Post on 08-Mar-2018
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 bd@tracxn.com
Reference hackers.ai conferenceand write to us at bd@tracxn.comand learn how some of the largest Venture Funds and corporates are leveraging Tracxn everyday.
Table of contents
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 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
Artificial Intelligence, May 2016
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
Artificial Intelligence, May 2016
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
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 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)
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
. 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.
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
. 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.
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 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
Artificial Intelligence, May 2016
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
Artificial Intelligence, May 2016
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
Artificial Intelligence, May 2016
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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
Artificial Intelligence, May 2016
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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
nd
ing
(In
$M
illio
ns)
Funding Year
Machine Intelligence SystemTotal Funding
No. of fundingtransactions
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
No. of fundingtransactions
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
No. of fundingtransactions
Funding Teardown: AI - Infrastructure
Artificial Intelligence, May 2016
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
No. of fundingtransactions
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
No. of fundingtransactions
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
No. of fundingtransactions
47.9%
42.5%
5.9%
3.7%
Consumer- Funding Distribution
Intelligent Robots
Virtual Assistants
RecommenderSystems
Search Engines
Funding Teardown: AI - Applications