REPORT ON
ARTIFICIAL INTELLIGENCE
May 2016
Sponsored by
APPLIED ARTIFICIAL INTELLIGENCE CONFERENCE #AAI16

Artificial Intelligence, May 2016
2
2
Topic Page
AI Key Milestone Events 03
Overview 05
Tracxn BlueBox 09
Acquisition Trends 12
Business Model Description 13
Funding Teardown 16
Contributors :
Lead Analyst – Vijaya Bhaskara Rao
Twitter Handle –
http://twitter.com/VijayBhaskar_Q
Analyst – Sharad Maheshwari
Twitter Handle –
https://twitter.com/sharadm159
Tracxn Website –
tracxn.com
Sales
bd@tracxn.com
Reference hackers.ai conference
and write to us at bd@tracxn.com
and learn how some of the largest
Venture Funds and corporates are
leveraging Tracxn everyday.
Table of contents

Artificial Intelligence, May 2016
3
AI Key Milestone Events
No. of transistors per sq. inch

Artificial Intelligence, May 2016
4
Dropping Storage, Bandwidth & Computation Costs Increase in Digital (mostly unstructured) data
Open Source AI Libraries Access to AI Platforms
Source: radar.oreilly.com Source: IDC
Global Digital Data (in Exabyte)
Enabling forces behind Artificial Applications

Artificial Intelligence, May 2016
5
Scope of report
This report covers companies that provide the infrastructure for creating Artificial Intelligence. These Infrastructure companies include
those working on Machine Learning, Deep Learning based platforms, libraries. Some of theses companies also provide platforms for
Natural Language Processing and Visual Recognition. In the Applications section, the report covers companies leveraging AI techniques
to build applications tailored for end use in Enterprise, Industry & Consumer sectors.
Over $1B has been invested in AI-Infrastructure startups since 2010 with ¬$340M being invested in 2015. Over $7.5B has been invested
in AI-Applications startups since 2010 with $2.3B being invested in 2015.
Notable investments in 2016
• Persado (Enterprise – Marketing) - $30M, Series C from Goldman Sachs, Bain Capital Ventures and others – Apr 05, 2016.
• Globality (Stealth) - $27M, Series B from Al Gore, Ron Johnson, John Joyce, Michael Marks and Ken Goldman – Apr 07, 2016.
• X.ai (Consumer – Virtual Assistants) - $23M, Series B Two Sigma Ventures, SoftBank and others – Apr 07, 2016.
• Mintigo (Enterprise – Marketing) - $15M, Series D from Sequoia Capital – Apr 05, 2016.
• Twiggle (Industry – Retail & E-Commerce) - $12.5M, Series A from from Naspers, State of Mind Ventures and J Capital – Apr 07,
2016.
• Luka.ai (Consumer – Recommender Systems) - $4.4M, Series A led by Sherpa Capital with participation from Y Combinator,
Ludlow Ventures, and Justin Waldron – Apr 08, 2016.
• Comma.ai (Industry – Transport) - $3.1M, Unattributed from Andreessen Horowitz and others – Apr 03, 2016.
Sector Overview

Artificial Intelligence, May 2016
6
Notable
rounds
Palantir
$70M
Zest Finance
$73M
Mobileye
$400M
Palantir
$445M
Palantir
$880M
Knewton
$52M
511
669
1565
2343
2652
711
88
116
172
208 205
83
0
50
100
150
200
250
2011 2012 2013 2014 2015 2016 YTD
0
500
1000
1500
2000
2500
3000
No.offundingrounds
Funding Year
TotalFunding(In$Mn)
YoY Funding Rounds vs. Total Funding
Total Funding
Funding Round
• 2015 saw an increase in funding
amount with almost same no. of
funding rounds as that of 2014,
indicating increased average
ticket size of each round.
• Total funding in the Artificial
Intelligence sector has seen CAGR
of 29.7% during the period 2011 –
2015.
• In 2016 as well, artificial
intelligence sector has already
seen a considerable interest in
terms of funding.
• Palantir nearly garnered $1.5B of
the funding in the AI space over
the last 6 years. One of the few
decacorns who have not gone for
an IPO.
Total funding in AI has seen a consistent upward
trend since 2011

Artificial Intelligence, May 2016
7
Start-up activity around the world

Artificial Intelligence, May 2016
8
Number of late stage deals has gone up
significantly since 2012
• Seed, Series A and Series B rounds
were considered to be early stage
funding. Debt and grant rounds
are excluded assuming they have
no ownership interest.
• Year 2015 saw a dip in early stage
funding rounds while the number
of late stage funding rounds saw
an upward trend since 2011
• Majority of the late stage rounds
in 2013-15 went to Enterprise
software in the BI & Analytics
space, Healthcare and Transport
(Autonomous Vehicle Technology)
industry verticals.
•
63
87
142
166 15725
29
30
42
48
0
50
100
150
200
250
2011 2012 2013 2014 2015
Roundsoffunding
Funding year
Early vs. Late Stage funding rounds
Late Stage Early Stage

Artificial Intelligence, May 2016
9
Cumulative funding in the sectorPractice Area – Technology Global | Analysts: Vijaya Bhaskara Rao , Sharad Maheshwari
May 2016Tracxn BlueBox : Artificial Intelligence
930+ companies tracked, ~$8.0B invested in last 5 years, $3.3B invested in 2015/16
INFRASTRUCTURE
ENABLING TECHNOLOGIES
Nvidia (1993, IPO)
VISUAL RECOGNITION
Face++ (2011, $47M)
$1.3B
MACHINE INTELLIGENCE
SYSTEMS
DEEP LEARNING
Sentient (2007, $144M)
MACHINE LEARNING
Data Robot(2012,$57M)
COGNITIVE SYSTEMS
IBM (1911, IPO)
NATURAL LANGUAGE
PROCESSING
SPEECH RECOGNITION
Mobvoi (2012, $77M)
TEXT & SPEECH ANALYTICS
Idibon (2012, $6.9M)
$463M $242M $181M
$437M
APPLICATIONS
ENTERPRISE
BI & ANALYTICS
INDUSTRY
ADVERTISING
Voltari (2001, $274M)
PHARMA & HEALTHCARE
Butterfly Network (2011, $100M)
FINANCE
Zest Finance(2009, $112M)
$5.4B
SECURITY & SURVEILLANCE
Cybereason (2012, $89M)
TRANSPORT
Mobileeye (1999, IPO)
AGRICULTURE
The Climate Corp(2006, Acq.)
SALES
InsideSales (2004, $199M)
MARKETING
Attensity (2000, $105M)
CUSTOMER SERVICE
ClaraBridge (2006, $103M)
HUMAN RESOURCES
Bright Media(2011, $20M)
BUSINESS
INTELLIGENCE
Palantir(2004,$2.01B)
ALTERNATE DATA INTELLIGENCE
Premise Data(2012,$66.5M)
SOCIAL MEDIA
INTELLIGENCE
Dataminr(2009,$180M)
EDUCATION
Knewton (2008, $157M)
RETAIL
Prism Skylabs(2011, $24M)
$2.3B
APPLICATIONS
CONSUMER
VIRTUAL ASSISTANTS
INTELLIGENT ROBOTS
Anki(2010, $105M)
PRODUCTIVITY
X.ai(2014,$34.3M)
HEALTH & MEDICAL
Your.md(2013,$7M)
GENERAL PURPOSE
Siri(2007,Acq.)
$430M
$8.1B
RECOMMENDER
Luka.ai(2014, $4.5M)

Artificial Intelligence, May 2016
10
16
39
25
52
34
1
0
10
20
30
40
50
60
2011 2012 2013 2014 2015 2016
No.ofcompaniesfounded
Founding Year
The highest number of companies in
AI – Infrastructure were founded in the year 2014
• Majority of the companies
founded in 2014 are focused on
Deep Learning based technology.
• Companies developing Deep
Learning Technology are focused
on developing better (read better
recall and precision) algorithms &
hardware systems for faster
processing.
• Startups developing Deep
Learning techniques for
image/visual recognition have
increased in the recent past.
Google has been applying these
techniques to improve image
search, provide autonomous cars
the ability to recognize objects.
One of the other key areas where
such techniques are being used is
the healthcare industry to predict
the probability of disease by
analyzing diagnostic scans.

Artificial Intelligence, May 2016
11
69
91
82
106
116
8
0
20
40
60
80
100
120
140
2011 2012 2013 2014 2015 2016
No.ofcompaniesfounded
Founding Year
The highest number of companies in
AI – Applications were founded in the year 2015
• In a recent trend startups are
focusing on improving
customer service by creating
Virtual Agents which can
interact/engage with
customers in natural language,
understand the context and
provide intelligent solutions.
IBM Watson again is one of the
most prominent enabling
players in this area in the
Finance and Healthcare
Verticals.
• Enterprises are trying to
complement their existing Big
Data Systems with AI (Machine
Learning/Deep Learning) layer
to add depth to the insights
generated from data and
process more complex
analytical tasks.

Artificial Intelligence, May 2016
12
• Out of 934 companies tracked, 100 companies have been acquired
• Acquisitions have been increasing significantly since 2013.
• The first quarter of 2016 has seen significantly increased acquisition activity
with Technology Goliaths like Apple and Salesforce leading the way.
Company Name Year Business Model Acquired By
Airwoot Apr 2016 Enterprise - Customer Service FreshDesk
Metamind Apr 2016 Infrastructure – Deep Learning Salesforce
Cruise Automation Mar 2016 Industry – Transport General Motors
PredictionIO Feb 2016 Infrastructure – Machine Learning Salesforce
Nexidia Jan 2016 Enterprise – Customer Service NICE Systems
Emotient Jan 2016 Industry - Advertising Apple
Recent Major Acquisitions
Business Model No. Of Acquisitions
Infrastructure – Natural
Language Processing
15
Infrastructure – Visual
Recognition
13
Applications – Consumer
– Virtual Assistants
10
Applications – Enterprise -
Marketing
10
Infrastructure – Machine
Intelligence Systems
9
Business Model wise Acquisition trends
Year No. Of Acquisitions
2011 4
2012 5
2013 13
2014 22
2015 28
2016 YTD 10
Year-wise acquisition trends
78%
8%
3%
3%
2%
6%
Acquisitions by Geography
United States
United Kingdom
India
France
Canada
Others
Major Acquirers
Company No. Of Acquisitions
Google 12
Apple 7
Salesforce 5
Yahoo 5
Nuance 5
Twitter 4
Acquisition Trends

Artificial Intelligence, May 2016
13
Overview
AI – Infrastructure represents companies that develop Machine Learning , Deep Learning , General Artificial Algorithms for processing
data(mostly Unstructured Data in the form of Natural Language Text and Images). Some of these companies do provide the distributed
systems/specialized hardware platforms/full stacks for efficient computation as most of the algorithms are designed to work with vast
amounts of data(esp. Big Data). The segment is classified in to 4 major business cut based on the technology provided and their use case.
It also includes hardware/software which enable AI-platforms. The AI – Infrastructure companies are mainly aimed at individual
developers or development teams in companies who want to integrate AI technology such as Natural Language Processing, image
recognition, analytics into their applications for various end use cases.
* MIS – Machine Intelligence Systems
MIS* – Machine Learning
Cloud hosted machine learning platforms or
companies providing APIs/Libraries for Machine
Learning
MIS – Deep Learning
Cloud hosted machine learning platforms or
companies providing APIs/Libraries for Deep
Learning
MIS – Cognitive Systems
Cloud hosted systems or companies developing
Machine Learning/Deep Learning Algorithms which
can demonstrate Artificial General Intelligence
AI-Infrastructure – Business Model Description

Artificial Intelligence, May 2016
14
4
NLP – Speech Recognition
Startups providing technology for creating
intelligent interfaces which can understand
natural language queries
NLP – Text & Speech Analytics
Startups providing platform for analyzing text and
speech to extract insights
Visual Recognition
Startups providing platform for analyzing text and
speech to extract insights
Enabling Technology - Hardware
Companies providing hardware enabling AI algorithms to run faster and
efficiently.
Enabling Technology - Software
Companies providing software to collect data from various sources into a
single place (data preparation) either for training algorithms or further
analysis
AI-Infrastructure – Business Model Description

Artificial Intelligence, May 2016
15
5
Overview
AI – Applications represents companies that use/develop Machine Learning , Deep Learning , General Artificial Algorithms for processing
data(mostly Unstructured Data in the form of Natural Language Text and Images) for a particular sector. The segment is classified in to 3
major business cut based on the sector the application is aimed at.
Enterprise : This segment covers companies which provide software based on AI technology for various departments within an
enterprise.
Industry : This segment covers companies which provide software based on AI technology for various Industry Verticals.
Consumer : This segment covers companies which provide applications based on AI technology aimed primarily at consumers.
Majority of the applications leverage AI technologies to make the existing automated solutions more intelligent. The remainder are
developing applications for end use cases where intelligent automation was earlier not possible or not efficient enough.
Consumers
Startups creating AI – Based applications
for Consumers
Industry
Startups creating AI – Based applications
for different industry verticals
Enterprise
Startups creating AI – Based software
for Enterprises
AI-Applications – Business Model Description

Artificial Intelligence, May 2016
16
15 15
51 50
59
111
91
5 5 5
8
5
8
6
0
1
2
3
4
5
6
7
8
9
0
20
40
60
80
100
120
2010 2011 2012 2013 2014 2015 2016
No.offundingtransactions
TotalFunding(In$Millions)
Funding Year
Enabling Technologies
Total Funding
No. of funding
transactions
4 12 22 20
209
103
61
4
3
5
9
18
15
5
0
2
4
6
8
10
12
14
16
18
20
0
50
100
150
200
250
2010 2011 2012 2013 2014 2015 2016
No.offundingtransactions
TotalFunding(In$Millions)
Funding Year
Machine Intelligence System
Total Funding
No. of funding
transactions
1 9
21
27
52
84
47
3
8
13
11
18
10
6
0
10
20
30
40
50
60
70
80
90
0
2
4
6
8
10
12
14
16
18
20
2010 2011 2012 2013 2014 2015 2016
TotalFunding(In$Millions)
No.offundingtransactions
Funding Year
Natural Language Processing Platforms
Total Funding
No. of funding
transactions
14
7
31
11
65
43
6
6
7
9
8 8
14
3
0
10
20
30
40
50
60
70
0
2
4
6
8
10
12
14
16
2010 2011 2012 2013 2014 2015 2016
TotalFunding(In$Millions)
No.offundingtransactions
Funding Year
Visual Recognition Platforms
Total Funding
No. of funding
transactions
Funding Teardown: AI - Infrastructure

Artificial Intelligence, May 2016
17
4
10
8
15
17
21
8
19
12
27
54
64
58
32
0
10
20
30
40
50
60
70
0
5
10
15
20
25
2010 2011 2012 2013 2014 2015 2016
No.offundingtransactions
TotalFunding(In$Millions)
Funding Year
Consumer
Total Funding
No. of funding
transactions
54.9%
13.4%
11.6%
10.6%
4.6%
4.0%
Enterprise - Funding Distribution
BI & Analytics
Marketing
Security &
Surveillance
Sales
Customer Service
Others
27.6%
19.0%
18.0%
14.2%
6.1%
3.1% 12.1%
Industry - Funding Distribution
Transport
Pharma & Healthcare
Advertising
Financial Services
Agriculture
Retail & eCommerce
Others
112 132
254
615
379 365
202
17
13
25
49
56
53
32
0
10
20
30
40
50
60
0
200
400
600
800
2010 2011 2012 2013 2014 2015 2016
No.offundingtransactions
TotalFunding(In$Millions)
Funding Year
Industry
Total Funding
No. of funding
transactions
214 307 276
747
1507
1810
267
22
41
48
71
84
84
23
0
10
20
30
40
50
60
70
80
90
0
200
400
600
800
1000
1200
1400
1600
1800
2000
2010 2011 2012 2013 2014 2015 2016
No.offundingtransactions
TotalFunding(In$Millions)
Funding Year
Enterprise Software
Total Funding
No. of funding
transactions
47.9%
42.5%
5.9%
3.7%
Consumer- Funding Distribution
Intelligent Robots
Virtual Assistants
Recommender
Systems
Search Engines
Funding Teardown: AI - Applications

www.tracxn.com