2024-06-14 09:30:33
The manufacturing industry is constantly looking for new ways to increase automation, improve operational visibility, and accelerate product and technology development. Artificial intelligence presents opportunities, but also many challenges.
In a few months, the planet has discovered LLL or “Large Language Models” with the launch of OpenAI’s ChatGPT. These basic models use generative AI (or GenAI) for natural language processing (NLP) and natural language generation (NLG). Thus, ChatGPT is based on GPT (Generative Pretrained Transformer), the language model developed by OpenAI and which includes 175 billion parameters.
These models are capable of performing a variety of tasks, such as machine translation, text generation, question answering, etc. LLLs are mostly used in computer science to develop chatbots, machine translation tools, and other artificial intelligence applications.
On the negative side, various professions would be threatened by GenAI. Business administration positions are the most threatened. AI could replace up to 90% of their tasks, given that many can be automated according to a Indeed study.
On the positive side, the industrial sector can benefit from the capabilities of LLLs by making it possible to understand and organize complex information and generate human-like interactions.
The manufacturing industry indeed generates huge volumes of complex unstructured data (sensors, images, videos, telemetry, LiDAR, etc.) generated by vehicles, machines and even workers connected via sensors. For the industry, it is essential to distribute this information in real time and merge it with large contextual data sources in order to respond to important events in a meaningful way.
The flexibility and predictive capabilities of large language models open the way to a wide range of industrial use cases, presenting capabilities to revolutionize operations and offering substantial efficiency gains.
There are several cases to consider. The most obvious one for industry is maintenance support. LLMs can provide real-time support to specialist personnel by accessing and interpreting information from the company’s maintenance database and related documents, allowing for immediate and accurate troubleshooting advice.
Human control remains essential
By analyzing safety incident reports and near miss data, it is possible to identify patterns, extract insights and develop proactive safety measures, helping to make the work environment safer and healthier.
LLLs can also help assess project risks, estimate the impact of different risk scenarios, and develop strategies to mitigate them. These risk analyses make it easier for highly regulated industries to meet ongoing compliance. In particular, they can identify deviations from regulatory requirements in a project’s data and implement corrective actions.
More broadly, LLLs help manage complex projects. Automating the generation of project documentation including plans, schedules and progress reports reduces human effort (which can be prone to errors) and improves accuracy.
However, building an LLL is a complex project that typically requires a significant level of expertise, substantial computational resources, and access to extensive data. This process indeed includes several key steps, including data collection, preprocessing, selection of an appropriate neural network architecture, model design, pretraining on large datasets for specific tasks, continuous evaluation and refinement, and finally deployment.
Other challenges include inherent biases in their results and data privacy issues. These models should therefore be used with caution and their results should always be verified by humans.
Cover image credit: Image generated by Adobe Firefly
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