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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
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