AI vs. Machine Learning: Differences, Applications, and Benefits
Explore the core distinctions between artificial intelligence and machine learning, their unique applications, and the advantages they bring to technology.

Artificial intelligence (AI) and machine learning (ML) are taking the worlds of technology and computer science by storm, but many people are unaware of what makes each field unique in its own right. While they are related to each other, they don’t function or operate the same way. Moreover, their different makeups and uses highlight the incredible potential of this growing field of technology.
Understanding the differences and distinctions between AI and ML can be beneficial, as each has unique applications in critical fields such as health care, robotics, and data science.
This article will discuss the unique applications and benefits of each field in a way that helps you understand what makes them different. Whether you’re a data scientist looking to expand your knowledge or simply someone who’s curious about how AI works, you’ll find plenty of great information in this article.
Table of contents:
- Understanding artificial intelligence (AI)
- Understanding machine learning (ML)
- The relationship between AI and ML
- Key differences between AI and ML
- Benefits of AI and ML
Understanding artificial intelligence (AI)
Artificial intelligence is the ability of computer systems and intelligent machines to perform tasks that usually require human intervention. These functions can include decision-making, speech recognition, data processing, and other common tasks.
Although AI has grown in popularity in recent years, its roots date back to the 1950s to a conference hosted by John McCarthy and Marvin Minsky. AI has since made great advancements (along with the broader field of computer science) to bring it to the place where it is today.
Although AI cannot replace human intelligence and doesn’t have special knowledge or the ability to replicate human behavior, it can improve the way people solve problems and make decisions. AI makes it easy to quickly analyze large pools of data and gives individuals a wider range of information to take into account. AI’s ability to recognize patterns and recommend helpful solutions also equips decision-makers to handle complex tasks.
Understanding machine learning (ML)
ML is a subset of artificial intelligence in which a computer is able to quickly identify patterns and create corresponding models. The system becomes more accurate as the computer takes in more data sets, eliminating the need for developers to write additional code to produce more learning algorithms.
The machine learning model was first introduced in 1949 in a book written by Donald Hebb titled “The Organization of Behavior,” in which Hebb shared his thoughts on how neurons communicate with each other. The model evolved over time as discoveries in computer science during the 1960s opened the door for more extensive research into both artificial intelligence and machine learning.
Machine learning sometimes runs unsupervised tests without human intervention. In these tests, machine learning algorithms analyze and cluster large amounts of unorganized or unstructured data. The ability to run unsupervised tests is what opens many doors of possibility for greater data analysis and customer segmentation.
However, the other alternative is supervised learning, where labeled datasets are fed to algorithms during the process of training or development. The benefit of supervised learning is that it enables organizations to anticipate real-world problems and prepare to respond accordingly. The main difference between unsupervised and supervised learning comes down to whether or not the training data is labeled.
The goal of machine learning is to enable action without constant human intervention. Machine learning enables businesses and individuals to save time on ongoing decision-making by offering the ability for decisions to be made automatically. Through pattern recognition, forecasting, and optimization, machine learning can potentially save companies significant time, money, and manpower.
The relationship between AI and ML
In short, ML is a subset of AI, and AI encompasses more than just ML. AI is a broad term, while machine learning refers to one potential tool we can use to develop AI. At times, AI and ML can function in a complementary manner to advance intelligent machines, but they are still separate and distinct entities.
Although machine learning includes important components of AI, such as deep learning, it doesn’t fully encompass other domains like neural networks, which refer to the ability of a collection of nodes to perform simple tasks depending on the other nodes it’s connected to.
While machine learning and neural networks can work in conjunction with each other, they are not terms to be used interchangeably. However, they both fit under the wider umbrella of AI, along with concepts like reinforcement learning and natural language processing (NLP).
Some AI systems (such as rule-based expert systems) do not use ML. Rule-based systems usually require advanced human programming, while machine learning systems can adjust their programming over time based on the data inputted.
Key differences between AI and ML
While the above sections provided a brief overview of the differences between AI and machine learning, the following sections will further expand the key distinctions.
Scope
AI has a broad scope of potential applications in robotics, natural language processing, and complex problem-solving tasks. NLP is what makes apps and chatbots like ChatGPT possible, and AI also explains why we’re seeing more AI-powered delivery robots. AI can impact a broad range of industries, from the medical world to aviation, and many other fields in between.
ML, on the other hand, has fewer uses. Mainly, ML focuses on algorithm development for prediction and classification. ML also contributes to larger AI systems through its data analysis capabilities. While machine learning will make automation more widespread in the future, it may not have quite as far-reaching impacts as AI.
Learning
AI aims to develop systems that perform simple tasks. It has the ability to encompass both rule-based learning and data-driven learning. While it can’t replicate the human brain or make decisions on its own, it can streamline and improve the way that people solve problems and address issues.
ML focuses more on algorithms learning from data, improving over time, and making predictions. This eliminates the need for human beings to be involved in a more hands-on manner during training and programming. Exposure to data is especially important for ML to conduct these functions, as the system will improve as it processes more datasets.
Goal
The goal of AI is to create systems that can help make decisions and solve problems. As AI develops to meet these standards, it will provide more opportunities for automation as well as enhancements and improvements in a number of different industries. AI can also support creativity and innovation by assisting content creators, authors, artists, and designers as they search for new ideas.
ML’s purpose is to enable machines to learn from data and improve task performance over time. It can also quickly analyze data and present findings or make recommendations based on the information. ML aims for accuracy and efficiency in task completion.
Data usage
AI may or may not require data for learning. Rule-based systems are usually preprogrammed, while learning-based systems are built to improve over time through the receipt and processing of a wider range of data. Algorithms are another example of a form of AI that’s less dependent on data for learning.
On the other hand, data plays a central role in ML for training models and improving algorithm performance. During the process of training and testing, reliance on data is paramount.
Without the ability to test and improve operations through large groups of data, machine learning’s impact will be limited. Often, this happens through decision trees, a supervised function of machine learning that is used to make predictions or organize data based on how questions were previously answered.
Learning model
AI involves a variety of learning models, including rule-based and data-driven models. What makes the two different is how they use data. Rule-based models contain explicitly programmed algorithms, while a data-driven system is built to identify patterns and learn relationships and behaviors from the data it processes.
Although AI encompasses ML, not all AI systems use ML models. ML solely relies on data and data-driven models. This is why training, validating, and testing ML models is so important.
Examples and applications
Most people are familiar with real-world examples and applications of AI like Siri, Alexa, and self-driving cars. These entities use a variety of AI subfields like speech recognition and computer vision.
ML examples and applications are also common in everyday life. For example, Netflix and Amazon use ML to offer recommendations. These ML systems rely largely on data sets to improve and personalize the user experience.
Error handling
AI’s error-handling capabilities enable it to approximate human-like error handling due to its broader scope. AI can also use ML to improve error handling.
Error handling in ML is more statistical in nature. Well-defined problems are crucial for effective error handling, and ML models can be refined over time to decrease the occurrence of errors.
Benefits of AI and ML
Let’s briefly cover some of the main benefits of both AI and ML.
AI
Some of the main benefits of AI include:
- Automation of repetitive tasks. AI can automate monotonous tasks like data entry, software testing, invoice building, and many more.
- Handling big data. AI can draw conclusions from large pools of data in very little time. It can also quickly extract relevant data and further process it through various forms of interpretation and transformation.
- Improved processes and workflows. AI enables companies and teams to work more efficiently, which can drive greater productivity and revenue.
- Ability to take on risky or dangerous tasks. Roles that may be hazardous to humans can be delegated to AI, such as sea exploration, coal mining, and assistance during natural disasters.
- Constant availability. When human customer service representatives aren’t available, AI can be on call 24/7 to assist customers or handle repetitive tasks.
- Reducing errors. When programmed correctly, AI-enabled systems and frameworks won’t produce any errors. This saves time and resources as you look to produce efficient, accurate results each time.
- Increased creativity. AI enhances human creativity by identifying unique patterns in extensive data, fostering innovative solutions and breakthroughs in diverse fields.
ML
A few benefits of machine learning are:
- Recognizing images. Algorithms can recognize different images and sort them into various categories in real time. Sometimes, this may involve recognizing different items in an image. Other times, it may look like identifying a particular face.
- Data mining. Machine learning can quickly identify trends and patterns in data, even large sets of raw data that isn’t processed yet. Practically speaking, this could mean evaluating credit risk or identifying junk emails.
- Better marketing. ML can boost marketing or advertising campaigns by processing customer data to see which customers are most likely to purchase a particular product.
- Speech recognition. Similar to natural language processing, speech recognition is the capability of machine learning to interpret vocal inputs and complete tasks. You’ll see this in voice chat applications or dictation software.
- More accurate predictions. Since machine learning algorithms train on large datasets, they can incorporate a wide range of information to reference when making predictions or suggestion decisions.
- Fraud detection. By analyzing spending patterns and customer behavior, machine learning enables banks to assess when fraud or theft may be taking place.
Find AI and ML experts on Upwork
Machine learning and AI are closely connected, but they each have their own capabilities and uses. Both have a place in the tech-driven world we live in today. Understanding the major differences will help you better imagine how both could make future advancements in technology possible.
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Disclosure: Upwork is an OpenAI partner, giving OpenAI customers and other businesses direct access to trusted expert independent professionals experienced in working with OpenAI technologies.