Tracxn - Report - Artificial Intelligence

REPORT ONARTIFICIAL INTELLIGENCE

May 2016

Sponsored by

APPLIED ARTIFICIAL INTELLIGENCE CONFERENCE #AAI16

Artificial Intelligence, May 2016

22

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 RaoTwitter Handle –http://twitter.com/VijayBhaskar_Q

Analyst – Sharad MaheshwariTwitter Handle –https://twitter.com/sharadm159

Tracxn Website –tracxn.com

Sales [email protected]

Reference hackers.ai conferenceand write to us at [email protected] learn how some of the largest Venture Funds and corporates are leveraging Tracxn everyday.

Table of contents


3

AI Key Milestone Events

No. of transistors per sq. inch


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


5

Scope of report

This report covers companies that provide the infrastructure for creating Artificial Intelligence. These Infrastructure companies includethose working on Machine Learning, Deep Learning based platforms, libraries. Some of theses companies also provide platforms forNatural Language Processing and Visual Recognition. In the Applications section, the report covers companies leveraging AI techniquesto 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 investedin 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


6

Notablerounds

Palantir

$70M

Zest Finance

$73M

Mobileye

$400M

Palantir

$445M

Palantir

$880M

Knewton

$52M

511669

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

. o

f fu

nd

ing

ro

un

ds

Funding Year

Tota

l Fu

nd

ing

(In

$M

n)

YoY Funding Rounds vs. Total Funding

Total Funding

Funding Round

• 2015 saw an increase in fundingamount with almost same no. offunding rounds as that of 2014,indicating increased averageticket size of each round.

• Total funding in the ArtificialIntelligence sector has seen CAGRof 29.7% during the period 2011 –2015.

• In 2016 as well, artificialintelligence sector has alreadyseen a considerable interest interms of funding.

• Palantir nearly garnered $1.5B ofthe funding in the AI space overthe last 6 years. One of the fewdecacorns who have not gone foran IPO.

Total funding in AI has seen a consistent upward trend since 2011


7

Start-up activity around the world


8

Number of late stage deals has gone up significantly since 2012

• Seed, Series A and Series B roundswere considered to be early stagefunding. Debt and grant roundsare excluded assuming they haveno ownership interest.

• Year 2015 saw a dip in early stagefunding rounds while the numberof late stage funding rounds sawan upward trend since 2011

• Majority of the late stage roundsin 2013-15 went to Enterprisesoftware in the BI & Analyticsspace, Healthcare and Transport(Autonomous Vehicle Technology)industry verticals.

•

63

87

142

16615725

29

30

4248

0

50

100

150

200

250

2011 2012 2013 2014 2015

Ro

un

ds

of

fun

din

g

Funding year

Early vs. Late Stage funding rounds

Late Stage Early Stage


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 INTELLIGENCESYSTEMS

DEEP LEARNINGSentient (2007, $144M)

MACHINE LEARNINGData Robot(2012,$57M)

COGNITIVE SYSTEMSIBM (1911, IPO)

NATURAL LANGUAGEPROCESSING

SPEECH RECOGNITIONMobvoi (2012, $77M)

TEXT & SPEECH ANALYTICSIdibon (2012, $6.9M)

$463M $242M $181M

$437M

AP

PLI

CA

TIO

NS

ENTERPRISE

BI & ANALYTICS

INDUSTRY

ADVERTISINGVoltari (2001, $274M)

PHARMA & HEALTHCAREButterfly Network (2011, $100M)

FINANCEZest Finance(2009, $112M)

$5.4B

SECURITY & SURVEILLANCECybereason (2012, $89M)

TRANSPORTMobileeye (1999, IPO)

AGRICULTUREThe Climate Corp(2006, Acq.)

SALESInsideSales (2004, $199M)

MARKETINGAttensity (2000, $105M)

CUSTOMER SERVICEClaraBridge (2006, $103M)

HUMAN RESOURCESBright Media(2011, $20M)

BUSINESS INTELLIGENCE

Palantir(2004,$2.01B)

ALTERNATE DATA INTELLIGENCEPremise Data(2012,$66.5M)

SOCIAL MEDIA INTELLIGENCE

Dataminr(2009,$180M)

EDUCATIONKnewton (2008, $157M)

RETAILPrism Skylabs(2011, $24M)

$2.3B

AP

PLIC

ATIO

NS

CONSUMER

VIRTUAL ASSISTANTS

INTELLIGENT ROBOTSAnki(2010, $105M)

PRODUCTIVITYX.ai(2014,$34.3M)

HEALTH & MEDICALYour.md(2013,$7M)

GENERAL PURPOSESiri(2007,Acq.)

$430M

$8

.1B

RECOMMENDERLuka.ai(2014, $4.5M)


10

16

39

25

52

34

1

0

10

20

30

40

50

60

2011 2012 2013 2014 2015 2016

No

. of

com

pan

ies

fou

nd

ed

Founding Year

The highest number of companies in AI – Infrastructure were founded in the year 2014

• Majority of the companiesfounded in 2014 are focused onDeep Learning based technology.

• Companies developing DeepLearning Technology are focusedon developing better (read betterrecall and precision) algorithms &hardware systems for fasterprocessing.

• Startups developing DeepLearning techniques forimage/visual recognition haveincreased in the recent past.Google has been applying thesetechniques to improve imagesearch, provide autonomous carsthe ability to recognize objects.One of the other key areas wheresuch techniques are being used isthe healthcare industry to predictthe probability of disease byanalyzing diagnostic scans.


11

69

91

82

106

116

8

0

20

40

60

80

100

120

140

2011 2012 2013 2014 2015 2016

No

. of

com

pan

ies

fou

nd

ed

Founding Year

The highest number of companies in AI – Applications were founded in the year 2015

• In a recent trend startups arefocusing on improvingcustomer service by creatingVirtual Agents which caninteract/engage withcustomers in natural language,understand the context andprovide intelligent solutions.IBM Watson again is one of themost prominent enablingplayers in this area in theFinance and HealthcareVerticals.

• Enterprises are trying tocomplement their existing BigData Systems with AI (MachineLearning/Deep Learning) layerto add depth to the insightsgenerated from data andprocess more complexanalytical tasks.


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 activitywith 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


13

Overview

AI – Infrastructure represents companies that develop Machine Learning , Deep Learning , General Artificial Algorithms for processingdata(mostly Unstructured Data in the form of Natural Language Text and Images). Some of these companies do provide the distributedsystems/specialized hardware platforms/full stacks for efficient computation as most of the algorithms are designed to work with vastamounts 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 individualdevelopers or development teams in companies who want to integrate AI technology such as Natural Language Processing, imagerecognition, analytics into their applications for various end use cases.

* MIS – Machine Intelligence Systems

MIS* – Machine LearningCloud hosted machine learning platforms or

companies providing APIs/Libraries for Machine Learning

MIS – Deep LearningCloud hosted machine learning platforms or companies providing APIs/Libraries for Deep

Learning

MIS – Cognitive SystemsCloud hosted systems or companies developing

Machine Learning/Deep Learning Algorithms which can demonstrate Artificial General Intelligence

AI-Infrastructure – Business Model Description


14

14

NLP – Speech RecognitionStartups providing technology for creating

intelligent interfaces which can understand natural language queries

NLP – Text & Speech AnalyticsStartups providing platform for analyzing text and

speech to extract insights

Visual RecognitionStartups providing platform for analyzing text and

speech to extract insights

Enabling Technology - HardwareCompanies providing hardware enabling AI algorithms to run faster and

efficiently.

Enabling Technology - SoftwareCompanies 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


15

15

OverviewAI – Applications represents companies that use/develop Machine Learning , Deep Learning , General Artificial Algorithms for processingdata(mostly Unstructured Data in the form of Natural Language Text and Images) for a particular sector. The segment is classified in to 3major 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 anenterprise.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 aredeveloping applications for end use cases where intelligent automation was earlier not possible or not efficient enough.

ConsumersStartups creating AI – Based applications

for Consumers

IndustryStartups creating AI – Based applications

for different industry verticals

EnterpriseStartups creating AI – Based software

for Enterprises

AI-Applications – Business Model Description


16

15 15

51 5059

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

. of

fun

din

g tr

ansa

ctio

ns

Tota

l Fu

nd

ing

(In

$M

illio

ns)

Funding Year

Enabling TechnologiesTotal Funding

No. of fundingtransactions

4 12 22 20

209

103

61

43

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

. of

fun

din

g tr

ansa

ctio

ns

Tota

l Fu

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ing

(In

$M

illio

ns)

Funding Year

Machine Intelligence SystemTotal Funding


1 921

27

52

84

473

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

Tota

l Fu

nd

ing

(In

$M

illio

ns)

No

. of

fun

din

g tr

ansa

ctio

ns

Funding Year

Natural Language Processing PlatformsTotal Funding


147

31

11

65

43

6

67

98 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

Tota

l Fu

nd

ing

(In

$M

illio

ns)

No

. of

fun

din

g tr

ansa

ctio

ns

Funding Year

Visual Recognition PlatformsTotal Funding


Funding Teardown: AI - Infrastructure


17

4

108

1517

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

. of

fun

din

g tr

ansa

ctio

ns

Tota

l Fu

nd

ing

(In

$M

illio

ns)

Funding Year

Consumer

Total Funding


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

5653

32

0

10

20

30

40

50

60

0

200

400

600

800

2010 2011 2012 2013 2014 2015 2016

No

. of

fun

din

g tr

ansa

ctio

ns

Tota

l Fu

nd

ing

(In

$M

illio

ns)

Funding Year

Industry

Total Funding


214 307 276

747

1507

1810

267

22

41

48

71

8484

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

. of

fun

din

g tr

ansa

ctio

ns

Tota

l Fu

nd

ing

(In

$M

illio

ns)

Funding Year

Enterprise Software

Total Funding


47.9%

42.5%

5.9%

3.7%

Consumer- Funding Distribution

Intelligent Robots

Virtual Assistants

RecommenderSystems

Search Engines

Funding Teardown: AI - Applications

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Tracxn - Report - Artificial Intelligence