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

Sections 1. Definition

2. Branches

3. Applications

4. Why now

5. Risks

6. Startups (Examples)

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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 now Definition RIsks Startup Examples

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Recent leaps of progress in AI has triggered an explosion of startups

Source: Venture Scanner

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

Present Weak AI: Expert

Systems

Deep Learning

RIsks Why now Branches Applications Definition

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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

AI listens, thinks and communicates...

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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.

Sees, moves and learns...

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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 things Knows one or limited things

RIsks Why now Applications Branches Definition

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Applications Artificial Intelligence

Startup examples

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)

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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

Branches Why now RIsks Applications Definition

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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

6bn

Network

connections per

person on earth

2.5 People have

smartphones.

World population =

7bn

30bn Pieces 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)

Why now Applications Definition

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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

Branches Applications Why now Definition

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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)