EIT Dean, Dr. Steve Mackay, explores the substantial potential of artificial intelligence in industrial automation. He underscores AI’s transformative capabilities in optimizing processes and enhancing efficiency, allowing for more streamlined operations. Additionally, Dr. Mackay highlights the advancements AI brings to predictive maintenance, enabling industries to anticipate equipment failures and perform timely interventions. However, he also acknowledges the challenges and disruptions associated with the widespread adoption of AI, emphasizing the importance of a balanced approach to fully harness its benefits while addressing potential drawbacks.
With the current hype around AI (Artificial Intelligence), its impact on instrumentation, control, and industrial automation must be considered, according to Dr. Steve Mackay, the Dean of the Engineering Institute of Technology (EIT). He posits that software companies that reference it are undoubtedly enjoying an upward blip in share price, but is it really that useful? Essentially, the AI algorithm is based on a vast repository of content extracted from the web and underpinned by computing power.
In November 2022, OpenAI announced the Large Language Model (LLM) and associated ChatGPT – an interactive ‘chatbot’. Graphics, avatars, and songs are also possible with various associated tools and software. Recently, Dr. Mackay was privy to a demonstration of AI technology involving a suitably attired AI-driven professor.
The professor taught statistics and interacted with students very ably. In a similar vein, and with some success, we have trained up an AI-based Chatbot to interact with students working on our online industrial automation labs (without divulging the solutions, of course).
The tussle between Google and Microsoft and their AI offerings continues; this competition will result in comprehensive improvements.
ChatGPT’s Take on AI’s Impact on IA
ChatGPT provided the following note on AI’s impact on Industrial Automation. It is fairly predictable and prosaic:
“The most significant impact of AI on industrial automation is the ability to optimize and improve processes through data analysis and machine learning. This results in increased efficiency, reduced downtime, and improved product quality. Additionally, AI has enabled predictive maintenance, allowing companies to prevent equipment failures before they occur, saving time and money.
To apply AI quickly and effectively, users should start by identifying the specific pain points in their workflow that AI can address. They should then evaluate the available AI technologies and select the ones that best meet their needs. Once the appropriate AI technologies are identified, users can work with vendors or consultants to integrate them into their existing systems.
Additionally, users can leverage AI development platforms to quickly build and deploy custom AI solutions without needing extensive programming skills. Lastly, users should prioritize ongoing training and upskilling to stay up-to-date with AI technologies and techniques.”
The immediate value of AI tools is the automation of mundane, repetitive tasks: writing emails and reports, for example. It can act as an office chum, providing a starting point before a necessary content rework to ensure it applies to the plant.
Despite the inevitable disappointments, innovation has improved life and business through time: steam during the industrial revolution; electrical engineering in the 1900s; electronics in the 1960s, and the internet in the Nineties.
AI should see the same positive growth in industrial automation plants. There is a caveat, however: the overriding need for safety will ensure that the uptake is cautious. For instance, ChatGPT could ‘hallucinate’ (the actual term) while transferring sulphuric acid.
A possible way forward for you:
- Install ChatGPT.
- Investigate the Microsoft and Google options.
- Use Prompt Engineering: to evince accurate answers.
- Use ChatGPT for AI queries and communication but be critical!
Finally, the spread of AI may also be impeded by some legal kick-back. The current model relies on harvesting data without recompense to the content’s originators.
