Examples of engineering surround us, all day, every day, but few people think about them very much. Engineers built the modern world and engineering is what keeps it running.
So it’s a given that the rise of artificial intelligence is going to have a big impact on the lives of billions when it comes to the field of engineering.
Artificial intelligence, the technology that gives machines the ability to see, to understand, and to reason, is a transformational concept in computer science. But without practical applications and tools built on those concepts, it’s nothing but a flashy set of parlor tricks.
It’s only through the integration of engineering and AI that most of the promises of the field will come true.
What Is Engineering?
Engineering is a big field. Engineering is defined as the discipline of applying scientific knowledge to practical problems. That practice is needed in almost every aspect of modern society: everything from electrical engineering to bioengineering to construction to mechanical systems go into powering and improving our daily lives.
Engineers turn dreams into reality.
So any introduction of artificial intelligence into this broad field is going to have big impacts on all our lives. And that major meeting of amazing technologies is coming soon.
Different Forms of Engineering Will Feel Different Impacts From AI
Not every kind of engineering will use AI in the same way. Additionally, different engineering fields may be changed to a greater or lesser degree by artificial intelligence.
Engineering is a discipline that has trickled out to so many different areas of use over hundreds of years that one of the first uses for AI in the field may be to build a comprehensive list of engineering professions and articulate its impact on them.
But there are five main fields in engineering practice, each of which has several sub-fields. They are:
- Civil Engineering - Tunnels, airports, highways, sanitation… the big infrastructure projects that keep the country humming happen in civil engineering. The scale of these engineering efforts mean that simulation and modeling will be important in these fields:
- Structural
- Transportation
- Environmental
- Mechanical Engineering - Mechanical engineers deal with vehicles and machinery. They will also take advantage of AI simulations, but also will be involved closely with robotics in areas like:
- Aeronautical
- Marine
- Automotive
- Electrical Engineering - Electrical engineering backs right up against information technology, dealing with signals, systems, and automation. Those are areas where AI is being developed to take over operations and to provide services to other aspects of engineering.
- Network
- Computer
- Chemical Engineering - Chemical engineers are in many ways closer to biotech and healthcare professionals than traditional engineering. They will use AI’s advanced generative abilities to come up with new substances and new ways to manufacture everything from plastics to drugs.
- Pharmaceutical
- Textile
- Biochemical
- Industrial Engineering - In the manufacturing world, robotics and automated systems are already a big deal. Engineers in these fields will continue to tap into AI that helps build things and get them where they are needed.
- Manufacturing
- Supply Chain
- Systems
How the Changes that AI is Bringing to Engineering Will Change the World
The impact of AI in particular areas of engineering has already been significant. The entire automotive engineering industry, for example, is in the middle of realigning itself to deal with autonomous vehicles. Those same vehicles are also making waves among transportation engineers, who will have to deal with the larger patterns they disrupt and create.
But the largest changes are yet to come. Artificial intelligence is still in the early days. And most of the developments so far have been in areas of generative content and language use… not high-impact skills in a field that all comes down to numbers.
Yet engineering is a field that has been pushing other AI technologies forward for decades. Robotics and industrial automation are old news to engineers. But AI control systems are enhancing their capabilities, making production lines, warehouses, and even construction sites more efficient.
Simulations will be an area that some branches of engineering will rely on AI to perform. Particularly in aeronautical and naval engineering, complex models of fluid dynamics are already performed by machines. AI will bring more realistic and insightful abilities to those sims.
One place that generative AI will find a home in engineering is in the creation of new materials and new ways of using those materials in building and manufacturing. Chemical and materials engineers, in particular, count on AI systems to quickly explore and find interesting physical properties in a huge range of theoretical substances. The kind of experimentation required for such a discovery today is time-consuming and expensive. But AI can simulate and predict those properties quickly and entirely in software.
Similarly, generative systems may be able to rapidly evaluate new construction and building techniques and suggest solutions that human engineers would stumble across only by chance.
Put together, these changes will make engineering in almost any field faster, more responsive, and safer. The very act of altering engineering processes will inevitably have even larger effects on our infrastructure and technology.
Engineering AI Jobs Will Merge with Engineering Jobs in General
While most people don’t spend a lot of time thinking about how the world works, engineers are a different breed. When they view a machine, or a building, or some process of industry, their minds immediately go to work: deconstructing, understanding, revising.
A good scientist is a person with original ideas. A good engineer is a person who makes a design that works with as few ideas as possible.
Much the same is true of AI professionals. There’s so much overlap between attitudes and skillsets that in many industries, the positions will not have a great deal of separation.
With a strong technical grounding, engineers are already ideally suited to pick up skills in artificial intelligence. Many engineering roles will absorb AI-related responsibilities without any change in title; AI will just be another tool, like CAD, that they use to get their jobs done.
Specialists, however, will also be in demand. In particular, robotics and automation engineers will continue to increase in importance in many industries.
AI programmers will also be needed to develop the advanced simulations that engineers will use.
Because engineering is a discipline built around practicality and applications, the kind of positions that emerge will usually involve directed uses of AI versus pure R&D. But research and development will be crucial in the early stages of developing tools and techniques, so AI scientists and researchers focused on engineering will also be common.
What Is the Best Way to Get an Education for AI Uses in Engineering?
There are already many college degrees in artificial intelligence and machine learning that have a strong engineering emphasis. Offered by engineering schools, programs like a Master of Science in Computer Science and Engineering AI concentration, a Master of Science in Applied Artificial Intelligence, or a Master of Engineering in Artificial Intelligence have all the usual AI and ML coursework with an engineering focus.
Many professional engineering jobs require state licensure, which only requires a bachelor’s degree. It’s an open question what kind of licensure AI professionals in the field will need, if any, but standards in artificial intelligence are higher today anyway. Many positions require a master’s or even a PhD in AI. But as AI becomes more commonplace in engineering, it’s also likely that the early cutting-edge R&D demands will ease up and more engineering AI positions will be available with something like a Bachelor of Science in Engineering in Artificial Intelligence.
The undergraduate level is where most of the core math, physics, statistics, and elementary programming skills are learned. Master’s studies build on those foundations, with more advanced coursework in algorithm analysis and design, human-robot interaction, and computer-aided research in chemical and material sciences.
Engineering school AI degrees often have more professional engineering coursework. You’ll find classes like occupational safety engineering, interdisciplinary innovation for engineers, or lean Six Sigma.
There are many degree specializations that focus on specific engineering disciplines, such as:
- Robotics
- Bioengineering
- Automation
- Computer Vision
- Data Science and Analytics
There are so many synergies that most AI degrees will be sufficient preparation for careers in engineering AI. But for candidates who graduated with a degree in a related field who want to shift fields, or for anyone who is looking for even further specialization in a specific area of engineering, educational certificates in AI offer a path forward.
A Graduate Certificate in AI for Engineering, or a Graduate Certificate in AI Engineering Fundamentals in Mechanical Engineering are both examples of college certificates designed to offer an update aimed at building AI skills. These take undergraduate training in the essentials that come with both engineering and computer science programs and update them with courses in machine learning, deep learning, and artificial intelligence algorithms for engineers.
Similarly, there are many programs like a Robotics Certificate Program or a Robotics and Autonomous Systems Graduate Certificate that deliver focused training in other common engineering AI fields.
Professional Certifications May Be a Practical Requirement in Engineering AI Credentials
There’s another kind of certificate program that is common in both technology and in engineering: professional certification.
Unlike an educational certificate, these certs are less focused on training than on assessing the state of knowledge and skill a candidate has. Often offered by either vendors who create tools or software or by non-profit industry organizations, they may come with some training component. But they are usually only awarded after candidates prove their skill and knowledge through written exams, by recorded time on the job, and by holding specific education levels.
In both engineering and tech, these certs tend to revolve around specializations: chemical engineering, network engineering, quality assurance, and the like. For artificial intelligence, only a handful of certifications are offered today, and none that are specific to engineering uses. Still, depending on the requirements of employers and the toolsets used in engineering AI jobs, any of those options could boost employment prospects.
The demand for professionals who understand both AI and engineering principles will skyrocket once AI has moved further out of the research and development stage. New frontiers will open up in every field for practical applications of AI expertise: exactly the kind of work that engineers are best at.