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The AI Spring - Innovations for the next decade
The document outlines significant advancements in artificial intelligence (AI) from the mid-20th century to 2015, emphasizing breakthroughs in autonomous systems, deep learning, and applications across various industries such as automotive and healthcare. It highlights the evolution of AI technology, the role of tech companies and startups, and the increasing availability of funding for research and development in this field. Additionally, it discusses the potential of AI to disrupt traditional business practices and the importance of collaboration and innovation in shaping the future of technology.
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The AI Spring - Innovations for the next decade
- 1. The AI spring -Innovations for the next decade May 2015 1 Pari Natarajan, Hardik Tiwari Rishabh Saraswat, Amrit Murali
- 2. Introduction of Turing test AIhas evolved rapidly in the last few years 1950 Computational Power Data Platforms Better Algorithms First AI program to play Tic Tac Toe 1960 RDBMS LOGIC THEOREMS -Single layer learning, Perceptron, Adaline IBM Deep blue defeats Gary Kasparov 1997 OLAP NEURAL NETWORKS - Multilayer Back propagation 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 -Convoluted Neural Network 2 DeepMind’s self-taught AI can beat human players at 29 of 49 Atari games
- 3. • 10360 possiblemoves • 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.. “That’s a strange move. I thought it was a mistake.” – Lee Sedol It may be a hundred years before a computer beats humans at Go — maybe even longer!! -AI Experts in 1997, NY times 3
- 4. “You get inthe 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. MANUFACTURING FINANCIING OWNERSHIP EXPERIENCE DESIGN & DEVELOPMENT AI-DesignedCar 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 million research centre in collaboration with MIT for autonomous vehicle technologies Autonomous Vehicles AI Based Designs Simulated Testing Baidu invested in ZestFinance, a startup 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 it’s users. Ride-Sharing $721 MnTotal Funding 2011Average Founding year 192Disruptors Adds value across the industry value chain Automotive 5Source: GEIP
- 6. And is disruptingall 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 NLP platform Alexa BFSI Healthcare PotentialtoDisruption1 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. 6Source: GEIP
- 7. Artificial Intelligence isa sum of many algorithms.. Constraint Satisfaction Probabilistic reasoning Logical Reasoning Machine Learning Search & optimization Control theory Facial Recognition Autonomous VehiclesDrones & Robotics Personalized Marketing Financial 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. Deep Learning isa 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 Deep Learning Algorithms Deep Boltzmann Machine Deep Belief Network Convoluted Neural Networks Stacked Auto Encoders 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 Deep learning is the most dominant of all 8
- 9. AI innovations aredominated 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 Dominated by the startups who build verticalized applications for various use cases. Applications – The Startup Zone Focused their 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 9Source: GEIP
- 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 $500M 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 Healthcare 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 Computer Vision Data Platforms Hardware USD 6 B Enterprise Software #Startups Funding 10Source: GEIP
- 11. 2277 Start-ups $14.28 Billion GlobalAI start-up distribution USA China Germany Spain UK Israel 85 40 55 188 1170 55 169 India 86 Canada Netherlands 25 26 Australia Brazil 18 France 43 25 Singapore Hong Kong12 Total FundingNumber of Start-ups And is 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 Computer Vision AI based consumer robotics start-up Massively scaled deep learning Threat detection ML based recruitment ML for retail Personalised healthcare NLP API PLATFORMS- DEEPLEARNING, NLP 11Source: GEIP
- 12. European start-up ecosystemis evolving and leveraged by US companies 441 Start-ups $ 1 B Investment APPLICATIONS PLATFORMS INFRASTRUCTURE HARDWARE SOFTWARE DEEP LEARNING- $ 170M NLP - $33M SMART ROBOTS -$20M AUTO - $64M FINTECH - $97M HEALTHCARE -$115M ADTECH - $26M SECURITY -$100MPRODUCTIVITY - $130M COMPUTER VISION - $50M USGlobalEU 60%30%10% 30 Acquisitions $1.2 B Investment Start-ups Deep Learning NLP Robotics Computer Vision Productivity 12Source: GEIP
- 13. Healthcare Home Automation CloudPersonal Assistants TechMafias are building an AI-first future Automotive Wearables AI Talent Acquisition 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 Siri 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 Now Tensorflow Project Jacquard Deepmind 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 ML CNTK 13Source: GEIP
- 14. And are acquiringyounger 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 14 Indicates Average
- 15. Leveraging global talentpool 9300 19600 Seattle Area Bay Area 2100 3200 Boston New York 9502700 Bangalore 1600 • Traditional Hubs for Engineering for the 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 & IsraelIndian Hi-Tech Cities 460 Singapore 660 BeijingIsrael Hyderabad • Top universities like CMU & MIT have hot focus on Artificial Intelligence research; • EU’s Human Brain Project for spending close to 1 billion euros on AI over the next decade. • OEM’s like Renault, Volkswagen partnering 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 for voice-based searches 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 15Source: GEIP
- 16. X User Base on GITHUB Andopening their innovation to others to build on 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 it’s release in Nov. 2015. Makoto Koike uses TensorFlow to sort Cucumbers Cornel University project on Cyber-Security Projects based on Platforms 16
- 17. “Success in creatingAI would be the biggest event in human history. It might also be the last, unless we learn how to avoid the risks.” - Stephen Hawking Public failures not withstanding.. Trending fake news articles 7 reported accidents (1 fatal) since April 2016 Facebook Tesla Google Deepmind failed at describing dumbbells Microsoft’s Tay became a racist bot Microsoft 17
- 18. Top R&D spendersare lagging behind.. Plan to release Xeon Phi processor line for AI applications-~$400M investments Leveraging ML for network threat products – Cognitive Threat Analytics Invested $5B in building an AI powered Gigafactory. $1B investment to establish the Toyota Research Institute for AI $500M investment in a 200 member AI R&D lab in Sillicon Valley 1 : Investment in AI in terms of talent & acquisition or Funding raised ( for startup) 2 : Focus on emerging technologies vs Older algorithms, Focus on Ecosystem creation and Platform adoption/maturity Google Microsoft Amazon Facebook Apple IBM Bosch Volkswagen Intel Oracle Cisco Foxconn SAP Airbus Mobileye Sentient Zoox Datarobot X.ai AI Focus1 FutureReadiness2 Startups G500Tech Mafia G500 Start-ups Tech Mafia Size of the bubble indicates R&D spend Source: GEIP 18
- 19. But can acceleratethrough four simple steps Identify Business Case 1 Build a Data Ecosystem 2 Collaborate with the Ecosystem Leverage newer talent hotspots 4 3 Algorithm & Platforms Data Talent Use Cases AI Puzzle 19
- 20. Identify and prioritizeAI’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 DeepLearningRuleBasedEngine AI bot designed car AI based website design platform 20
- 21. Build a Dataecosystem and own it 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 21
- 22. Toyota leverages multiplefacets 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 Centre for AI Research Partnered to develop autonomous car technology. New Age Innovation Fabric Porous innovation permeating beyond the walls of the organization Balance internal and external innovation 22
- 23. Venture Fund Accelerators Evangelize Ecosystem Collaboration • Extensivehands 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 Partner with start-ups 3rd Party Accelerators • Partner with other accelerators, innovation workshops with stakeholders Arena 120 Microsoft Start-up accelerator 23Source: GEIP
- 24. Build the rightplatform 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 24 EXAMPLES
- 25. Leverage global talent USA Canada 65110 10 UK 35 China 70 85 Available AI & related Talent in Country (‘000) 2015 2025 20 Israel India A global satellite and is a part of the global CoE for ML reporting to Berlin Global engineering hub and has small team working on ML Global engineering satellite and drives activities in Computer vision space Global CoE for IoT and Advanced ML space. 18 6 15 155 Beijing a global engineering satellite and works on NLP and Computer vision tech Partnered with Didi for crowdsourcing challenge for optimising route algorithm 25Source: GEIP
- 26. Some of theEuropean peers have a head start. Enterprise Software Have opened up multiple research labs all over the world, focused on collaborating with start-ups and scientific institutions with 420M investment. Intelligent Machines BMW is teaming up with Mobileye and Intel to work on its first fully autonomous self-driving car, which it plans to ship by 2021 Self-Driving Car Smart Car Audi confirmed that it's self-driving car will use the new Nvidia superchip future automotive self- piloting capabilities. Cognitive Computing , industry specific applications Collaborating and integrating IBM’s cognitive computing technology with the HANA Cloud. Self-driving car Robotics, Cyber-Secuirty and IoT Self-driving car 26
- 27.