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AMP New Ventures
Perspective on Artificial Intelligence
September 2016

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Artificial Intelligence is already everywhere. It powers our
smartphones, drives our cars and sorts our newsfeeds.
Companies globally and across industries are participating in the
race for true AI, to reduce operational costs, make faster, more
accurate decisions and personalise customer experiences.
Perspective

3.
Sections
1. Definition
2. Branches
3. Applications
4. Why now
5. Risks
6. Startups (Examples)
3

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Artificial Intelligence
The theory and development of computer systems able to
perform tasks normally requiring human intelligence, such as
visual perception, speech recognition and decision-making
Branches Applications Why nowDefinition RIsks Startup Examples

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Recent leaps of progress in AI has triggered an explosion of startups
Source: Venture Scanner

6.
1956
John McCarthy coins
‘Artificial Intelligence’
at Dartmouth
Conference
Theory
6
1950
Alan Turing
publishes paper on
concept of machine
intelligence
1995
US Department
of Defence uses
Predator UAV in
Balkan war
1997
IBM’s Deep Blue
wins chess against
World Champion
Gary Kasparov
2011
IBM Watson
computer defeats
Jeopardy game
show champions
2011
Debut of Virtual
Assistants Apple Siri
and Microsoft
Cortana
Jan 2014
Deep Mind
Team’s algorithm
wins Atari games
May 2015
Google self-driving
cars complete 1M
miles autonomously
June 2015
Deep Mind
teaches
program how to
read
AI has materialised from a theory in 1950 to widespread technological
applications that we use today in our daily lives
March 2016
AlphaGo beats Go
Grandmaster Lee
Sedol in a 5 game
series.
Strong AI: Machine Learning
Selected Milestones of AI
PresentWeak AI: Expert
Systems
Deep Learning

7.
RIsksWhy nowBranches ApplicationsDefinition
7
Branches
Artificial Intelligence
Startups examples

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Machine Learning
(Learn)
Computer Vision
(See)
Speech Recognition
(Hear)
Natural Language Processing (NLP)
(Communicate)
Expert Systems
(Think)
Motion Planning
(Move)
AI can be split by unique human capabilities

9.
AI listens, thinks and communicates...
9
Speech Recognition is the
process of mapping audio speech
data to textual sentences or key
phrases. As humans can speak
150 words per minute on average,
but can only type 40, speech
recognition has great potential in
computer efficiency.
As more voice usage data
becomes available, speech
recognition accuracy will get better
and better. In 2010, accuracy for
technology companies hovered
around 70, and today sits between
95 and 99.
Natural language processing
(NLP) focuses on human–
computer interaction, enabling
computers to derive meaning from
human language input; and also
generate natural language
responses. Today, machines
proficiently understand natural
language syntax but face great
challenge in interpreting sentiment
(i.e. sarcasm, excitement).
Expert Systems emulate human
expert decision-making abilities.
It allows the computer to solve
for complex problems by
reasoning about knowledge,
navigating if–then rules.
(Communicate)
(Think)
(Listen)
From the creators of Siri, Viv
enables developers to create
anything on top of its,
conversational interface, making
‘her’ smarter.

10.
Sees, moves and learns...
10
Computer vision is the
ability to electronically
perceive and understand
image/video sources,
extract meaningful
information and take action.
Up until now, image
recognition has been driven
by rules-based
categorisation. Today,
machines are fed data so
they build their own vision.
Motion Planning is the process
of forming a strategy of action
sequences to achieve a desired
movement, typically for execution
by intelligent agents,
autonomous robots and
unmanned vehicles. Today, we
are at advanced levels of simple
motion planning problems, such
as ‘move from A to B, while
avoiding collision with any
obstacles.’
Machine learning is training computers with
datasets to recognise patterns, develop
algorithms and self-improve. Machine
Learning has been central to today’s
unprecedented momentum in AI, as it enables
the progress of other AI branches.
(See)
(Move)
(Think)

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Machine Learning techniques are used to create self-learning capabilities
1. Raw Data is formatted and cleaned
so scientific conclusions can be drawn
without error/skew. Accuracy and
insights increase with relevance and
amount of data.
2. Algorithms are applied for
statistical analysis. This includes
things like regression models and
decision trees. The results are
examined and algorithms are re-
iterated until a best model emerges
that produces the most useful results.
Under the hood
3. A Chosen Model is now used
to produce probability scores
(usually between 0 and 1) that can
be used to make decisions, solve
problems and trigger actions.
Source: Azure
Supervised Learning :Data is labelled and
there is a specific outcome
Unsupervised Learning : Insights are drawn
from data without a specific purpose

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The goal of AI is to create Strong and Broad platforms using Machine
Learning techniques
Strong AI
Weak AI
Executes tasks within a rules-based
programmed domain
Narrow AI
Built to perform limited,
specific tasks
Broad AI
Systems that can be
applied to many contexts
Self-improves through Machine Learning
based on raw input
Goal
Knows one thing and improves Knows many things and improves
Knows many thingsKnows one or limited things

13.
RIsksWhy nowApplicationsBranchesDefinition
13
Applications
Artificial Intelligence
Startup examples

14.
Agriculture
• Drone vision to monitor crop conditions like water
stress, nutrient condition, plant population, soil moisture
content etc.
• Predicting pest and disease outbreaks using data
• Drones capable of delivering customized fertilizers and
pesticides based on the requirement of each plant
• Autonomous GPS guided harvesting systems
• Facial recognition for livestock (e.g. cows)
14
Healthcare
• Expert systems to instantly weigh factors in a patients
circumstance and shortlist possible diagnoses with
confidence ratings
• Surgery Robotics to assist in the operating theatre
• Virtual nurses and remote patient monitoring
• Data to streamline the selection process of drug
development to show investigators which developments
show the most promise
• Insight and pattern induction from huge data deposits
from connected devices
Military
• Unmanned drones providing sustained surveillance and
swift precise attacks on high-value targets
• Small robots are used for missions to counter
improvised explosive devices
• Systems for faster collection and information analysis to
improve reaction and decision-making time to
implement effective military actions
• Smart pilot helmets (e.g. F35 fighter jet helmet)
Manufacturing
• Computer vision with robotics to automate assembly
line tasks
• Computer vision and machine learning to track and
isolate physical fault causes
• Mail routing using computer vision based on human
written (and often badly) postal codes
• Data-driven rapid prototyping for 3D printing
AI is being adopted across all industries

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Customer Service Chat Bots
NLP powered chat bots used to
answer general FAQ and action
simple tasks, reducing volumes
and waiting times for customers
Predictive Credit Analysis
Machine Learning algorithms
are applied to credit scores and
other personal data to assess
risk for loan applications and
loan pools as a whole
Insurance Underwriting
Underwriting AI systems are
used to automate the
underwriting process and utilise
wider and more granular data
such as health and social media
Personal Budgeting
AI is used to recognise and
report personal spending
patterns, detailing location,
merchant and spend category.
Alerts can be pushed for
irregular fees and patterns
Algorithmic Trading
Investment managers use
trading Algorithms to
automatically place trades,
generating profits at speeds
that are humanly impossible
Fraud Detection
By analysing historical transaction
data, models can be built to detect
fraudulent patterns. These models
can then be applied to real-time
financial transactions and be given
fraud scores.
Operations and Risk Product Sales & Marketing Customer Service
Marketing
AI used to personalise offers,
A/B test advertising content,
and decide when is the
optimal time to release that
content
AI is being deployed across the Financial Services value chain
Robo-Advice
Automated financial advice
and investment portfolio
rebalancing based on risk
profile and life stage

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41 startups bringing AI to Fintech
Source: CB Insights

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Branches Why now RIsksApplicationsDefinition
17
Why now?
Artificial Intelligence
Startup examples

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More Fuel
Better
Engineering
Cheaper
Material
Improved
engines
Avalanche of
Data
Repurposing
GPUs
Cheaper
Computation
Stronger AI
AI is booming now due to the convolution of more data, the repurposing
of GPUs and cheaper computation

19.
6bnNetwork
connections per
person on earth
2.5 People have
smartphones.
World population =
7bn
30bnPieces of content
shared on
Facebook every
month
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More fuel (data)
Just as human brains require dozens of examples before it
can naturally distinguish cats and dogs, Artificial minds
require large datasets to upskill in categorisation accuracy.
Social networks, mobile phones and wearable devices,
powered by improved connectivity and cloud economics,
have created an explosion of data to feed AI engines.
90% of all the data in the world being generated in the past
2 years.
Why is AI booming now?
Avalanche of Data
Data is growing at a 40% compound rate, reaching ~45
Zettabytes (ZB*) by 2020. To put things in context, 1 ZB =
1.1 Trillion Gigabytes = 2 billion years of music .
More
Fuel
Better
Engineering
Cheaper
Material
Improved
engines

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Why is AI booming now?
Repurposing of GPUs
Better Engineering
Up until now, AI applications have needed to process large amounts of data in a sequential pattern,
limiting processing speeds.However, n 2009, Andrew Ng’s team at Stanford discovered that GPU
(Graphic Processing Units) chips, typically used for gaming, could be organised to run data processes in
parallel manner.
This is important as ‘neural networks’, the primary architecture of AI software today, require many
different processes to take place simultaneously in parallel. To recognise images for example, every pixel
must be seen in context to each other, a deeply parallel task.
More
Fuel
Better
Engineering
Cheaper
Material
Improved
engines
CPU: 1-4 Serial
Processing
Cores
GPU:100’s of
Parrallel
Processing
Cores
Serial
Parallel

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Cheaper material
Computational power has steadily become cheaper over the
past 50 years as per Moore’s law, which states that overall
processing power (number of transistors on an affordable
CPU) for computers will double every two years.
This is achieved through shrinking transistors, which in turn
makes digital devices significantly cheaper and more energy-
efficient to power AI applications.
Why is AI booming now?
Cheaper Computation Power
Computer cost/performance (1992 – 2012)
Microchip transistor sizes (2000 – 2020)
More
Fuel
Better
Engineering
Cheaper
Material
Improved
engines

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Source: CB Insights
This breakthrough in AI has attracted large amounts of investment, in
turn further accelerating growth
AI Landscape: Global Yearly Financing History
Investments in AI startups have
increased nearly 6x to ~400 in 2015,
up from ~70 in 2011

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Tech giants (Google, Facebook, Amazon, IBM) are aggressively
acquiring AI startups to capture market share
Race for AI: Most Active Acquirers in Artificial Intelligence
Google is the most active in
the space (21 companies)
followed by Facebook
(10 companies).
Source: CB Insights
By 2020, the market for machine learning applications will reach ~$40bn and 60% of those applications will run on
the platform software of 4 companies (Amazon, IBM, Google and Microsoft)

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Why nowApplicationsDefinition
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Risks
Artificial Intelligence
Branches RIsks Startup Examples

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Risks of transferring responsibility and knowledge to AI
Existential risk to humanity
Futurist and Google’s Director of Engineering Ray
Kurzweil, predicts that machines will surpass humanity
in intelligence by 2029 and become ‘Superintelligent’, a
powerful state that could be difficult to control and pose
existential threats to humanity. Other technology leaders
such as Bill Gates and Elon Musk have expressed
similar concerns. Superintelligence is ranked as the 3rd
highest existential threat to humanity, after
Bioengineered pandemics and Nuclear war.
More serious cyber attacks
AI algorithms are equally as susceptible to cyberattacks
as regular software. However, because AI algorithms
are often depended on to make high-stakes decisions,
such as driving cars and controlling robots, the impact
of successful cyberattacks on AI systems could be
much more devastating than attacks in the past.
Elon Musk, Founder of Tesla and SpaceX, tweets
concerns about AI
Hackers remotely kill Jeep on a highway
(July 2015)

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Replacing human jobs
Boston Consulting Group predicts that by 2025, up to
a quarter of jobs will be replaced by either smart
software or robots. The first jobs most likely to be
affected are industrial jobs (manufacturing, cleaning),
routine information processing tasks (bookkeepers,
travel agents) and basic customer service roles (call
centres, cashiers).
Amplification of bugs
The shift from traditional programming to machine
learning means that code is often self-produced in neural
nets, as opposed to being hand-programmed. While this
is much faster, this means the code is harder to audit,
and early-stage errors or bugs can be easily amplified if
undiscovered. Extra validation measures should be
taken with machine learning to achieve high degrees of
quality assurance.
Microsoft’s Twitterbot ‘Tay’ goes rogue with tweets
Jobs requiring empathy and intuition (e.g. psychologists, clergies)
are least likely to be threatened by technology.
Risks of transferring responsibility and knowledge to AI

27.
Branches Applications Why nowDefinition
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Startup examples
Artificial Intelligence
RIsks Startup Examples

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Banjo
Gods eye view
Description
Banjo delves through public social media posts and uses
algorithms to identify deviations from the normal activity at a
given location. Apart from breaking news, Banjo’s use cases
include things such as track disease outbreaks and predict
insurance claim in natural disaster events. Banjo is now used by
thousands of news outlets, insurance firms, security contractors
and more.
How it works
The company divided the globe into 35 billion football-field-
size squares and spent years determining baseline activity
levels for each portion of the virtual grid. Now, any deviation
from this baseline triggers an alert to the Banjo team.
Why it matters
During the Boston bombing on April 15, 2013, the Banjo team
were able to instantaneously look at the scene in real time and
identify people of interest just minutes after the bombing
occurred.
Inception: 2011, California (US)
Social Media activity heat map used at Banjo HQ
Banjo’s computer vision classification
Funding to Date: US $121m

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Affectiva
Emotion as a service
Description
Affectiva offers a cloud based solution that reads facial
expressions, which it calls “Emotion as a Service”. Its emotion
analytics platform ‘Affdex’ is used by one third of Fortune Global
100 companies and over 1,400 brands (Unilever, Kellogg's,
MARS etc.) to understand consumer emotional engagement,
optimise business processes and improve customer
experiences.
How it works
Affectiva has collected the world's largest repository of emotion
data – 3.2 million faces analysed from 75+ countries amounting
to more than 12 billion emotion data points.
Why it matters
Affectiva allows developers to create hyper-personalized
experiences across multiple industries. For example, in gaming,
developers can create adaptive games that change based on a
player’s mood. In healthcare, clinical researchers can develop
applications that respond to a patient’s emotional state. Video
communication platforms can even modify presentations in real-
time, based on an audience’s engagement.
Funding to Date: US $33.72m
Inception: 2009, Massachusetts (US)
Testing advertisement reception using Affectiva software
Affectiva’s facial analysis to label emotional states

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Jibo
Every family needs a Robot
Description
Jibo is the world’s first social robot for the home, at 11 inches
tall and weighing 3 pounds. It’s uniquely empathetic in the way
it takes voice commands, recognises individuals, takes
photos/videos, answers queries and more.
How it works
Jibo uses machine learning, speech and facial recognition, and
natural language processing to learn from its interactions with
people. Jibo will familiarise with individuals, recognising voice
print and appearance, and alter its behaviour accordingly.
Why it matters
Interest from larger players in the smart home and
entertainment fields has grown since Jibo's 2014 reveal. In May
2016, Jibo’s team released an SDK (software developer kit) that
allows developers to create their own skills for Jibo. Jibo is a
step ahead of Amazon’s Alexa or Apple’s Siri in that it is built to
coexist socially with humans, a step closer towards fictional
characters such as Starwars’ R2D2.
Funding
$33.72m (FTD)
Location
Massachusetts (US)
Founded
2009
Founder
Rana el Kaliouby
Inception: 2012, Massachusetts (US)
Funding to Date: US $52.3m
Jibo

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Prisma
AI with a paintbrush
Description
Prisma uses machine learning algorithms to instantly transform
smartphone into stylized artworks based on unique artistic and
graphical styles.
How it works
Styles are extracted from artworks are mashed with photo data
using neural networks on a blank canvas to produce a final new
image. This is not to be confused with ‘filters’ as used in
Instagram.
Why it matters
This counters the argument that ‘machines can never develop
creativity’, as Prisma’s art has become virally popular. The app
is now being used in ~30 countries, with 300,000 installs across
10 of those countries per day.
Inception: 2016, Moscow (Russia)
Funding to Date: US $1m - $2m

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ZestFinance
Big data credit scoring
Description
Founded by ex-CIO of Google, Douglas Merrill, ZestFinance
applies algorithms to thousands of data points to make a credit
decision within seconds. Its loan product ‘Basix’ can approve
personal loans ($3000 - $5000) in minutes.
How it works
In evaluating borrowers, ZestFinance pulls data from various
credit agencies and other sources, looking at factors such as
college attendance, online restaurant ratings, phone bills and
even the way you type online. This allows the company to re-
create the holistic view of the borrower.
Why it matters
Alternative credit scoring allows Fintechs to lend to borrowers
typically not served by banks due to a lack of credit history. For
example, ZestFinance’s ‘Basix’ lends to near-prime borrowers
who just miss the cut to borrow from banks. Secondly, the
speed of data crunching means loans can be funded to
customers within minutes, much faster than traditional bank
processes. Finally, according to ZestFinance, ‘all data is credit
data’.
Inception: 2009, California (US)
Funding to Date: US $112m
Douglas Merrill, Founder (ZestFinance)