Do I need a degree to have a career in AI? Part 2
Now we are at a new crossroads of human history. The term to describe the new revolution is coined as “Society 5.0”. This new Society 5.0 is spearheaded by Artificial Intelligence (AI) which has the potential to produce some of the most significant and disruptive breakthroughs of the twenty-first century. AI is expected to produce 2.3 million employment by the end of 2020, resulting in a net gain of 500,000 possibly new jobs, according to Gartner.
However, there is a talent shortage in AI capabilities, which is the most significant obstacle to AI adoption. The main source of concern for the AI crisis is that academic and training programs are out of step with current industrial developments. So, if you’re interested in starting a career in AI or you want to know if a college degree is necessary or not, we will try to answer your query.
The most successful AI professionals frequently share traits that allow them to flourish and grow in their jobs. Working with artificial intelligence necessitates an analytical thought process as well as the capacity to address issues in a cost-effective and efficient manner. It also necessitates forethought in terms of technical advancements that convert into cutting-edge programs that help firms to remain competitive. AI professionals must also have technical abilities to build, maintain, and repair technology and software systems. Finally, in order to do their jobs, AI experts must learn how to interpret highly technical knowledge into language that others can comprehend. This necessitates effective communication skills as well as the capacity to collaborate with coworkers on a team.
Easiest way to fulfil or gain the traits of most successful AI professionals is education, specifically degree programs. Most artificial intelligence programs are built on fundamental computer technology and mathematics knowledge. Those at the entry level need at least a bachelor’s degree, whereas positions requiring supervision, leadership, or administrative responsibilities typically require a master’s or doctorate degree. Typical coursework entails the study of:
● Mathematics: probability, statistics, algebra, calculus, logic, and algorithms.
● Bayesian networking and graphical modelling, including neural nets, are two examples.
● Engineering, physics, and robotics
● Computer science, programming languages, and coding are all examples of coding.
● Theory of cognitive science
Candidates can look for degree programs that offer AI-specific degrees or pursue an AI concentration within majors like computer science, health informatics, graphic design, information technology, or engineering. You can learn about top universities that offer AI degree programs here.
The benefits of having a degree are:
● To correctly learn machine learning. A machine learning degree will educate you about machine learning in a systematic manner. Degree programs are created by academics who are knowledgeable about the subject topic and how to teach others. The degree programs are focused and clearly describe what is expected of a student before they enroll in the program and what they will be capable of once they complete the program.
● To find work. Obtaining a higher degree in machine learning will allow you to apply for machine learning employment. Organizations will advertise positions that need certain skill sets and will choose requirements that will allow them to efficiently filter candidates. Machine learning job postings generally demand a bachelor’s degree or higher in machine learning or a closely related subject.
● To start out machine learning research. Obtaining a better degree in machine learning will allow you to do machine learning research. The vast bulk of machine learning research is conducted in university and corporate research laboratories. In such labs, competition is strong, and the criteria for advertised employment include particular undergraduate degrees and honors programs.
The completion of a degree in machine learning does not ensure the desired outcome. It may improve your chances, but success is not guaranteed. Limitations are:
● A degree is not cheap. A degree program might cost tens of thousands of dollars or more, and you are foregoing any money you could have earned during that period in the hopes of having a higher earning potential in the future. Granted, you may be able to offset those fees with a scholarship or delay those costs into the future.
● A degree takes a long time to complete. A degree takes years, and a higher degree might take several years, if not a decade. If you are interested in implementing or employing machine learning now, that is a very long time to wait.
IBM from 2017 also considers individuals with hands-on experience gained through a coding boot camp or an industry-related vocational training, as 15% of hires do not complete four-year degrees. So, they are options where you can skip a university degree. You may finish official machine learning training at your own speed, from the comfort of your own home. Three formal training possibilities are available:
● Completing an online machine learning course. Attend the lectures, complete the assignments, and engage with the other students.
● Read a book on machine learning from beginning to end. Take notes, do the tasks, and put what you’ve learned into practice.
● Create and implement your own course. Draw on high-quality free and paid materials on the topics that interest you the most, then build the course and include the formality you want.
You can get started in the field AI and achieve the development you desire without a bachelor’s or higher degree. You discovered that there are several roads accessible, and a degree is only one of them, which may take a significant amount of time and cash. You also learnt about alternatives to degree-level organized learning and research apprenticeships for higher-level degree programs. University degree is a difficult subject, and I’d love to hear your thoughts on this. Please let me know what you think by leaving a comment.
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