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The AI spring
- Role of Product Management in the Age of Intelligent Products
1
Pari Natrajan, CEO – Zinnov LLC
Introduction of
Turing test
Convergence of innovations has moved AI forward in the last few years
1950
Computational
Power...
• 10360 possible moves
• Monte Carlo tree search & Q Learning
• Statistical, learned and general purpose
• Learned from 30...
“You get in the car, press the button, and
it will drive you all the way from
Mountain View to San Francisco”
- George Hot...
MANUFACTURING FINANCIING
OWNERSHIP
EXPERIENCE
DESIGN &
DEVELOPMENT
AI-Designed Car
Developing the first AI
designed car
Pa...
Disrupting all major industry verticals
1- Analysed basis data maturity, software penetration, regulatory restrictions acr...
Artificial Intelligence is a sum of many algorithms..
Constraint Satisfaction
Probabilistic reasoning
Logical Reasoning
Ma...
Deep Learning is a branch of Neural Networks, where there are ‘n’ intermediate processing layers between the input and
out...
AI innovations are dominated by Tech Mafias and Start-ups
Hardware
APPLICATIONS
PLATFORMS
INFRASTRUCTURE
MACHINE INTELLIGE...
Quarterly funding trend (2013-16 YTD)
Q1,
2012
Q1,
2013
Q1,
2014
Q1,
2015
Q1,
2016
$94
$137
$253
$121
$302
$552
$926
$901
...
2277
Start-ups
$14.2
8 Billion
Global AI start-up distribution
USA
China
Germany
Spain
UK
Israel
85
40
55
188
1170
55
168
...
Healthcare
Home Automation
CloudPersonal Assistants
Tech Mafias are building an AI-first future
Automotive Wearables
AI
Ta...
And are acquiring younger start-ups to accelerate innovation pace….
Amazon Apple Facebook MicrosoftGoogle
Average Acquisit...
..Leveraging global talent pool….
9300
19600
Seattle
Area
Bay Area
2100
3200
Boston
New York
9502700
Bangalor
e
1600
• Tec...
X
User Base on
GITHUB
….and opening their innovation to others to build on
Computer Vision
KINDRED
Robotics
Project
Malmo
...
Public failures not withstanding
Trending fake news articles
Facebook
7 reported accidents (1 fatal) since April
2016
Tesl...
17
Will AI replace product management function?
Grid.io
AUTO DESK DREAMCATCHER
SENSOR AND HISTORICAL DATA
REITERATE & IMPR...
18
How should Product Managers respond
Build deep understanding of changes in the market1
Understand AI impact on the prod...
19
Build deep understanding of change forces in the market and the ecosystem
Technology innovations that have the potentia...
20
Identify and prioritize AI based product features
DATA
COMPLEXIT
Y
APPLICATION COMPLEXITY
Wide range of interconnected ...
21
Centralized AI/ Data Teams ( Earlier)
Product
Management
Sales &
Marketing
Customer
Support
AI Research
Scientists
Engi...
Build and own a data ecosystem
Conceptualization
Design &
Development
Product Usage ServiceabilityManufacturing
ERP
Geo-lo...
Build the right platform partnerships
GPU Hardware Platforms
Big Sur Tensor Processing Unit
Github – Public
Datasets
Infra...
24
25
And AI is deeply impacting the products of today
Recommendation System
Dynamo DB -
Managed NoSQL
Database Service
ML mo...
26
AI is changing the way we build products- Self Design Websites
Leveraging Artificial Intelligence across the design pro...
27
AI is changing the way we build products- Self Design cars
“When you start to add in AI/machine learning, it’s like you...
28
AI at the center of all products - Facebook
FBLearn
er Flow
• Each machine learning
algorithm should be
implemented in ...
29
AI at the center of Organization’s future - Tesla
Eight surround cameras
Twelve ultrasonic sensors
Forward-facing radar...
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AI Spring : Role of Product Management in the Age of Intelligent Products

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The presentation on the AI Spring is a part of Product Management Summit during NASSCOM Product Conclave 2016 at Bangalore on 26 - 27 October 2016. The session was conducted by Mr. Pari Natarajan, CEO, Zinnov LLC.

Over USD 14 billion has already been invested in AI focused start-ups to leverage the combination of cheap and fast computing, data abundance and decades of refinement to AI algorithms. The session will focus on the current trends in AI and how it will impact product management function.

Published in: Technology
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AI Spring : Role of Product Management in the Age of Intelligent Products

  1. 1. The AI spring - Role of Product Management in the Age of Intelligent Products 1 Pari Natrajan, CEO – Zinnov LLC
  2. 2. Introduction of Turing test Convergence of innovations has moved AI forward in the last few years 1950 Computational Power Data Platforms Better Algorithms First AI program to play Tic Tac Toe 1960 RDBMS LOGIC THEOREM IBM Deep blue defeats Gary Kasparov 1997 OLAP NEURAL NETWORKS 512 Core GPU 2880 Core GPU 12000 Core GPU Cost of Computing $200 Per million transistors $50 Per million transistors $0.05 Per million transistors 2011 2015 Watson became Jeopardy Champion BIGDATA PLATFORMS -HDFS DEEP LEARNING 2 DeepMind’s self-taught AI can beat human players at 29 of 49 Atari games
  3. 3. • 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.. “If I look back on the three matches, the first one, even if I were to go back and redo the first match, I think that I would not have been able to win, because I misjudged the capabilities of AlphaGo.” It may be a hundred years before a computer beats humans at Go — maybe even longer!! -AI Experts in 1997, NY times 3
  4. 4. “You get in the car, press the button, and it will drive you all the way from Mountain View to San Francisco” - George Hotz 2015 Built Autonomous Car in a month in his garage <$1000 Off-the-shelf ADAS Kit in Beta $3.1 Million Funding received 3 Total Team Size 7.25 Hours worth of driving data open sourced AI has moved on from games to the real world 4Source: Comma.ai
  5. 5. MANUFACTURING FINANCIING OWNERSHIP EXPERIENCE DESIGN & DEVELOPMENT AI-Designed Car Developing the first AI designed car Partnered with the robotics and intelligent systems group Intelligent Systems PRODUCT FEATURES $25 million research centre with MIT for autonomous vehicle Autonomous Vehicles  AI Based Designs  Simulated Testing Baidu invested in ZestFinance, which uses ML for 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 it’s users. Ride-Sharing $721 MnTotal Funding 2011 Average Founding year 192Disruptors Adding value across the industry value chain AUTOMO BILE 5Source: GEIP
  6. 6. Disrupting all major industry verticals 1- Analysed basis data maturity, software penetration, regulatory restrictions across the value chain representing disruption potential over next 5 years 2 – Analysed basis current investments ( talent + acquisition) for all players Amazon Echo- equipped with the NLP platform Alexa. BFSI Healthcare PotentialtoDisruption1 AI Maturity 2 Retail Predictive diabetes management solution AI based Robo advisory service. Enterprise Software Semicon Consumer Electronics Microsoft cloud platform Azure, has NLP capabilities Nvidia’s Machine Learning Enabled Hardware - 12x faster. NASA software to enable damaged aircrafts, find a safe landing spot. Recommends what styles to wear based on current customer photographs Driven 130 million miles using the autopilot feature since 2014. Auto Aerospace Consumer Software 95% of its $17B revenue was generated through ML enabled Advertising. 6Source: GEIP
  7. 7. Artificial Intelligence is a sum of many algorithms.. Constraint Satisfaction Probabilistic reasoning Logical Reasoning Machine Learning Search & optimization Control theory Facial Recognition Autonomous VehiclesDrones & Robotics Personalized MarketingFinancial Trading Artificial Intelligence Building computer programs that make data driven decisions without being explicitly programmed and also automatically improve over time What is NLP Computer Vision 7
  8. 8. Deep Learning is a branch of Neural Networks, where there are ‘n’ intermediate processing layers between the input and output. WHY DEEP LEARNING  Automated Feature Extraction – Can recognize patterns by itself  Less training data required  Reuse the same algorithm or different applications Input Input Input Output Convoluted Neural Nets ‘ n’ Intermediate Layers Output Output Input Input NLP Natural Language DB Queries Spam Detection Early Detection of curable diseases Personalized Ad Recommendations Automated Conversation Coach DAVE2 – Autonomous Vehicle Vision Pattern Recognition Speech Recognition Speech to Text Conversion SIRI Deep Search 2 for voice search With Deep learning being the most dominant of all 8
  9. 9. AI innovations are dominated by Tech Mafias and Start-ups Hardware APPLICATIONS PLATFORMS INFRASTRUCTURE MACHINE INTELLIGENCE NLP COMPUTER VISION DEEP LEARNING ADAS GESTURE CONTROL Enterprise Software Assistants Productivity FinTech AdTech Data Platforms HealthTech Auto Applications – The Startup Zone Platforms – Tech Mafia Playground Infrastructure – G500 Domination Intensity Start-ups G500Tech Mafia 9Source: GEIP
  10. 10. Quarterly funding trend (2013-16 YTD) Q1, 2012 Q1, 2013 Q1, 2014 Q1, 2015 Q1, 2016 $94 $137 $253 $121 $302 $552 $926 $901 $602 $1,049 Raises $100M for Deep learning based ultrasound Google acquires Deepmind for $600M Raises $65M for ML based threat detection Q1, 2011 Focussing on reverse engineering the neocortex raised series A Ten fold increase in start-up funding in the last five years 200 USD 0.4B APPLICATIONS 600 USD 2.5 B PLATFORMSENABLERS 1400 USD 11 B USD 1 B USD 0.9 B USD 0.7 B Healthca re Fintech Auto USD 2.3 B USD 0.7 B USD 0.5 B USD 0.3 B USD 0.1 B Machine Learning NLP Comput er Vision Data Platforms Hardwa re USD 6 B Enterpris e Software #Start-ups Funding 10Source: GEIP
  11. 11. 2277 Start-ups $14.2 8 Billion Global AI start-up distribution USA China Germany Spain UK Israel 85 40 55 188 1170 55 168 India 86 Canada Netherlands 25 26 Australia Brazil 18 France 43 25 Singapore Hong Kong12 Total FundingNumber of Start-ups And dominated by the US Global AI start-up distribution $11.5 B $0.6B $0.3 B $0.5 B $0.6 B $0.1 B $0.1 B $0.1 B APPLICATIONS-FINTECH, HEALTHCARE APPLICATIONS –HRTECH, HEALTHCAREAPPLICATIONS – AUTO, FINTECH, RETAIL PLATFORMS- DEEPLEARNING, VISION ENABLERS –BIGDATA PLATFORMS Vision based advanced assistance system AI based consumer robotics start-up ML based threat detection ML based recruitment solution ML for retail ML for personalised healthcare NLP API Data cataloging and cleaning PLATFORMS- DEEPLEARNING, NLP 11Source: GEIP Massively scaled deep learning
  12. 12. Healthcare Home Automation CloudPersonal Assistants Tech Mafias are building an AI-first future Automotive Wearables AI Talent Acquisiti on Patents - 30K $10 B 300 + Wearables for Health monitoring Project Titan Siri controlled home kit Apple Smartwatch iPhone iOS 10 image recognition Spotlight for images & text Si ri Aerospace Google Home Verily –algorithms for Diagnosis Deepmind for Healthcare Google X nanoparticle research Google X –Self Driving Android Wear Smartwatch Google Loon AI Robot- GoogleX Google Prediction API AlloGoogle NowTensorflo w Project Jacquard Deepmin d Google for Work AI Platforms Cortana for Healthcare Microsoft –Volvo Self driving Kinect Cortana SwiftKey Microsoft Graph – Sales lead scoring Hololens Azure ML DSSTNE Oculus UAV Aquila Facial recognition Facebook M Facebook Deeptext FAIR Wit.ai AlexaDrone Delivery Recommender systems AWS MLCNTK 12Source: GEIP
  13. 13. And are acquiring younger start-ups to accelerate innovation pace…. Amazon Apple Facebook MicrosoftGoogle Average Acquisition Year AcquireeMaturity Dot Com Era Smartphone Era Cognitive Era 2004 2010 2016 1 2 3 4 5 Bulk 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 Google begin work on Maps NLPVision ML NLP Vision ML NLP Vision Robotics 13 Indicates Average
  14. 14. ..Leveraging global talent pool…. 9300 19600 Seattle Area Bay Area 2100 3200 Boston New York 9502700 Bangalor e 1600 • Tech Mafia - Machine learning 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 460 Singapor e 660 BeijingIsrael Hyderaba d Top universities like CMU & MIT have hot focus on AI research; • EU’s Human Brain Project will spend to 1 billion euros on AI over the next decade. • OEM’s like Renault, Volkswagen partnered with Autonomous start-ups like Mobileye IBM set up its Watson unit in India in 2012 to work for Healthcare and BFSI clients in US. Baidu is investing in Deep speech that leverage speech recognition; 910 Spain 4100 UK 2000 France 3300 Germany Hong Kong & Singapore X ER&D Workforce in AI 950 Netherlands 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 14Source: GEIP
  15. 15. X User Base on GITHUB ….and opening their innovation to others to build on Computer Vision KINDRED Robotics Project Malmo Tensorflo w DSSTNE 35 K Faceboo k 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 Describes neural networks as a series of computational steps6K 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 AI Library. User base has grown tenfold since it’s release in Nov. 2015. Makoto Koike uses TensorFlow to sort Cucumbers Cornel University project on Cyber-Security Projects based on Platforms 15
  16. 16. Public failures not withstanding Trending fake news articles Facebook 7 reported accidents (1 fatal) since April 2016 Tesla Google Deepmind failed at describing dumbbells Microsoft’s Tay became a racist bot Microsoft 16 FAIL FAST FAIL CHEAP FAIL FORWARD
  17. 17. 17 Will AI replace product management function? Grid.io AUTO DESK DREAMCATCHER SENSOR AND HISTORICAL DATA REITERATE & IMPROVE ON THE GO Eight surround cameras Twelve ultrasonic sensors Forward-facing radar Nvidia’s new Drive PX2 Fully Autonomous Driving Software Upgrade by 2018 Million Miles of Driving data Leveraging Artificial Intelligence for design Self Design by AI AI for Product Personalization Building Software using sensor + AI data FBLearner Flow Building more than 1.5 billion AI agents—one for every person who uses Facebook or any of its products,
  18. 18. 18 How should Product Managers respond Build deep understanding of changes in the market1 Understand AI impact on the products2 Transform team structure and skill profiles3 Build a data moat and a data ecosystem4 Product Managers in the AI Storm Orchestrate partnerships to accelerate innovation5
  19. 19. 19 Build deep understanding of change forces in the market and the ecosystem Technology innovations that have the potential to disrupt existing ecosystem and create new opportunities TECHNOLOGY Changing consumption models across global marketsCONSUMPTION Business models that have the potential to disrupt the industryBUSINESS MODEL Creation of new markets for existing products and servicesMARKETS Ecosystem include engineering service providers, start-ups, universities and expert networks ECOSYSTEM The segments across industry vertical where the value gets capturedVALUE CHAIN Future Lens Framework Zinnov’s ‘Future Lens Framework’ helps enterprises understand the dynamic relationships between an organization and its external ecosystem Source: Zinnov Research and Analysis
  20. 20. 20 Identify and prioritize AI based product features DATA COMPLEXIT Y 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 DeepLearningRuleBasedEngine AI website that designs itself e.g. Grid.io NLP & graph search to identify models Testing Service Chat based bots e.g. Sobot Technologies AI sits between Tester & programmer. Generates possible diagnosis & plan new tests A/B Testing Prototy pe Bug report and instantly deploy hot fixes in app runtime. E.g. Hansel.io Dynamic & personalised changes to websites Design Customer Support Customer Interface Complex customer queries using dynamic algorithms
  21. 21. 21 Centralized AI/ Data Teams ( Earlier) Product Management Sales & Marketing Customer Support AI Research Scientists Engineering Product Management Sales & Marketing Customer Support AI Research Scientists Engineering CXOs ML Engineer AI Research Scientists Data Scientists De - Centralized AI Teams (Need Now) CXOs ML Engineer Data Scientists AI Research Scientists Product Management AI Research Scientists Data Scientists Product Engineering Teams Sales & Marketing Customer Support ML Engineer ML Engineer ML Engineer AI Research Scientists Data Scientist Machine Learning Engineer Education: MS or PhD in CS & Mathematics Neural networks, NLP, machine learning, statistical modelling, pattern recognition Education: Bachelor’s/Master Degree in CS Problem solving, and programming Python, Java / C++, as well as ML toolkits such as Theano, Tensorflow, Keras or similar Education: Masters, PhDs Analytics: SAS/R CS: Python, Hadoop, SQL, Data Derivation from Unstructured data New Team StructureNewer Roles Reorient team structure and skill profiles
  22. 22. Build and own a data ecosystem Conceptualization Design & Development Product Usage ServiceabilityManufacturing ERP Geo-location data Social data Compete Data Usage data Bug Reports Product Data 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 22
  23. 23. Build the right platform partnerships 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 23
  24. 24. 24
  25. 25. 25 And AI is deeply impacting the products of today Recommendation System Dynamo DB - Managed NoSQL Database Service ML models based on data stored in AWS cloudUser Applications Predictions with Amazon Real time ML 29% increased Sales in 2014 Self Learning Robots FANUC Intelligent Edge Link and Drive (FIELD) system to increase robotics productivity using Deep Learning Many organizations have already innovated their products through investments in AI Improved Data Centers 40% reduction in the amount of electricity needed for cooling in 2015
  26. 26. 26 AI is changing the way we build products- Self Design Websites Leveraging Artificial Intelligence across the design process Decide Composition Auto Tag Areas Define Flow Of Content Wireframe based on flow Pair Optimal content Define Styling basis emphasis Create HTML wrps and add Google Pug ins
  27. 27. 27 AI is changing the way we build products- Self Design cars “When you start to add in AI/machine learning, it’s like you have 1,000 engineers working for you solving problems in a fraction of the time that it used to take. It’s the democratization of manufacturing,” - Mike McCoy, Founder AUTO DESK DREAMCATCHER Design software Dreamcatcher uses AI to suggest 1000s of alternative Chassis designs Collected actual driving data from multiple sensors installed on cars SENSOR AND HISTORICAL DATA REITERATE & IMPROVE ON THE GO Every time car is driven, newer data is collected to improve design
  28. 28. 28 AI at the center of all products - Facebook FBLearn er Flow • Each machine learning algorithm should be implemented in a reusable manner • Engineers should be able to write a workflow that can be shared over many machines • Training a model should be easy for engineers of varying experience • Users should be able to easily search past experiments, view results, share with others, and start new variants of a given experiment. Of Facebook’s workforce use Flow (including non-engineering employees) 25% Users on GitHub 9k Workflows run per month 500k 1. Catch errors early 2. Improve readability of code 3. Facilitate tooling 4. Improve runtime Advantages Applications Search News Feed Advertising
  29. 29. 29 AI at the center of Organization’s future - Tesla Eight surround cameras Twelve ultrasonic sensors Forward-facing radar Nvidia’s new Drive PX2 ‘Tesla Vision’ End-to-end image processing software with neural net Fully Autonomous Driving Software Upgrade by 2018

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