What our clients need to know about the use of AI in the engineering sector

Recently, we predicted that AI will become the most prevalent technology to impact the temp work markets and our core sectors in future years. It is an undeniable truth that the world of work is embracing AI and that certain roles and tasks are becoming increasingly automated. 

Our clients need to be aware of this sophisticated technology and how it is developing with the times so that their businesses can adapt and move with it.

In this blog series, we aim to explore the impact of AI across our five key sectors. This is to help you to understand how AI will affect your particular industry or business.

Firstly, we will take a look at the main areas of AI which are impacting the engineering sector so that our clients are aware of the pros and cons of adopting AI versus temp workers.

Some of the most exciting current and prospective uses of AI lie within the field of engineering and are as follows:

Manufacturing

The rise of AI promises the development of machines capable of performing ever more complicated manufacturing, and even design, tasks. Machines that are abe to learn and improve without human intervention are the ultimate goal, and this would have significant and far-reaching implications. 

Many engineers fear that their jobs could soon be taken over by sufficiently advanced robots. As manufacturing and design capabilities continue to expand, machinery has been built that is capable of replicating just about everything that a human can do on an assembly line. This means that automation has taken jobs away from people in a number of different areas.

However, a Stanford University study entitled ‘One Hundred Year Study of Artificial Intelligence’, reported that there was no imminent threat to workers. The study argued that even if or when AI does have a significant impact on jobs, this will be balanced by numerous other positive effects on society.

Perhaps the most prominent example of AI being used in engineering is in the field of automobile manufacturing and on its assembly lines.

The combination of software and hardware that has made its way into the manufacturing process has grown progressively more sophisticated over the years. Initially, these robots were performing simple engineering tasks that involved relatively large components and movements. Today, they are capable of precision movements and of emulating the most intricate parts of the process.

Big data

Data is a commodity unlike any other that the world has known. It is extremely valuable financially, but it can also be used directly in order to give a business a massive edge over the competition.

AI, especially in its most sophisticated implementations, relies heavily on large data sets and algorithmic learning.

One of the most exciting applications of artificial intelligence within the field of engineering is machine learning. Machine learning is dependent upon the constant generation and analysis of data. It is via this process, of extensively collecting data about performance and subsequently analysing it, that AI is able to learn. If the program is equipped with the right algorithms to identify mistakes and formulate solutions then it can perform a process and continuously refine it.

For engineers who are working on large scale public projects, big data will be a staple of their work. Big data analysis can tell researchers, in unprecedented detail, where the flow of people in urban environments is at its densest. This, in turn, means that public infrastructure decisions can be based on objective scientific analysis.

Also, within the context of engineering for public works, big data can be used to analyse how well certain solutions have performed when implemented elsewhere. Big data can also allow for an objective and detailed comparison of how similar the current environment is to ones where the solution has been used before. This is relatively simple when using big data analytics techniques, but would be a long and expensive process to complete otherwise.

Machine learning

One of the most significant technological concepts for the future of AI engineering is machine learning. Machine learning is the study of exactly how machines learn. The ultimate goal of AI is not just to have machines that can learn, but to have machines that are capable of self-analysis. Such a machine could assess the efficiency of its learning methods and refine its processes to a much greater degree.

But what would the practical applications of machine learning look like? Imagine if every one of the robotic arms that put cars together contained a tiny camera. Each arm could then review the work of previous robots along the assembly line. If they were to identify an issue they could then formulate a solution.

The technology is in place to accomplish the first part. A high-resolution video of a half-assembled car can be taken and algorithms developed to identify whether there are any clear faults. The next step is having robots respond to the fault, based on what they ‘see’.

Machine learning takes this process to the next level. Within machine learning, the data collected by all of the robots involved in production can be pooled together and it is possible, via a central control, to learn which problems are most likely to appear. That central AI would subsequently be able to formulate solutions to problems, rather than simply following predefined outcomes and routines.

Natural language processing

Natural language processing is a field of study dedicated to improving the ability of humans and machines to communicate. In particular, natural language processing aims to improve the sophistication with which machines can respond to the human voice. As with machine learning, natural language processing makes use of large data sets and algorithm-based learning.

Think of the voice assistant as a smartphone. Over the past decade, the accuracy with which they hear and transcribe our voices has improved. But while a phone might be able to identify the spoken word, this is by no means the same as understanding.

Currently, a phone looks for certain keywords that it understands to work out what someone is asking, based on context. It then responds or performs an action and sometimes vocalises a response. Natural language processing aims to refine this process by allowing the machine to develop a deeper understanding of language. If this understanding is refined enough, then it will reach a point where the machine can deduce what someone wants when presented with an entirely new command or request.

If an engineer is trying to work out how to reinforce a particular feature in their design, it would be beneficial if they could simply ask their computer. Or in the case of an assembly line, imagine the advantages if a human overseer could give the robots feedback. They could ask the robots to perform their roles in a slightly different way, to make adjustments, or even to try new things and analyse the result for greater time and cost saving efficiencies.

Image processing

It might not be immediately obvious as to what image processing has to do with engineering but this is another technology which is vital to implementing AI to its full potential in this field.

When humans see an object, it is because light is entering the eye and being converted into an electric signal. This signal is then carried to the brain via the optic nerve. The brain turns this electronic signal into an image, it is this image that people ‘see’.

Machines work in a very similar way. A camera can be set up in order to record an image which may be displayed to a user. However, this is not the same as the machine understanding the image. With image processing algorithms, machines can analyse what they see and react accordingly. From an engineering perspective, this means that machines would be able to identify structural abnormalities and other issues that have identifiable visible signs.

This kind of image processing technology could also make a significant difference to the workplace safety of engineers. There may often be visual clues indicating structural deficiencies and weaknesses that are not immediately obvious until the structure fails. By combining image processing with data input from other sensors, AI can be used in a variety of contexts. For example, on both construction sites and the scenes of fires, structural integrity can become a concern. Having a more reliable way for engineers to assess integrity could save lives.

Internet of Things (IoT)

Today, people are used to having vast amounts of data flying through the airwaves all around them as they are constantly connected to WiFi and 3G and 4G networks. As smart devices become more common in homes, people are also beginning to see the practical potential of being able to link multiple devices together.

The IoT refers to a hypothetical network, which would connect everyday devices and things together, in the same way that the internet connects computers from around the world. Allowing various devices to collect and share data would open up some exciting new possibilities.

As the IoT gradually becomes a reality, it will increasingly become something that engineers consider during the design process. The virtually endless number of ways that devices can be connected to work together will allow new and innovative solutions to many problems.

Jobs

No discussion of the impact that artificial intelligence is having on engineering would be complete without mentioning the impact of automation on jobs. In many places, there are widespread fears and anxieties surrounding automation. As machines begin to replace humans in certain jobs, there are worries that there will eventually have no need to hire people at all.

It should be acknowledged that the threat to jobs is very real, and in some areas, it has a significant effect on communities. However, most researchers agree that the long-term benefits of automation outweigh the potential drawbacks.

In the case of engineers specifically, AI is opening some exciting new horizons for the field. These new opportunities should be embraced. It is important to realise that many of these advances will make a big difference to our ability to tackle the biggest issues facing our civilization.

Conclusion

Right now, robots are executing jobs that are otherwise unsafe or overly labour intensive for humans. This means that, for the most part, humans and robots are living in harmony. Above all, humans, despite their qualms of  a robot takeover, will always have control over robots and would never sacrifice one of their own in favour of AI.