Updated deeper overview of investor's look at machine learning / deep learning startups, with slight Russian accent. =) Some slides are courtesy of Russia.ai and personally great friend @Petr Zhegin: #23, #28 are from http://www.russia.ai/single-post/2016/09/21/Ten-Russian-speaking-venture-capital-funds-one-may-consider-to-back-an-AI-startup #30 insights are from http://www.slideshare.net/RussiaAI/artificial-intelligence-investment-trends-and-applications-h1-2016 Victor Osyka of Almaz Capital, http://fb.com/victor.osika, http://medium.com/@victorosyka

1. Investor's View on
Machine Intelligence
startups, 2.0, Jan 2017
osyka.victor@gmail.com

2. Who is Victor
1
Been on both sides of the table: startup founder,
venture investor at US/Russia fund
www.almazcapital.com. On boards of Carprice,
StarWind, Nival, 2Can-iBox, Yaklass, RoboCV etc.
4 years in VC, 1 yr. startup co-founder, 3 yrs. in
consulting, engineering + LBS MBA edu.
Reach here osyka.victor@gmail.com
http://medium.com/@victorosyka I post here and
at facebook
www.linkedin.com/in/victorosyka
http://facebook.com/victor.osika

3.  Google trends stats on AI/ML/DL/bigdata + deep learning patents
 Technology: deep/machine learning helped a lot in many domains, more
progress to come
 Portrait of a fundable startup is probably:
– May aim to taking some technology barrier
– Not hardware, agnostic if b2c/b2b, biz co-founder(s), creates barriers for entry,
arbitrages R&D cost by CIS geo, ideally HQ in SV
 Globally, funding is steadily growing
 Exits are done in Russia in 2016 even under sanctions: Itseez, Api.ai
Takeaways
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4. Patents
3

5. Patents on “deep learning”
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6. Patents on “deep learning”
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7. Words from google trends
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8. =
Google trends – with AI
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9. =
Google trends – let’s remove AI to see details
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10. =
Google trends
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11. =
Google trends
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12. Technology
11

13. When general intelligence will come? =)
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Schmidhuber:

14.  Data
 Computing
– Demand for data and computing power will increase even more as too
much data and power is required to slightly decrease the error rate in
models that power AI. Buy Nvidia stocks? =)
– AI moves into real time – e.g. live video analytics, driving
 Progress in architectures
– Complexity of architectures will go up at the hardware and the neural
networks level
– # DL developers: 2.2K => 55K in 2016
 # GPU developers globally: 120K in 2014 => 400K in 2016. (ML is also
done at GPUs, but many operate on non-DL stack)
Inflection point is a result of abundance of:
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15. Computing– last 3 dots Nvidia GPU’s, not Intel CPUs
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16. According to type of data used
Visual
 Computer vision
– Online
– AR
– Offline: cameras, robotics, self-
driving, self-flying
 Image processing
Sound
 Voice/Music synthesis
– Deepmind’s WaveNet
 Speech recognition
– One user
– Dialogues, team talks
Text
 Auto-translation
 Text processing / dialogues
Other
 Control systems (reinforcement
learning)
 Scientific problems
Current state of the art in machine learning
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17. Current state of the art in machine learning
Confidential 16

18. Visual
 Computer vision
– Who will recognize faces more successfully
than 60-70% level of quality at scale more than
0.5-1M+ pics?
– Who will be able to identify various objects by
SKU?
– Breakthroughs to current state of health
images processing
– Extract meaning from content
 Image processing
– Real-time video filters? Realtime AR?
Sound
 Voice/Music synthesis
– Who will increase speed of WaveNet by factor
of 100-1000x so it would be usable in real life?
 Speech recognition
– Who will recognize dialogues better than 50-
60% level of quality?
Text
 Text processing / dialogues
– Chats with end user satisfaction of more than
20-30-40% Or more complex talks?
– Who can extract meaning?
 Auto-translation
– What is better than google?
Other
 Control systems (reinforcement learning)
– Who will do gaming better?
 …and make autonomous agents based on
gaming spaces?
– Who will apply RL to other control domains
than power of servers etc.?
 Scientific problems
– Any meaningful breakthroughs to current
states
What one should seek in technology?
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19. Startups to seek
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20. Other than “purely product co” types of ML startups
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Scientific company -
ML company with
radical tech
improvements
• Cross-disciplinary
team
• Aims to develop
new tech
• E.g. DeepMind,
Vicarious etc.
Research lab, not
company
• Develops new
knowledge
• And outsources it
• E.g. Open.ai, Caffe
library etc.
ML company with
incremental tech
improvements
• Inspired by others’
papers
• In house tech
optimization by
computer science
people
• Very clear product
focus
Product company,
productizing some
open sourced ML
tech stack but doing
very fast business
● e.g.: Prisma etc.

21.  Software, not hardware
 Doesn’t matter if b2c or b2b customers
– B2c good that scales virally if goes well +
uses crowdsourced data (see below)
– B2b is good that monetize-able +
accumulates proprietary data (see
below)
 Many techs are replicated by followers
in 1-3 years, so business advantage
should be more complex
– Creates some barriers for entry/switch
costs.
 e.g. acquire data either unique (e.g.
crowdsourced, not publicly
downloadable/parce-able), or at scale?
 e.g. vertical market is targeted in a self-
reinforced data loop (more data = tech
performs better = customers are more
loyal). Example: health data, telco data,
industrial data, banking.
– Still, companies aiming to the taking
technology barriers are welcome
 Team of not only tech ppl, some founder
must be product or biz obsessed
– Tech team can be big now, field seems to
be complex now
 Exploits geo arbitrage for labor costs
– Gives more R&D headcount for the same
runway OR less $ needs to be raised
each time
 Ideally, Russians in Valley: to be very
product/biz conscious by their living in
the ecosystem around + helps with next
rounds of fundraising
Portrait of an ideal fundable startup?
Confidential 20

22. Biz is critical. Sci/engineers problem is…
Confidential 21
Curiosity and freedom as a core value:
• “Disturbing me in my curious introspective
research”
• “Don’t touch me, boring biz guys.”
• “Customers are lamers” = no customer
listening, in essence`
• Market feedback is often perceived as an
annoying factor, limiting curiosity

23.  Russian entrepreneurs
– Appear to miss some hot spots of the AI
landscape and focus their efforts on a
limited number of applications?
– Overly focused on consumer and robotics,
founders do not embrace cybersecurity,
finance and healthcare sectors, which are
considered to be among the hottest
themes…
Russian AI startups last few years
Confidential 22
Startup examples
 Robotics
– Software
 Toytemic, Krisaf, ExoAtlet
– Bots, drones, vehicles, DYI kits
 Aeroxo, Endurance, Umki,
Sensepace, Wicron, OMI Plow,
Robodrom, Alpha Smart
Systems, xTurion, Anywalker,
Promobot, Bitronics Labs
 Computer vision/Imaging
– 3DiVi, VisionLabs, CompVision,
Prisma, Life.Film, Vocord
 Predictive analytics
– RCO, Medialogia, Eventos,
Promodern, Prometei,
Gloubhopper, Statsbot
 AR/AR
– VRD, VR Systems, Kvadratik,
Bazelevs Innovation
 Intelligent assistants
– Cubic, Findo, Lexy

24.  Predictive analytics
– People.ai
 Driving / robotics
– IntelinAir
– Cognitive Technologies
– Starship
 Visual / computer vision
– NTech Lab
– Icon8, Malevich, Altera and other
derivatives of Prisma
– Scorch – visual recogn., vid
surveillance
– Entropix
– Kuznech
 Consumer
– lifetracker.io
 Audio/Voice
– Mubert
 NLP/NLU/dialogues
– Edwin
– Digital Genius
– Deephacklab
 Search
– Inten.to
 Security
– Unfraud (Italian co)
 Gaming
– Mobalytics?
Examples – Russian roots
Confidential 23

25.  Probably, around 2000 AI startups in the world as CBInsights says
Startups globally
Confidential 24

26. Funding
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27. Funding in the field – dynamics globally
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28. By industry
Russian investors in foreign AI co’s (# = 38)
Confidential 27
By core technology
 Altair – Youappi, Socure
 Flint – CyberX, Youappi
 Grishin Robotics – Occipital, RobotLab
 I2BF – Planetary Resources, Autnomous
Marine Systems
 LETA – Unomy, Visilights
 Maxfield – Visilights, SpeakingPal
 RTP – ReportGrid, WorkFusion
 Runa – LendingRobot, TellmePlus, Digital
Genius
 Titanium Investments – Feedviser, Mantis
Vision
 Vaizra – PrimeSense, Face.com

29. Foreign investors
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 Top AI investors last 5 years
– Bloomberg Beta
 Thesis is future of work/enterprise tech
– Google Ventures (series B-C-D, no seed or A)
– Samsung
 Personal assistants and alike
– Rakuten
– Horizons
 Assistants, text processing (e.g. ViV, made by founders of Siri)
– Intel Capital
 Computer vision, hardware
– In-Q-Tel
– Khosla
 Healthcare, general AI (Vicarious), ML platforms (Scaled Inference, Russian guy in USA)

30.  In these 3 countries is the following industry breakdown of funded startups:
USA, UK and Toronto are AI clusters abroad?
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31. Exits dynamics
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32. Acquisitions in the field – dynamics
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33. Acquisitions in the field – some names
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34.  Itseez by Intel, acquired in May 2016: 100 people in Nizhniy
Novgorod – sanctions does not matter if the target is so special
for the acquirer
 Api.ai by Google, acquired in September 2016: also Russian
company
Exits in Russia still viable
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35.  http://idlewords.com/talks/superintelligence.htm
 http://mobile.nytimes.com/2016/12/14/magazine/the-great-ai-
awakening.html?utm_campaign=Artificial%2BIntelligence%2BWeekly&utm_medium=email&utm_source=Artific
ial_Intelligence_Weekly_53&_r=0&referer
 https://techcrunch.com/2016/12/14/why-we-are-still-light-years-away-from-full-artificial-intelligence/
 http://www.forbes.com/sites/louiscolumbus/2016/12/18/mckinseys-2016-analytics-study-defines-the-future-
machine-learning/#374cd999d0e8
 http://www.kdnuggets.com/2016/12/ibm-predictions-deep-learning-2017.html
 https://blog.ought.com/nips-2016-875bb8fadb8c#.ea50o72eg
Superintelligence philosophy and ML recent posts
Confidential 34

36. Startups supply side - EU
Confidential 35

37. Startups supply side - World
Confidential 36

38. Thanks
http://medium.com/@victorosyka
http://facebook.com/victor.osika
Confidential 37