KNOWLEDGESTREAM AT-A-GLANCE
The Future of Industrial Manufacturing
ABSTRACT
The future of industrial manufacturing
PARTICIPANTS
OBJECTIVES
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End Date: May 21, 2018
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.
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!