Jeffrey Jensen, Arundo

1. HOUSTON │OSLO │ PALO ALTO
Social Convergence of Machine Learning in IIoT
IoT With the Best – 29 October 2016

2. Today’s Talk
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• Who am I? Who is Arundo? Why am I talking? Why this matters
to you?
• Machine learning – where we are in the space?
• What is “social convergence” anyway?
• Some examples.

3. Me.
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• Actually an antennas/controls person (Ph.D.
in electromagnetics/stochastic methods)
• Diverse software development experience
• Triathlete
• Previous employers:
• Intel
• Toshiba
• Siemens

4. Arundo.
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Not the invasive plant…

5. 5
Industrial experience All employees have a MSc or PhD
We have a unique diverse team

6. Locations and Notable Logos
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Three offices to meet global growing demand
of data science in oil & gas and marine
industries
Ongoing work with many clients, a couple I can
mention here…

7. A post data science, data science company
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In addition to building new algorithms, deploying models, and solving big data problems for
customers…
.. We are beginning to ask questions about how data science is being communicated and used
within companies in order to not only make an impact but also to scale the impact.
Therefore, from our learnings, we realize there are several challenges ahead with regard to
implementing data science solutions…
… because of the amount of people, machines, and data that the solution must impact and the
amount of coordination it will take across these aforementioned stakeholders
So – we do data science and have placed a good amount of focus on the “social convergence”
(including machines) of data science solutions for big data problems.

8. What are we seeing?

9. Music that makes you dumb…

10. Why are Facebook posts dropping?

11. Facebook can watch you fall in love…..

12. The anticipation of the industrial internet
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Expects the Global IIoT to
be $220Bn in 2020
Expects the Global IIoT to
be $14.4Tn by 2022

13. Collection of data – even in Iiot
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Machine Learning
Images from iconfinder.com and pixabay.com

14. Several groups and people with different objectives
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Machine Learning
Images from iconfinder.com

15. How do we connect every source to maximize impact for all?
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Machine
Learning
Machine Learning

16. Actions/decisions
Products for turning data into value
16
Data
Real-time failure
predictions and
performance
optimization
Real-time data
Historical asset
performance
Batch data
Annotations and
interaction across
assets and
companies
Meta data
LiveQ
Q
DeepQ SocialQ

17. Arundo DeepQ generates insight from historical data
17

18. Arundo SocialQ is data drive collaborative problem solving over the
cloud
18

19. An analysis and collaboration of people solving around data science
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20. Our approach is unique*
20
Enabling our customers to get control of all data across equipment and sensors,
on all assets - even industry wide
* Patent pending
1. System-wide and equipment
agnostic deployment
2. Enhanced predictability
through model-sharing*
3. Industrial network
infrastructure

21. Copyright © 2016. Arundo. All rights reserved