The potential of artificial general intelligence tools for design facilitation

Article By : Richard O. Ocaya

Investigating the potential of artificial general intelligence to supplement the engineering design process.

Artificial general intelligence (AGI) is here, and it is changing the way we live and work. This article highlights the potential of AGI as a powerful design facilitation tool and aims to dispel the misconception that AGI is merely an elaborate encyclopedic network that recites answers from a database. While much has been written about AGI from the perspectives of the humanities and social sciences, this article takes a STEM-based approach to demonstrate how AGI can be incorporated as an ally in design strategies.

ChatGPT, a technology developed by OpenAI and currently at Version 4.0, is a prime example of AGI in action [1]. Preliminary reports suggest that this new technology is already threatening up to 80% of skilled jobs in the United States alone, spanning a wide section of the humanities and social sciences from writers to editors to advisors and many more [2]. However, the potential of ChatGPT extends far beyond its ability to automate tasks traditionally performed by humans. By asking the right questions, we can unlock the true power of AGI as a design facilitation tool.

This article demonstrates how AGI can be incorporated into the design process to drive innovation and progress. Through a series of simple questions posed to ChatGPT, we show that AGI has the potential to transform the design process by generating new ideas, identifying potential flaws, and providing real-time feedback. By embracing AGI as a powerful tool for innovation and progress, we can stay ahead of the curve and remain competitive in an ever-changing world. It is time to ask the right questions and embrace AGI as a powerful tool for innovation and progress.

In the 2004 film “I, Robot”, loosely based on Isaac Asimov’s 1950 novel of the same name, the protagonist, played by Will Smith, engages in a series of exchanges with the virtual AI interface, Dr. Alfred Lanning. For each misguided question he asks, he receives the response, “I’m sorry, my responses are limited, you must ask the right question.” Only when he finally asks the right question does he receive the answer, “That is the right question.” This scenario is reminiscent of interactions with ChatGPT. Preliminary assessments of this technology have mainly come from non-technical fields, leading some to belittle it as an elaborate encyclopedic network that simply reads and recites answers from a database. However, in this DI, we aim to show that this sentiment is not entirely accurate.

To maintain concision, we present ChatGPT with four questions covering key aspects of engineering, including mechanics, mathematics, and electronics. These questions are listed below together with the ChatGPT’s responses. The questions themselves are kept simple. They are framed as sentences that have a mixture of numerical and worded values in an attempt to confound ChatGPT. Furthermore, note that the responses are copied as-is from the ChatGPT terminal. The reader will note that the output formatting is mine, but identical in content to ChatGPT.

Question 1:

Response 1:

Question 2:

Response 2:

Comment: While ChatGPT’s ability to decipher the input is remarkable, the final answer is INCORRECT. The correct answer is y = 0.9503x + 1.8571.

What went wrong? It is likely ChatGPT’s reliance on some algorithm from an obscure source.

Question 3:

Response 3:

Question 4:

Response 4:

ChatGPT’s ability to generate new knowledge through unsupervised learning is unprecedented. However, it is clear that this AGI language model still requires human input to provide the necessary guidance and focus. It is uncertain how long this will remain the case, as AGI continues to evolve into something more powerful. The potential for AGI to problem solve and generate new knowledge independently can make it an extremely useful tool in engineering repertoire at any point in the design cycle.



  1. ChatGPT,
  2. Motherboard: Tech by Vice, OpenAI Research Says 80% of U.S. Workers’ Jobs Will Be Impacted by GPT,

This article was originally published on EDN.

Professor Ocaya specializes in electronics and solid-state physics, which he teaches at the Qwaqwa Campus of the UFS. He is active in computing, mathematical methods, new techniques for device characterization, material science and microcontroller-based instrument design. He holds a C3 rating from the National Research Foundation (NRF) of South Africa.


Leave a comment