Even in the field of generative AI, there are huge differences: one is generative AI trained on specific data sets that are specific to specific manufacturing facilities and their equipment and software systems; The other is generative AI that is fed data on a wide range of topics from a variety of sources - many of which may not be reliable enough to begin with.
To help clarify this issue, let's take a look at the applications of AI in data analytics and generative AI in manufacturing production operations, and how they interact with industrial automation technologies.
The difference between AI for data analysis and generative AI
Let's start with AI for data analysis. While this is a relatively new addition to the field of automation technology, it has been in use for several years, with applications ranging from production analytics to predictive maintenance. At its most basic, in a manufacturing environment, data analytics AI essentially processes the data input from a company's plant equipment and software systems and applies algorithms to sift through it to highlight trends and anomalies and provide insights about business possibilities based on the correlation of the data collected by these different systems.
Generative AI can generate original content - including text, images, video, audio, or software code - based on user prompts or requests. Because generative AI can receive large amounts of data from so many different sources, we see issues such as "hallucinations," which need to be fully vetted by humans before the results are put into practice. Note, however, that this is general-purpose generative AI.
In a more controlled environment, the results will be more reliable if the data fed into a generative AI system is provided by a trusted source and is focused on the equipment and systems of a specific company or a group of partner companies.
This is why you see many automation technology companies implementing generative AI technologies to develop systems commonly referred to as "Copilot." These systems are trained on relatively closed data sets that are specific to the user's application scenario and the technologies associated with it, rather than scraping various resources from the Internet.
How can automation technology vendors implement generative AI
Just as AI for data analytics has become ubiquitous in all types of manufacturing systems over the past few years, the use of generative AI in manufacturing operations and design applications is rapidly increasing today. To promote industrial cybersecurity and drive the integration of generative AI into shop floor operations.
The interaction between static and dynamic machine data will provide users of the platform with a new level of control over operational processes. The "new level of control" means that users will be able to interact with Copilot technology in their own language and receive detailed instructions and recommendations based on their requirements. ServiceNow says its ability to automate workflows - from maintenance scheduling to real-time problem solving - helps ensure that the AI-powered insights provided by Copilot translate into tangible, effective actions that increase productivity and minimize downtime.
Generative design has long been used by automation manufacturers to design their products, and with the integration of generative AI, generative design is undergoing a major evolution. Generative AI brings a new dimension to generative design, changing the way engineers and manufacturers conceive, create, and optimize automation technologies by introducing "human-in-the-loop" capabilities.
It is important to distinguish between existing generative design capabilities using traditional AI and the emerging trend of integrated generative AI. Unlike traditional generative design methods, which rely solely on AI algorithms, the addition of generative AI introduces a more interactive and iterative approach where engineers can provide feedback to guide AI systems to more optimized solutions. This allows them to explore a wide design space and generate a large number of potential designs based on specified parameters, constraints, and performance goals. This approach is particularly suitable for automated systems, where there is often a need to balance multiple variables and competing objectives.
Applying generative AI-driven generative design to automated systems can increase the speed at which multiple design alternatives are generated and evaluated. In a matter of hours or days, Tony says, the system can generate hundreds or even thousands of design options, each optimized for a given parameter.
Another application cited relates to the alignment of technology with industry standards and best practices. Generative AI can be used to verify that a system meets cybersecurity standards by highlighting areas where the system deviates from established norms, helping engineers maintain consistency and quality across projects. The technology is also used to standardize the practices of engineering teams, especially in situations where engineers with different levels of experience need to adhere to the same design standards and use consistent libraries. This consistency is very valuable when copying systems across different sites or environments, as generative AI can suggest appropriate adjustments while maintaining overall design integrity.
Keep an open mind about industrial generative AI applications
The problem with general-purpose generative AI tools, which get the most media attention, is that they are dismissive of new AI applications emerging in automation technologies. Industrial generative AI tools from automation vendors focus on specific data sets and data sources to ensure the accuracy of results.
To keep your mind open to industrial generative AI, consider this case: Some 20 years ago, many manufacturing engineers did not consider Ethernet to be an effective choice for factory floor networking.
The further development of generative AI technology is important for the manufacturing industry to focus on acquiring the knowledge of its professional engineering, operations and maintenance staff to guide the next generation of industry workers. These manufacturing focused generative AI tools are expected to be the technologies that make that goal easier to achieve.
