Machine Learning vs. AI: Differences, Uses, and Benefits

Artificial intelligence (AI) and machine learning (ML) are often used interchangeably, but they're actually distinct concepts that fall under the same umbrella.

In this article, you’ll learn more about both of these fascinating fields, how they're impacting our world today, and how they may impact it in the future. Afterward, if you're interested in learning even more, consider enrolling in Stanford and DeepLearning.AI's Machine Learning Specialization, where you'll master fundamental AI concepts and develop practical machine learning skills in a beginner-friendly, three-course program by AI visionary Andrew Ng.

What's the difference between machine learning and AI?

In simplest terms, AI is computer software that mimics the ways that humans think so that it can perform complex tasks, such as analyzing, reasoning, and learning. Machine learning, meanwhile, is a subset of AI that uses algorithms trained on data to produce models that can perform those complex tasks.

Most AI is performed using machine learning, so the two terms are often used synonymously, but AI actually refers to the general concept of creating human-like cognition using computer software and systems, while ML is only one method of doing so. 

Generative AI vs. machine learning

Generative AI, or GenAI, is a subset of AI capable of creating new content, such as text, images, or music, based on user input prompts. While machine learning is used to perform more narrowly defined tasks like categorizing data or making predictions, GenAI can respond dynamically to user inputs, and so is used for more creative tasks like composing text or conversing with customers via AI agents.

What is artificial intelligence? 

Artificial intelligence (AI) is computer software that mimics human cognitive abilities in order to perform complex tasks that historically could only be done by humans, like decision-making, data analysis, and language translation. 

In other words, AI is code on computer systems explicitly programmed to perform tasks that require human reasoning. While automated machines and systems merely follow a set of instructions and dutifully perform them without change, AI-powered ones can learn from their interactions to improve their performance and efficiency. 

AI is an umbrella term covering a variety of interrelated but distinct subfields. Some of the most common fields you will encounter within the broader field of artificial intelligence include: 

• Machine learning (ML): Machine learning is a subset of AI in which algorithms are trained on data sets to become machine learning models capable of performing specific tasks. 

• Deep learning: Deep learning is a subset of ML, in which artificial neural networks (AANs) that mimic the human brain are used to perform more complex reasoning tasks without human intervention.  

• Natural Language Processing (NLP): A subset of computer science, AI, linguistics, and ML, natural language processing focuses on creating software capable of interpreting human communication. 

• Generative AI: A type of AI and subset of deep learning that is used to create new content like text and images. Typically, GenAI is powered by large language models (LLMs), which are models trained on massive data sets, to dynamically create outputs based on user prompts.

• Robotics: A subset of AI, computer science, and electrical engineering, robotics is focused on creating robots capable of learning and performing complex tasks in real-world environments.  

Watch this video from AI expert Andrew Ng and preview the AI for Everyone course:

What is machine learning? 

Machine learning (ML) is a subfield of artificial intelligence focused on training machine learning algorithms with data sets to produce machine learning models capable of performing complex tasks, such as sorting images, forecasting sales, or analyzing big data. 

Today, machine learning is the primary way that most people interact with AI. Some common ways that you’ve likely encountered machine learning before include:

• Receiving video recommendations on an online video streaming platform. 

• Troubleshooting a problem online with a chatbot, which directs you to appropriate resources based on your responses. 

• Using virtual assistants who respond to your requests to schedule meetings in your calendar, play a specific song, or call someone. 

AI vs. machine learning vs. deep learning 

AI, machine learning, and deep learning are sometimes used interchangeably, but they are each distinct terms.

Artificial Intelligence (AI) is an umbrella term for computer software that mimics human cognition in order to perform complex tasks and learn from them.  

Machine learning (ML) is a subfield of AI that uses algorithms trained on data to produce adaptable models that can perform a variety of complex tasks. 

Deep learning is a subset of machine learning that uses several layers within neural networks to do some of the most complex ML tasks without any human intervention.  Read more: Deep Learning vs. Machine Learning: Beginner’s Guide

Real-world examples of AI

Chances are you’ve used an AI-powered device or service in your everyday life without even realizing it. From banking programs that check for shady transactions to automated spam filters that keep your inbox virus-free and video streaming platforms that recommend shows to you, AI and machine learning are increasingly woven into the fabric of our daily lives. Here are just a few of the ways that AI – and machine learning by extension – are used every day: 

1. Health care 

Health care produces a wealth of big data in the form of patient records, medical tests, and health-enabled devices like smartwatches. As a result, one of the most prevalent ways humans use artificial intelligence and machine learning is to improve outcomes within the health care industry. 

Some common applications of AI in health care include machine learning models capable of scanning x-rays for cancerous growths, programs that can develop personalized treatment plans, and systems that efficiently allocate hospital resources.

Read more: Digital Health Explained: Why It Matters and What to Know

2. Business

AI has had a significant impact on the world of business, where it has been used to cut costs through automation and to produce actionable insights by analyzing big data sets. As a result, more and more companies are looking to use AI in their workflows. According to 2024 research conducted by EY, for example, 95 percent of surveyed senior leaders reported their organizations were currently investing in AI, which they saw as significantly disrupting the industry [1]. 

3. Supply chains 

Supply chains keep goods flowing worldwide. Yet, as supply chains become increasingly complex and globally interconnected, so too do the potential hiccups, stalls, and breakdowns they face. Supply chain managers and analysts are increasingly turning to AI-enhanced digital supply chains capable of tracking shipments, forecasting delays, and problem-solving on the fly to ensure speedy deliveries.

Read more: Supply Chain Analytics: What It Is, Why It Matters, and More

Benefits and the future of AI

AI and machine learning provide various benefits to both businesses and consumers. While consumers can expect more personalized services, businesses can expect reduced costs and higher operational efficiency. 

It’s little surprise that the global market for AI is expected to increase exponentially in the coming years. According to Grand View Research (GVR), the global market size for artificial intelligence is projected to expand from $136.6 billion in 2022 to a whopping $1.8 trillion in 2030 [2]. Some common benefits for businesses using AI and machine learning in the real world include:

• The ability to quickly analyze large amounts of data to produce actionable insights

• Increased return on investment (ROI) for associated services due to decreased labor costs

• Improved customer satisfaction and experiences that can be tailored to meet individual customer needs

AI capabilities

Artificial intelligence has a wide range of capabilities that open up a variety of impactful real-world applications. Some of the most common include pattern recognition, predictive modeling, automation, object recognition, and personalization. In some cases, advanced AI can even power self-driving cars or play complex games like chess or Go.

Consider starting your own machine-learning project to gain deeper insight into the field.

Build your AI and machine learning skills today

Want to learn more about AI and machine learning? Start with one of these courses or specializations on Coursera: For an overview of AI and machine learning, take DeepLearning.AI's AI for Everyone course. Learn AI terminology, what AI is used for, and how you can implement it in your organization with this short six-hour course.

To develop practical machine learning skills, explore Stanford and DeepLearning.AI's Machine Learning Specialization. In as little as two months, you'll learn how to build machine learning models, apply best practices for their development, and train a neural network with TensorFlow.

To explore AI-based solutions for business challenges, enroll in IBM's AI Foundations for Business Specialization. In this popular program, you'll learn about AI from a business perspective, the role of data science in the modern business world, and a framework for deploying AI in your organization.