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

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

In this article, you’ll learn more about how both are used in the world today. You’ll explore how each are changing the way we live and work and discover courses we think you'll learn the most from.

Explore machine learning and AI with us

Interested in a machine learning career? Consider taking Stanford and DeepLearning.AI's Machine Learning Specialization. Or what about AI? You can build job-ready skills with IBM's Applied AI Professional Certificate. Explore our collection of the best machine learning and AI courses.

What's the difference between machine learning and AI?

In simplest terms, AI is computer software that mimics the ways that humans think in order to 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 such 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, while ML is only one method of doing so. 

Hop on the generative AI trend and learn how ChatGPT can improve your workflow:

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, such as 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. 

• 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: 

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

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 2020 research conducted by NewVantage Partners, for example, 91.5 percent of surveyed firms reported ongoing investment in AI, which they saw as significantly disrupting the industry [1]. 

Supply chains 

Supply chains keep goods flowing all around the world. Yet, as supply chains become increasingly more complex and globally interconnected, so too does the number of 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

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.

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

Build skills in machine learning and AI

DeepLearning.AI’s AI For Everyone course introduces those without experience in AI to core concepts such as machine learning, neural networks, deep learning, and data science. Build job-ready skills in just four weeks.