Applied Machine Learning:

Beyond the Hype

E360 Annual Conference • Atlanta, Ga. • April 11 and 12

John Wallace Ron Chapek

Director — Innovation, Retail Solutions Director of Product ManagementEmerson Emerson

Applied Machine Learning: Digital Transformation

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The profound and accelerating transformation of business

activities, processes, competencies and models to fully

leverage the changes and opportunities of digital,

data-driven technologies — including the application

of machine learning/artificial intelligence.

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Applied Machine Learning: What Is Different?

Sensors, switches, actuators, communication protocols,

cloud storage and other core components of IIoT are not new.

What is NEW are lower storage costs, more

intelligence/computing power, ubiquitous networking and

affordable “subscription” access to powerful analytics platforms.

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Applied Machine Learning: New Business Models

These technologies and the resultant (massive) increase in

available data they generate will enable entirely new

value-creation opportunities, business models and

revenue streams.

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Applied Machine Learning: Reality Check

Many companies are in “digital shock” and are struggling to

make the digital culture shift:

• Not started 37%

• Playing catch-up 24%

• On the adoption curve 14%

• Ahead of the adoption curve 25%

Applied Machine Learning: Focus on Value Generation

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Think big but start small, focusing on a specific task with a compelling business (value) proposition.

• Predictive asset management

• Asset life cycle management• Maintenance cost optimization

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How do you plan to implement

AI technology in your

enterprise?

Applied Machine Learning: Question

What Is Machine Learning?

• Machine learning refers to being able to provide a computer with the ability to learn without programming.

• Machine learning is NOT Big Data, IoT, data analytics, dashboards, augmented (or virtual) reality, etc.

• There’s a lot of math, but no “magic”.

• It’s been around for awhile (1959) but recent events (i.e., Cloud processing, high-speed computer processors [CPUs], cost-effective data storage, etc.) have accelerated development and enabled real-world applications.

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https://en.wikipedia.org/wiki/Artificial_intelligence

https://en.wikipedia.org/wiki/Machine_learning

Some Real-World, Everyday Examples; Machine Learning Is All Around Us

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https://www.wired.com/2016/01/the-rise-of-the-artificially-intelligent-hedge-fund/

How do they know what I am searching for?

How do they know what I am saying?

How do they know what to translate?

How do they know where to invest?

https://www.coursera.org/certificate/machine-learning

It’s the machine!

(and a

cloud)

(and connectivity)

thyssenkrupp Utilizing Machine Learning (Predictive Maintenance) to

Drive Optimization in Elevator Maintenance

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https://max.thyssenkrupp-elevator.com/en/

Development championed

by thyssenkrupp

Innovation Center located

at Tech Square

How Does Machine Learning Work?

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It Starts With Inputs and Resulting Actions (Data).

Process or

FunctionInputs

Results

or

actions

The Problem

Given a set of inputs, can we

predict with sufficient accuracy

a result or action taken as a

result of the inputs?

Machine

Learning

Prediction

Model

Inputs

What We Are Trying to Do

Note that Inputs can be anything (i.e., human

language, sensor data, stock market data,

etc.) and Results can be either human

(i.e., translate English to French) or machine

(i.e., predict a failure will occur).

Results

or

actions

Simplified Machine Learning Process;

Creating a Model Is a Data-Intensive, Iterative Process

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Create training

data set(s) and

validation data

set(s) from inputs

and results.

Analyze and

understand what

you are trying

to predict.

Use training data

to evaluate

different models’

performance and

accuracy.

Select initial

model based on

training data

performance.

Input 1 Input 2 Input 3 Result

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Input 1 Input 2 Input 3 Result

I11 I21 I31 R1

I12 I22 I32 R2

I13 I23 I33 R3

I14 I24 I34 R4

I15 I25 I35 R5

I16 I26 I36 R6

I17 I27 I37 R7

I18 I28 I38 R8

I19 I29 I39 R9

Input 1 Input 2 Input 3 Result

I11 I21 I31 R1

I12 I22 I32 R2

I13 I23 I33 R3

I14 I24 I34 R4

I15 I25 I35 R5

I16 I26 I36 R6

I17 I27 I37 R7

I18 I28 I38 R8

I19 I29 I39 R9

Utilize new model

to predict results

based on new

inputs.

Use validation

data to check

performance.

1 2 3 4 5 6

A

BA

B

Other Examples: Machine Learning RTU Management (“Overlapping RTU’s”)

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Sales Floor Area RTU’s

RTU’s operating independently

generate demand “peaks”

which impact utility bills.

Supervisory Control App

learns (and predicts) response

of space to RTU state and

coordinates control.

Coordination of RTUs

facilitates comfort and

reduces demand peaks.

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Applying Machine Learning to Refrigeration Systems Data

Cloud-Based

Machine

Learning

Algorithms

Sensors and

Other Data

Operational Insights

Machine Learning Algorithm Predicts Refrigerant Leak

Additional Data Can Predict System Health and Performance.

Dashboard

Delivers

Platforms Are Tools, but Not a Solution; Creating a Solution Requires

Domain Knowledge and a Keen Understanding of the Problem

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Machine Learning Platforms Make the Math Easier, but Still Need Domain Experts as Well as Other Roles to Create a Solution That Drives Value.

The Tool

Domain Expert

The Solution

Microsoft

Azure

IBM

Watson

Amazon

AWS

Google

Data

ScientistsCoders

Domain

Experts

Lots of very

good machine

learning “platforms”

available today

Solution requires the

right platform and a

keen understanding

of the problem

Keys to a Successful Machine Learning Deployment

• Lots of confusion, activity and buzzwords

• Domain knowledge key to understanding the problem to be solved and creating a solution

• Lots of data critical to creating a good model

– Models are only as good as the data used to create them

• Analyze data “inventory” to understand what is available and ensure key data is being collected

• A “platform” is not enough (but can help with the math)

• Start small, but look for something with impact

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Questions?

DISCLAIMER

Although all statements and information contained herein are believed to be accurate and reliable, they are presented without guarantee or warranty of any kind, expressed or

implied. Information provided herein does not relieve the user from the responsibility of carrying out its own tests and experiments, and the user assumes all risks and liability for

use of the information and results obtained. Statements or suggestions concerning the use of materials and processes are made without representation or warranty that any such

use is free of patent infringement and are not recommendations to infringe on any patents. The user should not assume that all toxicity data and safety measures are indicated

herein or that other measures may not be required.

Thank You!

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