Expert Q&A: What does AI mean for engineers?

The disruptive potential of AI has rarely been away from the front pages in recent months. And despite some positive news (for instance the recent AI-enabled discovery of a new class of antibiotics) the tone of much of this coverage has been largely chilling, with warnings of redundant professions and even existential threats to humanity itself now landing on an almost weekly basis.  

Whilst the future is unclear, there’s little doubt that AI will have a profound impact on many areas of economic and human activity, including engineering.  So what might it mean for engineering and engineers specifically? What new opportunities will it open up? What challenges does it present? And how can engineers ensure they are equipped to make the best possible use of these powerful new tools?

We put these questions to a panel of AI experts including Dr Richard Ahlfeld, CEO and Founder of Monolith, an AI platform used by some of the world’s leading automotive, aerospace and industrial engineering firms; Professor Mark Girolami, Chief Scientist at the Alan Turing Institute, the UK’s national institute for data science; and ChatGPT, the AI chatbot at the heart of much recent coverage.

Describe your involvement in the field of AI

MG: I practiced as a professional (chartered) engineer for a decade at IBM where I worked on process automation. After this I undertook a PhD in Statistical Signal Processing looking at the problem of separating multiple sources of a signal from a multiplexed reading. Since 2016, I have worked at The Alan Turing Institute, the UK’s national institute for data science and AI. I was one of the founding executive directors, after which I led the development of Data Centric Engineering as a recognised discipline within the engineering sciences and related professions, and in 2021 I was appointed as the first chief scientist of the institute.

RA: Monolith’s mission is to empower engineers to use AI to solve even their most intractable physics problems. This was underscored in The State of AI in Engineering, a recent study that we commissioned with Forrester Consulting of over 160 engineering leaders in the US and Europe, where 71 per cent said they need to find ways to accelerate product development to stay competitive. Powered by machine-learning, our AI software enables engineering teams to significantly reduce their dependence on time- and investment-intensive physical testing programmes.

The no-code platform built specifically for domain experts gives engineers an opportunity to efficiently use their existing valuable test data. Analysing these high-quality data inputs, the software creates instant, accurate predictions for test outcomes – allowing engineers to identify problem-areas, resolve them swiftly, and enable them to make faster, more efficient improvements to a product.

ChatGPT:  As an AI language model developed by OpenAI, my role is to provide accurate and helpful responses to a wide range of questions and tasks, including those related to AI and its various applications across different industries and domains.

How is AI already impacting the world of engineering and the role of engineers?

MG: We first of all need to define what we mean by AI. If we think about the recent advances in AI, they are down to largely three major components.  The first is the availability of enormous amounts of data and the ability to collect, curate, and process it. An example is the amounts of data that is being scraped from the world wide web and fed into large language models. The second is the advances in computing capability, new system processes such as cloud-based computing, new processing architectures, new software languages, that have made large scale computing with big data viable. And the third component is the advances in algorithm development enabling, for example, the large-scale optimisation of models with hundreds of millions of degrees-of-freedom. 

So by viewing AI technologies as an outcome of vast amounts of data, fantastic compute capability, and smart algorithms, we can ask how data centric approaches to engineering are transforming the world of engineering. It is well known that we tend to over-estimate the immediate and short-term impact of emerging technologies and under-estimate their long-term impact. One can view the impact of AI technologies on engineering as a continuation, possibly an acceleration, of the digital revolution from computing, personal computing, mobile computing, internet, cloud computing, and all the ways that these have charged and driven the inventive imagination of engineers in transforming the way that problems are solved.

RA: In The State of AI in Engineering study, 55 per cent of those surveyed lack the required tools to analyse large amounts of data. While AI is perceived by some as difficult to integrate, Monolith addresses this gap in the market as it was built specifically for engineers and their test data. It gives engineers the power to solve intractable physics problems and achieve better product quality, improved performance and faster development.

When developing highly complex products, legacy R&D processes can be time-consuming to set up, and yet the learnings remain insufficient to pass validation requirements. 55 per cent of those surveyed in the study said existing virtual validation tools are not reliable enough. In the automotive industry, where competition is fierce – and delivering innovative products such as electric solutions today, and hydrogen in the future, is paramount – these traditional methods aren’t sufficient. AI is an invaluable tool for quickly understanding product areas critical to performance, streamlining workflows, and saving valuable time and costs. Furthermore, the study found respondents who have implemented AI are 43 per cent more likely to realise increased revenue, profitability and competitiveness over those who haven’t.

ChatGPT:  AI is significantly transforming the engineering industry, and its impact is being felt across various sectors. Engineers are increasingly relying on AI technologies to design, develop and improve products, processes and systems. AI-enabled tools such as machine learning algorithms, natural language processing, and computer vision are being used to automate repetitive tasks, analyse complex data sets, optimise designs, and create new solutions to engineering problems.

 By using AI tools, engineers can process vast amounts of data, identify patterns and optimize designs faster than ever before. Additionally, AI is enabling engineers to design more complex systems with greater accuracy and precision, while reducing the risk of errors. Moreover, AI is playing an essential role in the development of autonomous systems and smart products, such as self-driving cars, drones, and robots. These systems rely heavily on AI algorithms to perceive and interpret the world around them, make decisions, and act accordingly.

However, the rise of AI is also creating new challenges for engineers. As more systems become automated, engineers need to develop new skills and competencies to design, develop, and maintain these systems effectively. They also need to ensure that these systems are secure, reliable, and can be trusted, especially in safety-critical applications.

How do you think AI will affect engineering and the role of engineers in the future?

MG: The new skills requirements are difficult to foresee as there will be new markets and businesses that are going to emerge and develop. Take the automotive sector as an example. For the last century the skills that have been needed have been in mechanical engineering but that’s changing now to skills that are more based in electrical, electronic, computing, and software reliant. It's a complete shift in the skills required of engineers by the industry and we can expect to see these changes in every area of engineering.

RA: Just in the last few months, the potential value of AI has become more understood - both in the engineering sector and in broader society. While some may view AI as a threat to their job security, this is a critical misconception. We believe tools like Monolith will make engineers more productive and give them time back for more creativity and innovation to develop, for instance, longer-lasting, safer electric batteries or smarter energy meters. That’s why we wanted to ensure our software doesn’t require Python-coding knowledge, or years of technical training, and that it’s entirely no-code. This means that engineers can immerse themselves in it and extract all the benefits, by leaning primarily on their engineering expertise to know how to make the most out of it.

ChatGPT: AI is likely to continue transforming the engineering industry by enhancing the capabilities of engineers and driving new advancements in the field. Engineers will increasingly rely on AI tools to analyze large amounts of data, optimize designs, and automate routine tasks, allowing them to focus on more complex and creative aspects of their work. AI will also enable engineers to design and develop more intelligent and autonomous systems, such as smart factories, self-driving cars, and drones, that can operate with minimal human intervention.

However, the rise of AI will also require engineers to develop new skills and competencies to effectively design and maintain these systems. They will need to become proficient in programming, data analysis, and machine learning to effectively work with AI-driven tools and systems. Additionally, engineers will need to address ethical and societal concerns related to the impact of AI on the workforce, privacy, and security.

How can engineers ensure they aren’t left behind by the technology?

MG: The way that engineers have always ensured that they do not get left behind by the technology and that is to be at the vanguard defining and developing the technologies of the future.

RA: Put simply: embracing curiosity and immersion. Once engineers who aren’t familiar with AI explore the technology, witness the speed and accuracy with which it can predict tests, and learn to work with it effectively, they will be the professionals that will come to define the sector. Engineers who upskill in AI will speed product development and make even greater significant contributions to their teams and customers. This is the skillset that OEMs and suppliers will be seeking in the future. AI won’t replace engineers; engineers using AI will.

ChatGPT: Engineers need to take a proactive approach to learning and development. This includes staying up-to-date with the latest AI tools and technologies and identifying opportunities to incorporate them into their work. Engineers should seek out training and development opportunities, such as online courses, workshops, and certifications, to gain new skills and competencies related to AI.

Additionally, engineers can collaborate with AI experts to gain a better understanding of how AI can be applied to their work and to identify new opportunities for innovation. By working with experts from different fields, engineers can gain a broader perspective on the potential applications of AI and how it can be integrated into their work. By taking a proactive approach to learning and development, collaboration, and ethical considerations, engineers can ensure they remain relevant and effective in the age of AI.