A prominent feature in science tale over the years has been artificial intelligence. Since the earliest days of computing, scientists and other intellectuals have been fascinated by the notion of creating a machine capable of replicating the human brain. It used to be thought that the analogy of the human brain is like a network ran deep. However, we now know that the plot is much more complex, the way that the brain works goes beyond a simple computer.
We still do not fully appreciate how knowledge arises in the human brain, and there is still much debate surrounding whether consciousness can be separated from a seasoned intelligence. But artificial intelligence need not be this complex; we see far simpler examples of what we might describe as artificial intelligence regularly.
The voice assistants pre-installed on every modern smartphone are just one example and now these same AIs are being integrated into alarm clocks and speakers so that they can be used to control a variety of smart devices around the home.
Artificial intelligence is increasingly gaining its way into industrial and manufacturing contexts. There are even AIs being used to conduct high-frequency trading on the stock market. AIs are now throughout, meaning that it is becoming easy to forget just how amazingly complex they are. AIs have a numerous deal to offer the world of engineering. Some of the most exciting current and prospective uses of artificial intelligence are within the field of engineering.
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What is Artificial Intelligence?
However, while artificial intelligence has long been considered and discussed in an abstract, theoretical sense, it is only in the last decade that we have begun to see it being used in consumer technology. It has now become so ubiquitous in our everyday lives that it is easy to forget what a complex demonstration of technological prowess and understanding artificial intelligence represents.
In answering the question of what artificial intelligence is, and what the term means today, we need to consider what constitutes intelligence. This is not as simple as many people assume it should be. For example, would you consider all animals to be intelligent? Or rather, to have intelligence?
Some animals, such as cats, octopuses, and even dolphins, among others, demonstrate high levels of intelligence. When comparing two different animals, such as a mouse and a gorilla, there are several ways that scientists can measure their relative intelligence. But objectively defining and measuring intelligence is difficult.
The AIs that are used in the engineering sector combine both software and hardware components. Think of the robots on a car assembly line and the software that controls them. They are in themselves quite impressive feats of engineering, but are they intelligent?
You might be surprised to learn just how smart and sophisticate our uses of artificial intelligence in engineering are becoming. Smart production lines are the future. But how exactly does artificial intelligence make such a big difference to the engineering sector?
The rise of artificial intelligence promises to allow us to develop machines capable of performing ever more complicated manufacturing, and even design, tasks. Machines that are capable of learning and improving without human intervention are the ultimate goal, and this would have significant, and far-reaching implications. Furthermore, in our pursuit of creating ever more powerful AIs, we are discovering information about how our brains work and how we approach the learning process, both consciously and unconsciously.
Many engineers fear that their jobs can soon be over by sufficiently advanced robots. As our manufacturing and design capabilities have continued to expand, we have been able to build machinery that is capable of replicating just about everything that a human can do on an assembly line. These fears are not unfounded then, as automation is continuing to take jobs away from people in several different areas.
Things aren’t entirely bleak, however, a Stanford University study entitled reported that there was no imminent threat to workers. The study argued that even if or when artificial intelligence does have a significant impact on jobs, this will be balanced by numerous other positive effects on society.
Perhaps the most prominent example of artificial intelligence being used in engineering is in the field of automobile manufacturing.
The combination of software and hardware that has made its way on to the manufacturing line 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.
It would not be unreasonable to say that we are now living in an age of data. Data is a commodity unlike any other that the world has ever known. It is extremely valuable financially, but it can also be used directly to give a business a massive edge over the competition.
Artificial intelligence, 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 analyzing it, that artificial intelligence can learn. If the program is equipped with the right algorithms to identify mistakes and formulate solutions, then it can perform a process and continually refine it.
One of the most significant technological concepts for the future of artificial intelligence-led engineering is machine learning. Is the study of exactly how machines learn. The ultimate goal of artificial intelligence isn’t 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 so refine its processes to a much greater degree.
But what would the practical applications of machine learning look like? Well, imagine if every one of those robotic arms you see putting cars together contained a tiny camera. Each arm could then look over the work of the previous robots along the assembly line. If they identify an issue then they could formulate a solution.
We already have the technology to accomplish the first part. We can take a high-resolution video of a half-assembled car and develop algorithms to identify whether there are any clear faults. We could then have the robots respond to the fault-based on what they ‘see’.
You might be wondering what image processing could have to do with engineering? The connection might not immediately seem obvious, but this is another technology that is vital to implementing artificial intelligence to its full potential in the field of engineering.
When humans see an object, it is because the 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.