Can we take advantage of Gen-AI to increase human efforts in industrial automation?

by | May 7, 2024 | Artificial Intelligence, Digital Transformation | 0 comments

As the hype around generative artificial intelligences (Gen-AI) like ChatGPT, Gemini, Bing Chat, increases, there is a false belief that it will replace many human creative products like written content, computer code, technical documents, graphic works…

So far, these affordable and free AI tools have been widely used to create content that has non-critical implications.

But some concerns may arise, if we consider to apply AI tools to the industrial world, where uptime, reliability and security are critical elements to deliver solid and accurate results, and people and companies that rely on Gen-AI in an open-source world, are ultimately responsible for any negative outcome.

There are some considerations to be made, before we come to understand how artificial intelligence can effectively but carefully be applied within the context of industrial automation and before answering the following question: can AI accelerate industry in the right direction?

What is “intelligence”?
Recently, an article from Emerson’s Director of PACSystems Controls and Compute Portfolio, Darrell Halterman on how Artificial Intelligence can be leveraged in the industrial world to generate a positive impact and help experts to maximize their efforts, has appeared in the magazine Controls Engineering US.
In his article, Halterman, who owns a decennial expertise helping customers finding the right automation strategies to enable new levels of performance, reliability, and value in their enterprises, outlines the meaning of “Intelligence”.

We are used to consider intelligence what a person knows.

Historically, knowledge was built through the study of books, documents, and other written materials.

With the advent of the internet, as tons of data and facts are immediately accessible to anyone online, the definition of intelligence has shifted.
It can be defined as the capacity of a person to identify the best source of information and use it as useful insights.

An insight is a deep understanding of a situation or problem that goes beyond a simple information: it’s a form of clarity that reveals key elements and helps disclosure hidden connections.

Still, the verification of the accuracy of source data is crucial, as errors have always existed.

With so many data available and the continuous stream of new information, it is increasingly difficult for humans to find that “real” and true information, and we tend to rely on AI responses without questioning them, simply because we don’t own better resources or a comprehensive background.

This is where the most relevant differentiator comes, according to the author: discernment.

The first way to apply discernment is by posting the right question.

Secondly, discernment should be applied to assess if the answer generated by AI is correct: this ability can be only based on real life experiences, context, and background and it’s essential to verify if the answer is not only inaccurate but even dangerous.
Discernment can be therefore defined as the experience applied over knowledge, or in other words, subject matter experts (SMEs).

The possibility alone, to access to a greater amount of data, does not create the expertise. In fact, the opposite is true: it’s the “expert” who has developed sufficient discernment by experience, that can take advantage of AI data to maximize efforts.

That’s why manufacturing and processing organizations that aim to optimize their operations and overall efficiency must consider to equip SMEs with AI tools to accelerate their work, suggests the author.

For example, Gen-AI capability to generate language models can be used to produce industrial automation design or optimization activity, but it is only when training data sets are carefully curated by the SMEs, that it is possible to avoid any potentially erroneous data source.

Not only: SMEs competencies are still required to verify the outputs.
In fact, using Gen-AI to create entertainment contents has a lower probability to cause harm to human security, while generating the “wrong” code for driving machinery or operations, can be very dangerous.

At Emerson we are realizing how Gen-AI in the hands of SMEs can accelerate repetitive tasks. Instead, using AI for complex tasks, cannot be faster than traditional methods that involve human capabilities.

The idea that Gen-AI will be managing complex processes in the factory autonomously and without the supervision of human expertise, is utopian. Instead, skilled experts remain the irreplaceable resource to realize this kind of projects.

While companies and users continue to explore Gen-AI technologies, the author lists a number of proven and easy to implement strategies that Emerson has already in place and that can be seamlessly deployed in the factory, from Floor to Cloud™.

Read the full article here and discover how to build a strong technology foundation to increase productivity and optimizing manufacturing. Emerson experts are carefully evaluating AI and other advanced technologies to empowering users to leverage their industry expertise, and eventually incorporating AI tools in the most beneficial ways.

Example of Generative AI chat

The nature of intelligence has evolved over time, and the advent of the internet means that finding reliable information has risen in importance, compared with studying written materials for years to gain knowledge.

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The opinions expressed here are the personal opinions of the authors. Content published here is not read or approved by Emerson before it is posted and does not necessarily represent the views and opinions of Emerson.

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