Practical AI AI-driven automation in manufacturing
Jan 20, 2020
In this engaging discussion, Costas Boulis, Chief Scientist at Bright Machines, delves into the world of AI-driven automation in manufacturing. He shares insights on how AI enhances production efficiency and the unique challenges posed by manufacturing data. Costas introduces the concept of microfactories and highlights the importance of advanced robotics and digital twins. He also discusses the potential impacts on workforce dynamics, emphasizing a shift towards more creative roles as automation evolves.
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Lagging AI Adoption in Manufacturing
- Manufacturing has lagged in AI adoption due to historically cheap labor, especially in China.
- Increased labor costs and product demand now necessitate smarter robotics.
Automation Gaps in Manufacturing
- Manufacturing has existing software solutions for process control, but end-to-end automation is lacking.
- Automating tasks like inserting components requires sophisticated methods to handle object variations.
Bright Machines' Automation Goals
- Current manufacturing automation solutions are slow to deploy and not robust.
- Bright Machines aims to reduce deployment time and build more robust solutions.
