It is a virtual certainty that AI and all the anticipated developments and advances in technology are going to shake up the engineering profession like never before. It doesn’t matter where you live, what you do for a living, or how much you rely on technology, the world is going to have to respond and adapt to the changes which are coming. Engineers are at the forefront of these impending and already occurring advancements. How should they prepare to adapt and embrace the implementation of AI in future projects?
AI and Engineering: What Should Be Expected?
Whilst it is still in the early stages, there are many ways that we expect AI to change the profession of engineering. From the tools that engineers use to the processes applied to projects, everything will fall under the influence of AI. It is always evolving to make engineering more efficient, open new roles, and provide engineering professionals with more time and energy to spend on tasks and projects of a higher value.
Not only will AI help engineers work smarter and more efficiently, but it will also dramatically improve processes and workflows. AI provides the opportunity for engineers to fully flex their skills and work on projects currently held back by logistics, time restrictions, and inherent complexities.
Although some within the profession are resistant to AI and would rather things stay as they are, most openly embracing the inevitable changes it is going to bring. Given that STEM professionals are, according to the University of Oxford, the demographic least likely to have their jobs threatened and automated by AI, this is hardly surprising.
Adapting to Upcoming Innovations
Of all the potential changes and advancements which AI may bring to the profession, three stands out amongst the rest. It is key to be aware of and understand how each of these will directly impact not only engineering but the professional and personal areas of life overall. Only then can you begin to respond and adapt to the changes with less apprehension and more anticipation.
Machine learning brings unlimited potential by widening the scope for further innovation within the engineering profession overall. With the ability to learn from processes over time, computers and machines automatically optimize themselves and carry out many of the tasks which we as humans currently complete daily. The primary benefit of machine learning is that it is completely automated and learns on its own over time to be more efficient, accurate, and overall successful.
For engineering professionals, this brings not only the automation and improved execution of routine tasks but the possibility of job evolution and the creation of new roles. Engineering workflows are now largely being improved by machine learning tools which, from the beginning to the end of a project, aids the management with intuitive designs and application of skills. By embracing current changes that machine learning has established and mindfulness of what is yet to come, engineers can work alongside computers for more efficient workflows and a competitive edge.
Similar to the Internet of Things (IoT), cloud computing, and cyber-physical systems, Industry 4.0 is the umbrella term given to the current trends we see with automation and data exchange. Often hailed as the fourth industrial revolution, it is the extension of what took place during the third industrial revolution — the early adoption of computers. The effects of Industry 4.0 enhance computers with autonomous systems backed by data and improved by machine learning.
In engineering, one place where Industry 4.0 is having the biggest impact is the IoT. The IoT streamlines the collection and management of data through new AI tools and physical collection points, ultimately culminating in increased knowledge and better application of new tools. The automation of data collection, knowledge distribution, and management of projects make the design and production processes effortless. By embracing Industry 4.0—namely big data and cloud management—engineers can grow alongside these core technologies which will define a generation, rather than be overshadowed by them.
From everyday appliances to complicated machinery, the overall design process is being reimagined across the board through generative design. An iterative design process which generates several outputs that meet potential design constraints, generative design harnesses AI software and cloud computing to generate thousands of design options. It does this by defining design constraints and variables such as maximum weight, material options, and requisite strength.
Generative design is perhaps the most applicable to engineering and its effects on the development of any upcoming technological developments. To prepare for it and its mainstream use, engineers should begin experimenting with generative design in its current rudimentary state. In doing so, you end up more prepared than those who haven’t paid it due attention, along with machine learning applications, utilization of Industry 4.0, and other relevant professional AI possibilities.