GTC. S Create Deep Intelligence TM in the Internet of Things (IoT) Nobuyuki Ota

Transcription

1 GTC S Create Deep Intelligence TM in the Internet of Things (IoT) Nobuyuki Ota

2 Preferred Networks Preferred Networks, Inc. (PFN) founded in 2014, located at Tokyo, Japan. Subsidiary company, PFN America is at San Mateo CA. PFN specializes in distributed machine learning technology, with a focus on Deep Learning, for the Internet of Things (IoT) PFN s goal is the realization of Distributed Deep Intelligence TM the synergistic implementation and integration of Distributed Deep Learning intelligence throughout the IoT networks 2

3 Major problems in the IoT and PFN s approach for its resolution Applications of Deep Intelligence TM technologies using GPU Distributed Deep Learning for Drug Discovery 3

4 Problems faced by IoT/IoE applications: Massive increase in volume, velocity, and variety of data Massive amounts of data are generated at the edge of the network This data is large, noisy, and has low-value density Collecting and analyzing this data in the Cloud is not practical 4

5 PFN s Solution: Online Edge-Heavy Computing and Global analysis on Cloud Computing Devices analyze data locally at the edge of the network Edge-devices learn autonomously in real-time for superior accuracy Machine learning models and extracted information only are sent to the Cloud for global analysis 5

6 What is Deep IntelligenceTM? Intelligent platform using Deep Learning through entire Networks Automatic & Real-Time Sense Optimize / learn Organize Analyze Cloud EdgeHeavy Deep Learning 6 Act

7 PFN s Strategy of Deep Intelligence to IoT 1. Development of proprietary, state-of-the-art, flexible Deep Learning method 1. Deployment in diverse edge devices and network components to achieve Distributed Deep Intelligence 1. Integration of network and edge device control through a comprehensive Deep Learning management system 7

8 Realization of Deep Intelligence in IoT/IoE with a strong partnership with NTT, Cisco, Toyota 8

9 Edge Heavy Computing: Video Intelligence Box using GPU (Tegra K1) Feature Advanced algorithm: deep neural nets recognize video inside box All-in-one: Web-cam, cpu, gpu, wifi, power and streaming service Battery-powered: running up to hours without external power An example of advanced intelligence that works on IoT devices 9

10 Retail Product Using Deep Intelligence Video Analytics: Feedback closes the loop for greater customer understanding Customizable deep learning video analytics models Recognition of user-defined customer features Prediction of shoppers behavior Video ad selected based on recognized features and predicted customer behavior Personalized ad shown to shopper on video screen Was the ad effective? Deep learning model improves through observing customer response 10

11 Retail Intelligence Video Analysis Prototype at ITpro EXPO 2014 Demo included feature recognition, location detection, sending targeted ads, security features, and real-time learning Day two included customized feature recognition based on video feed from day one Dashboard snapshot illustrating visualization of the distribution of recognized features by location on floor plan of expo site 11

12 Retail Product Using Deep Learning Video Analytics: Product Features: Customizable recognition of customer attributes: Gender Age Clothing type or color Any other user-specified features Location tracking of individual customers Targeted actions based on customer location and recognized features Delivery of personalized ads, offers, or product information to displays or mobile devices Ability to close the loop and learn from customer response System automatically captures customer response and uses it to update its model in real-time for improved accuracy Complete surveillance and security suite 12

13 Collaborative Car to Car Intelligence: Smart Car Networks Integration of local knowledge Global analytics Model repository Intelligent V2X communication Collaborative understanding Model mixing and sharing Self driving technology Autonomous Real time Learning Multi-model recognition 13

14 Deep Intelligence for Automobiles and Smart Cities Self-driving car technology PFN began exclusive collaboration with Toyota Motor Corp in Oct for development of self-driving technology using Deep Learning Dash cam analytics Deep Learning can add meta-information to dash cam video streams to provide useful information for a variety of purpose, such as a safer driving. Inter-car distributed machine learning and V2V communication Connect Automobile to Smart City to provide integrated services Parking prediction Traffic control Energy control 14

15 Medical Science: Deep Learning application for Drug Discovery Hinton s group won the Kaggle competition to predict Drug Activity Multi-task Neural Networks for QSAR Predictions (GE Dahl, et al 2014) 15

16 Deep Learning Application for Drug Discovery PubChem Database Multi-task improved accuracy Chemical compound Assay Data 19 assays 2M substances Fingerprint + Activity Deep Neural Net 2 3hidden layers units Dropout Minibutch SGD 16 B 1 0 Active!! Active!! Inactive!! Prediction of Drug Activity multiple targets (Multi-task)

17 Distributed Deep Learning Architecture for Drug Discovery using Parallel Distillation and GPU Cluster ~10 Nodes PubChem Database Each Node optimizes with Hard target + Soft target Soft target (Dark Knowledge) Community Learning 2M Substances 19 Assays Soft target 17 Node - 3GPU K40-54GB memory

18 Result: Scalability Distributed Processing of Deep Learning using Parallel Distillation is successfully implemented and shows better scalability Scalability # of Nodes elapsed time node * time communicat ion time Elapsed Time # of Nodes 18

19 Result: Improved Accuracy using Community Learning Distributed Processing of Deep Learning using Parallel Distillation shows improved accuracy D Community Learning AUC values 19

20 Massive Distributed Deep Learning Architecture for Drug Discovery using Parallel Distillation and GPU Cluster >100 Nodes PubChem Database.... Soft target Community Learning 200M Substances 1M Assays.... Soft target 20 Node - 3GPU K40-54GB memory

21 Practical Applications for Drug Discovery Kinase and GPCR Deep Learning can predict Cross Reactivity, Side Effect, Toxicity as Multi-task. No structural information of target proteins is necessary Reduce R&D cost Drugs Assays 21

22 Deep Intelligence Application for Medical Science Genome Database Chemical compound Database Bio Assay Database Integration of multiple data type Community Learning Predictive Model Generalized deep learning model solves multiple tasks Personalized Medicine Drug Discovery Diagnosis

23 Deep Intelligence for IoT Edge Device Middle Network Cloud Autonomous and Real Time Integration of multiple data type Global analysis, data center Community Learning & sharing knowledge Deep Intelligence Entire Network is connected as Deep Neural Net Generalized deep learning model solves multiple tasks Healthcare Retail Automobile Smart city

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