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AI vs. Machine Learning: What's the Difference?

By Steve Smith

The information presented here is true and accurate as of the date of publication. DeVry’s programmatic offerings and their accreditations are subject to change. Please refer to the current academic catalog for details.


June 3, 2024

8 min read

AI vs. Machine Learning: What’s the Difference?

Whether the conversation is about generative artificial intelligence that can create text, videos or images, or artificial intelligence that can take on repetitive tasks in manufacturing, AI is a hot topic. Machine learning (ML) is mentioned so frequently with AI that the two are almost used interchangeably. But are these terms just different ways of describing the same thing, or what’s the AI vs. machine learning difference?

In this article, we’re going to break down the AI vs. machine learning story. We’ll define what each of these terms mean, how they relate to one another, then talk about their connection and real-world applications.

What Is AI?

Artificial intelligence is a term that describes the various methodologies that make machines behave in a way that mimics human intelligence. For example, AI-enabled applications can analyze data to provide information or trigger certain actions without the need for a human to be involved. AI can be found in many of the technologies we commonly use today, like the voice assistants we use at home, the chatbots that personalize our online experiences or the smart devices that keep us connected. AI can also be found in the robotic technologies that are emerging in the healthcare field and the auto industry in the form of self-driving vehicles.

What Is Machine Learning?

Machine learning is just one of several subsets found under the broadly defined AI umbrella. In ML, algorithms are trained on data sets to become machine learning models capable of performing specific tasks. Machine learning enables a system to learn from large amounts of data and improve autonomously using its subsets, neural networks and deep learning capabilities.

Machine learning is being deployed in a vast array of industries, opening up a new realm of possibilities for the use of computers and the massive amounts of data being generated by an Internet of Things world. For example, “big data” helps the manufacturing industry optimize everything from inventory management to streamlining their supply chain. In retail, machine learning is being used to personalize online shopping experiences. In entertainment, machine learning is helping to deliver personalized on-demand content and is used by advertisers to design everything from trailers to ads.

The AI and Machine Learning Connection

In our artificial intelligence vs. machine learning comparison, the connection between AI and ML can be further explained by exploring machine learning’s subsets.

Both AI and ML are rooted in computer science and data science, going beyond basic automation and computer programming to generate outputs that are based on complex data analysis. As a result, they are both suited for intricate tasks that involve precise outcomes.

Examples of this include the way a self-driving car might use computer vision to recognize objects in its field of vision and tap into its knowledge of traffic laws and patterns to steer the vehicle and maintain speeds. An algorithm used in the real estate industry might apply knowledge of previous sales, current market conditions and the location of a particular home to predict its market value.

AI and machine learning are both areas of computer science that concentrate on creating software that analyzes, interprets and comprehends data in complex ways. As scientists attempt to program a computer system to perform tasks that involve humanlike self-learning, they are striving to design software that will eventually complete tasks as fast as or faster than a human being.

Now that we know the ways they are similar, let’s look at how AI and ML are different. Key differences of AI vs. machine learning include:

  • Objectives: The aim of AI technology is to complete a task like a human would, with efficiency and speed. This may involve concepts like learning, problem-solving and pattern recognition. The goal of ML, however, is to evaluate large volumes of data, using statistical models to identify patterns and draw a conclusion.

  • Methods: While the field of AI encompasses a broad variety of problem-solving methods, like neural networks, deep learning, search algorithms or rule-based systems, machine learning methods are divided into 2 broad categories: supervised and unsupervised. In supervised learning, ML algorithms learn to solve problems using data values labeled as input and output. Unsupervised learning is more investigative, attempting to find hidden patterns in unlabeled data.

  • Implementations: In building an ML solution, the process typically involves selecting and preparing a training data set, then choosing a preexisting ML model, like linear regression or a decision tree. Particular data features are then selected and fed into the model so it can learn. As data is refined through various updates and checks, the accuracy of the ML model is improved over time. For AI products, the process is a bit more complex, and typically utilizes prebuilt AI solutions to integrate with products through application programming interfaces (APIs).

Real-World Application of AI and ML

With AI as the overarching system, think of machine learning, deep learning and neural network subsets as a series of AI systems in order, from the largest to the smallest, with machine learning being the largest. Just as machine learning is a subset of AI, deep learning is a subfield of machine learning, and neural networks make up the framework of deep learning algorithms.

Deep learning uses neural networks, or a process that teaches computers to simulate the decision-making capabilities of the human brain, to weigh options and arrive at conclusions. Deep learning is enabling many of the AI-driven technology we see today and is the driving force behind many apps and services that improve automation.

It’s projected that machine learning will become a $209.91 billion industry by 2030. Considering how that machine learning is being adopted across industries and is showing up more and more into our daily lives, this eye-popping projection is not surprising.

Some example of the ways companies use machine learning every day include:

Machine learning in sales and marketing

Data science and machine learning algorithms are used by sites like Amazon, Netflix and others that make suggestions based on your browsing, purchasing or viewing history. Marketers use machine learning for lead generation, data analytics, online searches and search engine optimization. Machine learning can also tailor marketing materials to match customers’ unique interests.

Machine learning in customer service

When responding to inquiries, machine learning can understand a customer’s tone as well as what they’re saying and direct them to the appropriate agents for support. Voice-based inquiries use natural language processing and sentiment analysis for speech recognition, while text-based inquiries are handled by chatbots. By using chatbots, customers don’t have to wait for human assistance, even during peak traffic times.

Machine learning in finance

Banks and other financial institutions are using machine learning in several ways, including training ML models to recognize suspicious online transactions that may need to be investigated. Lenders are also using ML classification algorithms and predictive models to make decisions on loans and who they will offer them to. Many market transactions are the result of decades of stock market data used by AI and ML to forecast trends and make recommendations to investors. About 60-73% of stock market trading is conducted by algorithms that can trade at high volume and with great speed.

Machine learning in healthcare

The advanced analytics driven by machine learning can lead to better recommendations for patients needing medications and treatments. In diagnostics, AI is often used to analyze mammograms and early cancer screenings, and help clinicians reduce the number of missed cancer diagnoses. ML is being trained to classify tumors, find bone fractures and detect neurological disorders.

Machine learning in transportation

Getting from Point A to Point B has been transformed by machine learning. This is apparent in the mapping applications that use ML technology to check traffic conditions, find the fastest path to a destination, avoid tolls or suggest places nearby or along your route. Ride-sharing apps also use ML to match riders with drivers, set pricing, check the traffic and optimize the driver’s route. In self-driving vehicles, ML algorithms allow vehicles to gather data from cameras and sensors to understand and see what’s happening around them.

Machine learning in cyber security

Machine learning is being deployed in several ways to enhance information security. In authentication techniques, ML and facial recognition are being used to safeguard data systems and networks. Antivirus programs may use AI and ML methods to detect and block malicious software. ML is being used by reinforcement learning to train models to identify and respond to cyberattacks, detect system intrusions, recognize and label various events as fraud and classify phishing attacks.

Prepare for Tomorrow’s AI-Enabled Workplaces

Here at DeVry, several of our programs are infused with a core group of data analytics and AI-integrated courses. These courses are designed to help provide foundational knowledge in data gathering and analysis and provide our students with opportunities to study how machine learning and other AI essentials are helping support problem solving and decision-making in today’s workplaces.

Our Associate Degree Specialization in Machine Learning and Design Techniques will help you gain hands-on experience with computer-aided design and study disciplines like natural language processing, data analytics and artificial intelligence, while helping you prepare to pursue industry-recognized certifications like CompTIA A+, CompTIA Linux+, CompTIA Network+ and CompTIA Cloud+.

Qualified students may receive up to a $300 reimbursement for the cost of one exam attempt. Other certifications may also be eligible for reimbursement. Speak to your Student Support Advisor for additional information.

Looking to earn a higher-level degree? Our MBA with a Specialization in Business Intelligence and Analytics Management can give students experience with analytics tools and tech-platforms used to manage data, as well as a chance to develop their leadership and collaborative skills as they prepare to pursue a career in corporate management.

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