For AI In Manufacturing - Start With Data –
Summary
Large language models are built on readily available universal languages that many people understand and that follow specific rules and sentence structures, Kalyan Veeramachaneni, principal research scientist at MIT’s Schwarzman College of Computing, said at the MIT Machine Intelligence for Manufacturing and Operations Symposium. These approaches put AI within reach of plant workers and manufacturing engineers, who understand everyday production requirements and process challenges but aren’t necessarily versed in the language of mathematics and complex modeling. Compared with high-value AI initiatives in other industries, manufacturing use cases tend to be more individualized, with lower returns, and thus are more difficult to fund and execute. If companies are going to rely on AI-generated insights, there will need to be a human layer that systematically governs data quality and automation results. Manufacturers should start applying generative AI or other technologies to targeted initiatives to learn, develop skills, and secure early wins that can be used to build organizational momentum and gain buy-in.