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What Artificial Intelligence Means to the Internet of Things

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Presentation on theme: "What Artificial Intelligence Means to the Internet of Things"— Presentation transcript:

1 What Artificial Intelligence Means to the Internet of Things
Justin Grammens Cofounder, Lab 651

2 Who is Justin Grammens? Background Serial Entrepreneur
Cofounder of Lab 651 20+ years Architecting & Engineering Software Solutions MS in Software Engineering from University of Saint Thomas Cofounder of IoT Fuse 501 (c)(3) non-profit to advance the Internet of Things in Minnesota and beyond Owner of IoT Weekly News Adjunct Professor at the University of Saint Thomas teaching an advanced graduate level course on IoT

3 Who is Lab 651? Decades of Experience Building “Smart” Products
Physical Product Design User Experience & Feel Mechanical Engineering Electrical Hardware & Firmware Connectivity / Wireless Design Power Optimization Electrical Engineering Mobile Applications Cloud / Web Applications Machine Learning / AI Software Engineering & Data Science

4 S4, Solution Specialist Sales Summit
© 2014 Microsoft Corporation. All rights reserved. MICROSOFT MAKES NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION IN THIS PRESENTATION. S4, Solution Specialist Sales Summit Where Did IoT Come From? 6/13/ :13 AM Term was coined key Kevin Ashton at MIT in 1999 People had connected and controlled machines for decades 1977 – Smart electric meter created 1980’s - Members of Carnegie-Mellon connected Coke Machine 1994 – Xerox PARC demonstrates wearable device recording interactions 1995 – Siemens demonstrates connected vehicle telematics & monitoring Really started to gain adoption in 2013 Kevin recently has started calling it: “The Internet For Things” Added cow icon

5 Google Trends for IoT Trends
© 2014 Microsoft Corporation. All rights reserved. MICROSOFT MAKES NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION IN THIS PRESENTATION. S4, Solution Specialist Sales Summit Google Trends for IoT 6/13/ :13 AM Trends Added cow icon

6 Google Trends for IoT & Big Data Trends
© 2014 Microsoft Corporation. All rights reserved. MICROSOFT MAKES NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION IN THIS PRESENTATION. S4, Solution Specialist Sales Summit Google Trends for IoT & Big Data 6/13/ :13 AM Trends Added cow icon

7 Google Trends for IoT, Big Data & ML Trends
© 2014 Microsoft Corporation. All rights reserved. MICROSOFT MAKES NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION IN THIS PRESENTATION. S4, Solution Specialist Sales Summit Google Trends for IoT, Big Data & ML 6/13/ :13 AM Trends Added cow icon

8 S4, Solution Specialist Sales Summit
© 2014 Microsoft Corporation. All rights reserved. MICROSOFT MAKES NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION IN THIS PRESENTATION. S4, Solution Specialist Sales Summit How To Think of the IoT 6/13/ :13 AM Companies need to move beyond the technology of IoT to applying an industry lens and relevant use cases IoT should be thought of in conjunction with other technologies Machine Learning / Artificial Intelligence Augmented Reality / Virtual Reality Data Visualization & Analytics Blockchain All of the above Added cow icon

9 What is AI

10 What is AI

11 What is AI & IoT What makes AI different when talking about IoT is that it can reach out and touch you.

12 Where IoT and AI is Today
Still in the early stage of acceptance Many companies don’t see the value of the data Aren’t aware of the possible sensor technologies Think they need to hire a team of data scientists Have not been through physical product development Don’t know where to start - technology alphabet soup Too much focus on the “what”, not enough on the “why” Stuck on data visualization and not on the AI that drives actions ( Indy Car Racing example ) To apply AI to data from IoT devices we need a lot of data Cellular connectivity costs are coming down Pushing more intelligence to the edge with gateways

13 Industrial Scenario(s)
You own a manufacturing company Biggest source of lost time and cost is machine failure – thousands of dollars per hour. What if you could track machine behavior? Predict movements that are indicators of failure Order parts for maintenance during scheduled downtimes Prevent waste by only doing maintenance when it’s need You are a machine manufacturer Currently you only receive revenue when the machine is purchased or need repair (low margin) Offer a service to monitor and prevent downtime with your customer (high margin) Better quality of service and experience by the customer using your product

14 Manufacturing Process
Spikes show potential failure before downtime occurs and poor quality in production

15 Quality & AI - Weld Splatter
•Avoid quality problems due to spatters from resistance welding by identifying previously unknown patterns in the process data •Learn how to detect and avoid splatters before they occur •Generate knowledge by data mining algorithms to minimize spatters •The weld controller can react – for example, by reducing the power for every individual welding process to avoid spatters

16 Consumer Usage Patterns
Usage of Craftsman and Kenmore Products by Consumers

17 Intelligent Consumer Products
Appliance monitoring Proactive maintenance (ex. before refrigerator compressor failure) Order replacement parts & alert service technicians Real-time usage and performance of models Energy savings Run electrical components at time of less energy cost Manufacturer performance Which manufacturers are performing better than others

18 Intelligent Hydraulic Hoses
© 2014 Microsoft Corporation. All rights reserved. MICROSOFT MAKES NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION IN THIS PRESENTATION. S4, Solution Specialist Sales Summit Intelligent Hydraulic Hoses 6/13/ :13 AM Eaton has been manufacturing hoses for a hundred years Process was to replace the hose when they failed What if they could predict before the hose broke? Customer avoids downtime Customer avoids environmental mess of hydraulic fluid Customer avoids potential injury to employee Eaton now could sell less hoses ( less margin ) However now has higher margin selling a monitoring service Added cow icon

19 Intelligent Classrooms
© 2014 Microsoft Corporation. All rights reserved. MICROSOFT MAKES NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION IN THIS PRESENTATION. S4, Solution Specialist Sales Summit Intelligent Classrooms 6/13/ :13 AM Wildflower Montessori Schools wanted to improve outcomes in the classroom What if they could monitor lessons, students and teachers relative location to create a super teacher? Team at Lab 651 built Intelligent lesson trays Sensor for each student’s slippers Sensor for the teacher’s pocket Added cow icon

20 Intelligent Waste Water Treatment Cost of Clean H2O
It takes 1-2 KW to create 264 gallons of water from wastewater (higher in states like CA) One NYC WWTP treats 1.4B gallons a day (5.3M KWh) Almost $1M a day in energy costs $690,000 spent on pumping and aeration Pumps being run at 100% capacity due to the best possible but unknown output

21 Intelligent Waste Water Treatment
21,200 WWTP in the US Each one averages 760,000 gallons a day Average of 4,318 kWh / day / facility $10.9M / day we spend on WWT 69% of energy costs are spent on pumping and aeration = $7.5M a day or $2.8B a year Cost $1, / IoT sensor At 10% energy reduction - upside of $260M a year IoT Data w/ Artificial Intelligence

22 “What AI Means to the IoT “
Due to the massive datasets required AI is a requirement for IoT to provide value

23 Thank You! Justin Grammens Cofounder, Lab 651 justin@lab651.com
6/13/2018 © 2012 Microsoft Corporation. All rights reserved. Microsoft, Windows, and other product names are or may be registered trademarks and/or trademarks in the U.S. and/or other countries. The information herein is for informational purposes only and represents the current view of Microsoft Corporation as of the date of this presentation. Because Microsoft must respond to changing market conditions, it should not be interpreted to be a commitment on the part of Microsoft, and Microsoft cannot guarantee the accuracy of any information provided after the date of this presentation. MICROSOFT MAKES NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION IN THIS PRESENTATION. Thank You! Justin Grammens Cofounder, Lab 651


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