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    Top 10 Skills to Gain in Artificial Intelligence in 2023

    by BML Munjal University February 16, 2021

    Artificial Intelligence is the buzzword for the technological revolution happening around the world in science, industry, and education.

    Creating machines that can “learn,” respond, and act according to the desired outcome is what everyone is endeavouring for.

    Given this focus, organisations are investing more and more in replacing older systems with newer ones powered through AI.

    Resolving employee queries using chatbots, the first level of job application screening through AI-powered systems, risk evaluation, learning, and development – there are many such areas where larger companies have invested in AI and are reaping benefits. Smaller organisations and start-ups, too, are not far behind.

    With the growing need for Artificial Intelligence, there is a huge demand for AI professionals and Machine Learning Experts getting built up.

    Engineering students, too, are showing interest in taking up this subject to enter the industry and grab up the opportunities.

    While Artificial Intelligence is the result of a series of technologies involved in creating a “smart” process, Machine Learning, Neural Networks, data science are some of the building blocks which need to be learned to build a career in AI. These are the “skills” learned with an Artificial Intelligence course.

    The BMU Perspective

    BML Munjal University was created to bring about a change in the way education is delivered to students. At BMU, we give a lot of focus to the course curriculum.

    It is designed and customised from a regular one with the help of industry experts. We have collaborated with the Imperial College of London, which makes our courses with a truly global mindset.


    Our Bachelor in Electronics and Communications Engineering course is no exception in this regard. We have several subjects involving the latest technology incorporated in the course – Artificial Intelligence and Machine Learning is one of them.

    To truly excel in any field, we believe that students must transition from academia to the corporate world. We make it possible through our multiple industry interfaces and relevant courses so that our students can settle smoothly in the corporate.

    For a career in AI, it is also important for students to learn to program in Python, Java. We ensure that students learn programming before leaving college.

    Talking about skills, we should also note that a successful career in Artificial Intelligence is not guaranteed with learning the “subjects” mentioned above.

    There must be some behavioural or attitudinal competencies inherent in an individual or acquired through practice, making someone a strong candidate in a career stream.

    Like, theoretical knowledge of medicines and human anatomy does not make someone a good doctor; he or she needs some other skills such as being resilient, persistent, eye for detail, and strong sensory abilities.

    Likewise, a career in Artificial Intelligence requires some skills that can be learned or acquired along with the course.

    For ease, we have divided the skills into 3 clusters based on the KSA format of competencies: Subjects (Knowledge), Languages and Tools (Skills), and Behavioural (Attributes). Let us have a look at some of these:

    A. Subjects

    1) Machine Learning

    If it is represented as a Venn diagram, Machine Learning is a subset of AI. Artificial Intelligence empowers a machine to “behave” intelligently.

    Machine Learning is the process of “teaching” the machine to exhibit such intelligence. Machine learning builds a model on data, which helps a machine make intelligent predictions without using a coded algorithm.

    There is no learning AI without skilling up on ML. Machine learning uses 3 approaches to “teach” a machine: supervised, unsupervised, and reinforced learning. Netflix suggestions (based on matches), email filtering, are some of the many applications of ML in daily lives.

    2) Deep Learning

    360-degree camera view by stitching together images from cameras located on four sides of a vehicle is a common example of Deep Learning. Deep learning is a subset of Machine Learning and is a method in which the AI system “learns.”

    It uses multiple layers to pick out deeper features from an image or a sound. Higher the layers, finer the details, while the lower layers extract only the outline from an image.

    Image recognition, speech, or audio recognition are some areas where Deep Learning is used. It has a wide range of applications and is a skill that is learned in an AI course.

    3) Data Science

    After theoretical, empirical, and computational, Data Science is the fourth paradigm of science. It combines mathematics, statistics, and algorithms to “read” insights and gain knowledge from structured and unstructured data.

    Data science is an important aspect of Artificial Intelligence and some other fields that depend primarily on the analysis of data. It is an essential skill that comes along with a course on AI.

    TensorFlow, PyTorch, Apache Hadoop, and Jupyter notebook are some of the platforms used in Data Science for data processing.

    4) Neural Networks

    The neural networks or artificial neural networks(ANNs) are built to replicates the biological neural networks in the human brain. Just like the neurons in the brain, the ANNs also have nodes that are interconnected in the network to transmit inputs from one to the next in the form of “signals.”

    Applications of ANNs include 3D reconstruction, handwritten note recognition, spam filtering, games, to name a few. This is a wide subject in itself. However, an AI course also provides a scope for an elementary understanding of the subject.

    B. Language & Tools

    5) Languages: 

    No matter which part of the world, Python is considered the most used language for programming AI systems. To become an AI expert, knowledge of programming using Python and a few other languages is important. Java, PROLOG, R, LISP, C++ are a few of the other languages used in Artificial Intelligence.

    It is possible to learn programming in these languages through several easily available online resources. Most of them are hosted by open-source software and can be made available to anyone. Python is widely used because of its extensive library and ease of use or simplicity. Python is learned by even people from non-technology backgrounds.

    Most AI systems are built on Python, and it will certainly give you an edge over others if you have mastered the language. Python is a language skill that needs to be developed to get into an AI career, from creating algorithms to encoding.

    6) Knowledge of advanced signal processing techniques

    A part of this is covered in Machine Learning itself. However, signal processing being an important aspect of Artificial Intelligence, it is imperative to learn some of the techniques.

    Physical waveforms such as sound when transduced into electrical signals using electronic processors. The same applies to images when converted into pixels to transform meaning electronic information.

    Projects in this field solving algorithms in signal processing can help become an AI expert.

    7) Unix tools

    Most AI inventions and work are currently focused on hardware and software changes to make life easier in banking, online search, user interface, healthcare, and customer care.

    And nearly all of this work of data processing happens in the Linux ecosystem. Therefore, it is difficult to work on different functions of AI without being accustomed to Linux-based systems and UNIX tools such as cut, sort, ark, tr, grep, etc.

    C. Behavioural

    8) Problem-solving

    Artificial Intelligence with problem-solving is not even possible. AI itself is a constant effort at solving problems that can make life easier for mankind.

    The AI Effect is all about that. Larry Tesler’s Theorem states, “AI is whatever hasn’t been done yet.” Once it is done, or a problem is solved, then it is no longer “intelligence”; it is just a repetition of codes/

    Given the above, problem-solving and analysis become a huge part of AI. Whether AI in banks or AI in warfare, AI in the medical world, or AI in Data Science, analysis of the problem is a must.

    What do AI professionals do? At the end of the day, AI professionals are tirelessly working to solve one of the many problems faced by man and innovate a technology that can make our lives easier.

    To make a machine follow a predetermined set of rules is not the task of an AI specialist. Creating a new set of rules each time to solve a new set of problems is what AI experts do.

    Microsoft CEO said, “The future we will create is a choice we make. Not just something that happens.” And it is this creation of the future where Artificial Intelligence experts contribute greatly through their problem-solving skills.

    9) Communication and collaboration

    Unline in the movies, which show a miraculously talented scientist, was working on codes or test tubes and jumping up from his seat saying, “Eureka,” researchers or scientists or engineers do not work like that in real life.

    In most cases, they work in the Research and Development department of an organization with ten other team members. These ten people are responsible for the initiation, development, completion, delivery, testing, and presentation of the technology they work with.

    In this environment, the team constantly debates, discusses, brainstorms, and communicates their thoughts and ideas through calls, meetings, presentations, reviews, and discussions.

    This is why it is important to understand that there is no respite from a very important skill required of almost all jobs – Communication and collaboration.

    To get the project running for an organisation, AI experts will probably need 15 other team members, the best minds hired from the industry.

    Like the team huddle, there will be scrum meetings, review meetings to discuss progress,  challenges, and daily tasks. The communication and collaboration skill is key to succeed in this environment.

    This is a skill anybody, especially students of any field, must develop to succeed in their careers even if it requires them to work with machines.

    10) Computing efficiency

    How do you make a machine “learn”? Machines do not have natural intelligence and cognitive abilities to learn through a social environment. They are “taught” through data.

    An enormous amount of data is fed into them so they can relate to patterns and can identify and therefore “learn.” To give the simplest example of AI.

    To make a machine “identify” an image as “Cat,” it must be fed with endless images of “cats” of different kinds, from different angles and told that “these are cats.” The machine “remembers,” and when shown a cat, the machine calls it a “cat.”

    Having said that, it is important to note that an AI engineer will work with data. Enormous amounts of data on which they will apply mathematics, programming, and algorithms.

    The data can be so huge that it may have to be distributed across a system of machines or clusters, and that is known as “Distributed computing.”

    It is not just enough for an AI aspirant to know about computing; they must also be comfortable distributing computing.

    AI aspirants should have a knack for computing data, statistics, applied mathematics, and working with probability. This skill is indispensable and a must for all AI engineers.

    Speaking about skills – knowledge or attributive, an Artificial Intelligence professional should also be innately creative and curious.

    These skills are inherent in a person. They are either there or not. Ask yourself, Am I curious to learn more even after I have failed a million times?

    Do I have the patience to work and fail, work, and fail to come up with a resolution? Am I someone who understands consumer needs?

    Artificial Intelligence is about innovating to design solutions for newer needs. Therefore an AI expert constantly strives to understand those needs – social needs and logistical needs of the end-user.

    It is only through understanding these needs that new inventions will come and new problem statements which will then be needed to solve.

    This self-evaluation, along with a deep sense of interest in the subject, will catapult you towards a career that is not only interesting but also highly in demand. A mastery of the skill sets mentioned above will ensure that you are successful in that career.

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