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On completeness of data models and SLM's


A good summary of some of the challenges that manufacturers are dealing with at the moment.


The article highlights (but only tangentially really identifies) a key missing building block for the true success of deploying AI in manufacturing to solve more than the isolated use-cases of e.g. predictive maintenance.

The key building block, we would argue, is a more nuanced data set to work with. We agree that SLM's (Small Language Models) hold significant promise -- but we also see that most manufacturing floors still lack a foundational data model that is enriched enough to deliver on the value that Manufacturing Engineers and CDOs alike are looking for.


This, yet again, highlights one of the core value propositions of MBrain and why the Method-Centric approach can create exponential returns on the shop floor.


"Despite the myriad challenges, there are also abundant opportunities thanks to significant advances in manufacturing technology, particularly AI. By harnessing a broader array of data and employing powerful machine learning and AI tools capable of identifying patterns and making predictions, manufacturers can realize various potential benefits..."




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