KNOWLEDGESTREAM AT-A-GLANCE

The Future of Industrial Manufacturing

ABSTRACT

The future of industrial manufacturing

PARTICIPANTS

Patrice L. Tiolet, INPG, MBA, CPSM
Entrepreneurship projects at (independent)
Lisa Mitchell MApp Stats MBA FAICD
Internationally Exp Director I Senior Exec I Trusted Advisor I Value Chain Transformation I Performance Imp Specialist
Cory Nation
Capability Manager, Innovation & Technology at Rolls-Royce
Thompson Mackey, ARM
Risk Management Consultant at EPIC ➟ Advising World's Leading Companies on Insurance + Due Diligence + Compliance + Risk
Adam Malofsky, PhD
Elemence - Help, Listen & Learn - $2.5B products sold/yr - $70mm+ raised - Collaborator, Coach, Innovator & Advisor
Evans Iyamu, MBA
Technology Enthusiast | Product Owner | Corporate Finance
Donnie H. Brooks
Owner metric design LAB
Mario Orozco
VP Strategy & Business Development, Investor, Board Member, Entrepreneur; former Management Consultant at Bain & Company
Ian Gibson
Professor at University of Twente
Ufuk Kivrak
Managing Director
Arsalan Minhas
Director Solution Consulting at OpenText
Michael A. Campagna, CPIM
Chief Marketing Officer at Mimo Monitors
Fanish Shukla
Lead CIO, Enterprise Architect, Innovator, Founder, Speaker and DeepListener
Srikant Menon
Director of IOT, Digital Transformation, ML/AI, Design Enthusiast, Member of Leader Excellence at Harvard Square
Mani V G S
Founder, The Chainworks

OBJECTIVES

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4. :

100% Complete
Start Date: Sep 28, 2017
End Date: May 21, 2018
1182

CONTRIBUTIONS

ACTIVITY

221 Days

13 Themes

22 Contributors

753 Posts

429 Comments

183 Followers

OUTPUTS

1 Video

THEME #1

The Changing Role of Manufacturing

THEME #2

Internet of Things (IoT) and the Evolution of Data Collection and Monitoring

THEME #3

Connectivity and Security

THEME #4

R&D-Intensive Industries within Industrial Manufacturing

THEME #5

Regional Processing and the Future of Industrial Manufacturing

THEME #6

Labor Intensive Trades and the Future of Industrial Manufacturing

THEME #7

Consolidating the Theme: Future Drivers

THEME #8

Artificial Intelligence and Machine Learning

THEME #9

Automation and Customization

THEME #10

Industrial Manufacturing Disruptors

THEME #11

Training and Education

THEME #12

AI/ML Specific Theme - Tagging

THEME SUMMARY

AI/ML depends on training and tagging to work well but can be data-heavy and time-consuming. We need to improve the current methods and expectations for training and tagging.

  • Aggregation of group thinking (i.e., improving collaboration)
  • Automated content classification into categories to reduce the human aspect
  • Tagging at the source of data creation (rather than after the fact)
  • Improved classification through data aggregation and computer supervision

Content classification can speed up the data tagging process.

Content classification is an intelligent way of classifying content into categories. And, using machine learning to automate these tasks, just makes the whole process super-fast and efficient

I’d love to see more AI(machine learning) actually being used to do the heavy lifting and actually classify the data then analyse it. (The example that comes to mind here is Watson being used to classify killer skin cancers from harmless ones whilst the patient waits at the doctors(GP not specialists) surgery.

Creating tags early can improve accuracy and efficiency.

Once the data is generated, trying to tag it afterwards is a very error-prone and inefficient process.

Accuracy of AI and ML algorithms depends on Data Availability, Quality and Quantity of Data, but again we should not wait for data to get matured, and start implementing what ever data available.

Data tagging should not be overlooked and is a crucial element to AI/ML becoming more trusted.

With the introduction of big data and a realization of the importance of analytics, the importance of good quality data, the importance of good quality analysis is receiving broader recognition and buy-in.

I’d love to see more AI(machine learning) actually being used to do the heavy lifting and actually classify the data then analyse it. (The example that comes to mind here is Watson being used to classify killer skin cancers from harmless ones whilst the patient waits at the doctors(GP not specialists) surgery.

THEME #13

WRAP-UP Time!