AI Frontiers: Where We are Today

and the Risks and Benefits of an AI

Enabled Future

GRI – FEBRUARY 2017

CONFIDENTIAL AND PROPRIETARY

In 1956 at a conference at Dartmouth, McCarthy coins the name “Artificial

Intelligence”

1956 – “Summer research” project on AI 2006 – AI @ 50

THE SEMANTICS

2

Recent advancement in AI has spurred narratives prophesizing the

obsolescence of not only jobs, but also the human race in itself

“With artificial intelligence we are summoning the

demons…If I had to guess at what our biggest

existential threat is, it’s probably [artificial intelligence].”

– Elon Musk

“First, the machines will do a lot of jobs for us and not be

super intelligent… A few decades after that, though, the

intelligence is strong enough to be a concern.”

– Bill Gates

“The development of full artificial intelligence could

spell the end of the human race.”

– Stephen Hawking

WHY NOW?

3

Three drivers have led to the rapid progression of AI

Machine learning is poised to

enter a “golden age” of

development

Better than human performance

with ability to teach machines to

▪ Perceive images and sounds

– Image classification

– Voice recognition

▪ Read text and identify concepts

– Natural Language Processing

(e.g. translation)

– Emotional sensing

▪ Prescribe the best course of

action by identifying patterns

– Behavioral analysis

– Anomaly detection

– Recommendation engines

Machine Learning

Deep Learning

Analytics

Advanced

hardware

New

algorithms

Big Data

4

WHY NOW?

Depending on the data and use case, there are generally three methods to

create a training “feedback” signal

MACHINE LEARNING PRIMER

When it works wells

▪ DO KNOW how to classify data

AND

▪ Large LABELED dataset

Example

▪ Predicting who will charge off

on credit debts

Methodology

▪ Learns how to classify data

based on labeled training set

▪ Classifies unseen data

quickly and accurately

When it works wells

▪ DO NOT KNOW how to classify

AND

▪ Large UNLABELED dataset

Example

▪ Cluster customers with similar

transaction behavior

Methodology

▪ Infers “hidden” structure in

data

▪ Encodes “hidden” structure

and returns pattern

Supervised Unsupervised Reinforcement

When it works wells

▪ DO NOT KNOW how to classify

AND

▪ System provides FEEDBACK

Example

▪ Autonomous vehicles and

robotics

Methodology

▪ Interacts with environment

and receives rewards

▪ Adjusts decision making to

maximize reward

5

Today, we are still in the early stages of AI development – all forms of

artificial intelligence are instances of Narrow AI

WHERE WE ARE TODAY

General AI Super intelligence

Evolution of artificial intelligence

Narrow AI

Description

• Performs a broad set

of intellectual tasks

• Used for highly

complex tasks

• “Single brain” as

smart and empathetic

as a human

• Operates in open

systems

• Outperforms humans

in intellectual tasks

• Capable of creativity,

innovation, social

skills

• Fully conscience

machines

• Operates across vast

set of open systems

• Designed for specific

tasks

• Applied to a narrowly

defined problem

• Integrated to produce

highly powerful

applications

• Operates in closed

systems

Examples

• “Brain of the bank”

• Fully intelligent

personal assistants

• Next-best-offers

• Language translators

• Autonomous vehicles

Today

• Next evolution of

intelligence

Future?

6

7

Several key factors could constrain AI’s advancement

Source: New York Times; Accenture and Frontier Economics; MGI

Finding

suitable

use-cases

Access to

big data

Scarcity

of talent

Lack of

platform

tech.

Mitigation

Honesty and

awareness about what

AI can and cannot do

Building privacy and

security into the design

Shift of AI developer

toward curation

Grow technical,

supportive and “business

translator” talent

Retraining of labour force

Key factors that could constrain development of AI

Risk

▪ Highly inflated

expectations

▪ Imperfection of

models

▪ Exploitation of

personal info

▪ Biased data

▪ Backlash

▪ Talent wars

▪ Technological

unemployment

▪ Complex

solutions for

specific use-

cases

Adopt, develop and train

talent on platform

technologies early

40% of activities can be automated

through AI

500,000 new data

scientists will be needed

by 2025

+30% productivity increase by 2035

Preparing for the future

of AI

$90B of growth in

the global AI solutions

market by 2020

KEY FACTORS THAT WILL SHAPE THE FUTURE OF AI

We built three broad scenarios that help us think about the future of AI THE RISK AND REWARD OF AI

Collobor-

ative AI

Winner

takes all

▪ Democratization– Large and small organizations

will be able to develop and deploy their own AI

▪ Complement to human intelligence – AIs will

be collaborative partners and an important

component of high performance teams

▪ Concentration of power – Handful of giants will

dominate AI, controlling high caliber technical

talent and access to enormous data sets

▪ Dissipation of industry boundaries – Early

adopters will disrupt vulnerable markets

Potential scenarios

▪ Excess hype – Disillusionment will result from

misuse, misapplication, and misunderstanding

▪ Socially wasteful innovation – Significant focus

on using personal information, resulting in

exploitation and backlash

Return to

AI winter 1

2

3

Benefits

8

1 Return to a (mild) AI winter

Reckless innovation could drive us

towards another AI winter

Excessive hype will

create short term expectations, yet

realizing the potential of AI will take

time.

Significant resources will be spent on unsuitable use

cases.

Socially wasteful

innovation will potentially be

created by over focus on personal

information

Positive

outcomes

Negative

outcomes

Some benefits will be captured but at

a price

▪ Some improvement in

human productivity

▪ Advancement in human

innovation

▪ Backlash from

exploitation of data

▪ Contraction of AI

investment & resources

due to overhype

This scenario would have minimal

effect on employment and wealth

inequality

SCENARIO

9

2 Winner takes all

25 19

6 7 4

2015 2016E

25

-351

2011 2012 2013 2014

Large investment by tech giants…

$10.8 billion in investment

from global tech giants in 2015, four

times the amount of 2010

$5-10 million per

employee in recent aqui-hire

transactions

Number of M&A

+50%

…is widening the innovation gap

▪ Announced investment in $3B

in Watson

▪ Acquired LinkedIn, the

Weather Network

▪ Hired 150 ML engineers in

Berlin alone

▪ Created $100M accelerator

▪ Launched new fund with

leading expert Bengio

▪ Acquired Maluuba

▪ Acquired DeepMind (~$500M)

▪ Hired leading expert Geoffrey

Hinton

SCENARIO

10

3 Collaborative AI

Integral part

of team

Pervasive use

across industries

Intelligence

partner

▪ Open and accessible AI technology will be available to

both large and small corporations, as well as small

businesses and individuals

▪ Heavy analytics and real-time intelligence will inform

management of changes to the internal business,

customer sentiment and external risk

▪ AI partners will take on burdensome tasks, inform

decision and allow teams to focus on more value added

tasks

▪ User friendly tools will be accessible to a broader set of

users with different experience levels allowing for the

development of custom AIs

▪ Transparent data collection and use will build trust in

customers and inspire the democratization of data

Platform

technology

Democratization

of data

Democratization of AI will not only automate

cognitive tasks, but augment our intelligence

11

SCENARIO

We predict Financial Institutions will quickly adopt Deep Learning for the

following use cases

Risk monitoring

Credit

worthiness

Next best action

Collections

Anti-money

laundering

Customer service ▪ Employment of AI tools based on Natural Learning Processing will help

monitor customer service interactions and estimate NPS in real-time

▪ Increased adoption of leading edge NBA engines will improve digital

engagement by helping customers with account opening and maintenance, and

offering personalized financial products

▪ Deployment of new deep learning models will reduce charge-offs by helping

collections agents select the optimal treatment strategy for each account

▪ Improved adjudication algorithms will use credit score data, transaction activity

and other “orthogonal” data (e.g. social media, telco) to offer instant adjudication

and credit increases to more retail and small business customers

▪ Replacement of “rule based” alert system with machine learning models will

mark a turning point against the prevention of illicit transactions, and criminal and

terrorist activity

▪ New generative machine learning models will monitor payment activity and

detect instances of fraud more quickly and accurately

▪ Adoption of predictive maintenance algorithms to analyze aggregate

consumer data will help identify early fault signals indicative of systemic risk

Fraud

detection

12

USE CASES