A few decades ago, engineering was all about sketches, physical models, and blueprints. It was a highly physical industry, in other words, with even preliminary designs sometimes finding their way into a physical model. Today, things are dramatically different. Instead of hand-sketching designs, engineers focus more on computer designs. Similarly, software tools are now often used instead of physical sketches, saving engineers both time and effort. With so much change in a relatively short time, you might wonder how much more the engineering industry could possibly evolve. The answer, perhaps unsurprisingly, is quite a bit. As artificial intelligence (AI) continues to grow in the coming years, it is poised to further transform engineering.
Let’s take a closer look at the future of AI in engineering, with an emphasis on computer engineering in particular!
Thanks to the dramatic increase in the number of computers used in everyday life, computer engineers have quickly become incredibly important. They develop hardware such as routers, motherboards, circuits, and other types of equipment and test them to ensure stability and function. When industry giants like Intel or AMD release major updates to their motherboards, computer engineers are some of the people who make them happen. Software developers and computer engineers work closely together to make sure that any updates, be they motherboard updates or software updates, do not cause anything to stop working.
Computer engineers also research new technology, as we mentioned briefly above. This role is particularly heavy on experimentation and testing new designs throughout the development process. Innovative solutions and products are the ultimate goals here, and once a successful design has been completed computer engineers may even oversee the manufacturing process.
If you are interested in a computer engineering career, you can expect to be paid well for your time. According to the U.S. Bureau of Labor Statistics, the median pay is roughly $130,000 annually, and the field is projected to grow about 5% year-on-year. Landing a job in the industry requires a thorough education from an accredited university. Earning an ECE master’s degree online is fine, but make sure the school is well-respected, like Kettering University Online, before formally enrolling. However, you do not necessarily need any experience working in a related field, so once you have your degree, you can start applying for a job.
Computer engineers must have both hard and soft skills to succeed in the industry. Some of the soft skills they should learn before completing their degree include communication, problem-solving, and analytical skills. Remember that this kind of engineer must work closely with others, sometimes across different departments. Good communication skills make that possible with minimal conflict or confusion. They must also be able to analyze the results from the tests they run and the hardware they build while thinking critically about how to improve their design.
Hard skills for computer engineers include testing and quality assurance, circuitry and microprocessors, and operating systems. To create useful and functional hardware, they must understand not only how to build it but also how to test it for quality assurance. Taking the time to learn these hard skills thoroughly before looking for work better enables computer engineers to tackle complex jobs such as those including heavy AI use. And, of course, they must be well-versed in the operating systems that depend on their circuitry.
Now that we have a handle on what a computer engineer does let’s talk about AI.
AI is a complex topic that is difficult to explain concisely. In simple terms, AI is a simulation of human intellect. It mimics how people think, in other words, and is used via machines in different applications. IBM puts it a bit more succinctly by defining AI as the science and engineering of intelligent machines, with an emphasis on intelligent computer programs.
Today, AI benefits human life in several different ways. From manufacturing to healthcare and everything in between, this technology is quickly transforming the way the world works. Before it can positively impact daily life, however, AI must be properly developed and implemented. Often, the people who are responsible for this are computer engineers.
Before we dive into computer engineers and how they see the future of AI, however, let’s take a quick look at how quickly the industry is growing.
According to Grand View Research, the field of AI is growing at a rapid pace. In 2022, the market was valued at around $130 billion. From 2023 to 2030, the field is expected to grow by approximately 37% in compound annual growth rate (CAGR). A recent study into AI development also found that from 1981 to 2017, the number of patents involving AI increased exponentially. This growth is thanks to the tireless research and innovation in AI by students, professionals, and laypeople alike. From building machine learning models to developing life-saving medical gear, AI is expanding in multiple markets globally.
What does this mean for the future? While this article is about how AI is used in the engineering industry, this technology is important in many different areas of life.
We will discuss this topic in more detail shortly, but let’s look at a broad overview of how AI is being used to help engineers. First, artificial intelligence makes it easier for engineers to do their jobs. Instead of spending time poring over designs in search of errors and developing smart solutions to problems, engineers can allow AI technology to find those issues on their behalf. This frees the engineer to focus more on advanced aspects of design and construction, leading to quicker projects with fewer mistakes. Consider Computer-Aided Design (CAD) software and how integral it has become to the engineering process.
AI can assist engineers in other ways, too. Some use the technology to create sophisticated designs, some of which incorporate sensor-based procedures for increased accuracy. AI can even deliver these designs to manufacturing facilities. This is not to say that it can replace human engineers. In fact, it is important to remember that AI has its limitations. Human designers can carefully tailor designs to suit the unique needs of customers rather than simply churning out designs that seem effective on paper but might be less efficient in real life.
Even more broadly, AI and machine learning can be used to improve production line efficiency and manufacturing success over time. Notably, this process does not necessarily require human input. AI-powered machines can analyze various metrics over time to determine areas where improvements can be made to improve the processes. Automobile manufacturers, for example, have long used AI-powered robots to complete precise engineering moves required in the most intricate steps of the manufacturing process. Assembly lines using these robots are known as “smart production lines.”
Computer engineers utilize AI in a few different ways. The most important is creating “smart” machines. Modern consumers expect technology to be convenient and actively make their lives easier, and computer engineers must be able to make that happen. While not all products are smart, a surprising number include AI and its sister field, machine learning.
Remember that computer engineers are some of the primary professionals behind the creation of innovative hardware – if anyone is going to consider how AI can help a project, it is them.
Let’s look at an example of how AI is used in computer engineering. Many computers and algorithms today include AI, and they require AI-enabled circuits to function properly. These circuits, better known as central processing units (CPUs), dictate how accurately and quickly AI functions on the device. Smartphones, for example, increasingly include some form of speech recognition. Computer engineers must create CPUs that allow AI and machine learning to work effortlessly so that the end user has a smooth and intuitive experience.
Regarding how AI is used to help computer engineers as they work, you need only look at how it is helping other engineers. The technology can be used even in complex applications by monitoring work and catching potential flaws or conflicts. This is especially useful when the AI is given specific parameters to keep these detections relevant. If a computer engineer is creating a circuit to be used with a specific program, for example, AI can help by scanning completed work and comparing the data to what they know about the program in question. Any conflicts or errors found can then be fixed before the equipment is sent out to end users.
We have explored how AI is used in computer engineering but remember that AI is not confined to this industry. In fact, many businesses with an engineering focus are embracing AI technology in a big way. That is because the way that AI functions, including tasks like deep learning, reinforcement learning, natural language processing, expert systems, and unsupervised/supervised learning, is inherently useful to many forms of engineering. This section explores how AI is used in mechanical engineering, allied fields, civil engineering, and electronic and electrical engineering. We will also look at how it is being used in the general manufacturing workflow.
AI is used in a few different ways in mechanical engineering, including predictive maintenance and fracture mechanics. In fracture mechanics, for example, AI is often used in the diagnostic process for rotating machinery, hot forging pressing, and reciprocating machinery. It makes the diagnostic process much quicker and more accurate, leading to smart solutions and less downtime.
Another area where AI is being used is in mechanical engineering centers around sensors in machines. The number of sensors used in various pieces of machinery is increasing relatively rapidly. This has led to a dramatic increase in the amount of data available pertaining to real-time machine states. Instead of finding out that a part is failing in a machine after it has already caused problems leading to an investigation, sensors can detect problems before they damage machines or lead to serious errors.
Beyond detecting problems, the data supplied by in-machine sensors can predict the degradation of the machine in question. This, in turn, can determine when machines should be maintained and what needs to be done. “Predictive maintenance” refers to using AI to predict machine maintenance needs. Deep learning and machine learning are two of the most important AI functions for this as they can lead to results with a higher degree of accuracy as the amount of data over time builds up. Accordingly, AI is most often used in mechanical engineering to help produce and design “smart” tools, especially for health assessment.
AI is commonly used in a few different allied fields in civil engineering, particularly those involving modeling complex tasks and determining design parameters. Structural engineering, for example, makes great use of AI in pattern recognition and machine learning. Construction management also uses AI to monitor the overall health of building structures by using data collected from sensors inside them (like the way AI is used in machine sensors, as described above).
AI-based tools are also used in solid waste management, where their main uses are predicting waste generation and monitoring waste collection systems and waste disposal and containment sites. Project management is another field in civil engineering and allied fields where AI is used. More specifically, AI-based tools are used to assess task risk and duration as well as to predict unforeseen labor shortages. These tools are even used in ocean engineering to help forecast maintenance needs before building.
AI is booming in electronic and electrical engineering. It is especially important in plant-wide control systems, where it helps maximize efficiency. Beyond managing electrical plants, AI is also used to help optimize a variety of domains of electrical engineering. This includes analyzing power systems and solving potential problems, whether they are currently impacting performance or are simply projected to do so in the future.
Electrical engineering is also used to diagnose faults and monitor the condition of electrical machines and equipment. This includes being used for computer vision applications and image processing.
The manufacturing sector is moving increasingly closer to Industry 4.0, and companies are adopting a variety of modern computing tools to take full advantage of the innovation. “Industry 4.0” is also known as the “fourth industrial revolution”. Much as the first industrial revolution introduced steam and water power instead of animal and human power alone and, in the process, transformed the industry, the fourth industrial revolution is poised to change the way we do things on a large scale.
Industry 4.0 is characterized by an increase in smart factories, smart machines, and automation. AI plays a critical role in these, making it invaluable to the manufacturing workflow. From bettering the value of informed data used to produce goods more productively and efficiently to improving flexibility so that manufacturers are better able to make use of mass customization to meet consumer demands, AI is the backbone of the modern manufacturing process.
More specifically, AI (and its related tool, machine learning) enable manufacturing companies to utilize the newfound volume of information in their plants. This data is produced not only on the factory floor but also from third parties, partners, and business units. AI is used to glean insights to improve business and operations processes by creating visibility, automation, and predictability. Consider the maintenance of industrial machines, for example, which are prone to fail during production. The data collected from these machines, including the failures, can be used to build predictive maintenance routines, as described earlier in this article.
AI is not the kind of technology that will fade into obscurity. On the contrary, it is poised to become increasingly important as time passes. We have talked about what this means for engineers in other fields, but how do computer engineers see AI in the future?
Computer engineers see AI as a potential addition to their workflow. It can help them analyze large amounts of data to predict trends and patterns, for example, as well as to optimize hardware and software. AI can also take over certain aspects of a computer engineer’s job, such as sifting through information in search of conflicts. This gives them more time to focus on creating innovative solutions and even more impressive products.
Are you interested in learning more about AI and how you can work with it in a computer engineering job? Keep the information above in mind as you search for the ideal educational institution to work with, and you will be well on your way in no time.
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