What is the future of engineering at a time when Artificial Intelligence is fast taking over the world?
It was all about blueprints, sketches, and real models back then. However, today's focus is primarily on software tools and computer designs. Artificial intelligence and digital technology are becoming increasingly popular. Smart systems and supervised machine learning are being transformed to a large part by advances in AI.
Engineers' tedious tasks, such as discovering relevant content, rectifying problems, and determining solutions, will be made easier by artificial intelligence systems. Smart systems can assist them in completing their task promptly. AI and digital technology can also help the system engineer create advanced designs, including sensor-based design procedures and delivery to intelligent manufacturing facilities.
However, AI may not approach the job in the same way that a human designer might. This can also go off the grid at times. Because today's machines are typically comprised of expert systems that make decisions using the software. Because the software's engine is based on if-then logic, it can only be learned by practice.
When new knowledge is added to the library, the software use if-then logic to extend additional facts or ifs, resulting in a list of possible solutions or events.
This is the process that AI and machine learning are built on. As more individuals get linked through the internet, smart machines will be able to provide new services and opportunities.
Expert systems presently make up the majority of smart systems, but by 2024, autonomous robots will have mostly replaced them due to technological advancements. There are both advocates and detractors of this tendency. Because the number of robotic appliances continues to grow, the cost of sensors decreases, which is the simple reason for artificial intelligence robots to proliferate quickly.
Over the forecast period, the robotic sensors market is expected to grow at a CAGR of approximately 8%. (2021-2026). Most industries, including automotive, transportation, industrial manufacturing, logistics, and defence, have begun to use autonomous robotics and digital technology as their primary means of production.
As a result of the rapid rise of smart technologies with their roots, networked artificial intelligence may cause ambiguity. Though computers are capable of intelligent behaviour, they are not capable of recreating the human brain's cognition processes. Artificial intelligence algorithms can only deal with known data, thus they can't forecast or make reasonable decisions in unpredictable scenarios.
Because of their broad connectivity and low-cost sensors, new technologies based on the most advanced AI and machine learning have emerged. These technologies are basic and have yet to be able to replicate the human brain.
It becomes evident that AI algorithms link facts to solutions that are based on experiential learning and do not take physics into account. AI has progressed from a scientific breakthrough to a tool for engineering. Engineers from many fields must understand and integrate AI tools into their engineering designs in order to keep up with the latest digital technology developments.
Many open-source technologies, such as Microsoft's DMLT, Google's TensorFlow, and Amazon's DSSTNE, have machine learning software libraries. Google's DeepVariant open AI programme can more precisely describe a person's genome from sequencing data than other ways, which is assisting engineers.
Natural language processing is used by personal productivity assistants such as Amazon's Alexa, Apple's Siri, and Microsoft's Cortana. Oncologists have taught IBM Watson to assist them in treating and diagnosing lung cancer. Tesla is getting closer to developing self-driving cars. Zebra Medical Systems, an Israeli startup, is developing radiological instruments that are more accurate than humans. All of this is feasible thanks to various types of engineers who are in charge of smart system training.
At this point, rather than designing and manufacturing items, a human engineer's position may soon be that of a director. Humans may not be performing the activity, but they are the ones who determine the route the machine should take. The engineering system will alter as machines learn to design things, but engineers will remain highly competent and relevant.
The uncertain future of technology necessitates resilient and adaptable engineers who can develop strong artificial intelligence technologies with a variety of skill sets, including teaching AI systems how to create and join future human-AI companies.
Reference- Analytics Insight