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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 Tren...
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 unstructur...
Artificial Intelligence, May 2016
5
Scope of report
This report covers companies that provide the infrastructure for creat...
Artificial Intelligence, May 2016
6
Notable
rounds
Palantir
$70M
Zest Finance
$73M
Mobileye
$400M
Palantir
$445M
Palantir
...
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 S...
Artificial Intelligence, May 2016
9
Cumulative funding in the sectorPractice Area – Technology Global | Analysts: Vijaya B...
Artificial Intelligence, May 2016
10
16
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25
52
34
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Artificial Intelligence, May 2016
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140
2011 2012 2013 2014 2015 2016
No.ofcompa...
Artificial Intelligence, May 2016
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• Out of 934 companies tracked, 100 companies have been acquired
• Acquisitions have ...
Artificial Intelligence, May 2016
13
Overview
AI – Infrastructure represents companies that develop Machine Learning , Dee...
Artificial Intelligence, May 2016
14
4
NLP – Speech Recognition
Startups providing technology for creating
intelligent int...
Artificial Intelligence, May 2016
15
5
Overview
AI – Applications represents companies that use/develop Machine Learning ,...
Artificial Intelligence, May 2016
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15 15
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Artificial Intelligence, May 2016
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2010 20...
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BootstrapLabs - Tracxn Report - artificial intelligence for the Applied Artificial Intelligence Conference 2016

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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.

Published in: Investor Relations

BootstrapLabs - Tracxn Report - artificial intelligence for the Applied Artificial Intelligence Conference 2016

  1. 1. REPORT ON ARTIFICIAL INTELLIGENCE May 2016 Sponsored by APPLIED ARTIFICIAL INTELLIGENCE CONFERENCE #AAI16
  2. 2. 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
  3. 3. Artificial Intelligence, May 2016 3 AI Key Milestone Events No. of transistors per sq. inch
  4. 4. 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
  5. 5. 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
  6. 6. 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
  7. 7. Artificial Intelligence, May 2016 7 Start-up activity around the world
  8. 8. 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
  9. 9. 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)
  10. 10. 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.
  11. 11. 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.
  12. 12. 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
  13. 13. 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
  14. 14. 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
  15. 15. 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
  16. 16. 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$Millions) No.offundingtransactions Funding Year Visual Recognition Platforms Total Funding No. of funding transactions Funding Teardown: AI - Infrastructure
  17. 17. Artificial Intelligence, May 2016 17 4 10 8 15 17 21 8 19 12 27 54 64 58 32 0 10 20 30 40 50 60 70 0 5 10 15 20 25 2010 2011 2012 2013 2014 2015 2016 No.offundingtransactions TotalFunding(In$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
  18. 18. www.tracxn.com

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