Digital avatars, smart voice assistants, self-driving cars, emotionally intelligent robots: all rely on machine learning and artificial intelligence (AI). These two computing processes have become so integral to modern living that it’s hard to remember they both seemed distant dreams not very long ago.
Most people understand that machine learning and AI involve machines that simulate the way the human brain works. But what, precisely, do the terms mean, and how do they differ? This article explores both questions. It also discusses the careers associated with each and the degrees required to enter and advance related computing professions.
According to Andrew Moore, former dean of Carnegie Mellon University’s School of Computer Science and now head of Google Cloud AI, “Artificial intelligence is the science and engineering of making computers behave in ways that, until recently, we thought required human intelligence.” These behaviors include decision-making, image and speech recognition, problem-solving, and translation.
Roomba, iRobot’s famous robot vacuum cleaner, offers an example of AI in action. The Roomba can scan a room’s size, identify any obstacles, and figure out the most efficient cleaning routes by itself. Newer models can even make a map of a house and figure out its floor plan.
Machine learning is a subset of AI. It involves learning from past data to make decisions or predictions. Using learning algorithms, machines process historical data, which they use to recognize patterns. These machines can then make decisions or predictions on their own.
Spotify uses machine learning to customize your recommendations and predict your musical preferences based on your previous activity and the songs on your playlists. That’s just one of many, many real-world examples of this technology in action.
The tech industry is known for welcoming those with significant hands-on experience. Plus, the demand for AI professionals is rapidly increasing. Many AI job postings you’ll spot today will only require an undergrad degree and a list of specific required skills. However, when you browse highly sought-after positions at well-known firms, the jobs often require a graduate degree (PhD or computer science master’s). If you’re looking for high-paying positions in the field, a master’s is crucial. (
The earning potential for these roles typically soars past the six-figure mark. The median salary of an AI engineer, for example, is $164,800. A University of San Diego salary chart lists other six-figure AI-related positions, including data scientists (who earn $127,000), research scientists ($111,000), and big data engineers ($131,000). ( )
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According to the Bureau of Labor Statistics, the U.S. job market for computer and information research scientists (the category into which AI and machine learning jobs are bucketed) is expected to grow at a whopping 21 percent rate between 2021 and 2031 (about three times the rate for the job market as a whole). That should create about 3,000 job openings in the field each year. The Stanford Institute for Human-Centered Artificial Intelligence (HAI) reports that the number of AI jobs worldwide is rising rapidly, with the US market leading the way.
LinkedIn’s 2021 Jobs on the Rise report ranks artificial intelligence practitioners at the top of its digital transformation jobs list. It notes that “demand is strong… in fact, this was the top emerging trend from last year’s report.” ZipRecruiter reports a median salary for artificial intelligence engineers of almost $157,000 annually, with the top 25 percent earning over $165,000.
The most common types of entry-level AI and machine learning jobs are in software engineering. The Bureau of Labor Statistics reports an average annual income for software developers of just under $131,000.
Other AI and machine learning jobs, with their average salaries, include:
As you improve your skills and gain years of experience, you can level up to a senior role, perhaps as a manager leading a team or an architect focused on design and performance.
An increasing number of industries are starting to utilize AI and machine learning technologies, including aviation and space, engineering and construction, medicine, and retail. Finding a promising field and developing specialized skills could help you advance more quickly than you would with generalized AI and machine learning capabilities only.
Machine learning and artificial intelligence jobs require strong analytical and problem-solving skills as well as solid math skills (including logic, probability, predictive analytics, and statistics). You’ll also need to know theory and fundamentals, especially when it comes to algorithms and data structures. You’ll need an understanding of deep learning models and artificial neural networks and, of course, a solid grounding in computing and computer systems.
In terms of programming, you’ll be expected to navigate commonly used languages in AI and machine learning such as C++, Java, Python, and R. Additionally, you’ll be required to have knowledge of software development methodologies and tools.
According to Indeed, New York City and San Francisco are the top employment markets for artificial intelligence and machine learning positions. No surprise there. Washington DC takes third place; again, no surprise given the concentration of intelligence, military, and other high-stakes government jobs in the city. The other big markets are, well, big markets for many professions: San Jose, Seattle, Boston, Los Angeles, Chicago, Dallas-Fort Worth, and Atlanta.
Most AI and machine learning jobs require a bachelor’s degree in math or computer science. These programs teach the fundamentals of algorithms and logic, programming, and software engineering. A growing number of universities include AI and machine learning courses in their curricula, and some have specialized tracks in these fields. Carnegie Mellon University even offers a bachelor’s degree in artificial intelligence.
Many AI and machine learning companies prefer to hire candidates with a master’s degree in computer science, often with a specialization in AI or machine learning, which can be obtained either on-campus or online (the latter is offered by Columbia University’s Computer Science Master’s Degree in Machine Learning or the Lyle School of Engineering’s MSCS-AI at Southern Methodist University).
Another way to specialize in AI or machine learning is through online courses or boot camps like Springboard’s AI/Machine Learning Career Track.
If you’re leaning toward an academic path, you’ll need to get a doctoral degree in AI or machine learning. A doctoral degree may also be required by research institutions or R&D departments of companies.
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