Deepdive in AIML venture landscape By Ajit Nazre Rahul Garg

1. A Deep Dive in the Venture Landscape of Artificial Intelligence and Machine Learning September 2015 Ajit Nazre Rahul Garg

2. Artificial Intelligence Evolution Artificial Intelligence is on its 3rd reincarnation since its first birth in 1950 and looks like it has longevity this time around 1950 -1970s – Start of AI as a concept but no real applications 1980 -2000 – AI/ML mainly used in Military & Academia 2005 onwards – Large tech companies such as IBM, Microsoft, Google, and Facebook have invested in AI/ML for commercial applications 1956 John McCarthy organized a conference at Dartmouth & named the field as Artificial Intelligence 1950 Alan Turing published a paper about the possibility of machines with true intelligence 1995 US Department of Defense uses predator UAV in Balkan War 1997 IBM’s Deep Blue wins chess against World Champion Gary Kasparov 2011 Debut of Virtual personal assistants like Apple’s SIRI & Microsoft’s Cortana 2011 IBM Watson computer defeats Jeopardy game show champions Jan 2014 DeepMind team uses deep learning algorithms to create a program that wins Atari games Oct 2013 Vicarious breaks any ‘Captcha’ passing the Turing test Jun 2015 Facebook launches Moments that detects faces and shares photos with friends to whom they belong May 2015 Google self driving cars complete 1M miles autonomously Jun 2015 DeepMind teaches program how to read

3. Artificial Intelligence / Machine Learning Classification Artificial Intelligence Deduction, Reasoning, Problem Solving Knowledge Representation Planning Perception: Computer Vision Machine Learning Robotics: Motion and Manipulation Natural Language Processing Social Intelligence Supervised Learning Unsupervised Learning Reinforcement Learning Decision Tree Learning Association Rule Learning Neural Networks Inductive Logic Programming Support Vector Machines Bayesian Networks Similarity and Metric Learning Clustering Deep Learning Manifold Learning Sparse Dictionary Learning Genetic Algorithms

4. Multiple Factors contributing to Dramatic Improvement in Prediction Accuracy Increasing Prediction Accuracy of Algorithms = Improved Decision Making Increase in affordable Compute Power Big Data Faster networks Cloud Infra- structure Sensor Networks Advances in Neuroscience “Whenever we can replace human judgment by a formula, we should at least consider it.” - Daniel Kahneman (2002 Nobel Prize in Economics)

5. Applications of AI/ML in Daily Use Already Info Search engines Sentiment analysis (or opinion mining) Information Retrieval Spam filtering for email Speech and handwriting recognition Spoken language understanding Stock analysis Structural health monitoring Syntactic pattern recognition Topic spotting: categorize news articles Weather prediction Tools Face Detection Finance – Derivatives Trading Game playing Software Testing Internet fraud detection Machine translation Medical diagnosis Mood analysis Brain machine interface in prosthetics Optical character recognition Recommendation systems Robot locomotion Services Advertising - Targeting Bioinformatics Automatic word completion Chemical Informatics Classifying DNA Sequences Computer Vision – Object Recognition Customer Segmentation Detecting Credit Card Fraud

6. R&D Activity in AI / ML as measured by Patents granted Source: MarketRealist.com IBM and Microsoft had more patents in AI / ML granted in 2014 than all others

7. M&A Activity in AI / ML is Heating up Google Dark Blue Labs, Vision Factory, Jetpac, Quest Visual, Titan Aerospace, Deepmind Technologies, Boston Dynamics, Bot & Dolly, Holomni, Redwood Robotics, Industrial perception, Schaft, Flutter, Wavii, Behavio, DNNresearch, Viewdle, Pittpatt, Saynow, Phonetic Arts, Metaweb 21 10 6 6 4 0 5 10 15 20 25 Google Facebook Yahoo Apple IBM Facebook Pebbles, Surreal Vision, Ascenta, QuickFire, Wit.ai, Oculus VR, SportStream, Jibbigo, Face.com, RecRec Yahoo Tomfoolery, Cloud Party, Skyphrase, LookFlow, IQ Engines, Qwiki Apple Metaio, Cue, Novauris Technologies, Polar Rose, Siri, FingerWorks IBM AlchemyAPI, Silverpop Systems, Curam Software, Languagae Analysis Systems No. of companies acquired in AI/ML Source: Wikipedia Google is most acquisitive (21 companies) in the space followed by Facebook (10 companies)

8. AI / ML Venture Funding Analysis: Methodology • Created a database of over 2800 AI/ML companies from several sources including Angel list, Crunch base, Tracxn, Venture scanner, Portfolios of major VCs, News Reports & Secondary Research • Weeded out public, acquired, closed down and non existent companies (with no website) arriving at a list of 1781 companies • Filtered the list further to 312 companies based on funding > $100k • In addition to the focus areas in AI / ML areas defined on slide 3, we have extended our analysis to areas like augmented reality, image / facial recognition, and drones that overlap multiple areas. 2800 AI & ML companies from sources: Angel List, Crunch base, Tracxn, Venture scanner, VC websites, News articles & secondary research 1781 Active companies after weeding out public, acquired, closed down, non existent companies 312 Companies that have external funding of >$100k

9. Active AI / ML Ventures by Country in 2015 More than half of the AI/ML companies are based in the US 929 127 108 77 52 44 34 32 29 19 330 0 100 200 300 400 500 600 700 800 900 1000 USA UK India Canada Germany France Israel Russia China Netherlands Others n= 1781

10. AI / ML Ventures based on Funding and Location SF Bay Area has the highest density of funded startups followed by New York & London Out of funded companies , nearly 40% are in the SF Bay Area & 70% in North America Nearly 18% of companies are funded 68 49 26 12 12 8 0 10 20 30 40 50 60 70 80 Silicon Valley San Francisco NewYork London Seattle Berlin 312 1469 Funded Companies n=312

11. AI / ML Startups are still in their Infancy 62% of the startups still pre series A 193 72 26 10 5 1 5 0 50 100 150 200 250 Seed/Angel Series A Series B Series C Series D Series E Other n=312

12. Venture Funding and Most Active VCs in AI / ML Khosla Ventures: Blue River, Scaled Inference, Atomwise, Lumiata, Kaggle, Idibon, Metamind, Pymetrics, Ayasdi, Catalia Health, Theatro, Thync Google Ventures: Expect Labs, Wonder Workshop, Clarifai, Orbital Insights, Airware, Agent, AltspaceVR, Bento, Building Robotics, Framed, Skycatch Intel Capital: API.ai, Cloudmade, Emotient, Expect Labs, Eyesmart, PrecisionHawk, Hooklogic, Prelert, Reflektion, Total Immersion, Fortscale,Whoknows, Incoming Media, Occipital Two Sigma Ventures: Anki, 3D Robotics, Rethink Robotics, Jibo, Kasisto, Dextro, Socure, Floored, Canary, Zymergen, Indico Data, RRE Ventures: DigitalGenius, Yhat, Clearpath Robotics, Jibo, Palantir, Giphy, Viglink, Airware0 50 100 150 200 250 300 350 2010 2011 2012 2013 2014 14 12 12 12 8 0 2 4 6 8 10 12 14 16 Intel Capital Khosla Ventures Two Sigma Ventures Google Ventures RRE Ventures Source: Company Websites Source: CB Insights No. of Investments in AI/ML companies Venture Investments in $M in AI/ML companies 300% increase in venture funding from ‘13 to ‘14. Intel Capital, Google, Two sigma & Khosla ventures most active in investing in AI / ML

13. Funded Ventures in various fields of AI / ML Machine learning, Robotics, AR and Image recognition are most actively funded areas n=31292 58 36 34 33 26 11 10 8 4 0 10 20 30 40 50 60 70 80 90 100 ML (excluding Deep learning and predictive analytics) Robotics & Drones AI (excluding NLP, ML, Robotics, Computer Vision) Augmented Reality Image & Face Recognition NLP Predictive Analytics (clustering, SVM, Bayesian networks) Computer Vision Deep Learning & Neural Networks Others

14. Major Fields for AI / ML Venture Investment by Geography North America is leading the way but Europe has significant presence in AI & AR areas n=312 68 44 22 20 20 19 11 6 6 2 17 10 9 14 6 13 0 2 2 2 7 4 2 2 2 0 2 0 0 10 20 30 40 50 60 70 80 ML (excluding Deep learning and predictive analytics) Robotics & Drones AI (excluding NLP, ML, Robotics, Computer Vision) Augmented Reality Image & Face Recognition NLP Predictive Analytics (clustering, SVM, Bayesian networks) Computer Vision Deep Learning & Neural Networks Others North America Europe Asia/Other

15. Areas of Application of Venture funded companies in AI/ML 72% of the funded startups targeting enterprise applications n=312 (consumer apps, consumer electronics, educational applications for consumers & toys) 224 88 0 50 100 150 200 250 Enterprise Consumer

16. AI / ML Ventures with Applications across Industries Impact across a wide range of industries 50 38 30 23 23 23 16 14 14 9 8 7 6 6 5 5 35 0 10 20 30 40 50 60 ConsumerApps IndustrialAutomation AI/MLasaService Healthcare Media Marketing Advertising Security FinancialServices Ecommerce HR Agriculture Education Enterpriseproductivity ITServices CustomerService Others n=312

17. Aerospace Cumbersome & manual process of maintenance and repair using field manuals that have to be frequently updated Field service agents can call or chat with support from his/her augmented reality view Today Future Companies making it happen: Atheer Labs, Augmate, Infinity AR, Total Immersion, Vuzix Pic Courtesy: Atheer Labs

18. Agriculture Farming decisions based on tradition and intuition Machine learning algorithms using sensor data and aerial imaging help farmers make intelligent data based decisions increasing yield Today Future Companies making it happen: Blue River, Farmlogs, Greensight, Mavrx, Pulsepod, Terravion Pic Courtesy: Farmlogs

19. Automotive Cars driven by humans, prone to errors (nearly 1.3M people die in road accidents every year) Driverless cars – leading to comfortable experience & less human fatalities Today Future Companies making it happen: BMW, Daimler Benz, Google Pic Courtesy: Google

20. Background Checks Checking of background information, documents and identity manually taking 1-2 weeks Automated background checking process through APIs, real time ID authentication using image recognition. Nearly 60% reduction in costs Today Future Companies making it happen: Onfido Pic Courtesy: Onfido

21. Communications People can talk to one another only in a common language that they speak People will be able to talk to one another without knowing the other person’s language – Nothing lost in translation Today Future Companies making it happen: Lexifone Pic Courtesy: Clker.com Hi, How are you I’m fine thank you Hi, How are you मैं ठीक हूं धन्यवाद

22. Customer Care Time consuming process of Authentication Biometric Voice recognition based instant authentication Today Future Companies making it happen: Agnitio, Nuance, Verbio, Voicebase, Voiceitt, VoiceVault Sir, Please help me with the authentication & tell me your DOB, Address, Mobile No. My Password is XXXXX

23. Customer Care Cumbersome & time wasting Voice Menu Virtual assistant that can converse like humans & assist without Menus Today Future Companies making it happen: DigitalGenius, Expect Labs, Nuance Press 1 for Credit Cards, Press 2 for Debit Cards, Press 3 for Loans, Press 4 for Netbanking……

24. Healthcare Doctor reads static patient charts before or after visiting a patient Doctor can read all relevant reports dynamically using gestures during her patient visit Today Future Companies making it happen: Atheer Labs, Augmedix, Infinity AR

25. Media Watching ads on TV is avoided as they are not actionable and mostly irrelevant Automatic content recognition will allow advertisers/ programmers to inject interactivity & context relevant content Today Future Companies making it happen: Cognitive Networks, Datascription, Dextro, Enswers, Magic Pony Technology, Persado, Videntifier Technologies Wonder if that Jersey came in my size Just ordered the Jersey directly Pic Courtesy: Forbes

26. Navigation Driver needs to divert his gaze or has to give rigid voice instructions On windshield navigation with heads up display that recognizes the surrounding and gives instructions Today Future Companies making it happen: Nuviz, Wayray Pic Courtesy: Wayray

27. Office Productivity Mundane tasks in office leading to decreased productivity Automatic meeting schedulers, note takers, speech to text transcription & virtual personal assistants will improve productivity Today Future Companies making it happen: Assistant.to, Gridspace, Idibon, Robin Labs, Thoughtly, X.ai, Zahdoo Pic Courtesy: Typepad & Coolcontourproducts.com

28. Oil and Gas Crack Detection: It takes human 5 hours per inspection with 92% accuracy (Avg cost ~ $7.5) 20 seconds with 94% accuracy through image recognition (Average Cost ~ $2) Today Future Companies making it happen: Tractable Pic Courtesy: Wikipedia

29. Recruiting Impossible to manage candidates with required behavioral skills Neuroscience based games will match candidates with required skills Today Future Companies making it happen: Connectifier, Pymetrics, Talentoday Pic Courtesy: Pymetrics

30. Wealth Management Stock trading & investing happens mainly on personal skills and divine luck Sentiment analysis, crowd sourced research & algorithms will make investing more transparent and better informed Today Future Companies making it happen: Betterment, Estimize, Narrative Science, Personal Capital, Sigfig, Wealthfront

Deepdive in AIML venture landscape By Ajit Nazre Rahul Garg

Deepdive in AIML venture landscape By Ajit Nazre Rahul Garg

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