Is AI ready to take on engineering?
A few weeks ago, my youngest daughter had a fever for a week so my older girls turned to Google to learn more. They panicked, as Google’s AI summary said a 104° F fever is cause to rush to the ER. But I knew better, so showed them dozens of legitimate medical sites, all stating the exact opposite.
This got me thinking — just how much should we be using AI in our jobs? Not only is it inaccurate at times, it consumes more energy than most users realize. According to the World Economic Forum, Microsoft said its CO2 emissions had risen nearly 30% since 2020 due to data center expansion. Similarly, Google’s GHG emissions in 2023 were almost 50% higher than in 2019, largely due to the energy demand tied to data centers.
I dug deeper to learn how manufacturers are using it. One friend uses AI to research for his customer base. His team found that AI often has small but important technical and mathematical errors, so it’s critical to go back and check its work. He added that sometimes, a number might be off by a decimal point, or its source information may be inaccurate. He compared it thus, “Some days you can have graduate assistant level work, and then other days it’s more in line with a summer intern with good grades and a resume who landed their first real summer job.”
I also learned from a colleague how the company Monumo is redesigning electric motors with deep tech engineering. Its machine learning-friendly Anser Engine can now run 10 million simulations in 24 hours and identify the optimal parameters for specific motor use-cases to reduce cost, increase efficiency or improve sustainability.
These are both good examples of how we can use this constantly evolving technology to better our systems, machines and components. Here, AI is assisting engineers and technical personnel to design the machines of the future and benefit manufacturing.
Considering energy use, it’s wise for manufacturers who use AI to do extensive research to find the most beneficial uses. Like my story, Google search was already doing a better job of answering questions accurately. Perhaps it’s time to put that type of AI behind us and focus our energies on truly better engineering.
Mary C. Gannon • Editor-in-Chief
mgannon@wtwhmedia.com
linkedin.com/in/marygannonramsak
Filed Under: Digital Issues