investor's view on machine intelligence startups, 2.0, jan 2017

Investor's View on Machine Intelligence startups, 2.0, Jan 2017

osyka.victor@gmail.com

Who is Victor

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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 facebookwww.linkedin.com/in/victorosyka http://facebook.com/victor.osika

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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Patents

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Patents on “deep learning”

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Patents on “deep learning”

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Words from google trends

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Google trends – with AI

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Google trends – let’s remove AI to see details

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Google trends

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Google trends

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Technology

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When general intelligence will come? =)

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Schmidhuber:

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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Computing– last 3 dots Nvidia GPU’s, not Intel CPUs

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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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Current state of the art in machine learning

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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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Startups to seek

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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.

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?

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Biz is critical. Sci/engineers problem is…

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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

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

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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

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

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Probably, around 2000 AI startups in the world as CBInsights says

Startups globally

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Funding

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Funding in the field – dynamics globally

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By industryRussian investors in foreign AI co’s (# = 38)

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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

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)

In these 3 countries is the following industry breakdown of funded startups:

USA, UK and Toronto are AI clusters abroad?

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Exits dynamics

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Acquisitions in the field – dynamics

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Acquisitions in the field – some names

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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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http://idlewords.com/talks/superintelligence.htm http://mobile.nytimes.com/2016/12/14/magazine/the-great-ai-awakening.html?utm_campaign=A

rtificial%2BIntelligence%2BWeekly&utm_medium=email&utm_source=Artificial_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

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Startups supply side - EU

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Startups supply side - World

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Thanks

http://medium.com/@victorosyka

http://facebook.com/victor.osika

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