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What are the Opportunities in Artificial Intelligence?
Artificial Intelligence is one the hot topic in the information and technology which predominantly includes general AI, machine learning, expert systems (which can be the same thing) data mining, Neural Networks and fuzzy systems. These topics have become the most wanted and essential topics among the scholars, students, faculties and professionals.
As AI had already created enough impact among the public, this article would help the upcoming generation to know more about the career opportunities that are related to this domain. And we need to understand that fact that, Machine learning is the practical face of AI. It’s about identifying sources of data, then creating systems that analyze that data and make decisions based upon it. Below are the some of the career opportunities in AI.
AI/Machine Learning Researcher
Researcher or Research scholar is one of the important and key opportunities with the respective to AI. Since AI and other related terms are very new to the society, the number of requirements for the researcher on AI is very high. Research improvements to machine learning algorithms will be the major focus as the research scholar. Most of the research scholars are selected by the governments and give some real time problems. In some cases, research ways of applying it to new domains and technology which often combines one or two domain together.
Usually if someone has got a PhD have good job opportunity. And there are also positions working on nascent technologies such as general AI (deep mind, etc.) These positions are very rare, and usually restricted to strong PhD researchers and hand-picked team members. Apart from the PhD, the major requirements will be based upon the logical thinking and innovation in the field. More than the logical thinking, people with innovative ideas to solve the real time problem are always wanted as research scholars
AI Software Development
Requirement for AI knowledge for software has become essential, as most of the tools have incorporated AI into them. Developing the systems and infrastructure that can apply machine learning to an input data set. This is just like any other software engineering position. Often software engineers are recruited based upon the problem solving skills and the way in they think.
You can get these jobs with a bachelor’s in a related field (e.g. computer science), though it’s good to have some understanding of machine learning and AI, and good math skills. Masters degree with good research background(Research publication in National and International Journals) on these is also preferred for the role of AI software engineer or AI solution architect(Junior to Senior). Often these positions are started from the lower level and steadily increased to the level of architects.Most of the roles are filled by the engineers who are capable of thinking like Machines.
Automation Engineer
Artificial Intelligence had wide opened a new area which is closely related to it is called as Automation. Automation has become mandatory in the current scenario, which predominantly uses AI to help the human engineers to solve the variety of problems right from the simple to complex problems. Automation engineers would understand the nature of the environment and try to automate commonly repeated task which doesn’t require the support of human engineers. By automating this task, AI tools are helping the human engineers and organization to meet the demands on time with limited number of resources which also help them to reduce the human effort on the task which doesn’t require human presence.
Data Scientist
In the current Data has become gold. Data has become gold because, it plays a key role in the prediction of business by an organization. Although most of the predictions are based upon the data generated in the internet, it is often considered as important for the business. This has wide opened a new area of science which is termed as Data Science and the people who are involved in this are termed as data scientist.
Most often this job is related to the deep investigation of large data sources, and often creating and training systems to recognize patterns in them. A PhD in a related field is not unusual task but there are people who have involved in data science with just a bachelor degree. Requirement for the data science engineers and data scientist have steadily increased and created huge demand in the market. For the next 5 years, data science would emerge as one of the predominant area of opportunity for the fresh graduates. Understanding on the data structures and statistical mathematics are essential for a person to become data scientist.
ML Engineer
Machine Learning is one part of AI which often involves in applying a machine learning or AI framework to a specific problem in a different domain. Machine learning is considered an essential and peak domain by the Forbes technology 2017 article. Often machine learning are applied to gesture recognition, ad analysis, or fraud detection are the popular area in which AI has been used. The major focus for the Machine learning engineer is to understand the nature of the machines and solve the problems which are related to the society. Often people with good mathematical knowledge and understanding on the hardware systems are recruited for the post of machine learning engineers. These machine learning engineers are required huge in number, as the industry is now moved towards AI.
Skills Required
Numbers of opportunities that are related to AI are always huge in number, but there is always shortage of engineers in this domain. Strong foundation on System Software, Data Structures and Algorithms, strong mathematics knowledge, abundant problem solving skill, programming skills, learning skill are the major skills which are required for a person to shine in the domain of AI.
Apart from these skills, strong innovative thinking is very much essential for a person to become the Artificial Intelligence Engineer or AI Architect. Often people in this domain are paid high, as they become the reason for reducing the cost spent by the organization on silly task.
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