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"The Vision AI Start-ups That Matter Most," a Presentation from Cognite Ventures


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Chris Rowen of Cognite Ventures delivers the presentation "The Vision AI Start-ups That Matter Most" at the February 2017 Embedded Vision Alliance Member Meeting. Rowen shares his unique perspective on the vision AI start-ups that matter most.

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"The Vision AI Start-ups That Matter Most," a Presentation from Cognite Ventures

  1. 1. The Vision AI Start-ups That Matter Most Chris Rowen, PhD, FIEEE CEO Cognite Ventures February 22, 2017
  2. 2. Outline • The Vision AI Innovation Scene • Quick Picture of Cognitive Computing Startups • Embedded Vision and Cognitive Computing • Embedded Vision Innovation Scene • New Hardware for Embedded Vision • Observations • The List
  3. 3. Vision AI Innovation Scene • Huge enthusiasm for cognitive computing aka artificial intelligence aka machine learning aka deep learning aka neural networks • Transformation of all modeling and recognition of complex data, scenes and actions • Applications from robotics to human-machine interface • Profound changes in how we develop systems: programming  labeling and training • Massive investment by major cloud operators: Google, Baidu, Facebook, Microsoft, Amazon  sucking up every deep learning PhD they can find • Massive startup activity around the world • Which startups are the most important and focused on serious cognitive computing? From raw catalog of ~2200 AI startups, we’ve identified ~275 of particular interest – 75 of them in embedded vision
  4. 4. Typical omission:’s chatbot platform helps brands increase revenue and customer loyalty by enabling personalized one-on-one conversations with their customers at scale through any messaging application. Typical inclusion: creates AI software for autonomous vehicles. It aims to build a hardware and software kit powered by artificial intelligence for carmakers. What applications are at the heart of AI? Autonomous Driving Cyber Security Surveillance Generic deep learning platforms Medical imaging Massive Scale Sensor IoT – e.g. Ag Legal Analysis Advertising Automation Drones Toys Robotics Generic Data Analytics CRM Language Translation Financial modeling Predictive Marketing Geoanalytics ERP Retail AnalyticsSelection Criteria: • Inherent huge data • Embrace and tight focus on deep learning methods for modeling and classification • Job listings and blogs show depth in machine learning
  5. 5. Quick Picture of Cognitive Computing Startups: What? Cognitive Computing: 277 Embedded: 82 Vision: 125 Embedded Vision:74 • Almost ¾ of 276 startups focus on cloud software: CRM, logistics, predictive marketing • Heavy emphasis on document and text processing in the cloud • Plenty of vision/image processing in the cloud: non-real-time analysis • Most of embedded includes vision. Rest is audio, voice and motion sensing
  6. 6. Quick Picture of Cognitive Computing Startups: Where? CA, 67% MA, 10% NY, 5% TX, 3% WA, 2% CA MA NY TX WA PA MI CT DC NM TN MD FL OR UT OH CO DE USA, 49% UK, 21% China, 8% Canada, 5% Germany, 3% India, 2% USA UK China Canada Germany India Japan Israel The Netherlands South Korea France Switzerland Spain Russia Austria South Africa Slovenia Norway Ireland Hungary Finland Denmark Belgium Argentina
  7. 7. Embedded Neural Network Product Segments Autonomous Vehicles and Robotics Monitoring, Inspection and Surveillance Human-Machine Interface Personal Device Enhancement Vision Multi-sensor: image, depth, speed Environmental assessment Full surround views Attention monitoring Command interface Multi-mode ASR Social photography Augmented Reality Audio Ultrasonic sensing Acoustic surveillance Health and performance monitoring Mood analysis Command interface ASR social media Hands-free UI Audio geolocation Natural Language Access control Sentiment analysis Mood analysis Command interface Real-time translation Local bots Enhanced search
  8. 8. Why Cognitive Vision Now? • Computing and communication driven by new data in/out • CMOS sensors trigger imaging explosion • 99% of of captured raw data is pixels (dwarfs sounds and motion) 1010 sensors x 108 pixels/sec = 1018 raw pixels/sec • Rapid growth of vision-based products and services • Starting 2015: more image sensors than people • New Age: Making sense of pixels requires computer cognition 0 2E+09 4E+09 6E+09 8E+09 1E+10 1.2E+10 1.4E+10 1.6E+10 1.8E+10 2E+10 1990 1995 2000 2005 2010 2015 2020 World Population Three-year sensor population
  9. 9. Vision 0 10 20 30 40 50 2011 2012 2013 2014 2015 2016 ImageNetTop-1Error % Year Rapid Progress on Accuracy 0 10 20 30 40 50 0 5 10 15 20 25 ImageNetTop-1 Error% GMACs per image Bounded Compute Load 0 10 20 30 40 50 0 50,000,000 100,000,000 150,000,000 ImageNetTop-1Error % Model Coefficients Models Getting More Manageable • Computer vision is big, obvious NN domain • Many related tasks: classification, localization, segmentation, object recognition, captioning, generation • Huge computation in embedded inference • Vision is fundamentally hard – even for humans!! • Example: ImageNet Classification: • 1000 categories • 120 species of dogs 23.5 24 24.5 25 25.5 0 2 4 6 8 ImageNetTop-1Error % GMACs Optimization Doubles Efficiency ResNet 50,101 Cadence Tibetan mastiff Shih-Tzu Norwegian elkhound
  10. 10. Embedded Vision Startup Scene: What and Where? • Identified 74 startups focused on machine learning for embedded vision • Half in US, >30% in CA. Half doing robots, drones and cars 0 5 10 15 20 25 30 35 Cognitive EV Startup Countries CO MI PA TX MA Surveillance Vehicles Human- Machine Interface Drones and Robots Silicon 0 5 10 15 20 Application for Cognitive EV Startups
  11. 11. Some Examples DeepGlint (格灵深瞳) [China] •18M in 3 Rounds, most recent June, 2014 •Deep Glint focuses 3D computer vision and machine learning technologies, to provide automatic human trajectory analysis solutions to banks and shopping centers. Mashgin [US] •$620k in 3 Rounds, most recent September, 2015 •Mashgin is building a self-checkout kiosk that uses 3D reconstruction, computer vision and deep learning to identify items. FiveAI [UK] $2.7M Seed on July, 2016 We're building the world's most reliable autonomous vehicle software stack to solve the most difficult problem of all - delivering a solution that's safe in complex urban environments, without any driver involvement.
  12. 12. Embedded Vision Drives Hardware Need scale and efficiency: • Conventional wisdom - deep neural networks much less efficient than hand-tuned feature recognition methods (but more effective) • Convolutional neural networks allow • High parallelism • Low bit resolution • Structured, specialized architectures • Manageable memory bandwidth • ~1000x energy improvement over GP CPU may compensate for efficiency gap 10 100 1,000 10,000 10 100 1,000 10,000 100,000 GMACSPerWatt GMACs Neural Network Platforms Vision DSP core 1 Vision DSP core 1 cluster Vision DSP core 2 Vision DSP core 2 cluster Embedded GPU core cluster Data Center GPU 1 cluster Embedded GPU cluster FPGA 1 FPGA 2 Convolutional neural network (CNN) engine CNN engine cluster Data Center GPU 2 cluster Data Center GPUs FPGAs Embedded GPUs Vision DSPs Vision + NN DSPs CNN engines GPCPU
  13. 13. Hardware Startups for Embedded Vision • Modest number of startups reflects challenging funding for silicon • Rapid pace of change in basic neural network algorithms dictates programmability • Major semis and IP providers investing heavily in vision and deep learning Name Description Website Country State BrainChip Spiking Neuron Adaptive Processor USA CA Cambricon Device and cloud processors for AI China Cerebras Systems Specialized next-generation chip for deep-learning applications USA CA Deep Vision Low-power silicon architecture for computer vision USA CA Deephi Compressed CNN networks and processors China Graphcore Graph-oriented processors for deep learning UK Isocline Ultra-low power NN inference IC design based on flash+analog+digital USA TX KNUPATH Ultra-scale processor ICs for vision and ML USA TX Leapmind Embedded deep learning platform Japan Reduced Energy Microsystems Lowest power silicon for deep learning and machine vision USA CA Tenstorrent Deep learning processor: designed for faster training and adaptability to future algorithms Canada ThinCI vision processing chips USA CA
  14. 14. The Cognitive Embedded Vision List Abundant Robotics Accelerated Dynamics AIMotive Airware AKA Alchera Technologies Algocian Anki Argo AI Auro Robotics Blue Vision Labs BrainChip Cambricon Cerebras Systems Clearpath Robotics CloudMinds Cognitive Pilot Comma AI Deep Vision Deep Vision DeepGlint Deephi DeepScale Emotibot Emovu Emteq Evolve Dynamics Face++ FiveAI Graphcore Horizon Robotics Intuition Robotics Iris Automation Isocline Kindred Kneron KNUPATH Leapmind Lily Camera Machines with Vision Mashgin Memkite Minieye Momenta MorpX Nauto Netradyne Neurala Novumind Noxton Analytics nuTonomy Osaro Oxbotica Pilot AI Labs Quanergy Reduced Energy Microsystems RobArt RoboCV Rokid Scortex Shield AI Skydio Sportcaster Tenstorrent TeraDeep ThinCI Third Eye Systems Universal Robotics Velodyne Viz White Matter Zero Zero Robotics Zoox
  15. 15. Observations 1.Yes, the cognitive startup space is extremely lively, but demand exceeds supply – many under-served niches 2.Deep learning disrupts embedded vision – massive retraining of people and retooling of solutions 3.New technology enables new business models, especially • new services – e.g. “emotion as a service” • cloud-device hybrids – e.g. base layers trained in cloud, transfer learning in device • human augmentation – e.g. guided labeling tools, remote supervision data programming • autonomous agents – e.g. knowledge structures in support of task reasoning (AIBrain) 4.Expertise and grit gets funded
  16. 16. Sources and Caveats • Crunchbase: Comprehensive database of startups but needs lots of sifting: includes unfunded dreams, research programs, acquired companies, etc. • Shivon Zilis and James Cham in the Harvard Business Review: “The State of Machine Intelligence, 2016” • MMC Ventures: “Artificial Intelligence in the UK: Landscape and learnings from 226 startups” • 机器之心选出全球最值得关注的100家人工智能公司: (“The heart of the machine selected the world's most noteworthy 100 artificial intelligence companies”) Caveats: • Somewhat subjective selection based on estimated degree on concentration on machine learning and neural network methods • The scene is constantly changing, with companies appearing and disappearing daily • Hard to get every link and summary correct • Send me corrections and suggestions:
  17. 17. The Whole Cognitive Computing List 4Paradigm ABEJA Abundant Robotics Accelerated Dynamics Affectiva AIBrain AiDO AIMotive Airware AKA Alchera Technologies Algocian Algorithmic Intuition Alpha I Amplero Anki Argo AI Arimo Arimo Atomwise Auro Robotics Aurora AI Automat Avalon AI Aylien Bay Labs Behavox Benevolent BenevolentAI Bloomsbury AI Blue Vision Labs BMLL Technologies BrainChip Butterfly Networks Calipsa Camio Capio Captricity CareSkore Cartoaware Celaton Cerebellum Capital Cerebras Systems Cirrascale Citrine Informatics Clarifai Clear Metal Clearpath Robotics CloudMedx CloudMinds Cogitai Cognative Cognicor Cognitive Pilot Comma AI Content Technologies Cortexica Cortica CrossingMinds CrowdAI CrowdFlower Cyberlytic CyCorp Cylance Cyra DarkTrace Datalogue Dataminr DataRobot Deep Genomics Deep Instinct Deep Vision Deep Vision Deep Vision Deep6 Analytics DeepGlint Deephi Deepomatic DeepScale* DeepVu Descartes Labs Digital Reasoning DigitalGenius Ditto Labs DroneDeploy Eloquent Labs Emotech Ltd Emotibot Emotient Emovu Emteq Enlitic Evolve Dynamics Eyeris Face++ Fashwell FeatureSpace FiveAI FoodVisor Grail Inc. Graphcore Graphistry Greedy Intelligence GridSpace H2O Hocrox Horizon Robotics Hubino Hyperverge ICarbonX Idio Imagia Imubit Indico Insilico Medicine Intelligent Voice Intelnics Intuition Robotics Iris Automation iSentium Isocline JukeDeck Kaggle Keen Research Kheiron Medical Kimera Kindred Kneron KNUPATH KONUX Last Mile Technologies LastMile Leapmind Level 6 AI Leverton Lexalytics Lily Camera Linguamatics Loop AI Luminist Luminoso Lunit Maana Machines with Vision Mad Street Dan Marax AI Mashgin Matroid MEDANN MedWhat Memkite Mentat Innovations micropsi MindMeld* MINDSET Minieye Mobvoi Momenta MorpX Nara Logics Nauto Neo AI Netra Netradyne Neural Painting Neurala Neurence NNaisense Novumind Noxton Analytics Nudgr Numenta Numerate nuTonomy Oncora Medical OpenAI OpenCapacity Orbital Insight Osaro Oseven Oxbotica Pat Phrasee Pilot AI Labs PitStop Pixoneye Planet Pop Up Archive Preferred Networks pulseData Quanergy Rainbird Technologies RapidMiner Ravn Systems Re:infer Realeyes Recursion Pharmaceuticals Reduced Energy Microsystems Replika Resnap Retechnica RobArt RoboCV Rokid Sage Senses Scaled Inference Scortex Seamless.AI Seldon Semantic Machines SenseTime Sensifai Sentenai Senter Sentient Sentisum Sentrian Shield AI Sight Machine SigOpt Siwa Skindroid Skydio Skymind Sonalytic SoundHound SpaCy SparkBeyond SparkCognition Speechmatics Sportcaster Tamr Tend Tenstorrent TeraDeep Terrabotics Terraloupe TheySay ThinCI Third Eye Systems TickAI Tractable TUPU Twenty Billion Neurons TypeScore Unisound Universal Robotics Valoosa Velodyne Vicarious Visii Visio Ingenii Viz Volley Vuno Wave Computing White Matter Xihelm YITU Technology Zephyr Health Zero Labs Zero Zero Robotics Zest Finance Zoox
  18. 18. neural network technology and applications