Students Learning About Artificial Intelligence

Recently, we explored why AI is so important and why machine learning matters, and here we’ll explain the difference between the two fascinating disciplines. 

Some people consider AI and machine learning to be interchangeable and some organizations even use these terms as if they referred to the same processes, but the reality is that there are important differences between these fields.

To help you understand their many differences, this discussion will cover what artificial intelligence is, what machine learning is, how they’re related to each other, and how they differ in goals, processes, scope, and application.

After you’ve learned all about the many important distinctions between AI and machine learning, fill out our information request form to receive additional details about CSU Global’s 100% online Master’s Degree in Artificial Intelligence and Machine Learning, or if you’re ready to get started, submit your application today.

What is AI?

AI should be thought of as a field of science, as it is an incredibly broad discipline that focuses on attempting to mimic human intelligence in a computer or machine.

Accordingly, AI engineers seek to create computer systems that can think and behave just like humans, but at faster speeds and with more processing power, typically for the purpose of outperforming humans at tasks requiring traditional forms of intelligence.

The field of AI has evolved so quickly and grown so capable in recent years that many AI applications do significantly outperform their human counterparts.

What is Machine Learning?

Machine learning should be thought of as a subfield of artificial intelligence, since it isn’t about creating a stand-alone intelligence, but simply helping an AI system learn more quickly so it can do a better job of accomplishing some particular task.

Where AI engineers seek to build systems capable of demonstrating human-like intelligence, machine learning specialists are typically only interested in helping any given intelligent system make faster, more accurate decisions.

In this way, the scope of machine learning is far more narrow, which is why machine learning can therefore be thought of as an optimization process used to improve some particular component of an AI-driven system.

The important distinctions that separate AI from machine learning will become easier to understand as we look at other differences in their goals, process, scope, and application.

Differences in Goals

AI scientists utilize a vast array of processes and technologies to build complex computer systems which are able to think like people and solve complex problems.

The goal of an AI system is to solve problems, answer questions, and complete tasks typically done by humans.

Therefore, the system must be able to operate fully autonomously as an independent intelligence that can be supplied with various data sets, which then must be capable of analyzing and interpreting to generate a series of different types of conclusions.

In comparison, machine learning engineers aren’t necessarily looking to solve a whole array of different problems, but instead, seek to help AI systems solve one particular problem more efficiently and more effectively.

There’s a fundamental difference then, between the goals of AI and machine learning. To put it quite simply:

  • AI’s goal is to create an independent intelligence that can solve a wide variety of complex problems.
  • Machine learning aims to help AI systems arrive at more accurate conclusions for a single problem and arrive at those conclusions more quickly.

Differences in Processes

The process of AI requires building a non-human intelligence that is capable of performing tasks just like a human would.

This means that the AI system needs to be capable of consuming data like a human, reviewing it as a human would, and offering a conclusion similar to the one that a human would arrive at.

Machine learning doesn’t care at all about mirroring human intelligence or building a system that operates like human intelligence would.

Instead, the process of machine learning uses iterative learning to make an AI-powered system smarter by letting it learn how to generate better, faster results, simply by doing the same task over and over again.

While an AI intelligence should be capable of solving many different problems, and may only look at each problem a single time, a machine learning system is purposefully designed to perform the same task over and over again, but deliver faster, more accurate results each time.

  • The process of AI is creative, utilizing all sorts of different thinking methods and forms of intelligence to come up with a specific solution to a number of different problems.
  • The process of machine learning is iterative, repetitive, and typically requires running the same exact problem over and over again to look for patterns in the data so it can reach conclusions more quickly, and with greater accuracy.

Differences in Scope

AI encompasses an enormous scope, looking to overcome one of the most complicated, broadest problems ever attempted, namely, building a replacement for human intelligence.

As such, AI systems need to be capable of performing a whole series of complex tasks, including all the different types of problem-solving abilities that human brains are capable of completing.

In contrast to AI’s wide and broad scope, machine learning programs seek to specialize in one specific process, program or task and are focused on returning better results for only that single problem.

Machine learning systems, therefore, don’t need to know anything or be capable of doing anything other than the single task they’ve been assigned, whereas AI systems should be capable of overcoming all sorts of different assignments.

To summarize:

  • AI has an extremely broad scope, seeking to create a new form of intelligence capable of solving a whole multitude of problems.
  • Machine learning systems only need to solve a single assignment and are typically specialized for completing a single task.

Differences in Application

To help you understand just how important the distinction between the two fields is, let’s look at the differences in application and give examples of what AI and machine learning are used to do.

One of the simplest ways to differentiate between the two disciplines is to think about some of the ways we interact with AI and machine learning programs each and every day, via smartphones and smart TVs.

An example of an AI-driven application is your Smartphone’s assistant, via Siri if you’re an Apple user, or Google Assistant if you’re on an Android device.

The role of these AI-powered programs is to interpret what you want help with and to come up with a creative, useful solution for you based on whatever you’ve asked them to do.

These systems need to be capable of interpreting what you want, then handling a whole series of tasks, whether that be looking something up, scheduling an appointment, offering advice, providing directions, or doing any number of other things requiring a human-like intelligence.

Understanding what you’re really asking for, and being able to interpret what you’ve said, hone in on your intent, then deliver something useful to you is what these AI applications are all about. That is a complex, broad challenge, requiring an extremely sophisticated intelligence.

In contrast, two of the most common machine learning-driven systems that people interact with each day are far simpler than Siri or Google Assistant; they’re programs tasked with accomplishing a very specific function, giving you recommendations for things to buy, or things to watch.

Amazon uses a machine-learning algorithm to suggest products “You might also like”, and Netflix uses machine learning algorithms to power their Recommendation Engine, which is responsible for suggesting shows and movies that you might want to watch.

Both of these systems rely on data, and large sets of data, but all they’re doing is comparing your behavior to the behavior of other users that purchased similar products or watched similar shows, uncovering patterns in the larger data set, then applying those patterns to provide you with suggestions about what you might want to do next.

This is far simpler than the tasks assigned to Siri and Google Assistant, which are responsible, again, for a whole variety of different things, including interpreting what you actually want, devising a solution, and delivering to you in a way that is useful.

An Example: AI-Driven Systems vs. Machine Learning Solutions

Let’s review the differences between an AI-powered Siri vs. a machine learning-powered Amazon “You might also like” to help delineate just how different AI is from machine learning:

  • Different Goals
    • Apple’s Siri - It’s complex, trying to make your life easier by understanding what you want help with and delivering a useful response.
    • Amazon’s “You might also like” - It’s simple, only seeking to suggest other things you might want to buy, based on what you’ve previously purchased, looked at, and how your shopping behavior aligns with other users.
  • Different Processes
    • Apple’s Siri - It’s complicated since this process requires interpreting your input language, determining what you truly want, and then giving you whatever it thinks you’re asking for.
    • Amazon’s “You might also like” - It’s simple since all it needs to do is accomplish a single task, reviewing what you’ve purchased, and looked at, comparing that to other users, then suggesting other things you may want based on patterns in the data.
  • Different Scopes
    • Apple’s Siri - It’s broad, needing to provide you with a whole series of different types of solutions, similar to what a human assistant would be capable of delivering.
    • Amazon’s “You might also like” - It’s narrow, getting you to purchase as many products as possible, without worrying about your intent, real needs at any give moment, or anything else. 

Which Field Should You Study?

That’s a great question, but the answer is difficult to provide without knowing more about your specific educational background, your career goals, and your interests.

There are good reasons to study both artificial intelligence and machine learning; after all, each field influences the other, and anyone looking to accomplish great things in either discipline should know at least the basics of the other.

To get the best of both worlds, consider enrolling in a degree program like CSU Global’s 100% online Master’s Degree in Artificial Intelligence and Machine Learning, where you’ll be given the opportunity to develop your skills, knowledge, and expertise in both of these critical disciplines.

Why Should You Choose to Study AI & Machine Learning with CSU Global? 

If you’re serious about launching a career in AI or machine learning, then you’ll certainly want to consider enrolling in our online M.S. in AI and Machine Learning program.

This program is regionally accredited by the Higher Learning Commission and widely respected by both AI and machine learning industry professionals, while CSU Global is recognized as an industry leader in online education, having recently earned considerable awards in the space, including::

Finally, to ensure that your education is affordable, we also offer competitive tuition rates and a Tuition Guarantee that promises your tuition rate won’t increase from enrollment through graduation.

To get additional details about our fully accredited, 100% online Master’s in AI and Machine Learning program, please give us a call at 800-462-7845, or fill out our Information Request Form.

Ready to get started today? Apply now!