Engineering is one of the most important industries. From electrical engineering to structural and civil engineering, it is key to the safe creation of buildings/products/items in society, and is also crucial for jobs. This, after all, is a sector that is not only interesting to work in but one that also employs lots of people across a wide range of engineering roles.
As with many other industries now, technology has become increasingly important in engineering, and this has seen not only AI but also machine learning make its mark recently. But what are these technologies all about, and how are they used in engineering?
What are AI and machine learning?
AI is a term that most people have heard of now and refers to artificial intelligence. It is the ability for computers/networking systems to not only collect data but also use the information to solve issues. This essentially allows computers to show a kind of artificial human intelligence, hence the name.
Machine learning is a niche within AI that refers to the ability that computers have to learn and adapt their behavior. This sees them able to constantly refine how they perform set tasks, but without needing explicit instructions from a human operator to do so.
Although there are lots of industries where both of these tech innovations come in handy, engineering is one of the most prominent. It is certainly something that specific sectors within engineering are using more now.
How do you pick up AI and machine learning skills for engineering?
Moving into roles in fields such as electrical engineering requires you to develop key skills to succeed first. Technology plays a crucial role in this for modern engineers. While this might have been focused on things such as CAD before, it is now also useful to develop skills in AI/machine learning.
The best way to go about this is studying an academic program that teaches you the latest AI and machine learning knowledge. The ECE master’s degree from Kettering University Online is a popular choice and enables you to learn more about both. This is an online course with the chance to graduate within just two years, and is the perfect starting point for any modern engineer.
What other skills do you need as a modern engineer?
Just as finding out more about UX is key for any company looking to attract more customers, finding out more about the skills that engineers need is worthwhile if you plan to move into this industry. While working as an engineer does mean that picking up skills in areas such as AI and machine learning is wise (especially for electrical engineers), this is not all you will need.
To begin with, engineers need the ability to communicate effectively with colleagues, stakeholders and senior management. This not only ensures that you can explain a design or process to people clearly, but also means that you can pass on any instructions to team members properly. As an engineer, you must also be able to work as part of a team. This is because you will be part of a larger team usually and working on projects/processes that involve other people.
Problem-solving is also a key skill that most engineering jobs ask for. This helps you to solve often complex engineering problems and use your ability to think outside the box to come up with workable solutions. Of course, high-level IT skills are also a must for any modern engineer. This is not just those related to AI/machine learning, but also things such as knowing how to use CAD, knowing how to use video conferencing software, and knowing how to use online team collaboration platforms.
When you also add in the ability to work calmly under pressure, good leadership skills and an eye for detail, you can see that working in engineering requires many things.
How are AI and machine learning used in engineering?
As well as finding out how to gain knowledge around machine learning and AI for a career in this sector, you may also want to know how AI and machine learning are used in engineering. This will, after all, enable you to see how they are implemented and if this is something that interests you. Even if you do not plan on moving into this industry, it is still interesting to find out how these tech innovations are shaping our world.
In terms of AI specifically, it is used to enable computers and robotics to perform certain tasks within an engineering setting. This could be certain AI-powered software used to guide engineering processes or AI-backed hardware that produces the finished product/item to a set design. AI tech gives computers/robotics in an engineering environment the ability to perform tasks they are programmed with and use the AI they contain to carry out tasks independently.
AI in engineering – autonomous vehicles and robotics
A good example of this is how AI is used within the automotive sector. Autonomous vehicles are the perfect illustration of this and something that is expected to become the future of the industry. As you would expect, AI systems play a crucial role in allowing autonomous vehicles to function and drive on our roads safely. This makes AI key within modern auto manufacturing and something that modern engineers should know about.
On a broader scale, robotics in engineering is something that we are seeing a lot more of. Many engineering companies and manufacturing environments now contain robotics that have AI capabilities. As noted above, this enables them to carry out set tasks and use their AI to help streamline the engineering process.
AI can also be handy for breaking down departmental silos within a business to support engineering tasks. This can see AI help with effective data management across a company and enable engineers to not only take key insights from this large-scale data, but also draw valuable details from it to use in their own role.
How is machine learning used in engineering?
Machine learning is a subset of AI as a whole and is used in its own way within engineering. It essentially sees machines constantly analyzing and reviewing data collected as they perform set tasks. This allows them to constantly be learning about what they are doing and looking for ways to improve on this. As you can imagine, this is used within engineering in a variety of ways.
The most common way is signal processing. Machine learning tech helps with the correct modeling of signals and deals with precise signal output. As a result, better-quality data is fed into an AI system, and this in turn sees better results overall. Machine learning can also be used within engineering to develop more useful inferences and pinpoint more meaningful patterns as a task is carried out. For engineers, this can help to refine processes and improve the final output.
Why is AI useful in engineering?
Now that we have looked at how AI and machine learning are employed in engineering, you might wonder why they are so useful to this sector. When it comes to AI, there are actually a whole host of reasons. To begin with, things such as AI-backed robotics can carry out repetitive, mundane tasks that human workers might not like.
It also avoids the issue of human workers losing concentration when performing these kinds of tasks and mistakes being made in the engineering/production process. In addition, AI-enabled hardware in engineering can also perform very intricate tasks that human engineers may not be able to perform precisely.
In terms of engineering specifically, AI also helps companies to create products that offer more advanced features to consumers. Autonomous vehicles are a classic example of this, as is face recognition tech in modern smartphones or digital voice assistants in tech devices.
AI can also help to make the whole engineering process more efficient and give engineers ever more innovative tools to do more in their job. In the same way as CAD brought a whole new dimension to this sector when it was first introduced, AI in engineering is now having a similar impact.
What benefits does machines learning have for engineering?
In the same way as AI has some superb benefits for the engineering industry, machine learning also has its own rewards.
As you might expect, it mainly centers on the ability that this tech gives to machines to learn as they carry out engineering tasks. By constantly analyzing and refining what they are doing on any set engineering job, machines can perform in a more efficient way moving ahead. This can in turn drive up efficiency and productivity levels in a company based in the engineering sector.
By using machine learning, engineers can also discover new insights they might not have stumbled on themselves. This tech also enables those in the sector to uncover new patterns thrown up by machine learning and fine-tune engineering processes in the future. It can also help engineers use these patterns to hone their own judgement for future tasks.
What else can machine learning bring to engineering?
Machine learning can also be very handy in this industry for driving new innovations. This could be in terms of the process that is followed on an engineering project, the best materials to use, or how best to incorporate certain engineering features into a design. In terms of lean design in engineering, hardware/software could use machine learning to fine-tune production processes and reduce wastage. This is not only better for the environment but also better for a business’s bottom line.
Perhaps the last major benefit of machine learning in engineering is the reduction of human intervention. After all, AI-enabled software/hardware that can learn no longer needs humans to get involved as much or intervene so often in what it is doing. This will help free up the human engineer to focus on their own jobs and make everything run in a more efficient, leaner manner.
What does this mean for the role of engineer?
If you do harbor dreams of moving into engineering as a career, you may feel a little worried about the rise of AI and machine learning in the industry. More intelligent hardware and software that can learn by itself might see you wondering where human engineers fit in anymore. The good news, however, is that human engineers will always be needed, and tech such as AI/machine learning will never replace them.
While the automation that these technologies bring to engineering means that some roles that humans used to do may be performed by machines moving ahead, this does not mean that human engineers will disappear. Instead, human engineers will be able to focus on more interesting tasks and concentrate their attention on areas that are more rewarding to work on.
Although the latest machines and robotics in engineering can do many great things, they will never replace the unique skillset that human engineers possess. This means that we will always need humans in this sector, however advanced the tech is around them.
Being fully prepared is key for engineers and companies
To reap the most benefits from both of these wonderful pieces of technology, engineers and companies simply need to be prepared for them. This means that you will not get left behind and can incorporate them into your role/business without any fuss.
But what does this mean exactly? For those working as engineers or looking to break into the sector, it is all about embracing AI and machine learning. By picking up knowledge in these areas, you will not only see their awesome potential but also be ready to use them in any engineering position. This in turn will see you ready to work at the cutting edge of the industry and not get left behind. It also means that engineers can make the most of the latest tools available in their job and work comfortably alongside AI-powered robots/machines.
It is a similar story for businesses that may wonder how to deal with the rise of these technologies within their own company. By learning how to blend AI-backed hardware/software and machine learning with big data, automation and human workers, they are able to fully embrace the Fourth Industrial Revolution. This can help firms within engineering drive up efficiency and productivity and cut back on wastage in their processes.
What’s next for AI and machine learning in engineering?
Although both technologies have become increasingly visible in the industry over recent years, it is likely that this is only the start of the journey. It is sensible to think that the benefits that both offer to engineers and the companies they work for will only see them become more crucial. As the public start to purchase and demand more products with AI tech inside, this should only spark a bigger role for this kind of tech in the future.
We have already mentioned autonomous vehicles in the auto industry, and you would fully expect to see these roll out on a global scale in the coming years. AI and machine learning are key to the production of these vehicles, and this means a bigger role for them as the years move on. It also means that engineers with skills in these areas will become more in demand. When you also think about how technology such as AI-based natural language processing software and AI within image processing could become huge in the future, it is easy to see the possibilities.
What else could greater use of these technologies mean for engineering?
A knock-on effect of all this, of course, is what the future might bring for those working in engineering. We have already noted that human engineers will always be needed, but it might see the specifics of any engineering position change over time. The rise of machine learning and AI seems to signal that what an electrical engineer might do in 10 years’ time might well be a lot different from what they do now.
You also have to imagine that greater use of AI and machine learning across engineering will also spark an appetite for even more technology in the sector. We all know the buzz around virtual reality (VR) in industries such as gaming, and you could well see more use of VR tech in engineering in the future.
Augmented reality is also something that you could imagine becoming a major presence in the industry. The ability that this gives to transpose virtual items into the real world could be very useful for engineers. It could, for example, enable them to take a digital part and see how it looks in the real world and/or how it looks when inside a physical product/design.
AI and machine learning key to engineering
As the above shows, machine learning and AI are both vital technologies in today’s engineering world. They not only allow for greater levels of automation within the sector but also free up human engineers to work on higher-level tasks. When you also factor in the insights that AI can help engineers draw from big data, the efficiencies it can bring to companies, and the positive impact of machine learning for the industry, it is no wonder that we hear about both more than ever now.
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