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What Engineers Need to Know About AI

A profile of a robot with a blue artificial intelligence brain extending out.

Artificial intelligence is more than just a buzzword; it is a new reality. Major enterprises are exploring the vast potential of AI-driven technologies to become more competitive, and they are looking for engineers who can aid in that mission. While AI is still in its nascent stages, its eventual impact in the engineering field will be significant. Artificial intelligence will limit the time human engineers need to spend on routine and repetitive tasks, ultimately rendering design and simulation tools more accurate.

The discipline's rapid growth has led some engineers to question whether their jobs are secure. The simplest answer is yes, they are. The next logical question is why? "We are allowing the computer to do the iterative work that previously would have been done by the engineers," explains Paul Haimes, vice president of generative design solutions company PTC, in an interview with the Institution of Mechanical Engineers. "The engineers get a range of options to choose from, but it’s still the engineer who is making the decision."

Artificial intelligence will transform engineering, but it won't render engineers obsolete so long as they adapt. The good news is that adaptation – especially when it comes to technology – is part of every engineer's toolkit. What matters is that they understand how AI is affecting engineering in the present, are comfortable with the technologies involved and see how artificial intelligence will impact the field in the future. This guide covers the basics, but engineers who want to excel in the long term need the kind of advanced knowledge they can get in a program such as SMU Lyle School of Engineering's Online Master of Science in Computer Science with Artificial Intelligence Specialization (MSCS-AI).

The Benefits of AI in Engineering

Fact: AI was never intended to replace engineers – or, more broadly, human workers. AI specialists use machine learning, natural language processing and deep learning to create systems that work alongside humans. An intelligent automated system might take over tedious, repetitive or overwhelming tasks, freeing up people to focus on more interesting and rewarding work. In settings where people do dangerous jobs, AI-powered monitoring systems can identify risks before anyone gets hurt. Intelligent systems are also capable of taking over precise and delicate engineering tasks that tend to be uncommonly stressful. And intelligent systems can generate product iterations letting engineers develop prototypes more quickly. Those products are often more reliable as well as easier and less expensive to produce than products designed solely by humans.

The computer vision platform PowerArena is already working with industrial engineering enterprises to predict and detect accidental defects in design and production. In this case, an intelligent machine utilizes a sensor to complete a task that humans often struggle with, lowering costs, reducing mistakes and leaving more time for engineers to focus on production.

Software engineers may be particularly worried about their futures when it comes to AI adoption because intelligent systems can improve upon existing applications without the need for human intervention. Speech recognition, computer vision and self-driving cars are examples of common types of programs common in the field of artificial intelligence. AI researchers are looking for more ways to imbue computers with what are essentially programming skills, but again, the fledgling use of AI in software development should not cause engineers to lose sleep.

A 2020 research-backed paper in AI Perspectives paints a compelling picture of how artificial intelligence could make software engineering better. According to the authors, AI will eventually "speed up development processes, realize development cost reductions and efficiency gains" because "human developers multiply their creative potential when using AI tools effectively." Software engineers will always be a part of the development process, but in the future, assistance from artificial systems will make debugging, documentation and data evaluation faster and easier.

Used strategically, artificial intelligence allows engineers across all the branches of engineering to do more – whether that means testing more, designing more, exploring more or developing projects that help more people.

How Engineers Use Artificial Intelligence

Just as there are myriad career paths in engineering, there are numerous applications of AI in engineering. Engineers use machine learning to forecast how the weather will impact transmission and distribution in the electric grid. They use big data to answer questions about human behavior that shape the evolution of public infrastructure projects. They use intelligent image processing to identify potentially problematic structural deformities in aging buildings. And cutting-edge AI applications make manufacturing processes more efficient and sustainable. The applications of AI in engineering are virtually limitless.

One of the most disruptive AI programs is one used regularly by civil engineers: 3D Building Information Modeling (BIM). BIM has become the new standard in computer-aided design because it helps civil engineers design accurate, to-scale 3D models using data from simulations, other models and previous designs. Because BIM objects are intelligent, they update in real-time when engineers make changes to any element in the design. BIM could become a crucial tool for architects, engineers and construction managers as the world's population continues to grow beyond the confines of our built environment.

It's wise to think of AI as just another data science or computer science tool – one engineers can leverage to boost the efficacy and resiliency of their work, now and in the future.

Must-Have AI Skills for Engineers

The best AI degree programs offer a curriculum that is varied, immersive and modern. A slate of core courses introduce the basics: algorithm engineering, data mining, natural language processing and machine learning, along with Python and other programming languages. Electives offer a deeper dive into database management, deep learning with neural networks and cloud computing.

Engineers don't have to be expert programmers or computer scientists – they don't even have to be preparing for AI jobs – but they should have foundational coding skills and a general understanding of software development, algorithm design and cloud computing concepts. Likewise, engineers don't have to become data scientists to advance in a world powered by AI technology, but they should be able to collect and model the right data because data powers artificial intelligence. They should also be familiar with and comfortable using off-the-shelf AI products—also known as AI-as-a-Service (AIaaS) or AI on tap—designed for engineering applications, e.g., IQ Ideas Plus.

Ultimately, engineers cannot ignore the growing demand for artificial intelligence skills. As Peter Jackson, member of London Futurists, put it in an interview, "an engineer's life is never static. One is always learning new skills in order to keep up with changes in the ways where developments actually happen. If one has to be a software engineer, or if one is another sort of engineer there will be several technological changes which will affect the course of their career."

Artificial Intelligence Is Only as Good as the Data

AI systems need to "eat." The only reason artificial intelligence systems work is that AI specialists and machine learning engineers feed them massive amounts of data, which provide those systems with patterns they can learn from. Widespread adoption of AI technology is only possible if organizations and researchers collect the useful data necessary to teach newly-developed intelligent systems how to behave. To improve upon them, researchers and AI engineers need to eventually introduce increasingly complex data so systems can draw more accurate conclusions. Engineers who use AI should think carefully about the quality and origin of the data they input into any system powered by artificial intelligence.

Collecting clean, high-quality data is more challenging than most engineers realize. Data cleaning, also known as data munging or wrangling, is the process of converting a raw set of data into something standardized and readable, without redundant entries, missing values or mismatched data types. Engineers who want to leverage the power of AI in their organizations must be able to identify and collect large amounts of high-quality data in the right format. Without that piece of the puzzle, intelligent systems may not work as planned, so engineers who want to work with AI need to hone their data mining and database management skills.

AI Won't Eliminate Engineering Jobs

One more time for the people in the back: AI is not going to eliminate engineering jobs. Robot movies tend to portray artificial intelligence as sinister – something for humans to fear – but in reality, intelligent systems enhance human ingenuity and expertise. AI does not replace human ingenuity because in many cases, it can't. According to McKinsey & Company, fewer than 5 percent of jobs can be fully automated. While AI may replace millions of roles in the coming decade, it will likely create more jobs than it eliminates – somewhere in the neighborhood of 97 million new positions. In fact, the University of Oxford found that jobs in science and engineering are actually less likely to be automated out of existence than other jobs.

Think about how different mechanical engineering was in the 1970s. Tools, processes and projects have changed, but mechanical engineers still have plenty of work to do. It is accurate to frame the impact of artificial intelligence across fields as just another transformation. More artificial intelligence careers will evolve, and some engineers will step into roles specifically related to AI. Others will keep their titles but have to learn artificial intelligence skills to stay competitive. The World Economic Forum reports there is accelerating demand for professionals ready to step into new specialist roles, including AI engineer, machine learning engineer, human-machine interaction designer and robotics engineer. Engineers who study artificial intelligence in a program like SMU's MSCS-AI have what it takes to ride the wave of transformation as it continues to wash over countless industries.

How to Prepare for AI-Enabled Engineering

The world will always need chemical engineers, civil engineers, electrical engineers and mechanical engineers. However, even if your title stays the same as engineering evolves alongside AI, your responsibilities may look very different in two, five or ten years. That is the nature of technological and industrial advancement – change is inevitable and skill sets are constantly evolving. Engineers have to evolve with them.

The best way to prepare for AI-enabled engineering is by reskilling. Core artificial intelligence skills for engineers include problem-solving, AI-powered data analytics, machine learning algorithm development and advanced programming in languages such as Python, Java and C++. Engineers who are ahead of the game when it comes to artificial intelligence will ultimately be more competitive.

SMU's online AI master's degree curriculum prepares engineers to leverage the power of AI by giving them a deeper understanding of intelligent digital technologies plus transferable technical skills and the critical thinking abilities necessary to implement AI in the field. The best thing about the part-time, online student experience is that MSCS-AI candidates don't have to take time off work or sacrifice income. Happily employed engineers can level up their skills while continuing to advance in their careers.

What the Future of Artificial Intelligence in Engineering Looks Like

Artificial intelligence is a rapidly evolving discipline. Innovators and AI engineers are coming up with new use cases every single day. Some of these developments will be news-worthy. For example, AI may be the key to providing clean, renewable energy to underserved regions of the world or increasing agricultural yields to feed a rapidly expanding population. Other applications of artificial intelligence may not make headlines but will have a disruptive effect. In PwC's 2020 AI Report, the company predicted that "much of the AI excitement will come from results that may sound mundane: incremental productivity gains for in-house business processes," such as reporting automation. Those developments, even the most minute, will change the way humans work and live in fundamental – and potentially unexpected – ways.

While previous technological revolutions involved technologies that took over manual tasks for humans, sparing us physical labor, advancements in artificial intelligence may free people from some of the mental load that keeps them from reaching their cognitive potential. AI may primarily become a tool with which engineers automate redundant, low-value tasks that keep them from working on more important projects and issues. Over the long term, AI could launch a new phase of invention and innovation in engineering.

That is why it is so crucial that engineers keep abreast of developments in AI related to their areas of expertise. Understanding how artificial intelligence will shape the future of engineering is important not because engineers are in danger of obsolescence but rather because it is engineers who should be leading AI implementation projects. Savvy engineers who reskill in AI master's degree programs will be the innovators who forge a path in human-computer interaction and overcome the tough engineering challenges of the future.

Enroll in SMU Lyle's 10-course master's in computer science with an artificial intelligence specialization and in just 20 months, you will gain the skills and knowledge to implement artificial intelligence in your organization in the present and adapt as the applications of AI in engineering evolve.