Social Convergence of Machine Learning in IIoT - Jeffrey Jensen

HOUSTON │OSLO │ PALO ALTO

Social Convergence of Machine Learning in IIoT

IoT With the Best – 29 October 2016

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.

Me.


• Actually an antennas/controls person (Ph.D. in electromagnetics/stochastic methods)

• Diverse software development experience• Triathlete• Previous employers:

• Intel• Toshiba• Siemens

Arundo.


Not the invasive plant…

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Industrial experience All employees have a MSc or PhD

We have a unique diverse team

Locations and Notable Logos


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…

A post data science, data science company


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.

What are we seeing?

Music that makes you dumb…

Why are Facebook posts dropping?

Facebook can watch you fall in love…..

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

Collection of data – even in Iiot


Machine Learning

Images from iconfinder.com and pixabay.com

Several groups and people with different objectives


Machine Learning

Images from iconfinder.com

How do we connect every source to maximize impact for all?


Machine Learning

Machine Learning

Actions/decisions

Products for turning data into value

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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

Arundo DeepQ generates insight from historical data

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Arundo SocialQ is data drive collaborative problem solving over the cloud

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An analysis and collaboration of people solving around data science


Our approach is unique*

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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

Copyright © 2016. Arundo. All rights reserved

Social Convergence of Machine Learning in IIoT - Jeffrey Jensen

Technology