and error, resulting in an

ever-improving ability to

assess inputs and derive

more accurate outputs

Cognitive systems mimic

– and ideally amplify –

human judgement and

(with robotics) human

actions

The AI Spring

“I was playing in front of a crowd

that wanted to see blood. It was an

away game for humanity…”

―Ken Jennings

AI: The new electricityFuture of work (and play…) will be powered by AI…

Types of cognitive technologies

Cognitive

technologies

Rules-based systems Machine advisors can help businesses comply with complex government rules

Machine TranslationImplications on

international relations, defense, intelligence and

domestic applications

Speech Recognition can help in dictations,

phone assistance

RoboticsExamples include disaster management robots, robot

assistance in home health care

Computer Vision

Applications include

identifying criminals from surveillance

footage

Machine Learning Can be used in large

government datasets to predict future events

Natural language processing

Used to extract terms and conditions from

contracts

Google Assistant can assist you in every task

All Seeing AI

Machine intelligence is surpassing human

capabilities across different fields

Now…

Voice recognition Expression recognitionImage recognition

Text translation Speech translation

And soon…

Copyright © 2017 Deloitte Development LLC. All rights reserved.

Facial expressions predicted poor driving behavior and identified accidents two seconds before they occurred with an average

accuracy of 76%

An online shopping study demonstrated that the face can

predict buyer intent with an average accuracy of 73%

Combined with cutting edge analytics, it is increasingly

possible to understand and predict future behavior

14 Copyright © 2017 Deloitte Development LLC. All rights reserved.

Neuro imaging with AIThe Gallant Lab at Berkeley has pioneered the

mapping of key words or images to parts of the

brain.

Like a carbon copy, researchers today can replicate the

images people are viewing based on brain activity

alone.


AI can detect deception in the courtroom

The team from the University of Maryland and Dartmouth College trained the AI to recognize micro-expressions known to indicate that someone is lying

Results showed that DARE managed to spot 92 per cent of the micro-expressions, which the researchers describe as a 'good performance'


These technologies can help us understand team &

individual performance


How can I understand more about my counterpart?

?Sexual Orientation

Political views

Likes/dislikesTravel history

Speech analysis

AI augmented face analysis

Text analytics

Social media profiling

Religious views

Demographics

Education

Family

Employment history

Sentiment analysis Public

database mining

Shopping habits

Household income

Social influence

Health

With AI you can know someone without ever meeting them


The Future of Work

How can we get 5X, 10X the output with the same resources?

What big shifts are

required?


WORK WORKER WORKPLACE

Every aspect of work will be redesigned What

work gets done?

Whocan do the work?

Whereis the work done?


Autonomous: Mind to Machine

Dull Dangerous “Dear” (high-value)


Sources: Deloitte analysis of OPM Fedscope data and Department of Labor O*Net Generalized Work Activities.

*2011 figure, latest available: Justin Falk et al, ‘Comparing the Compensation of Federal and Private Sector Employees.’

0.0

50.0

100.0

150.0

200.0

250.0

300.0

350.0

400.0

450.0

500.0

Docum

enting/R

ecord

ing I

nfo

rmation

Handling a

nd M

ovin

g O

bje

cts

Assis

ting a

nd C

aring for

Oth

ers

Gett

ing I

nfo

rmation

Com

munic

ating w

ith S

uperv

isors

, Peers

,…

Guid

ing,

Dir

ecting,

and M

otivating…

Monitoring a

nd C

ontr

ollin

g R

esourc

es

Monitor

Pro

cesses,

Mate

rials

, or…

Perf

orm

ing G

enera

l Physic

al Activitie

s

Inspecting E

quip

ment,

Str

uctu

res,

or…

Pro

vid

e C

onsultation a

nd A

dvic

e t

o O

thers

Perf

orm

ing A

dm

inis

trative A

ctivitie

s

Evalu

ating I

nfo

rmation t

o D

ete

rmin

e…

Analy

zin

g D

ata

or

Info

rmation

Contr

ollin

g M

achin

es a

nd P

rocesses

Thin

kin

g C

reatively

Pro

cessin

g I

nfo

rmation

Com

munic

ating w

ith P

ers

ons O

uts

ide…

Judgin

g t

he Q

ualities o

f Thin

gs,…

Repairin

g a

nd M

ain

tain

ing M

echanic

al…

Inte

rpre

ting t

he M

eanin

g o

f In

form

ation…

Makin

g D

ecis

ions a

nd S

olv

ing P

roble

ms

Sta

ffin

g O

rganiz

ational U

nits

Inte

racting W

ith C

om

pute

rs

Tra

inin

g a

nd T

eachin

g O

thers

Estim

ating t

he Q

uantifiable

…

Scheduling W

ork

and A

ctivitie

s

Perf

orm

ing f

or

or

Work

ing D

irectly w

ith…

Resolv

ing C

onflic

ts a

nd N

egotiating w

ith…

Org

aniz

ing,

Pla

nnin

g,

and P

rioritizin

g…

Develo

pin

g O

bje

ctives a

nd S

trate

gie

s

Updating a

nd U

sin

g R

ele

vant

Know

ledge

Opera

ting V

ehic

les,

Mechaniz

ed…

Sellin

g o

r In

fluencin

g O

thers

Identify

ing O

bje

cts

, Actions,

and E

vents

Esta

blishin

g a

nd M

ain

tain

ing…

Coachin

g a

nd D

evelo

pin

g O

thers

Pers

on H

ours

/ Y

ear

(Millions)

A Year in the Life of the Federal Workforce

How does the federal govt. workforce spend its time?

48%

25%

93%

42% 36%

28%

27%

5%

40%61%

24%

48%

2%18%

3%

High AutomationPotential

Medium AutomationPotential

Low AutomationPotential

Automation potential of the five most labor-Intensive federal tasks (Annual hours spent in million)

Four out of five most

labor-intensive activities

have 50% or more

medium / high

automation potential

Four out of five most labor-intensive tasks are highly

amenable to automation…

Documenting /recording information

Handling and

moving objects

Assisting and

caring for others

Getting information

Communicating with colleagues

Deloitte analysis of OPM Fedscope and DOL O*NET data

Potential savings for the federal government from AI

Source: Deloitte Research

1.1 billion hours

$ 9.6 billion

$37.7 billion

266 million hours

High Investment(Tasks speed up by 200 %)

Two scenarios Potential Savings

Hours Freed

Low Investment(Tasks speed up by 20 %)


Benefits of adding

cognitive

technologies to the

work flow…


The anticipatory enterprise


Human vs machine prediction and the HIPPO problem

The result: “Humanity barely bests [a] chimp throwing darts at the

possible outcomes.”

-Philip Tetlock

William Grove, a professor of psychology at the University of Minnesota, went through 50 years of data comparing “head-to-head” test approaches completed by humans and machines, and found

people were only superior 6% of the time

Between 1987 and 2003, Tetlock asked 284 people who "commented or

offered advice on political and economic trends" professionally to make

a series of predictive judgments about the world: 82,361, in total.


Removing cognitive bias

Broward County, Florida school district found that moving from teacher nomination to

nonverbal testing identified 80% more black and 130% more Hispanic students as gifted.


Augmented: Shifting from Human to Centaur


IBM Watson for Oncology synthesizes data from over

200 textbooks, over 290 medical journals, and 12

million pages of text to shed light on treatment

methods that no single human could find on their own


Anticipatory shipping model by


Text

Text

Text

Text

Text

Text

Text

Text

The AI advisor can play out security outcomes under various

scenarios


Algorithmic black box: The problem

Governments increasingly rely on data-driven insights powered by algorithms.

However, bias can arise as a result of

vulnerabilities in the-

Input data

Algorithm design

Output data


Bioenhancement


Forms of enhancement

Put onBake in

Put in


Neuroproductivity


CRISPR Super Plants, Super Animals

Gene editing of

mosquito parasites to

eliminate Malaria

Costs of producing

plants and animals will

fall

Genetic modification

to produce more

muscular pigsPlants resistant to pests

and herbicides- better

nutritional value


To come: Designer babies with CRISPR

Sprinter

High IQ

Taller

Perfect vision

Low risk of Alzheimer’s, breast cancer and strokes

Perfect pitch


“People are very open minded about new things.

As long as they are exactly like the old ones.”

- Charles Kettering

Just remember…

Bill Eggers

Email:

[email protected]

m

Twitter:

@wdeggers

Web:

williameggers.com

emerging trends and exponential forces - ffcoi · 2019. 3. 23. · 23 c opy righ t© 2017 d elott...

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