Artificial Intelligence - Specialist Skills vs. Generic Skills
AI is a vast field that someone needs a plethora of skills to succeed in. The skills required to become a successful AI developer are not obtained overnight. It requires several years of training and molding. The skills for AI jobs can be divided into specialist skills which are based on intensive subject knowledge and generic skills which are based on the attitude of the professional involved. Both these skills are equally important for the success of any AI specialist. The margin between both types of skills is not a clearly defined one.
The various skills required for AI jobs tend to overlap at various points in time. However, some of the major skills required can be grouped into divisions. These divisions can help aspiring AI specialists to move forward with their careers. The generic skills are easy to understand and have a lot of similarities to the basic attitude training required for demanding jobs. The specialist skills for AI are very difficult to learn and understand. That is probably why there is a lack of trained AI experts.
As mentioned above, the generic skills are not 'skills' in the common sense of the term. It is rather a compilation of the necessary attitudes required to survive in the competitive and cutting-edge environment, i.e. artificial intelligence. At the onset, people may find many similarities between these skills and the necessary attitudes required for a variety of other jobs. This is because the driving force behind AI, however advanced it is, depends completely on the ability of the people behind it.
Artificial intelligence, as any novice may know, is one field that requires more manpower than most other industries. This is because various AI is beyond the understanding of human intelligence until now. Only when humans realize AI supremacy, the need for manpower in the field of AI will be cut short. Therefore, some of the generic skills required for any AI job are given below:
The two C's-curiosity and creativity:
When talking about innate skills, the first things that come to mind are curiosity and creativity. This is something that has to be developed within a person. The only way to improve creativity is to try out new things as often as possible. It is also important to replenish yourself with new information to widen the scope of creativity and prevent stagnation. An AI specialist must be adept at finding innovative solutions to various problems by blending technical knowledge and creativity. The ability to think differently is very important for any AI professional.
Ability to learn and refresh:
The ability to learn is an important skill in any field as technology tends to update quickly and the future is kind to those who can adapt to change. To build and sustain a successful AI career, a person has to have the ability to learn quickly and assimilate information. People who are drawn by intensive fields like AI must be ready to invest a lot of time and energy in keeping themselves updated with the rising industry trends. An ability to grasp new concepts quickly will also give an impetus to the AI professional.
The field of AI is rewarding only for those who can remain patient and spend a lot of time on singular and unrewarding projects. Several science-fiction authors, TV shows and movies have incessantly romanticized with the concept of AI. Truth be told, real work to be done in AI is nowhere as easy or rewarding as portrayed on the big screen. The reality is that we are still another 50 to 100 years behind, reaching the type of AI involved in creating T-800s and Skynets. Hence, the field of AI will not be a bed of roses to lie upon immediately and will require tremendous amounts of patience and perseverance.
AI is certainly one of the most advanced technologies known to man. The work done by AI developers around the world may help to engineer the future. If at least 25% of the AI projects around the world become a success, then it will surely alter the world we live in and how we perceive it. An AI specialist is working at the top of the world. To ensure productivity, a healthy competitive streak is necessary. Many of the world's greatest discoveries and inventions have come due to the intense rivalries between scientists, explorers, and inventors. Thus, a mentality to compete, win and move forward is high in demand.
Taking risks is an important part of life. The experience people gain by taking a risk is a wholesome one. The modern AI specialist must not be afraid of taking risks. He/she should also have the ability to know when to take a risk and when to hold back. This is a skill that can only be procured through experience. It is a matter of fact that someone is called an expert only if they can pull through by taking calculated risks.
This is the area where many people tend to give a lot of importance. Someone interested can find true success only by having a blend of both specialist and generic skills. Specialist skills are what the employers look for on paper. If someone has the necessary specializations, employers will look to assess the generic skills as well. The term specialist skills itself defines its meaning. It consists of a set of tools which is a must for anyone venturing out into the field of artificial intelligence.
An AI specialist requires several specializations. Most companies demand post-graduates and Ph.D. scholars for filling various job openings. However, recent trends have shown that programmers with an interest in several aspects of AI have transitioned into AI experts after garnering a few years of experience. Though it doesn't cover every necessary skill in the book, some of the basic skills required for any AI specialist are given below.
Strong understanding of mathematics and algorithms:
The major underlying concepts in AI involve a lot of higher-order mathematics. The potential AI candidate must be well-versed in problem-solving and applied math skills. He/she must be able to perceive problems and identify the algorithms best suited for solving particular problems. Solving problems and optimization of the solutions to meet varying needs is an important part of the job profile for any AI expert. The ability to analyze and grasp problems involving complex mathematics is the most important skill required for AI and its subsets, notably machine learning. Many complex AI algorithms like neural networks are almost entirely of complex mathematics.
Command over programming languages:
An AI expert is also a programmer of sorts. A person cannot be called an AI specialist if he/she is not sufficiently good with various programming languages. An AI expert will have to filter through large amounts of code in different languages. Hence, a basic knowledge of common programming languages like C, C++, Java, Perl, R, Python, etc. is a must. By having an idea regarding the basic programming languages, a person can learn several programming languages of varying complexities. Each programming language has its benefits. For example, C++ can help speed up loading time while Python can help reduce the complexity of the programs.
Probability and statistics:
Probability and statistics play an unforgiving part in the development of AI programs. Both beginners and seasoned AI programmers require the help of various probability-based models during development. This primarily includes application in machine learning and other predictive analytics. Several models like Naive Bayes, Makarov and Gaussian models are prerequisites for AI program developers and programmers. Statistics also play a major part in the world of AI. Several important conclusions are drawn from statistics collected over a period of time. Someone can't hope to achieve big in the field of AI without an in and out knowledge of probability and statistics.
Data processing and Unix:
This is deep into the technical domains of AI. Clustering and classification of large amounts of data is an important part of an AI specialist's job. To meet this demand, the data has to be distributed among several computers and clustered among various computers. AI processing is done in Linux based machines. Therefore, knowledge of Unix tools like awk, grep, cat, sort, etc. will come in handy.
Advanced signal processing:
AI involves dealing with signals and assorted data continuously. Machine learning involves a lot of extraction of signals. For this, machine learning experts have to be well-versed in the processing of various forms of signals like bandlets, wavelets, curvelets, etc. Understanding of time-frequency analysis is also necessary. The importance is given to analysis approaches like Fourier analysis and convolutions. Click here to learn Artificial Intelligence Course and Data Science Course
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