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©2018 VMware, Inc.

Chief Research Officer

June 27th, 2018

Choose Optimism

David Tennenhouse

© 2017 VMware Inc. All rights reserved.

2 ©2018 VMware, Inc.

Disclaimer

 This presentation may contain product features that are currently under development.

 This overview of new technology represents no commitment from VMware to deliver these features in any generally available product.

 Features are subject to change, and must not be included in contracts, purchase orders, or sales agreements of any kind.

 Technical feasibility and demand will affect final delivery.

 Pricing and packaging for any new technologies or features discussed or presented have not been determined.


We are suffering just now from a bad attack of

economic pessimism…




The Pessimists



The Pessimists

1ABC News May 22, 2018 2Bloomberg, June 18, 2018

“Rise of the machines: has technology evolved beyond our control?” 3

3The Guardian, June 15, 2018

2 The AI Robots That Want Your Job

Robots creating a wages and

employment 'death spiral' warns IMF

1


John Maynard Keynes Economic Possibilities for Our Grandchildren (1930)

We are suffering just now from a bad attack of economic pessimism…

...I believe that this is a wildly mistaken interpretation of what is happening to us.



Innovation w Abundance w Jobs


PRICE AND SALES OF CARS 1908-1923 COTTON PROCESSING COSTS AND SALES 1784-1820

Spinning Welfare: The Gains from Process Innovation in Cotton and Car Production, Tim Leunig and Joachim Voth, May 2011.


The Analysts


Jobs Lost, Jobs Gained: Workforce Transitions in a Time of Automation. McKinsey Global Institute, December 2017

Personal Computers

Total US Jobs created (000s)

DIRECT

151

INDIRECT

524 ENABLED

2,904

UTILIZERS

12,176

Even with automation, the demand

for work and workers could increase

as economies grow

“Automation will bring big shifts to the

world of work, as AI and robotics

change or replace some jobs, while

other are created.”

9 ©2018 VMware, Inc. ©2018 VMware, Inc.

GREECE SPAIN UK USA CHINA INDIA

Source: Eurostat. “Unemployment Statistics." March, 2017.

9

0%

10%

20%

30%

40%

50%

60%

70%

2010 2015 2020 2025 2030 2035 2040 2045 2050

OLD AGE DEPENDENCY RATIO

0%

10%

20%

30%

40%

50%

60%

70%

2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016

YOUTH UNEMPLOYMENT RATE

What are the Important / Worthy Jobs for Human Beings to Focus On in the Future?

Pensionsataglance2015-©OECD2015


Demographics and AI

“Technology Will Redefine Not Reduce Manufacturing Jobs”

1Bloomberg, June 7, 2018 2MH&L, June 13, 2018 3MWorld Health Organization, November 11, 2013

“Global health workforce

shortage to reach 12.9 million in

coming decades” 3

4

“Firms Look to Robots, AI to

Plug U.K. Skilled Worker

Shortages” 2


The Analysts (2)

Source: Labor 2030: The Collision of Demographics, Automation and Inequality, February 07, 2018, Bain report

“The primary macroeconomic consequence of higher inequality is to constrain growth…”

“Industries with low productivity growth are increasing employment, slowing total productivity growth”

“As income shifts towards the top end of the spectrum, that group generally saves more rather than consuming more.”

10


Search Matching People with Opportunity

Geek Challenges

Wealth Circulation

Reinventing •  Education •  Healthcare •  Government


If the US government can’t explain

AI’s decisions it shouldn’t use it3

AI: The Pessimists

U.S. and Chinese Companies

Race to Dominate AI1

For AI to thrive, it must explain itself If it cannot, who will trust it?2

1The Wall Street Journal, January 18, 2018 2The Economist, February, 15, 2018 3Quartz Media, October 23, 2017

12


“What about the 99%?”



Hyper-Scale May Not be “Right-Sized” for the 99%

The Human

Element

Enterprises have many problems to solve…but most of them will not

require hyper-scaler technologies

Machine Learning

Model



DAWN: Machine Learning for All

Empower non-ML experts to conduct production quality domain-specific analytics

Automated ML workflow: From data acquisition to production

Peter Bailis Chris Re’ Kunle Olukotun Matei Zaharia



AI for All – Peter Bailis, Stanford


DAWN: Machine Learning for All

Empower non-ML experts to conduct production quality domain-specific analytics

Automated ML workflow: From data acquisition to production

Peter Bailis Chris Re’ Kunle Olukotun Matei Zaharia



RISE Lab: Real-time Intelligent Secure Execution


Enable real-time, low-latency decisions on live data with strong security. Software stack •  The next generation of big data analytics: •  Real-time applications •  Low-latency requirements •  End-to-end focus on ML pipeline •  Inclusion of security (e.g. computation on encrypted data)

Ion Stoica

Joey Gonzalez

Michael Jordan

Raluca Ada Popa

Joe Hellerstein


Hillview Research Prototype


Never compute something you cannot display

Distributed – but always compute

close to the data Data-source agnostic

State-of-the-art sampling /sketching

algorithms

Interactive Response

(Sub-second)

Hillview: Key Elements

23


Generic vs Differentiated AI/ML

 Generic §  Speech, text, image recognition – almost all about the scale of the training sets §  Consumed aaS from hyperscalers(?)

Differentiated §  Insights that depend on an enterprise’s unique data and domain expertise §  Key source of competitive advantage

Differentiated ML: Anomaly Detection

 VMware collects a large amount of multi-dimensional time series data •  Metrics about apps, VMs, hosts, devices •  Many metrics, frequent measurements per metric.

 Finding ‘anomalies’ in the data is a common requirement •  Create alerts; root cause analysis; intrusion detection

A step towards prediction…

Naïve Approach: One metric

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

Series 1

Alarm when a time series is out of range or

frequency

Two metrics

0

1

2

3

4

5

6

0 5 10 15 20 25 30 35 40

X-Value

Each dimension seems normal. Anomaly is in the relation between

the two values. Harder to catch.

Y-Va

lue

Tens of dimensions?

 Current Gold Standard: Principal Component Analysis (PCA)

  Generates anomaly scores for multi-dimensional data

ChooseaPrincipalSubspace•  Lowdimensional

Projec>onDistance•  Normaltoplane•  Measuresdistancefromthe

low-dimensionalsubspace

LeverageScore•  Measureslikelihoodofthe

projec>ononthelow-dimensionalspace.

PCA based scores

PrincipalSubspace

Gaussianonsubspace

PCA based anomaly scores

Pros:• Rigorousmathema>caljus>fica>on.• Clearexplana>onforwhypoint𝑥 islabelledanomalous:

-  𝑥isunusuallyfarfromthelowdimensionalprincipalsubspace.-  𝑥isunusualrela>vetothedistribu>ononthesubspace.

Cons:• PCAscalesquadra>callywithdimensionality.• Problema>cforhigh-dimensionaldata(>me-series,genomics,etc.)

Ten Thousand metrics / dimensions?

 Our goal: Find anomalies in high dimensionality (≈10↑4 ) datasets. •  Example: Many VMs simultaneously reaching peak load (each VM looks normal)

Challenge: PCA scales quadratically •  Computing all pairwise correlations is prohibitive ( 10↑8  pairs).

 Key idea: compression. •  Analyze composite metrics, which are random combinations of base metrics. •  Outliers in base metrics remain outliers even in the composite space.

Quick and Easy PCA?

Q:CanwehavethebenefitsofPCA-basedanomalyscoreswithoutfull-blownPCAcomputa>on?A:Yes.UsingtoolsfromMatrixSketching[Gopalan-Sharan-Wieder’18]•  Low-dimensionalencodingsofpoints

•  muchcheapertocomputethanPCA.

•  Sketchespreservebasicgeometricstructure(eg.distances).•  Theyalsopreservehigher-ordergeometricstructuresuchasclosenesstoalow-dimensionalsubspace,ordistancefromit.

•  2Xto6Xspeedupands>llgetrigorousguarantees!

MNIST dataset

•  Sketchdownfrom784to100dimensions(recallPCAscalesquadra>cally).

•  Top5anomalouspointsfromeachclassbyProjec>onDistance

MNIST dataset


•  Top5normalpointsfromeachclassbyProjec>onDistance

MNIST dataset


•  Top5anomalouspointsfromeachclassbyLeverageScores.

MNIST dataset


•  Top5normalpointsfromeachclassbyLeverageScores.

Some Experimental Results

DataSet DataDimension SketchDimension Run1meforPCA Run1meforSketch+PCA

P53Mutants 5409 200 29.2 7.5

Dorothea 100000 200 17.7 2.58

RCV1 47236 500 39.6 20.8

StandarddatasetsfromtheUCIMachineLearningRepository.


Explainable AI: Enterprise Strength Machine Learning



Explainable AI – David Gunning, DARPA


Explainable AI Challenges

• Constraining deep learning • Better understanding of deep learning • Alternatives to deep learning – for the 99%


Geek Challenges

  Democratize the use of AI/ML technologies

  Greater focus on differentiated AI/ML

  Increase the transparency of ML decision-making


John Maynard Keynes Economic Possibilities for Our Grandchildren (1930)

We are suffering just now from a bad attack of economic pessimism…

...I believe that this is a wildly mistaken interpretation of what is happening to us.




The Optimists

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