Will AI Replace Engineers? AI Applications & Challenges
THE LIGHT SIDE VISION. According to the World Economic Forum Jobs of Tomorrow article, engineering skills are one of the highest-demand skills. Indeed, engineering has always been considered an evolving industry. The rapid advancements of AI technology and related jobs, such as machine learning (ML), enable engineers to complete their work in a more efficient way and solve a wider range of problems, empowering their expertise and making them the main actors of the industry's future development and success, thus creating a growing need for their profession.
THE DARK SIDE VISION. "AI and automation in engineering may cause job losses and economic inequalities. Engineers must be cautious when implementing AI to avoid negative consequences, such as losing insight into what is happening and eventually losing their jobs, despite the overall benefits."
Will AI replace engineers? What vision for our future is more probable? Should we believe the Dark Side or the Light Side stories?
We wonder what will happen next, whether jobs will decrease or increase. And will AI automate and destroy jobs, or automate and enhance them?
Should we follow the legendary Ned Ludd in a sort of "Butlerian Jihad" (see below), or stick to the point of the World Economic Forum and Prof. Klaus Schwab, talking of a golden age thanks to new approaches like Industry 4.0 and AI?
Good News Ahead!
As AI tools are expanding, it seems that in the near future, data scientists, computer scientists, and other new jobs and profiles in AI technologies, such as DL (deep learning), will be the knowledge workers. However, in this article, we will show the application of AI tools for the product development process and that Artificial Intelligence (AI) is rather a help for critical thinking.
Will AI replace engineers? We feel the answer to this question is "no". The future is bright.
Follow us to know our arguments on AI technologies and their near future benefits. Rather than quoting market research analysts, we will give practical hands-on examples from the world of engineers.
By merging test data, simulation tools data, and technical drawings (CAD) data, data scientists and computer scientists are creating more opportunities and jobs for themselves but also for engineers involved in manufacturing processes and product development.
They do this by providing valuable data-driven AI insights to all engineers in the supply chain from concept to production.
Thus, the job market for engineers is going to shift from manual executions to more concept work, creating new jobs for humans in all industries rather than more power for an "evil AI".
Fortunately, It's Only Science Fiction...
The "Butlerian Jihad" is a fictional event in Frank Herbert's science fiction series "Dune". For those who watch movies and do not read books, it explains why in the recent "Dune" series, there are human computers or "Mentats" rather than artificial assistants. AI has been exterminated!
The Dune Universe and Its Relevance for Today
In the Dune Universe, the Butlerian Jihad was a human uprising against the dominance of thinking machines and artificial intelligence, sparked by fears of humans becoming overly reliant on technology to the point of losing control over their own destinies.
Similarly, the positive aspects of fully autonomous driving on the workforce that we will highlight later show the potential benefits of AI-driven automation in transportation.
However, just as the Butlerian Jihad was a response to unchecked AI dominance, the real-world adoption of autonomous driving technology necessitates careful consideration of potential drawbacks.
As the technology progresses, concerns arise about job displacement in the transportation industry, where autonomous vehicles could replace human drivers.
Harnessing the Positive Potential of AI Tools
The fictional Butlerian Jihad caution us to approach the advancement of AI with foresight and ethical considerations, ensuring that it serves as a complement to human expertise rather than a wholesale replacement. This way, we can harness the positive potential of autonomous driving while safeguarding the interests and well-being of the American workforce.
The Rise of Artificial Intelligence in Engineering
The use of artificial intelligence (AI), machine learning (ML), and deep learning (DL) in engineering has sparked a heated debate regarding the benefits of automation versus human expertise.
AI technologies have revolutionized the field, offering efficiency, accuracy, and problem-solving capabilities. Extensive manual labour and time-consuming calculations can now be accomplished with remarkable speed and precision, all thanks to AI-driven automation.
Semi and Fully Autonomous Driving
As a good example, AI technologies have revolutionized the automotive landscape with autonomous transportation. Engineers and researchers have leveraged AI, particularly ML and DL algorithms, to create sophisticated systems that can perceive the environment, make real-time decisions, and navigate safely without human intervention.
The Challenge of Perception
One of the most critical tasks in autonomous vehicle development is perception, where the vehicle must interpret its surroundings using sensors such as cameras, lidar, and radar systems.
Traditionally, engineers relied heavily on manual labour to develop handcrafted rules and algorithms for object detection, lane recognition, and other perception tasks. This process was not only time-consuming but also prone to errors and inefficiencies.
Positive Impacts of Technology
The impact of autonomous driving on the US workforce presents several positive aspects.
Firstly, it's expected to enhance road safety significantly, reducing accidents caused by human errors.
Secondly, autonomous vehicles handling transportation opens up new opportunities for workers to focus on other value-added tasks during their commutes. Additionally, this technology may lead to increased productivity, as delivery and logistics industries can streamline operations and reduce transit times.
Finally, developing and maintaining autonomous vehicles create new job opportunities in engineering, software development, and manufacturing sectors.
The Importance of Engineering Data
With the advent of AI and deep and machine learning techniques, engineers now have the ability to train neural networks to learn directly from vast amounts of data. For instance, convolutional neural networks (CNNs) have shown exceptional performance in image recognition tasks, enabling autonomous vehicles to detect pedestrians, other vehicles, and road signs with speed and accuracy.
AI Applications in the Engineering Field
Will AI replace engineers? We will now focus on this question, showing how AI models and generative AI systems can positively impact daily lives.
CAD and Design Automation
The standard design process in industrial product development consists of regular iterations between product designers using CAD and the simulation teams using CAE (Computer-Aided Engineering simulations). At different stages of the development process, with evolving requirements, new designs need to be assessed and improved using standard CAD/CAE tools.
These interactions involve consequent waiting times, and the simulation tools are not always compatible with the requirements of fast-paced projects.
Furthermore, different file formats and the complexity of simulation teams' tools are often further slowing down the process.
Quicker simulation approaches, integrated into design tools, are an attractive alternative. However, most of these so-called "simple CAE" or "upfront solutions" simulation tools have some serious disadvantages. Basically, they lack accuracy, do not correlate well with the “high-fidelity” simulation, and are limited to a few simplified scenarios proposed by the software vendors.
These are the bottlenecks that Neural Concept Shape (NCS) has solved with a new class of AI-based algorithms based on ML and DL.
Shape models handle raw 3D CAD and CAE data, allowing simulation to be conducted early in the design process. This provides designers with simplified and real-time access to simulation results.
NCS' CAD interface enables product designers to quickly and accurately iterate on designs, resulting in better solutions for customers.
AI Adoption in CAE Teams
Engineers remain at the core of the product design process in mechanical engineering and other industries. Using their knowledge and experience, CAE simulation domain experts are now becoming responsible for the quality, update, and deployment of the mentioned AI models.
For instance, mechanical engineers' expertise empowers designers with additional AI tools, enabling them to tackle complex engineering challenges more efficiently and accurately and with fewer iterations in the design process.
This symbiosis between AI and engineers is a new workflow, ushering in an era of innovation in CAE software applications for mechanical engineers and engineers in many other industries.
Advantages of AI in the CAE Workflow
One of the most significant advantages of integrating AI into the CAE workflow is a much smarter and more automated usage of CAE tools in the product development process.
AI-powered algorithms can assist engineers in automating time-consuming tasks, such as meshing and simulation setup, allowing them to focus on higher-level design decisions and analysis. This saves time and reduces the likelihood of human errors with more reliable and optimized engineering solutions.
Approaching Problem-Solving With AI Technologies
AI adoption has revolutionized the way CAE teams and consultancy companies approach problem-solving.
With access to AI-driven predictive analytics, engineers can now simulate and analyze a broader range of scenarios, enabling them to explore alternative design options rapidly. This iterative process leads to optimized designs and cycles in the product development process, giving adopting companies a competitive edge.
Furthermore, AI has opened up new possibilities in simulation-driven design optimization.
By harnessing the power of machine learning and optimization algorithms, CAE teams can efficiently search through vast design spaces to identify the most optimal configurations that meet multiple criteria.
This capability not only enhances product performance but also allows for the creation of innovative and cutting-edge designs that were previously challenging to achieve.
Summary: Why AI Won't Replace Engineers
The ideal approach here is to balance AI automation and human expertise. By leveraging AI as a powerful tool to complement human skills, engineers can harness the full potential of both worlds.
This symbiotic relationship enables engineers to achieve unparalleled levels of efficiency, innovation, and precision while retaining the invaluable qualities of human intuition and creativity.
Embracing AI as a collaborative partner rather than a replacement empowers the engineering community to address complex global challenges and drive progress towards a brighter future.
In conclusion, AI approaches will not replace simulation software or engineers.
AI will rather be used by simulation experts to validate concepts or explore much more complex physical phenomena (such as vehicle acoustics if we talk about a CFD application) while the early development process is done within the design teams.