Is Clarity between the Engineering Disciplines Blurring?Dr. M S Ganesha Prasad, Dean, New Horizon College of Engineering, Bangalore


Because of unprecedented situation, due to COVID 19, Job markets in INDIA is changing drastically; Industries like IT, IT support and services, Engineering service industries needs engineers who are very much comfortable with online tools.

Very recently to develop video streaming apps like Zoom, Skype etc, Govt of India sanctioned Rs one crore as start-up fund. To co-up with the present and future market trends and sustain in the competitive world one has to improve certain skill sets which are not only related to soft skill sets like – Customer services, Bilingual or multilingual, Strong interpersonal skills, Self-motivation and Creativity, Emotional intelligence, oral and written communication skills, In addition to these skill sets, for an engineer’s belongs to any domain must & should equip with technology skills like Artificial Intelligence, Machine Learning, Deep Learning, Autonomic computing, Cluster analysis, Cognitive computing, Data science, Genetic algorithm and Unsupervised learning. The simple definitions of these technologies are

Artificial Intelligence: The technology AI may be applied to any machine that exhibits traits associated with a human mind such as learning and problem-solving. Artificial intelligence refers to the simulation of human intelligence in machines that are programmed to think like humans and mimic like their actions.

Machine Learning: It is a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns and make decisions with minimal human intervention.  Machine learning is a method of data analysis that automates analytical model building.

Deep Learning: Also known as deep neural learning or deep neural network. This is a subset of machine learning in artificial intelligence (AI) that has networks capable of learning unsupervised from data that is unstructured or unlabelled.

Autonomic Computing : Autonomic computing is a computer’s ability to manage itself automatically through adaptive technologies that further enhance computing capabilities and cut down on the time required by computer professionals to resolve system difficulties and other maintenance such as software updates.

Cluster Analysis: This is a tool used for Machine Learning or Deep Learning which ultimately reduces the computing time. This basically groups similar contents either in organised or unorganized databases. This technique is more popularly used in heterogeneous database for segregating of the data.

Cognitive Computing: These are the computerized models, used to simulate the human thought process. Experts in this domain achieved great mile stones  initially by adopting these models to the pet animals like dogs / cats. In general where ever there is complex situations, where the answers may be ambiguous and uncertain, tools like Cognitive Computing really plays major role.

Data Science: This is an inter-disciplinary field which uses scientific methods, processes, algorithms and systems to extract knowledge and insights from many structured and unstructured data.

Genetic Algorithm: A genetic algorithm is a meta – heuristic tool inspired by the process of natural selection that belongs to the larger class of evolutionary algorithms. The genetic algorithm is a method for solving both constrained and unconstrained optimization problems that is based on natural selection.

Unsupervised Learning: UL is a type of machine learning algorithm used to draw inferences from datasets consisting of input data without labelled responses. The most common unsupervised learning method is cluster analysis, which is used for exploratory data analysis to find hidden patterns or grouping in data.

Generally with the added technical skill sets the engineer can comfortably connect to the surroundings. Most of the software / hardware products which are available with us are actually making use of such technologies. The primary responsibility of the engineers is to be aware of these technologies irrespective of the domain he/she belongs to, everyone definitely need a certain level of comfort around technologies.

Regarding these new technologies one needs to learn from basic level till to an extent of understanding oneself, engineers in most roles will be required to access data and determine how to act on it. On a more fundamental level, everyone needs to be able to understand the potential impact of new technologies of their industry, business, or in their job.

Today we need to understand the speed at which the technology is changing in the present and future workplaces, the responsible engineers have to be alert and able to manoeuvre and enjoy these technologies. Human brains not only are flexible, but are also need to be adaptable as we are required to adjust to shifting workplaces, the expectations from all the organisations are very high, particularly for beginners needs to work with their skill-sets. For the fourth Industrial revolution once ability is not only to see the technology changes as the burden but as an opportunity to grow and innovate.

People who can work comfortably in changing work environment will show that, they like changes in the working environment then to be static, this attitude of adopting to change management is very much essential, because skills required for the professional services is dropped drastically to an average of 6 years only, it’s time for all of us to begin acquiring skills that will make us valuable resources in the future workplace. Fast-paced technological innovations mean that most of us will soon share our workplaces with artificial intelligences and allied technologies. This may be possible by adopting a commitment to lifelong learning so you can acquire the skills you will need to succeed in the future workplace. At present the soft-skills are considered to be required skills, but to survive in this competitive world one at-least need to know the knowledge of Artificial Intelligence, Machine Learning or allied technologies.  Understanding these technologies may not requires good amount of technical knowledge base, but one need to distinguish between what is present and future technologies.

-For Computer Science and other IT Branch engineers must know the concepts of daily used used instruments / products to upgrade the same by using above technologies.

-For Mechanical Engineering and other aligned branch engineers must know the interfacing of the products / processes to the IT technologies mentioned above.

In totality as we progress for better tomorrow the lines between engineering disciplines are blurring. We like to mention to all the budding engineers that, if you are good in analysis better choose the branches like Core engineering disciplines. If you are good with algorithmic thinking then CS/IT branches may be good for you. Everything will be in place once you clear about your strength and do what you enjoy most. 

Dr. M S Ganesha Prasad

As mentioned by Sri Jaggi Vasudev Sadhguruji  “Memory is not intelligence. Having more information does not make one more intelligent.” Also “Alertness and consciousness are what will make a person superior,” as artificial intelligence takes up the task of remembering and carrying information but without good logic one cannot build machine/hardware/software which can perform like a human brain.

So, one has to upgrade the skill sets to meet the present day requirements, but with more ethical manner, because the logic will apply back to the humans.

Dr. M S Ganesha Prasad
Dean, Prof & Head
Department of Mechanical Engineering
New Horizon College of Engineering
Bangalore, Karnataka


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