We've all heard the rumblings about how the rise of artificial intelligence is eliminating jobs, but the reality may not turn out to be as bad as feared. The World Economic Forum has predicted that by 2025 AI will create far more jobs than it displaces—and by 2030, will add an impressive 26% to the world's GDP.
However, the World Economic Forum elaborated that for this forecast to come true, companies must train (and retrain) their workers in response to the ever-advancing technology: "Employers should view upskilling and reskilling as an investment in the future of their organization, not an expense."
So, whether you're a computer science professional, have a background in coding, or are looking to make a shift toward tech within your field, a Masters of Science in Artificial Intelligence can make multiple career pathways possible. While you don't necessarily need an advanced degree to get your foot in the AI door, it will make you eligible for higher salaries and leadership positions.
Let's take a look at which master's programs lead to roles in AI and how they're structured. We'll cover:
Are you an innovative computer science pro with stellar problem-solving skills? The ever-expanding AI and machine learning industry wants you. The US Bureau of Labor Statistics (BLS) has forecast that computer and information research scientist jobs are expected to grow 22% by 2030—at a much faster pace than other industries.
If you think AI only applies to software development, you’re sorely mistaken. The world of AI needs everyone from experts in data science to business strategy, as much as those on the technical research side of things. In other words, if you're considering a career path in AI, you’ll have a wealth of options. Here are some of the most popular industries currently hiring, specific roles, and the salaries you can expect.
It may not be long before you see artificial intelligence and machine learning weaving their way into nearly every type of job posting. There are a handful of industries, however, that are being truly transformed by this technology now and need trained professionals to make the transition a success. Let's look at a few.
Companies spend large portions of their budgets trying to get inside the minds of consumers to understand what motivates their purchasing decisions. Imagine taking the guesswork out of the process so marketing pros can focus more on promoting the features and benefits of their company’s product. Forbes notes that currently marketers are using AI to personalize ads for their potential customers and match influencers with the right products.
From predictive modeling to cyber security, banks and hedge funds often need machine learning engineers who understand finance. According to Forbes, about 70% of the financial sector is “using machine learning to predict cash flow events, adjust credit scores and detect fraud."
Intelligent systems are making the healthcare experience more accurate and less expensive every day. Health apps track the daily data of patients for their doctors, wearables detect disease earlier, and pattern recognition helps the diagnostic and course of treatment process. Healthcare AI technology is utilized by administrators, researchers, and even medical students.
These top three only cover a small fraction of industries using AI. Positions open up in software companies, manufacturing, the military, travel, transportation, and many more. Any industry that needs to understand the behaviors of everyday people can use AI to increase the efficiency and accuracy of its work.
Poke around the job boards and you'll start spotting a few common AI tiles. A handful of artificial intelligence positions are in the highest demand right now, and nearly all of them have six-figure salaries.
Salaries increase when you add a leadership element to any of these roles. Chief and lead engineers can make upwards of $200,000.
Entry-level and mid-level positions do not necessarily require a master's, but it certainly helps when you’re competing in a field of experienced candidates. Many IT companies like to see real-world experience, especially for highly technical roles like software engineers.
That being said, if you want to lead a team or manage large research projects, you will need either a Master of Science or a PhD in an AI-related field. Not only will the degree expand your technical knowledge in this area, but it also will connect you with top university-led research projects and the skills to transform prototypes into products.
The majority of master's programs aimed at AI professionals either specify artificial intelligence or machine learning in their title. However, most fall under an MS in computer science or engineering. Choosing a concentration is often at the core of the program. Students typically select a specialization by name or shape their specialty within the coursework of their degree program.
If you're looking to work specifically in a certain industry, such as healthcare or consumer products, some programs will offer electives that cover these fields.
What can you expect from an AI master's degree? Top universities know how quickly the industry evolves and design programs that can respond just as quickly. The curriculum typically breaks its curriculum into foundational AI and machine learning courses, programming-specific classes, and extensive concepts in AI. Let's take a deeper look at what to expect.
Most full-time artificial intelligence programs require a two-year commitment. However, with the rise of flexible, part-time, and online master's programs, some schools allow students to extend their studies to five years or more. If you're after a graduate certificate, you may only need a year to complete the program, but you should note that these do not result in a traditional master's.
Graduate programs in AI focus on providing hands-on experience, as well as a combination of core courses and electives. The length of your time at school may come down to your specialty and whether you're balancing it with a busy career.
Nearly all artificial intelligence master’s programs require a bachelor's degree for admission. The good news is that you don't always need an undergrad degree in a specific area. Some schools require a computer science bachelor’s or a related field. Others will waive these requirements in favor of real-world experience. In most cases, you will simply need a foundation of knowledge to start the program—even if you take several prerequisite courses after acceptance. Topics often include programming languages such as Python, calculus, and computer science. You'll also need the following:
Some universities also look at GRE test scores as well, particularly those who earned over a 300. However, many schools no longer require GREs.
Your school of choice will require between one and three letters of recommendation. Professional connections or past professors in your field can help flesh out the story of your resume and application.
It’s likely that you’ll be required to submit a written response to a prompt or explain why the school's unique program fits into your professional goals.
Some programs require a minimum undergraduate GPA, typically between 2.75 and 3.0 to be eligible to submit an application. You also may have to maintain a certain grade average for for the duration of the program to graduate.
As we mentioned earlier, most programs organize courses into foundational, advanced, and elective topics. Southern Methodist University starts its program with core classes in machine learning and AI before moving on to topics like neural networks, natural language processing, and deep learning. Chosen electives come down to your specialization, which we'll touch on more below.
Graduate students typically have the option of completing a thesis or capstone program, typically in their field.
Choosing a concentration is one of the most important keystones of a master's in AI. Since machine learning is such an extensive field, specializing helps focus and align a student's course of study with the job they plan to pursue after graduation. Students can choose pathways such as robotics, machine learning, knowledge reasoning, or human-computer interactions.
Here are a few of US News and World Report’s top picks for AI programs across the country.
Carnegie Mellon's new program, entitled the Master of Science in Artificial Intelligence and Innovation requires students to conceive and construct an AI product as a capstone project. The university has been at the cutting edge of AI research since the field first emerged, and even launched the country's first undergrad AI program.
Stanford University offers an AI concentration within its engineering and computer science department. The school is renowned for its AI lab, which has conducted groundbreaking research since 1965.
At Berkeley, you can earn a research-focused MS with a concentration in artificial intelligence—and have access to their world-class facilities and celebrated faculty. The school notes that many of their current AI research projects cover areas like networking systems, bioinformatics, and information retrieval.
On our list of top schools, Stanford notably offers a graduate certificate that runs alongside its master's program on campus. But online master's programs are common across the country, particularly catering to professionals with busy careers looking to advance their current knowledge of the topic.
Drexel's modular format allows ample flexibility and customization for working students. You can choose to complete a more traditional 45-credit master's program over two or three years or "stack" three graduate certificates toward the master's either online or in person.
SMU's online learning experience stresses the importance of balancing hands-on learning with asynchronous and flexible class schedules. Their MS in computer science with a concentration in AI takes two years to complete.
Students earn 30 credits over an average of 20 months in USD's Master of Science in Artificial Intelligence program. It focuses on the ever-changing demands of the AI field by incorporating relevant topics and technologies as they evolve. The program wraps up with a capstone project to develop their students’ professional tech portfolios.
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