Innovate for the Future

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Transcription:

Innovate for the Future A Historical Perspective of the Future Today Patricia Florissi, Ph. D. Global CTO for Sales, Dell EMC Dell Technologies Forum, August 2018

Life moves pretty fast. If you don t stop and look around once in a while, you could miss it. Ferris Bueller, 1986 2

Lost in thoughts, deep thoughts 3

Universe: all of space and time 4

Solar system 5

The sun is the main source of energy Sun Venus Mars Jupiter Neptune Mercury Saturn Earth Uranus 6

With main forces at play Electromagnetic Force Gravitational Force Sun Venus Mars Jupiter Strong Interaction Force Neptune Mercury Earth Dark Sector Force Weak Interaction Force Saturn Uranus 7

Dawn of the new era 8

Dawn of the digital era 9

Our universe Digital universe 10

Digital universe has an analytics galaxy 11

Data system 12

Digital Universe, Analytics Galaxy, Data System Data Search Management Governance Sustainability Protection Security Monetization Privacy 13

Technology Forces DATA Governance Management Security Monetization 14

Exploring the AI/ML/DL Technology Force AI/ML/DL Force DATA Governance Management Security Monetization 15

Artificial intelligence evolution since the 1950s DEEP LEARNING Example: MLPs Example: shallow autoencoders REPRESENTATION LEARNING Example: logistic regression Example: knowledge bases MACHINE LEARNING AI 16

In the early days In the now days Intellectually difficult for human being but relatively straight forward for computers Intellectually easy and intuitive for humans but hard to be described formally for computers 17 Deep Learning, Ian Goodfellow, Yoshua Bengio, and Aaron Courville

Classification Training Input Data ML/DL Algorithms Training Output Data New Input Data FACE NO Trained Model Output Data 18 Output Data

TEST ACCURACY 1.00 0.95 0.90 0.85 0.80 Memory-based Winnow 0.75 Perception Naïve bayes 0.70 0.1 1 10 100 1000 MILLIONS OF WORDS The unreasonable effectiveness of data 19 19

Learning algorithms Liner regression Logistic regression Polynomial regression K-nearest neighbors Anomaly detection Support vector machines Association rule learning Clustering K-means Hierarchical cluster analysis (HCA) Expectation maximization Decision trees Random forests Deep belief network (DBNs) Visualization and dimensionality reduction Principal component analysis (PCA) Kernel PCA Locally-linear embedding (LLE) T-distributed stochastic neighbor embedding (T-SNE) 20 Ensemble methods Association rule learning Apriori Eclat

Common denominator They perform better The more data They analyze 21

Lost in thoughts, deep thoughts in AI/ML/DL 22

Deep learning marks the beginning of fundamental Algorithmic Innovation in the Analytics Galaxy 23

Simple algorithms with lots of data will outperform sophisticated algorithms with less data 24

Off we go, to explore data sources AI/ML/DL Force DATA Governance Management Security Monetization 25

Exploring the IoT force IoT Force AI/ML/DL Force DATA Governance Management Security Monetization 26

Lost in thoughts, deep thoughts in IoT we must first define what a thing is... 27

The thing in the IoT Computer ability Context ability Connectivity ability 28

Things come in many, estimate to be 1 trillion in 2100 29

Things sense 30

Things compute 31

Things connect 32

Things connect and morph into systems Thing system A Thing system B 33

Things connect and morph into systems of systems Thing system A Thing system A Thing system B Thing system B Thing system A 34 Thing system A Thing system B Thing system B

Your umbrella 35

Digital tantrum 36

Your umbrella connected to your insurance 37

ACE vehicle Automated or Autonomous Connected Electric 38

ACE vehicle killer app that made IoT real VISION 360 view 39

IoT apps attempt to augment human s experience VISION 40

360 view falls short as an experience VISION 41

360 perception of the physical world VISION TASTE HEARING TOUCH SMELL 42

Amazing variety All types of situation aware edge technologies PADS CAMERAS ACCELEROMETERS MOTES SENSORS DOTS INCLINOMETERS CHIPS ACTUATORS SMART DUST STICKERS MAGNOMETERS MICRO CONTROLLERS CODES GPS GYROSCOPE TAGS RANGEFINDERS LED LABELS SPECKLES CARDS DISAPPEARING IMPLANTABLES 43

360 perception as a two way channel Perceive Communicate 44

Lost in thoughts, deep thoughts in the Internet of Things 45

IoT as a major Data Source in the volume of the data but most importantly in the diversity of the data 46

IoT as a major force to create a Digital Image of the Physical World 47

IoT as a major force to give back to Physical World insight from our Digital Image 48

Off we go, to explore the digital-physical bridge IoT Force AI/ML/DL Force DATA Governance Management Security Monetization 49

Exploring the AR/VR force IoT Force AI/ML/DL Force DATA Governance Management VR/AR Force Security Monetization 50

NASA has been using VR for the past 20 years to train astronauts. U.S. military uses VR for training and flight simulations. 1987: VR term is born 51

2016: year one of VR mainstream 52

1992: AR term is born 53

In September 2017, the app was downloaded 752 million times with $1.2 billon in revenue 2016: AR became popular 54

The immersive experience 55

The continuum of immersion experiences 56

Head Mounted Display (HMD) 57

360-video Augmented reality Virtual reality Mixed reality Holograms The interactive spectrum 58

Contextualize reality Full immersion Experiential presence Influence and interact with the surrounds Why xreality matters 59

Gaming Social Interactions Entertainment Business Operations Workforce xreality fuels the next generation of many things 60

Lost in thoughts, deep thoughts in the Augmented Reality and Virtual Reality 61

AR/VR enables presence in two universes Our Universe and Digital Universe 62

Off we go, to explore the other inhabitants of the Digital Universe IoT Force AI/ML/DL Force DATA Governance Management VR/AR Force Security Monetization 63

Exploring the Robotics force IoT Force AI/ML/DL Force Robotics Force DATA Governance Management VR/AR Force Security Monetization 64

What are robotics? 1) System of system of things Each system may be very different from others Some may be very sensitive to the context around them and depend on that context 2) Sophisticated AI systems 3) Locomotion or mobility 65

Evolution on robotics Source: Sony Humongous Rigid Context unaware Unintelligent INDUSTRIAL Fixed activities Sectioned off Hardware level intelligence Incapable of learning All sizes Flexible COLLABORATIVE AND SOCIAL Context aware AI enabled Adaptive activities Interwoven Software level intelligence Capable of learning They are capable of sensing their environment, comprehend, act and learn. 66

The up side Repetitive, monotonous tasks at large scale Processing humongous datasets 67

What is left for homo sapiens to do? Resolving complex, outlier cases Making executive decisions Advancing technology to make better robots 68

Lost in thoughts, deep thoughts in Robotics 69

It has been said that robots have NOT been replacing humans. 70

Instead... 71

Humans have been filling in for robots until... they became viable... 72

Robotics will forever change Human-Machine Partnership 73

Robotics demand all previous forces and Massive Amount of Processing Power 74

Off we go, to explore the processing force behind IoT Force AI/ML/DL Force Robotics Force DATA Governance Management VR/AR Force Security Monetization 75

Exploring the Accelerators force IoT Force AI/ML/DL Force Robotics Force DATA Management Accelerators Force Governance VR/AR Force Security Monetization 76

CPU 77

360-video Augmented reality Virtual reality Mixed reality Holograms The interactive spectrum 78

GPU 79

Linear algebra is cool again: A = xb MODEL TRAINING TRAINING DATA Testing Input Data ML/DL Algorithms COLLECTED SAMPLE DATA Testing Output Data Trained Model Feedback loop until the quality is good TESTING DATA Testing Input Data Output Data MODEL TESTING 80 Testing Output Data Quality Comparison

FPGA / GPU / TPU 81

Lost in thoughts, deep thoughts in Accelerators 82

Democratization of cores Processing Power Is Everywhere 83

Off we go, to explore the continuum IoT Force AI/ML/DL Force Robotics Force DATA Management Accelerators Force Governance VR/AR Force Security Monetization 84

Exploring the Continuum force IoT Force AI/ML/DL Force Robotics Force DATA Management Accelerators Force Governance VR/AR Force Security Monetization Edge- Core- Cloud Force 85

CENTRALIZED MAINFRAME MINICOMPUTER CLOUD 86

The perfect data in-mobility storm IoT effect Regulatory compliance Multi-Cloud Bandwidth constraints Traditioal Data Systems Security 87

DISTRIBUTED INTERNET CLIENT SERVER IOT/EDGE 88

Computing and connectivity have gone wild 89

Edge-core/fog-cloud continuum MIST CORE EDGE EDGE FOG MIST MIST CORE FOG CLOUD DATA CENTERS MIST MIST FOG EDGE EDGE MIST MIST FOG MIST EDGE 90 EDGE

Local normal, global outlier MIST CORE EDGE EDGE FOG MIST MIST CORE FOG CLOUD DATA CENTERS MIST MIST FOG EDGE EDGE MIST MIST FOG MIST EDGE 91 EDGE

Theano Golden mean The ideal moderate position between two extremes 92

THE GOLDEN MEAN 93

Data distributed and stranded MIST CORE EDGE EDGE FOG MIST MIST CORE FOG CLOUD DATA CENTERS MIST MIST FOG EDGE EDGE MIST MIST FOG MIST EDGE 94 EDGE

Analytics in place MIST CORE EDGE EDGE FOG MIST MIST CORE FOG CLOUD DATA CENTERS MIST MIST FOG EDGE EDGE MIST MIST FOG MIST EDGE 95 EDGE

Virtual data and analytics fabric 96

Federated analytics 97

Lost in thoughts, deep thoughts in Continuum 98

Different modes of computing Centralized Distributed Federated 99

As the processing sprawl Brings Along Decentralization of Power 100

Off we go, to explore the blockchain force IoT Force AI/ML/DL Force Robotics Force DATA Management Accelerators Force Governance Monetization Edge- Core- Cloud Force VR/AR Force Security 101

Exploring the Blockchain force IoT Force AI/ML/DL Force Robotics Force DATA Management Accelerators Force Governance Monetization Edge- Core- Cloud Force VR/AR Force Blockchain Force Security 102

Lost in thoughts, deep thoughts in the Future 103

Four transformations 105

Digital Transformation IT Transformation Security Transformation Workforce Transformation 106

Some final, final thoughts 107

Sebastiao Salgado The salt of the earth, 2014 108

Take picture \ capture a moment or document history 109

photo graph \ draw or write with light 110

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112

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115

techno graph? 116

techno graph \ draw or write with technology 117

digital transformation? 118

digital transformation \ technograph the way we work, live and think in new and innovative ways 119

Some people make it happen Some people watch it happen Some people do not know what happened 120

Dawn of a new era 121

Digital Universe, Analytics Galaxy, Data System IoT Force AI/ML/DL Force Robotics Force DATA Management Accelerators Force Governance 122 Monetization Edge- Core- Cloud Force VR/AR Force 5G Force Blockchain Force Quantum Computing Force Security

In the dawn of this new era... are you going to create a digital image of your today? 123

In the dawn of this new era... or are you going to become an explorer of the digital universe? 124

An explorer to re-imagine and technograph our future 125

Let the Transformations Begin 126

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For more information: carol.hildebrandt@dell.com Dell EMC One Changi Business Park, Singapore 129