Viewing entries tagged
manufacturing

Limiting bias and inexperience in AI-powered factories of the future

Published in techtarget.

The United Nations Sustainable Development Goals eight and nine are important in the context of Industry 4.0 and industrial IoT. SDG-8 calls for decent work and economic growth, while SDG-9 calls for innovation in industry and infrastructure. The purpose of the SDGs is to improve social conditions and advance humanity. AI plays a critical role in accomplishing this. For instance, let’s look at the innovation that’s happening in the Industry 4.0 space and where AI systems are proving efficient in preventing human errors and improving efficiency. The case studies from early AI systems clearly demonstrate that AI can not only improve efficiency metrics, like yield and throughput, but it can also reduce material waste and harmful emissions. In these scenarios, AI will create a net gain for us as society, improving human conditions.

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Data is not equal to knowledge

Published in Manufacturing.net. Full article here

A common pitfall a lot of machine learning (ML) companies run into is mistaking data as knowledge. Several enterprises think that having a lot of data makes them ripe for harvesting insights instantly through AI and ML techniques. It is not entirely true.

Data is not equal to knowledge, or more precisely, not the knowledge you think it equals.

Ernesto Miguel, 47 is a plant operator in a leading cement company. He has spent the last three decades working in the same cement plant. He knows each and every machine in his cement plant intimately. From the sound they make, he can tell what can be wrong. He is a champion in ensuring that the machines operate at their highest efficiency.

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