Source : DRAUP
1
1
May 2015
Next Generation Technology Spending Patterns in AISEPTEMBER 2017
Source : DRAUP
2
2
Artificial Intelligence is transitioning into the mainstream industry at warp speed
~ 5 MnPotential jobs to be impacted in the US by 2025
$25 BnEstimated revenue from AI products & services in 2025, to grow exponentially at a CAGR of 61%, from existing $0.7 Bn as of financial year ending 2016
$12 Bn Deep Learning market cap in 2025, holding the largest revenue slice. Estimated to grow at a CAGR of 58% from existing market cap of $0.3 Bn
~ 10,000Global AI Start-ups expected by 2025. Predicted to increase 5 times from the current ~2,200 start-ups in 2016
Note: DRAUP - The platform tracks engineering insights in the AI ecosystem using our proprietary machine learning algorithms along with human curation. The platform is updated in real time and analysis is updated on a quarterly basis
Source : DRAUP
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3
DRAUP followed a rigorous and structured research approach to analyze nearly 500 organisations currently working on Artificial Intelligence (AI) technology
Objective
Key questions answered
DRAUP analyzed
5 Industries including Automotive, Semiconductor, Software/Internet
~50 MSA locations across regions such as US, Canada, MEA (Israel), Europe and
Asia Pacific
~500 Artificial Intelligence (AI)technology Spenders shortlisted
~2,200 Start-ups working across AI and related technology areas
Ø To understand the current state of AI Industry Landscape consisting of global organisations: Tech Mafias, Start-ups and other G-500 players across diverse industries
Ø Who are the Drivers, Leaders and Lagers in the AI industry landscape ?
Ø What has been the technology spending patterns by these players across the AI stack ?
Ø What are the strategies adopted by top AI players to accelerate AI innovations ?
Ø What are the global technology hotspots for AI innovations ?
Ø How does the AI start-up landscape look like and what are the industry adoption patterns for AI applications ?
Ø How should global engineering organisations develop AI capabilities ?
Over 30 global stakeholders were interviewed as part of the analysis
Note: DRAUP has a database of nearly 500 engineering organisations and nearly 100,000 start-ups working across AI, IoT, Bigdata technologies in Automotive, Software/ Internet and other Hi-Tech industries. For validation of data multiple government reports have been referred such as OECD, World bank R&D Data, UNESCO Institute for Statistics, International Labour Organisation (ILO), US Energy Information Administration (EIA) and others
Source : DRAUP
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4
Who should read this?
R&D Decision Makers
Ø The study provides actionable insights for executives and decision makers to support their global R&D initiatives in the emerging AI technology segments
Ø The study can be leveraged to proactively track peer organisations’ current AI capability and future technology outlook
Ø Useful findings also include assessment of ecosystem for collaboration opportunities with peers, technology providers and new age emerging players in the AI technology segments
Sales executives of Engineering
ServiceProviders
Ø The DRAUP platform and our dedicated advisory expertise which extend beyond mere human curations, can be leveraged to proactively track and understand the recent advancements in the AI Ecosystem
Ø Sales decision makers, executives and organisational leaders of Technology Service Providers (TSPs) can leverage this to capitalize on C –level opportunities. The report provides insights on the advancements in AI, current trends, spending pattern of the companies, critical investment areas by companies etc.
Ø This could be used by the sales teams to effectively target the prospects. The study provides actionable insights for TSPs in the emerging product innovation areas of the AI Sub-Segments
Source : DRAUP
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5
AI Landscape: Start-ups, Tech Mafias and other G500 players01
Recommendations to build AI Capability 02
Case Study: India AI Ecosystem: Global R&D Spenders, Service Providers & Start-ups03
ü What have been the AI technology advancements during last 5 years ?
ü What are the type of players accelerating AI innovations?
ü What are their focus spend areas across the AI stack and industry use-cases?
ü Which are the global AI technology hotspots ?
AGENDA
Source : DRAUP
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6
AI has evolved rapidly in the last few years, enabled through rapid technology innovations
Introduction of Turing test
First AI programto play Tic Tac Toe
IBM’s Deep Blue defeats Gary Kasparov
DeepMind’s self-taught AI can beat human players at 29 of 49 Atari games
Deep learning start-up Gamalon claims to be 100 times more efficient than DeepMind
1950 1960 1997 2011 2015 2016 2017
Computational Power
Data Platforms
Better Algorithms
Cost of Computing
12,000 Core GPU
$0.05 Per million transistors
BIGDATA PLATFORMS -HDFS
DEEP LEARNING -Convoluted NeuralNetwork
512 Core GPU
$200 Per million transistors
RDBMS
LOGIC THEOREMS -Single layer learning, Perceptron, Adaline
Watson became Jeopardy Champion
Note: The list above illustrates landmark events in the AI EcosystemThe above analysis is based on the DRAUP’s proprietary engineering database and insights from industry stakeholders, updated as on Sept, 2017
Source : DRAUP
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7
“That’s a strange move. I thought it was a mistake.”– Lee Sedol
• 10360 possible moves• Monte Carlo tree search & Q Learning• Statistical, learned and general purpose• Learned from 30 million moves
• 1023 Trillion Possible Outcome• Brute Force Algorithm• Symbolic, hand crafted and domain specific• 700,000 Grandmaster chess games
Deep Blue AlphaGo
And is able to beat human champions in complex board games..
It may be a hundred years before a computer beats humans at Go — maybe even longer!!
-AI Experts in 1997, NY times
Source : DRAUP
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8
AI has moved on from games to the real world, disrupting all industry verticals…
NLP platform
Alexa
BFSI
Healthcare
Pote
ntia
l to
Dis
rupt
1
AI Maturity 2
Retail
Predictive diabetes
management
AI-based Robo advisory service
Enterprise Software
Semicon
Consumer Electronics Microsoft Cortana and Intelligent
Cloud
Machine Learning Enabled
Hardware
NASA software to enable damaged aircrafts, find a safe
landing spot.
Recommendation based on
photographs
Autonomous driving
Auto
Aerospace
Consumer Software
Machine Learning
enabled
Advertising
Note: 1- Analyzed basis data maturity, software penetration, regulatory restrictions across the value chain representing disruption potential over next 5 years
2 – Analyzed basis current investments ( talent + acquisition) for all players
The above analysis is based on the DRAUP’s proprietary engineering database and insights from industry stakeholders, updated as on Sept, 2017
Source : DRAUP
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9
MANUFACTURING FINANCIINGOWNERSHIPEXPERIENCE
DESIGN & DEVELOPMENT
AI-Designed Car
Developing the first AI designed car
Partnered with the robotics and intelligent systems group to drive innovations in cognitive systems in factories
Intelligent Systems
PRODUCT FEATURES
Set up a $25 Mn research center in collaboration with MIT for autonomous vehicle technologies
Autonomous Vehicles
§ AI-based Designs§ Simulated Testing
Baidu invested in ZestFinance, a start-up that uses machine learning to develop a credit scoring platform
Credit Scoring
§ ADAS§ Connected Car§ Speech Recognition
§ Intelligent Production Line
§ Integrated Systems
§ Ride Sharing§ On-Demand Transport
§ Credit Scoring§ Fraud Detection§ Predictive Modelling
Tesla’s Autopilot can, in real-time, learn the daily routes taken by its users
Ride-Sharing
$721 MnTotal Funding
2011Average Founding year
192Disruptors
Automotive
And adding value across the industry value chain : Case Study- Automotive
Note: The above analysis is based on the DRAUP’s proprietary engineering database and insights from industry stakeholders, updated as on Sept, 2017
Source : DRAUP
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10
AI innovations are dominated by Tech Mafias and Start-ups (1/2)
Average AI Headcount ~5,600
AI Talent as a percentage of
R&D talent
~177 ~330
16% 70% 0.6%
25$
Bn
Tech Mafia
R&D Spend
Google, Facebook, Microsoft, Apple and Amazon
DriversAI Start-ups
15$
Bn
Start-ups: Global AI start-ups have received financing from corporates, VCs and other angels
Funding
Leaders
Top 500 Global R&D Spenders (Not including Tech Mafias)
3$
Bn
G500
R&D Spend
Laggards
Note: The above analysis is based on the DRAUP’s proprietary engineering database and insights from industry stakeholders, updated as on Sept, 2017
Source : DRAUP
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11
Hardware
APPLICATIONS
PLATFORMS
INFRASTRUCTURE
MACHINE INTELLIGENCE NLP COMPUTER VISION
DEEP LEARNING
ADAS
GESTURE CONTROL
Enterprise Software Assistants
Productivity
FinTech
AdTech
Data Platforms
HealthTech
Auto
Dominated by the start-ups who build verticalized applications for various use cases.
Applications – The Start-up Zone
Focused efforts on building platforms that can then be leveraged by the ecosystem.
Platforms – Tech Mafia Playground
The AI focused companies can be found providing the infrastructure that enables the rest of the landscape.
Infrastructure – G500 Domination
Intensity
Start-ups G500Tech Mafia
Tech Mafias are building a robust platform infrastructure to accelerate the application ecosystem which is dominated by the AI start-ups
Note: The above analysis is based on the DRAUP’s proprietary engineering database and insights from industry stakeholders, updated as on Sept, 2017
Source : DRAUP
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Tech Mafias: Each of the Tech Mafias have developed strong platform capability in the fields of Chatbots, Deep Learning and Computer Vision; AI applications is their emerging focus segment
Note : The above analysis is based on the DRAUP’s proprietary engineering database and insights from industry stakeholders, updated as on Sept, 2017
Automotive Healthcare Consumer AI Platforms Infrastructure
Google WaymoVerily –for Diagnosis
DeepMind for HealthcareGoogle X nanoparticle research Google Home
Android Wear Smartwatch
Google Now
Project Jacquard AI Robot-GoogleXGoogle Prediction API
TensorflowDeepMind Google for Work
Project Titan
Wearables for Health monitoring
Siri controlled home kitApple Smartwatch
SiriiPhone iOS 10 image recognition
Spotlight for images & text
Microsoft –Volvo Self driving Cortana for Healthcare
Kinect
CortanaHololens SwiftKey
Microsoft Graph –Sales lead scoring
DSSTNEAzure ML
Alexa AWS MLRecommender systems
CNTK
OculusFacebook M
FAIRFacial recognitionFacebook Deep TextWit.ai
Total AI Spend $25 Bn
AI Talent 30K
Acquisition $10 Bn
Patents 300+
Source : DRAUP
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13
Tech Mafias: Tech mafias own ~45% of global AI talent leveraged from hotspots beyondtheir HQ locations
9,300
19,600
Seattle Area
Bay Area
2,100
3,200
Boston
New York
9502,700
Bangalore
1,600
• Traditional Hubs for Engineering for the Tech Mafia - Machinelearning to NLP & Computer vision.
• Driverless Cars, Drones, Data Science, Cyber Security are the hot areas
West Coast of USA East Coast of USA Western Europe & Israel Indian Hi-Tech Cities
460Singapore
660BeijingIsrael
Hyderabad
• Top universities like CMU & MIT are deeply focused on Artificial Intelligence research;
• EU’s Human Brain Project is spending close to 1 Bn euros on AI over the next decade.
• OEMs like Renault, Volkswagen are partnering with Autonomous start-ups like Mobileye
• IBM set up its Watsonunit in India in 2012 to work for Healthcare and BFSI clients in US.
• Baidu is investing in deep speech for voice-based searches that leverage speech recognition;
910
Spain
4,100
UK
2,000
France
3,300
Germany
Hong Kong & Singapore
X ER&D Workforce in AI
950Netherlands
TECH MAFIA HOTSPOT UNIVERSITY RESEARCH AUTO OEMs AI FOCUS OVERSEAS FOCUS CHINESE INTERNET AI DRIVERS
45%
55%
Tech Mafias
Rest
Tech Mafias own 45%of global AI talent
Source: GEIPNote : The above analysis is based on the DRAUP’s proprietary engineering database and insights from industry stakeholders, updated as on Sept, 2017
Source : DRAUP
14
14
XUser Base on GITHUB
Computer Vision
KINDRED Robotics
Project Malmo
Tensorflow
DSSTNE
35K
Facebook for Torch Swift-AI
CNTK
DSSTNE is designed to support problems with sparse data. 3KAI research built on top of the
game Minecraft.2K
Significantly faster than the default Torch and allow users to train larger neural nets
A unified deep-learning toolkit that describes neural networks as a series of computational steps
6K
Swift AI is a high-performance AI and machine learning library
Open sourcing their APIs allows the Tech Mafia to
democratize innovation
1K 1K
Computer Vision for refrigerators
Most popular Open Source AI Library. User base has grown tenfold since its release in Nov. 2015.
Makoto Koike uses TensorFlow to sort Cucumbers
Cornel University project on Cyber-Security
Projects based on Platforms
Tech Mafias: And opening their innovations for others to build on
Note: The list above may not be exhaustive . We shortlisted major open source initiatives as of Sept, 2017 which have been accelerating growth of the AI ecosystemThe above analysis is based on the DRAUP’s proprietary engineering database and insights from industry stakeholders, updated as on Sept, 2017
Source : DRAUP
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15
“Success in creating AI would be the biggest event in human history. It might also be the last, unless we learn how to avoid the risks.” - Stephen Hawking
Trending fake news articles
7 reported accidents (1 fatal) since April 2016
Tesla
DeepMind failed at describing dumbbells
Microsoft’s Tay became a racist bot
Microsoft
Note: The graphics above indicate major AI failure events for large technology giantsThe above analysis is based on the DRAUP’s proprietary engineering database and insights from industry stakeholders, updated as on Sept, 2017
Tech Mafias: Public failures notwithstanding
Source : DRAUP
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16
Start-ups are catching up, capitalising on the AI application segment across diverse industries
AI Talent 45K Patents 300+Total Funding $15 BnTotal
Start-ups 2,232
Consumer
Infrastructure
AI Platforms
Automotive
Healthcare
ZooxDrive.ai Nutonomy MobilEye ZenDrive
iCarbonX Lumiata Butterfly Zymergen Imagen Technologies
$ 721 Mn in investments
Total Funding
Api.ai x.aiAnki Jibo MagistoUgobe LukaGluru Emotech Sherpa
Attivio DiffbotTrifacta SentenAI SigOpt
AffectivaH20.ai Sentient.aiVicarious Systems Ayasdi
$ 996 Mn in investments
$ 1,032 Mn in investments
$ 365 Mn in investments
$ 1,217 Mn in investments
Top Start-ups
Note: The start-up list above is non exhaustiveThe above analysis is based on the DRAUP’s proprietary start-ups, updated as on Sept, 2017
Source : DRAUP
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17
Start-ups: Ten fold increase in AI start-up funding in the last five years
Quarterly funding trend (2013-16 YTD)
Q1, 2012
Q1, 2013
Q1, 2014
Q1, 2015
Q1, 2016
$94 Mn$137 Mn
$253 Mn
$121 Mn
$302 Mn
$552 Mn
$926 Mn $901 Mn
$602 Mn
$1,049 Mn
Raises $100Mn for Deep learning based ultrasound
Google acquires DeepMind for$500Mn
Raises $65Mn for ML-based threat detection
Q1, 2011
Focused on reverse engineering the neocortex; raised series A
AI based unicorns have emerged since July, 20165
Billion Dollar Valuation Line
Zoox
Valuation - $1.85 BnHealthcare
Valuation - $1.55 BnAutomotive
Valuation - $1.5 BnEnterprise
Valuation - $1 BnConsumer
Valuation - $1 BnHealthcare
Valu
atio
n
Age of Start-up
Zymergen
Note: No inflation assumed (all values in 2017 USD). Funding details are updated with nearly 70% accuracyThe above analysis is based on the DRAUP’s proprietary start-ups, updated as on Sept, 2017
Source : DRAUP
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18
Start-ups: DRAUP’s six lenses were used to examine the AI start-up landscape
Global Ecosystem Maturity
Congregation of AI start-ups in enabling ecosystems across the world for diverse
application areas
Use Case Adoption
Patterns on the top use cases adopted by highly scalable general purpose AI platforms
Value Chain Aggregation
Understand collaboration points with AI start-ups across the industry value chain
Absorption Pulse
Examine the focus areas and key drivers for M&A and investment strategies of prominent
incumbents
Deadpool Intensity
Analyse constraints for AI start-ups to scale and gain perspective on market conditions
Skillset Transformation
Adapt to the changing human capital needs to drive an AI-first business strategy
Source : DRAUP
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19
Start-ups: Autonomous cars and infotainment solutions are gaining investment traction in the Auto value chain
ADAS Driver Safety Autonomous carsConnected CarInfotainment
$24 Mn $10 Mn
$80 Mn
$18 Mn
$253 Mn
Fund
ing
in e
ach
of a
utom
otiv
e Va
lue
Cha
in S
egm
ent
GE acquired Cruise Automation and piloted a fleet of Chevrolet Bolt EVs in San FranciscoAutonomous Vehicles
Autonomous Vehicles
Acquired self driving technology provider Ottomatika following joint projects for CES
ADAS
Acquired the ML & Deep Learning solutions for image and video processing built by Israeli company SAIPS
Toyota acqui-hired the 16 member Jaybridge Robotics to be a part of its research institute focused on AI & Autonomous vehiclesAutonomous Vehicles
OEMs and Tier 1s are acquiring AI start-ups to bolster their value chain
Impact Assessment of AI start-ups in the automotive value chain
HighLowAI Tech Adoption
Machine Learning
NLP
Computer Vision
DeepLeaning
AI T
ECH
NO
LOG
Y
Note : The above analysis is based on the DRAUP’s proprietary engineering and start-ups database and insights from industry stakeholders, updated as on Sept, 2017;SWARM Disruption Framework for Start-up Analysis
Source : DRAUP
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20
Start-ups: AI start-ups are bolstering customer engagement and marketing in the retail value chain, applying different business models
Security & Surveillance
Supply Chain Management
In-Store Analytics
Customer Engagement
Multi-Channel Marketing
$ 24 Mn
$ 90 Mn
$159 Mn
$100 Mn
$105 Mn
Tota
l Fun
ding
of V
alue
Cha
in
Segm
ent
Predictive Intelligence Platform to reduce fraud and improve customer targeting
Customer Engagement
Supply Chain Management
A Machine learning platform that solves out-of-stock and overstocking problems
Top retailers are acquiring AI start-ups to bolster their value chain
Impact Assessment of AI start-ups in the retail value chain
HighLowAI Tech Adoption
Subscription Model
Licensing Model
One Time Payment
Affiliate Fees
Bus
ines
s M
odel
s
Note : The above analysis is based on the DRAUP’s proprietary engineering and start-ups database and insights from industry stakeholders, updated as on Sept, 2017;SWARM Disruption Framework for Start-up Analysis
Source : DRAUP
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21
Start-ups: Financial Services, Marketing and Healthcare are the first avenues for core AI start-ups that can be applied to diverse use cases
Total Funding
Patents
Vicarious Systems DataRobot
$135.8 Mn $97.9Mn $75.6 Mn $67Mn $57.4 Mn
8 16 9 - 3
Total Funding
Patents
$48Mn $37.4 Mn $34.2 Mn $30.6 Mn $30Mn
4 29 42 - 2
Sentiment
Analysis
Personal-
ization
Customer
Engagement
Digital
Marketing
Marketing
Fraud
Detection
Insurance
claims
Algorithmic
Trading
Risk
Modelling
Finance
Patient
Monitoring
Drug
Delivery
Population
Health
Precision
Medicine
Clinical
Variance
Healthcare
Use Case Adoption Index
LowHigh
Note : The above analysis is based on the DRAUP’s proprietary engineering and start-ups database and insights from industry stakeholders, updated as on Sept, 2017;
SWARM Disruption Framework for Start-up Analysis
Source : DRAUP
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22
Start-ups: US is the dominant innovation hotbed for AI start-ups led by the Bay Area Ecosystem
2,322Start-ups $14.74 Bn
Global AI start-up distribution
86
CanadaNetherlands
25
26
Australia
Brazil
18
France
43
25 Singapore
Hong Kong12
Total FundingNumber of Start-ups
$11.5 Bn
$0.6Bn
$0.03 Bn$0.5 Bn
$0.6 Bn
$0.1 Bn$0.1 Bn
$0.1 Bn
APPLICATIONS-FINTECH, HEALTHCARE
APPLICATIONS – HRTECH, HEALTHCAREAPPLICATIONS – AUTO, FINTECH,
RETAIL
PLATFORMS- DEEP LEARNING, VISION
ENABLERS –BIG DATA PLATFORMS
Vision based advanced
assistance system
AI based consumer
robotics start-up
Massively scaled deep
learning
ML based threat
detection
ML based
recruitment solution
ML for retail
ML for personalised
healthcare
NLP API
Data cataloging
and cleaning
PLATFORMS- DEEP LEARNING, NLP
Note : Coverage may be limited in China and other South East Asian counties
The above analysis is based on the DRAUP’s proprietary engineering and start-ups database and insights from industry stakeholders, updated as on Sept, 2017;
SWARM Disruption Framework for Start-up Analysis
Source : DRAUP
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23
Start-ups: Corporate acquisitions and investment strategies indicate the direction the industry is moving in
6
dealsBuild New
Products
Bolster Technology
Stack
23
deals
$625Mn
DeepMind
Enter New
Markets
10
deals$3,200Mn
Nest
Wit.ai
Not
Disclosed
Acquihire
Talent
6
deals
Intel
Indisys
Not
Disclosed
Technology and market expansion are primary drivers for M&A
Amazon Apple Facebook MicrosoftGoogle
Acquisition Year
Acq
uire
eM
atur
ity
Dot Com Era Smartphone Era Cognitive Era
2004 2010 2016
1
2
3
4
5Bulk of the acquisitions by Microsoft
and Google to boost their Search Tech.
MS and Apple begin
work on Gesture Control
devices.
The Tech Mafia investing
heavily in AI enablement
platforms Googlebegin work
on Maps
NLP VisionML NLP Vision ML NLP Vision Robotics
Indicates Average
Salesforce Intel Oracle IBM GE
Note : The above analysis is based on the DRAUP’s proprietary engineering and start-ups database and insights from industry stakeholders, updated as on Sept, 2017;
SWARM Disruption Framework for Start-up Analysis
Source : DRAUP
24
24
0 2 4 6 8 10 12Age of Start-up (In years)
Dead pool
Scal
e (B
ased
on
Inve
stm
ents
, Hea
dcou
nt G
row
th a
nd C
usto
mer
Tra
ctio
n)Start-ups: Examining the dead pool of AI start-ups reveals key challenges to scale
LegendApplication Companies
Platform Companies
Infrastructure Companies
Consumer Enterprise Industry
ML NLP Vision
Data Platform Hardware
Major factors that have prevented AI start-ups from crossing the value chasm
RegulatoryRestrictions
Autonomous car start-ups have faced problems with regulatory authorities to proactively demonstrate
Business Model
Consumer Application start-ups managed to gain user traction and customer growth but struggled to find a scalable revenue model
Market Definition
Core AI based start-ups struggled to define the right use case for their technology and proof of concept
Tech Roadmap
The development of the AI platform plateaued after a few years in operation
Series A
Seed. $3.1M
Series C
Series C
Comma.ai
*700 Start-ups plotted
Note : The above analysis is based on the DRAUP’s proprietary engineering and start-ups database and insights from industry stakeholders, updated as on Sept, 2017;SWARM Disruption Framework for Start-up Analysis
Source : DRAUP
25
25
Start-ups: AI start-ups are impacting roles in the engineering organizations
R&D
IT
Sales & BD
Product Management
Others
29.7% 33%
21.5% 7%
30% 38%
5.8% 4%
13% 17%
HC% of 10+ yrs exp
Hardware
Software
Architect
Analytics
UI/UX
24%
23%
5%
6%
14%
73%
50%
92%
55%
52%
HC% of 10+ yrs exp
12%
18%
4%
12%
10%
80%
30%
90%
36%
40%
ML/NLP
Release
QA
11%
8%
11%
42%
27%
73%
43%
-
2%
26%
-
-
Engineering Talent Hired From
Note : The above analysis is based on the DRAUP’s proprietary engineering and start-ups database and insights from industry stakeholders, updated as on Sept, 2017;SWARM Disruption Framework for Start-up Analysis
(Head-count split across business functions)
Source : DRAUP
26
26
MicrosoftAmazon
Apple
IBM
BoschVolkswagen
Intel
Oracle
Cisco
Foxconn
SAP
Airbus
MobileyeSentient
ZooxDatarobot
X.ai
Top R&D spenders are lagging behind
Plan to release Xeon Phi processor line for AI applications-~$400Mn investments
Leveraging ML for network threat products – Cognitive Threat Analytics
Invested $5Bn in building an AI powered Giga factory.
$1Bn investment to establish the Toyota Research Institute for AI
$500Mn investment in a 200 member AI R&D lab in Silicon Valley
Note : 1 : Investment in AI in terms of talent & acquisition or Funding raised ( for start-up);2 : Focus on emerging technologies vs Older algorithms, Focus on Ecosystem creation and Platform adoption/maturity;The above analysis is based on the DRAUP’s proprietary engineering and start-ups database and insights from industry stakeholders, updated as on Sept, 2017
AI Focus1
Futu
re R
eadi
ness
2
Start-ups G500Tech Mafia
G500
Start-ups
Tech Mafia
Size of the bubble indicates R&D spend
Source : DRAUP
27
27
AGENDA
ü How to build AI capability in the core business value chain?
ü How to build AI platform and data strategy to own key AI capabilities?
ü How to leverage collaborative AI innovation with the Ecosystem?
ü How to leverage global emerging AI hotspots?
AI Landscape: Start-ups, Tech Mafias and other G500 players01
Recommendations to build AI Capability 02
Case Study: India AI Ecosystem: Global R&D Spenders, Service Providers & Start-ups03
Source : DRAUP
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28
Organisations can accelerate AI innovations through four simple steps
Identify Business Case1Build a Data Ecosystem2Collaborate with the Ecosystem
Leverage newer talent hotspots43
4-Talent Hotspots
2-Data Ecosystem
3-EcosystemCollaboration
1-Use Cases
AI Puzzle
Note : The above analysis is based on the DRAUP’s proprietary engineering and start-ups database and insights from industry stakeholders , updated as on Sept, 2017
Source : DRAUP
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29
Identify and prioritize AI’s role
DATA COMPLEXITY
APPLICATION COMPLEXITY
Wide range of interconnected activities
Well-defined rules, procedures and criteria
Complete Autonomy
Augment Humans
Reliant on individual expertise and experience
Original, innovative work
Surgical RobotsChatbots
Echo, the home control device
Automated meetings scheduler
Image Search
Auto- Recommendations
Automated Factories
Robo- Advisory
Enterprise security through AI
Autonomous Car
Dee
p Le
arni
ngR
ule
Base
d En
gine
AI bot designed car
AI-based website design platform
1
Note : The above analysis is based on the DRAUP’s proprietary engineering and start-ups database and insights from industry stakeholders , updated as on Sept, 2017
Source : DRAUP
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30
Build a Data ecosystem and own it
ConceptualizationDesign & Development Product Usage Serviceability Manufacturing
ERP
Geo-locationdata
Social data Compete Data
Usage data Bug Reports
ProductData
Customer Data
Open Data
Web Data
Partner Data
Market Data
Design Data
Libraries
Enterprise Data
Customer Map Usage data
Govt. Data Content
Logs
SCADA
Sensor
Market Data
Machine Data
Energy Pricing MRO data
PLM Product Cloud
Supplier APIs
Data as a Product External Data APIs Internal Data APIs
2
Note : The above analysis is based on the DRAUP’s proprietary engineering and start-ups database and insights from industry stakeholders , updated as on Sept, 2017
Source : DRAUP
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31
Balance internal and external innovation
Toyota leverages multiple facets of the innovation fabric to drive innovations in aligned technology areas
Headquarters in Japan is supplemented by 5 engineering hubs
India, China, Thailand ,Mexico & Brazil
Palo Alto research labs for AI & Robotics research
Acqui-Hired IT born robotics start-up
The SAIL-Toyota Center for AI Research
Partnered to develop autonomous car technology.
New Age Innovation Fabric
Porous innovation permeating beyond the walls of the organization
3
Note : The above analysis is based on the DRAUP’s proprietary engineering and start-ups database and insights from industry stakeholders , updated as on Sept, 2017
Source : DRAUP
32
32
Partner with start-ups
Venture Fund
Accelerators
Evangelize
Ecosystem Collaboration
• Extensive hands on support & infrastructural support
• Connecting with clients and investors
• Limited platform & soft infrastructure support • Connect the startup teams with VCs & partners
• Partnership with accelerators/Universities• Mentorship support & events participation
Capital Investment Non-Capital Investment Examples
• Product GTM support• Senior level team hiring /restructuring
3rd Party Accelerators
• Partner with other accelerators, innovation workshops with stakeholders
Arena 120Microsoft Start-up accelerator
3
Note : The above analysis is based on the DRAUP’s proprietary engineering and start-ups database and insights from industry stakeholders , updated as on Sept, 2017
Source : DRAUP
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Build the right platform partnerships: CASE STUDY- Medtronic
GPU Hardware Platforms
Big Sur Tensor Processing Unit
Github – Public
Datasets
Infrastructure Platforms
Data Sets
Applications
ORCHESTRATION OF SPECIALISTS
Leverages Watson’s open API to
build MyCareLink Smart App that
predicts low blood sugar
Apache 2.0 open
source libraries
EHR, Clinical data
through pharmacies
and universities
H20.ai python-based ML
libraries
AWS ML optimized
infrastructure
Fraud Detection Diabetes Detection Cart checkouts Drones
AI Platforms
Driver Safety
Medtronic leverages open source infrastructure in
multiple areas of its product stack
3
Note : The above analysis is based on the DRAUP’s proprietary engineering and start-ups database and insights from industry stakeholders , updated as on Sept, 2017
Source : DRAUP
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Hire new talent and rebuild team structures4
Centralized AI Teams
Product Management
Sales & Marketing
Customer Support
AI ResearchScientists
Engineering
Product Management
Sales & Marketing
Customer Support
AI Research Scientists
Engineering
CXOs
ML EngineerAI Research Scientists
Data Scientists
De - Centralized AI Teams
CXOs
ML EngineerData Scientists
AI Research Scientists
ProductManagement
AI Research Scientists
Data Scientists
ProductEngineering
Teams
Sales & Marketing
Customer Support
ML Engineer
ML EngineerML Engineer
AI ResearchScientists
Data Scientist
Machine Learning Engineer
Education: MS or PhD in CS & MathematicsNeural networks, NLP, machine learning, statistical modelling, pattern recognition
Education: Bachelors/Master Degree in CS Problem solving, and programmingPython, Java / C++, as well as ML toolkits such as Theano, Tensorflow, Keras or similar
Education: Masters, PhDsAnalytics: SAS/RCS: Python, Hadoop, SQL, Data Derivation from Unstructured data
New Team Structure
New Talent
Note : The above analysis is based on the DRAUP’s proprietary engineering and start-ups database and insights from industry stakeholders , updated as on Sept, 2017
Source : DRAUP
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35
Leverage AI talent from emerging hotspots such as India and China
USA
Canada
65 110 UK
35
China70
85
Available AI & related Talent in Country (in thousands)
2015 2025
20Israel
India
A global satellite that is part of the global CoE for ML, reporting to Berlin
Global engineering hub that has small team working on ML
Global engineering satellite that drives activities in Computer vision space
Global CoE for IoT and Advanced ML space.
18
615 155
Beijing is a global engineering satellite that works on NLP and Computer vision tech
Partnered with Didi in crowdsourcing challenge for optimising route algorithm
Note : Coverage may be limited in China and other south east Asian counties;The above analysis is based on the DRAUP’s proprietary engineering and start-ups database and insights from industry stakeholders , updated as on Sept, 2017
4
10
Source : DRAUP
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36
ü Which are the AI talent hotspots in India ?
ü What are the key AI programmes from global R&D organisations based in India?
ü What is the maturity of AI start-up Ecosystem in India?
ü What are the top AI innovations incubated by Indian TSPs?
AGENDA
AI Landscape: Start-ups, Tech Mafias and other G500 players01
Recommendations to build AI Capability 02
Case Study: India AI Ecosystem: Global R&D Spenders, Service Providers & Start-ups03
Source : DRAUP
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Case Study (India) - Almost half of the total installed AI talent in India is concentrated in Bangalore
AI Talent Split Across Locations
14,500 – 15,500
Experience Split for AI Talent
Note: Others include the following cities: Chandigarh, Jaipur, Ahmedabad, Baroda, Kolkata, Vishakhapatnam, Mysore, Coimbatore, Kochi, Madurai, Trivandrum; IT GIC + R&D GIC includes Computer Vision, Machine Learning, NLP and Robotics only;The above analysis is based on the DRAUP’s proprietary engineering and start-ups database and insights from industry stakeholders , updated as on Sept, 2017
COMPUTER VISION
MACHINE LEARNING NLP ROBOTICS
~ 4,000 ~ 6,000 ~ 1,500 ~ 3,500
Source : DRAUP
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Case Study (India) - Various global companies are looking to develop AI-based solutions in India
Enterprise Software
The Bangalore center is focused on data analytics products to ensure that customers globally have a seamless experience across multiple channels -physical stores, the web, mobile etc.
Customer Experience
Microsoft India is launching a research group todeliver large-scale eye care in collaborationwith Hyderabad-based L V Prasad EyeInstitute.
Healthcare
Retail
WalmartLab's Bangalore center is focused onintegrating various data objects (e.g. customerbehaviour) to create solutions that can beimplemented across their stores
Cognitive Computing , industryspecific applicationsThe research lab in India is working closelywith financial institutions through India and AsiaPacific.
Machine Learning, Big Data &Analytics
Data Analytics Image processing
Note : The above analysis is based on the DRAUP’s proprietary engineering and start-ups database and insights from industry stakeholders , updated as on Sept, 2017
Source : DRAUP
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Case Study (India) - The start-ups in India have been quick to tap the AI potential
BangaloreDelhiHyd
40%20%10%
APPLICATIONS
PLATFORMS
INFRASTRUCTUREDATA PLATFORM -$2Mn
COMPUTER VISION -$16Mn
NLP - $1Mn
GESTURE CONTROL
AUTO - $1Mn RETAIL -$0.5Mn
VIRTUAL ASSISTANT -$2M
HR - $5Mn
E-COMMERCE MARKETING - $1Mn
MACHINE LEARNING -$1Mn
8Acquisitions
Undis.Investment
Applications
Deep Learning
Virtual Assistants
Machine Learning
tuplejump
ZoyoAI
Cruxbot
~170Start-ups
$0.03 Bn
Investment
Note: No inflation assumed (all values in 2017 USD). Funding details are updated with nearly 70% accuracy;
The above analysis is based on the DRAUP’s proprietary engineering and start-ups database and insights from industry stakeholders , updated as on Sept, 2017
Source : DRAUP
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Case Study (India) - Indian IT-BPM companies are already responding with their proprietary AI platforms
Recent AI-driven Headlines in the Indian IT-BPM
space
Note : The above analysis is based on the DRAUP’s proprietary engineering and start-ups database and insights from industry stakeholders , updated as on Sept, 2017
Source : DRAUP
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Case Study (India) - AI Platforms of top Indian IT service providers
TCS Wipro Infosys
• Reposition to serve ‘Heart of Business’
• Technology / AI Advantage
HCL
• Broad based (BPM Focus) • Broad based (including engineering, ADM & BPM)
• Broad based (Infrastructure services)
• Plug and play deployment requiring customization and learning
• Stand alone platform for core infrastructure services
• Plug and play deployment requiring customization and learning
• Stand alone platform offering a menu of multiple cognitive services
• Bespoke deployment
• AI capabilities bolt-on to existing automation architecture (IIP, IKP, IAP framework)
• Bespoke deployment
• AI modules bolt-on to existing automation platform; collaboration with Watson, S-Now, Dynatrace, Splunk
DEPLOYMENT & PLATFORM
OVERVIEW
• End to end infra services such as
• Infra blueprint• Self healing • Deployment• Predictive
maintenance
• Digital Virtual Assistants
• Prediction systems
• Robotics & Drones
• Engineering (aircraft floor beam development)
• Forecasting as a service
• Detect and correct Infra and App issues
• Watson power chat agent
STATED DOMINANT USE CASES
KEY FOCUS AREAS
Note : The above analysis is based on the DRAUP’s proprietary engineering and start-ups database and insights from industry stakeholders , updated as on Sept, 2017
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SUMMARY
• Artificial intelligence has finally gotten out of university research labs and Hollywood studios to impacting our daily lives. Technology innovations across fourdimensions is resulting in the faster evolution of AI: 1. Computational power is faster and cheaper 2. Data availability has exploded through the use of smartphone and IOT devices 3. AI algorithms developed painstakingly across research labs and universities over the last five decades have come to the forefront due tothe access to low cost computing power 4. Access to training data and real-time platforms
• The challenge is on how AI will advance in the real world where the constraints are not known and there is a lot of unpredictability. The battle now will movefrom games to the real world. AI is just not enabling some new product features but is playing a role across the value chain of the industries. Take the example ofthe Automotive industry - Over 700 million dollars of funding has gone into start-ups focused on AI-led disruptions
• AI is becoming one of the fastest technologies to be deployed across industry verticals. Companies such as Facebook, Google who are really advertisingplatforms, are using AI to better match the advertisements to the user preference. Companies such as Microsoft and Google are using AI in creating self-healingnetworks and even in reducing the cost of cooling at their data centers
• There are three kinds of companies that are investing into AI. First is the disruptors – start-ups who are building AI platforms and applications, second is TechMafia – Apple, Google, Facebook, Microsoft and Amazon- which are dominating the AI platform space and third - G500 companies that are still figuring out thespace but will eventually play a key part in industry applications
• Tech Mafias, led by Google has invested over USD 10 billion dollars in acquiring AI start-ups and collectively employing over 30,000 engineers working on AIplatforms and applications. Google’s CEO describes Google as an AI-first company. These companies are opening up a lot of their innovations in AI by makingthem open source and are making them accessible through APIs
• Venture capitalists across the world are seeing the potential in AI and have increased their investments five-fold in the last 10 years. The funding is higher forapplication layer than other areas. Companies such as Sentient have already got over USD 100 million in investments and are building massive scale AIplatforms using Deep learning
• US start-ups have dominated VC investments. The rest of the investments are spread across Europe, China, India, Israel, Singapore and Australia
Source : DRAUP
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SUMMARY
• DRAUP has defined 4 easy steps for large companies to adapt AI. First is to identify the key business use cases that can help reinvent existing products and
services or create new opportunities for growth. The second is to create and access a large set of data sources that will help train the AI engines. Third is to ensure
the presence of a platform that takes advantage of all the AI-led innovation happening in the ecosystem between the Tech Mafias, start-ups and universities. Fourthis to ensure the engineering talent capability internally is transformed to include talent pool in locations that will drive the next generation of AI platforms and
applications.
• Jobs can be categorized based on the application complexity and data complexity of the task. The jobs where the data complexity and application
complexity are low, are ideal candidates for full automation – use cases could be personalization, image recognition etc.
• The second step is to create and access all the data that is required to train the AI platform. The data should be across the product value chain and come
from internal sources, customers and partners. It is also critical to access external context data that is relevant from other data sources. It is critical to own as much
data as possible as it might be a key differentiator
• The third step is to develop an open innovation fabric to engage with the ecosystem. Organizations need to create systems that seamlessly integrate internal
innovations with external innovations. A strong process to understand the key disruptions in the industry by keeping tab of the innovations happening across the tech
Mafias and start-up ecosystem is critical
• Lastly the organisations need to structure the global engineering organization into hub-satellites to ensure they can tap into the right level of talent and
competency across the globe. The availability of affordable talent with AI skills is going to rapidly increase in countries such as India and China. Create a hub or a
center of excellence for AI in locations that have the potential to quickly scale AI talent. Amazon, for example, has a center of excellence in Bangalore for Machine
learning
• Organisations need to orchestrate partnerships across the AI stack – GPU and Hardware, Infrastructure, AI Platforms, Data sets and Applications. In order to build
their applications, organisations should use the best of breed data sets and partners across the AI stack. Indian Service Providers such as Wipro, TCS andInfosys have already taken a step ahead to develop the capability across the AI stack and use them to integrate the various layers.
Source : DRAUP
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