3. Accelerate the design and development of automation system

CTB810 HN800 In large-scale projects in the process industry, whether it is the expansion of an old project or the development of a new project, the design and development of automation systems requires the collaboration of many suppliers, users, partners and third-party partners such as regulators. From project initiation to project implementation to the project operation stage, large language models can be used to standardize requirements and parameters from various parties, thus significantly saving time and helping all participants improve their competitiveness.

This is because from the beginning of the system development from the needs of customers and partners, there will be a variety of requirements on the solution architecture, bill of materials, security plan, risk management plan, and so on. Based on current technology, it takes months, if not years, of rigorous work, clarification, and collaboration among experts from multiple functions to ensure final quality and feasibility. If the needs and data of all parties are quickly sorted out with the help of a large language model, the entire process can be accelerated while meeting compliance requirements at all levels.

CTB810 HN800 Ethical considerations and limitations of large language models

The use of large language models in industrial automation may present some ethical considerations and risks that must be taken seriously to ensure that the technology is used responsibly and ethically.System safety: When discussing the possibility of AI models performing industrial automation operations, clear safety measures must first be developed.
CTB810 HN800 Data security: Large language models require large amounts of training data. This data may include personally sensitive information or confidential information about specific processes, so it is important to ensure that the data is secure, that confidentiality is adhered to, and that individual privacy is respected.
Bias: Large language models may perpetuate social biases present in training data and continue to amplify the effects of these biases. This can lead to unfair and discriminatory consequences. Therefore, it is important to identify bias and mitigate the effects of bias to help ensure fair and equitable outcomes.

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CTB810 HN800 Information security: Large language models are vulnerable to malicious attacks such as model stealing or adversarial attacks. These models must be secured and protected against threats.
• Interpretation: Large language models are difficult to interpret and understand, so responses to them can be difficult to interpret. This can be problematic when using these models in the decision-making process, as it is difficult to understand what the basis of these decisions is, and thus to ensure their fairness and reasonableness.
• People-oriented: Through this article, we have seen some positive applications of large language models in the field of industrial automation. However, any output would still need human scrutiny. These models can only be used as a complement to human capabilities. For example, a machine can perform a task, but someone must check on it after the task is completed. Or machines can improve or challenge human creative thinking.
Reliability: The results of large language models may not always be true and should always be reviewed by human experts before using them.

Industrialization of large language models

As mentioned above, to take advantage of the benefits of large language models,CTB810 HN800  they must be properly deployed, taking into account their ethical considerations and limitations. In addition, it is important to consider the potential scale of large language models and where they can or need to be deployed. For example, the GPT-3 model has 175 billion parameters, while GPT-4 has 1.8 trillion parameters and is more efficient. It is clear that model execution will require very large amounts of computing power and storage space.

It is worth noting that pre-training large language models with domain-specific data can improve their performance and simplify their deployment. Different industries should evaluate how to create these foundational models and when large models are needed versus smaller, more specific solutions. For example, domain-specific large language models – such as manufacturing GPT, healthcare GPT, travel GPT, etc. – mean that smaller and more domain-focused models can be created and then built upon. This also helps to resolve erroneous output due to the overlaying of unrelated backgrounds. It is also possible for organizations in the same industry to collaborate to create a GPT that is suitable for general use in their domain, while still retaining their specific IP.