For most people, the proliferation of artificial intelligence seemed to happen overnight. Just a decade ago, AI was something people outside of technology and engineering only encountered in movies or articles about leading-edge research. Now that artificial intelligence powers everything from robo-advisors in finance to risk analysis platforms in law enforcement, it attracts a lot of media attention. All the buzz around AI has not only accelerated the implementation of related technologies in some sectors but also spread misconceptions about what artificial intelligence is and isn’t.
Experienced technology specialists probably want to shout this from the rooftops: artificial intelligence and machine learning (ML) are not different terms for the same technology. In brief, all ML is AI, but all AI is not ML. They are related but not synonymous. Both are revolutionizing the ways we use data and interact with machines, and both influence how we work, live and play. According to the IDAP group, 97 percent of mobile users rely on voice assistants powered by AI. Most people also interact with AI and machine learning technologies when they message with chatbots, scroll through personalized online shopping recommendations or ask their smart home devices for the weather forecast.
In 2021, research by PwC found that AI is already central to decision-making in 86 percent of businesses. That will continue – 60 percent of companies reported that digital transformation will be essential to their growth in 2022. If your goal is to help these businesses implement practical AI and machine learning solutions, you must be able to differentiate between these emerging technologies. Artificial intelligence master’s programs such as the online Master’s in Computer Science with Artificial Intelligence Specialization (MSCS-AI) offered by SMU Lyle School of Engineering can give you the skills and knowledge to implement artificial intelligence and machine learning solutions effectively. By the end of this article, you should understand the difference between artificial intelligence and machine learning and how each will factor into our digital future.
What Is Artificial Intelligence?
Artificial intelligence is an umbrella term encompassing the theories and technologies that allow computer systems to mimic the human brain. AI-enabled tech imitates the way we learn and solve problems. Artificial intelligence is a branch of computer science that uses data to “train” computer systems to think as we do. Data science has been an integral part of the proliferation of AI. Artificial intelligence went from a sci-fi dream to a practical tool once we had the computing systems and statistical techniques necessary to work with large, complex datasets.
Think of data like the input you take in every day about the world. Your ability to reason and make judgments based on that input is a measure of your intelligence. For example, you know that strawberry ice cream is usually pink, so when you see a cone full of pink ice cream, you probably assume it is strawberry. By feeding a computer system data about how ice cream colors relate to flavors, we can teach it to correlate pink with strawberry.
AI is so versatile because once sufficiently trained, it analyzes data very quickly and efficiently. It can also identify relationships humans tend to miss. While artificial intelligence is still a technology in its infancy, it can still outperform humans on many tasks. High-level AI systems power facial recognition systems that generate tagging recommendations on Facebook, fraud identification in the financial sector, language translation apps such as Google Translate, and speech recognition engines in digital assistants and real-time subtitle generators.
AI systems can take over tasks humans find boring, like simple data entry or customer service interactions. However, we’re still years away from truly autonomous artificial intelligence. There are myriad ways in which AI requires human oversight because the technology is far from perfect. Craig S. Smith wrote for the New York Times that while “we’ve seen the AI sun, we have yet to see it truly shine.”
Returning to the ice cream analogy, humans recognize that pink ice cream might also be cotton candy flavored or cherry, even if we have never tasted either. In other words, we can recognize and overcome biases AI cannot. At present, AI devices are not adept at reacting to surprises or unpredictable circumstances and tend to reinforce – rather than correct – biases present in training data.
Career Pathways and Opportunities in Artificial Intelligence
While many people worry that artificial intelligence will automate jobs out of existence, it is currently creating new opportunities. A World Economic Forum analysis found that demand for new specialists with titles such as human-machine interaction designer and robotics engineer is growing quickly. Technology firms still hire the most artificial intelligence professionals, but companies in financial services, healthcare, engineering, manufacturing, agriculture, energy and education also seek AI talent to power innovations and optimize their practices.
Technology professionals who become AI experts will be able to explore ways humans can use new technologies to solve some of our most pressing problems, like energy overuse and hunger. The Partnership on AI, a nonprofit research conglomerate that includes Apple, Amazon, Facebook, Google, IBM and Microsoft, as well as academic and industrial organizations, has said that “artificial intelligence technologies hold great promise for raising the quality of people’s lives.”
AI is already being used to identify and help address social problems. For example, UN Women partnered with Quilt.AI to analyze Big Data from eight countries to determine the extent of violence against women-related searches during the COVID-19 lockdowns. UN Women and the UN Populations Fund used the findings to recommend strategies for increasing digital literacy across vulnerable populations and developing online resources that can reach more women and girls.
Completing an artificial intelligence master’s program is about more than just earning another degree. SMU trains MSCS-AI candidates to think outside the box when it comes to the potential applications of AI. Coursework in the MSCS-AI degree program at SMU Lyle includes advanced machine learning and neural networks, natural language processing and internet applications and advanced data mining, which improves AI-enabled analysis algorithms. Students get real-world experience in the online MSCS-AI program‘s core courses, learn how to work with Big Data and receive guidance from world-class faculty members and accomplished peers.
How are AI and Machine Learning Different?
Machine learning is a subset of AI that uses mathematical data models to train computer systems to “think” independently and learn semi-autonomously. If artificial intelligence describes a computer system that can think like a human, then machine learning can aptly be described as one of the processes that make that possible. If AI is a parent, then machine learning is a kindergarten teacher. If you need a visual reference, picture computer science as the tree’s trunk, branching off into application development, operating systems and intelligent systems. Machine learning, deep learning, natural language processing and computer vision are all fields of study that stem from the AI branch.
Common examples of machine learning-powered technologies include image recognition, email spam filters and smart replies, in-text plagiarism checkers and smart assistants. All of these are based on algorithms that work by gathering and analyzing data, and then using that data to identify patterns and make predictions about new content. Machine learning allows developers to train machines to complete complex tasks independently without traditional programming.
Career Pathways in Machine Learning
Most job descriptions that mention machine learning will also mention AI and vice versa, making differentiating between the two complicated. There also isn’t much standardization in the field yet. The most common machine learning job is machine learning engineer, but there are plenty of titles beyond that, including computational linguist, data scientist, AI engineer and data analytics engineer. New job titles and responsibilities will emerge as the demand for machine learning skills grows.
Machine learning skills are useful in several career pathways – many of which don’t involve machine learning jobs. The Electric Power Research Institute has launched more than 20 initiatives that take advantage of AI in electrical engineering. Using machine learning algorithms, engineers train systems to analyze thousands of drone images and learn to recognize malfunctioning equipment in transmission and distribution infrastructure without human intervention. Other fields using machine learning in new ways include cyber security, healthcare, marketing and retail.
A 2020 Deloitte survey found that 97 percent of companies surveyed already use machine learning or plan to implement it in the next year. Statista data from the same year shows that demand for machine learning skills is high for 82 percent of large organizations worldwide. Yet, only 12 percent of those enterprises can find an adequate supply of professionals with the requisite expertise. Elective courses in the MSCS-AI curriculum teach graduate students skills related to machine learning in classes such as Machine Learning and Neural Networks.
How You Can Harness the Power of AI in SMU’s Online MSCS-AI
Industries are scouring applicant pools for professionals who know what they’re doing when it comes to artificial intelligence and machine learning. The IDC projects organizations across 16 industries will spend $120 billion implementing AI over the next three years. Employers need candidates who can help them use related technologies strategically. SMU’s graduate-level artificial intelligence program develops experts who understand the foundational elements of computer science, have advanced technical skills and can use critical thinking when leveraging AI- and machine learning-driven technologies.
One of the most valuable components of SMU’s online graduate program is that it incorporates ethics into the curriculum. Lyle School of Engineering faculty encourage students to consider the larger ramifications of AI and its potential to do the most good while affecting the least harm in real-world settings. At SMU’s Artificial Intelligence Laboratory, for example, students and faculty volunteered their time during the pandemic to mine data from a large number of scientific papers. They then fed the information to a supercomputer. Their goal was to help infectious disease researchers enhance their understanding of coronavirus to speed up therapy and vaccine development in the future.
“We are modeling for our students what it looks like to respond in real-time to real-world challenges, regrouping and refocusing our research on the pandemic, and inviting them to make discoveries,” professors Fred Chang and Jo Guldi wrote in the Dallas Morning News.
These are the challenging issues that artificial intelligence master’s program students should tackle, which overlap with the real-world, hands-on experience organizations want to see in potential employees. SMU Lyle encourages MSCS-AI candidates to think not just about technology’s applications but also about the bigger picture.
Upskilling to meet the growing demand for AI expertise across industries doesn’t require leaving your current job, as SMU’s master’s program is 100-percent online and takes less than two years to complete. Since you can continue earning while you study part-time, the ROI of an MSCS is impressive compared to full-time AI master’s degree programs. While AI and machine learning aren’t the same linguistically, together they offer an extraordinary number of pathways to success. Taking advantage of these emerging opportunities is just a matter of figuring out how you can harness these technologies to fuel your personal and professional growth.