Artificial Intelligence vs. Machine Learning: What’s the Difference?

Artificial intelligence (AI) and machine learning (ML) are terms that have created a lot of buzz in the technology world, and for good reason. They’re helping organizations streamline processes and uncover data to make better business decisions. They’re advancing nearly every industry by helping them work smarter, and they’re becoming essential technologies for businesses to maintain a competitive edge.

These technologies are responsible for capabilities like facial recognition features on smartphones, personalized online shopping experiences, virtual assistants in homes, and even the medical diagnosis of diseases.

Demand for these technologies—and professionals skilled in them—is booming. According to a report from research firm Gartner, the average number of AI projects in place at an organization is expected to more than triple over the next two years.

This exponential growth is posing problems for organizations. They report that their top challenges with these technologies include a lack of skills, difficulty understanding AI use cases, and concerns with data scope or quality.

AI and ML, which were once the topics of science fiction decades ago, are becoming commonplace in businesses today. And while these technologies are closely related, the differences between them are important. Here’s a closer look into AI and ML, top careers and skills, and how you can break into this booming industry.

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What Is Artificial Intelligence?

Artificial intelligence is a poorly defined term, which contributes to the confusion between it and machine learning, says Bethany Edmunds, associate dean and lead faculty for Northeastern’s computer science master’s program.

“Artificial intelligence is essentially a system that seems smart. That’s not a very good definition, though, because it’s like saying that something is ‘healthy’. What exactly does that mean?” she says. “On a basic level, artificial intelligence is where a machine seems human-like and can imitate human behavior.”

These behaviors include problem-solving, learning, and planning, for example, which are achieved through analyzing data and identifying patterns within it in order to replicate those behaviors.

What Is Machine Learning?

Machine learning, on the other hand, is a type of artificial intelligence, Edmunds says. “Where artificial intelligence is the overall appearance of being smart, machine learning is where machines are taking in data and learning things about the world that would be difficult for humans to do,” she says. “ML can go beyond human intelligence.”

ML is primarily used to process large quantities of data very quickly using algorithms that change over time and get better at what they’re intended to do. A manufacturing plant might collect data from machines and sensors on its network in quantities far beyond what any human is capable of processing. ML is then used to spot patterns and identify anomalies, which may indicate a problem that humans can then address.

“Machine learning is a technique that allows machines to get information that humans can’t,” she says. “We don’t really know how our vision or language systems work—it’s difficult to articulate in an easy way. For this reason, we’re relying on data and feeding it to computers so they can simulate what they think we’re doing. That’s what machine learning does.”

Artificial Intelligence vs. Machine Learning: Required Skills

Because artificial intelligence is a catchall term for smart technologies, the necessary skill set is more theoretical than technical. Machine learning professionals, on the other hand, must have a high level of technical expertise.

Artificial Intelligence Skills

People pursuing a career in artificial intelligence must have a foundation in:

• Algorithms, and techniques for analyzing them
• Machine learning and how to apply techniques to draw inferences from data
• The ethical concerns in developing responsible AI technologies
• Data science
• Robotics
• Java programming
• Programming design
• Data mining
• Problem-solving

Machine Learning

People pursuing a career in machine learning must have a foundation in:

• Applied mathematics
• Neural network architectures
• Physics
• Data modeling and evaluation
• Natural language processing
• Programming languages
• Probability and statistics
• Algorithms

Artificial Intelligence and Machine Learning Jobs

According to the World Economic Forum’s “The Future of Jobs 2018“ report, there will be 58 million new jobs in artificial intelligence by 2022—and a shortage of skilled professionals to fill them, according to Gartner. The following are the most in-demand jobs that require artificial intelligence and machine learning skills, according to a report from jobs site Indeed.

1. Machine learning engineer: $142,859

Machine learning engineers are advanced programmers tasked with developing AI systems that can learn from data sets. These professionals need to have strong data management skills and the ability to perform complex modeling on dynamic data sets.

2. Deep learning engineer: $75,676

These professionals are computer scientists who use deep learning platforms to develop programming systems that mimic brain functions. Experience developing neural networks is a must.

3. Senior data scientist: $134,346

A senior data scientist uses the business’s data to enhance business capabilities using advanced statistical procedures. These are highly skilled computer scientists and specialized mathematicians who are responsible for the collection and cleaning of data. They may use experimental frameworks for product development and machine learning to lay a strong foundation for advanced analytics. They are also responsible for monitoring junior data scientists and for driving the organization toward a data-driven culture.

4. Computer vision engineer: $126,400

A computer vision engineer determines how a computer can be programmed to achieve a higher level of understanding through the processing of digital images or videos. Computer vision uses massive data sets to train computer systems to interpret visual images.

Learn More: 5 High-Paying Careers in Artificial Intelligence

Pursuing an Advanced Degree in Artificial Intelligence

Northeastern University offers two avenues for people looking to pursue an advanced degree in artificial intelligence: a Master of Science in Artificial Intelligence (MSAI) and a Master of Science in Computer Science (MSCS) with a specialization in artificial intelligence.

The MSAI does not require a computer science undergraduate degree and is geared toward people looking for a broader understanding of AI, Edmunds says. “This is someone who needs to understand artificial intelligence, but isn’t necessarily trying to push the envelope of what’s trying to be done,” she says. “Instead, it’s about advancing how machines are being used and how they can be applied.”

In the MSAI program, students learn a comprehensive framework of theory and practice. It focuses on both the foundational knowledge needed to explore key contextual areas and the complex technical applications of AI systems.

This program incorporates data science, robotics, and ML, which enable students to pursue a holistic and interdisciplinary course of study while preparing for a position in research, operations, software or hardware development, or a doctoral degree.

“This program takes people from different backgrounds and gives them enough information to be able to talk with a team who’s responsible for the more technical artificial intelligence responsibilities,” Edmunds says. “They don’t need to know the nuts and bolts, but they’ll leave with enough to know the right questions to ask and make sure they’re being responsible with the technology.”

The MSCS with a specialization in artificial intelligence, on the other hand, is designed for people who are, or want to become a software engineer, computer science developer, or computer science researcher in which their focus is on creating new applications for algorithms, for example.

This program is designed for students with a background in computer science and includes courses on robotic science and systems, natural language processing, machine learning, and special topics in artificial intelligence.

“AI and ML are going to be how we solve some of the largest problems. We’re very focused on making sure that everyone can get access to those skills because that’s how we’re going to create a better world.”

To learn more about how a graduate degree can accelerate your career in artificial intelligence, explore our MS in AI and MS in Computer Science program pages, or download the free guide below.