Category: Artificial intelligence (AI) vs. machine learning (ML)

You might hear people use artificial intelligence (AI) and machine learning (ML) interchangeably, especially when discussing big data, predictive analytics, and other digital transformation topics. The confusion is understandable as artificial intelligence and machine learning are closely related. However, these trending technologies differ in several ways, including scope, applications, and more.  

Increasingly AI and ML products have proliferated as businesses use them to process and analyze immense volumes of data, drive better decision-making, generate recommendations and insights in real time, and create accurate forecasts and predictions. 

So, what exactly is the difference when it comes to ML vs. AI, how are ML and AI connected, and what do these terms mean in practice for organizations today? 

We’ll break down AI vs. ML and explore how these two innovative concepts are related and what makes them different from each other.Get started for free

What is artificial intelligence?

Artificial intelligence is a broad field, which refers to the use of technologies to build machines and computers that have the ability to mimic cognitive functions associated with human intelligence, such as being able to see, understand, and respond to spoken or written language, analyze data, make recommendations, and more. 

Although artificial intelligence is often thought of as a system in itself, it is a set of technologies implemented in a system to enable it to reason, learn, and act to solve a complex problem. 

What is machine learning?

Machine learning is a subset of artificial intelligence that automatically enables a machine or system to learn and improve from experience. Instead of explicit programming, machine learning uses algorithms to analyze large amounts of data, learn from the insights, and then make informed decisions. 

Machine learning algorithms improve performance over time as they are trained—exposed to more data. Machine learning models are the output, or what the program learns from running an algorithm on training data. The more data used, the better the model will get. 

How are AI and ML connected?

While AI and ML are not quite the same thing, they are closely connected. The simplest way to understand how AI and ML relate to each other is:  

• AI is the broader concept of enabling a machine or system to sense, reason, act, or adapt like a human 
• ML is an application of AI that allows machines to extract knowledge from data and learn from it autonomously

One helpful way to remember the difference between machine learning and artificial intelligence is to imagine them as umbrella categories. Artificial intelligence is the overarching term that covers a wide variety of specific approaches and algorithms. Machine learning sits under that umbrella, but so do other major subfields, such as deep learning, robotics, expert systems, and natural language processing.

Differences between AI and ML

Now that you understand how they are connected, what is the main difference between AI and ML? 

While artificial intelligence encompasses the idea of a machine that can mimic human intelligence, machine learning does not. Machine learning aims to teach a machine how to perform a specific task and provide accurate results by identifying patterns. 

Let’s say you ask your Google Nest device, “How long is my commute today?” In this case, you ask a machine a question and receive an answer about the estimated time it will take you to drive to your office. Here, the overall goal is for the device to perform a task successfully—a task that you would generally have to do yourself in a real-world environment (for example, research your commute time). 

In the context of this example, the goal of using ML in the overall system is not to enable it to perform a task. For instance, you might train algorithms to analyze live transit and traffic data to forecast the volume and density of traffic flow. However, the scope is limited to identifying patterns, how accurate the prediction was, and learning from the data to maximize performance for that specific task.

Artificial intelligence

• AI allows a machine to simulate human intelligence to solve problems
• The goal is to develop an intelligent system that can perform complex tasks
• We build systems that can solve complex tasks like a human
• AI has a wide scope of applications
• AI uses technologies in a system so that it mimics human decision-making
• AI works with all types of data: structured, semi-structured, and unstructured
• AI systems use logic and decision trees to learn, reason, and self-correct

Machine learning

• ML allows a machine to learn autonomously from past data
• The goal is to build machines that can learn from data to increase the accuracy of the output
• We train machines with data to perform specific tasks and deliver accurate results
• Machine learning has a limited scope of applications
• ML uses self-learning algorithms to produce predictive models
• ML can only use structured and semi-structured data
• ML systems rely on statistical models to learn and can self-correct when provided with new data

Benefits of using AI and ML together

AI and ML bring powerful benefits to organizations of all shapes and sizes, with new possibilities constantly emerging. In particular, as the amount of data grows in size and complexity, automated and intelligent systems are becoming vital to helping companies automate tasks, unlock value, and generate actionable insights to achieve better outcomes. 

Here are some of the business benefits of using artificial intelligence and machine learning: 

Wider data ranges

Analyzing and activating a wider range of unstructured and structured data sources.

Faster decision-making

Improving data integrity, accelerating data processing, and reducing human error for more informed, faster decision-making.

Efficiency

Increasing operational efficiency and reducing costs.

Analytic integration

Empowering employees by integrating predictive analytics and insights into business reporting and applications.

Applications of AI and ML

Artificial intelligence and machine learning can be applied in many ways, allowing organizations to automate repetitive or manual processes that help drive informed decision-making.

Companies across industries are using AI and ML in various ways to transform how they work and do business. Incorporating AI and ML capabilities into their strategies and systems helps organizations rethink how they use their data and available resources, drive productivity and efficiency, enhance data-driven decision-making through predictive analytics, and improve customer and employee experiences.   

Here are some of the most common applications of AI and ML: 

Healthcare and life sciences

Patient health record analysis and insights, outcome forecasting and modeling, accelerated drug development, augmented diagnostics, patient monitoring, and information extraction from clinical notes.

Manufacturing

Production machine monitoring, predictive maintenance, IoT analytics, and operational efficiency.

Ecommerce and retail

Inventory and supply chain optimization, demand forecasting, visual search, personalized offers and experiences, and recommendation engines.

Financial services

Risk assessment and analysis, fraud detection, automated trading, and service processing optimization.

Telecommunications

Intelligent networks and network optimization, predictive maintenance, business process automation, upgrade planning, and capacity forecasting.

Artificial intelligence (AI) and machine learning are often used interchangeably, but machine learning is a subset of the broader category of AI.

Put in context, artificial intelligence refers to the general ability of computers to emulate human thought and perform tasks in real-world environments, while machine learning refers to the technologies and algorithms that enable systems to identify patterns, make decisions, and improve themselves through experience and data. 

Computer programmers and software developers enable computers to analyze data and solve problems — essentially, they create artificial intelligence systems — by applying tools such as:

• machine learning
• deep learning
• neural networks
• computer vision
• natural language processing

Below is a breakdown of the differences between artificial intelligence and machine learning as well as how they are being applied in organizations large and small today.

What Is Artificial Intelligence?

Artificial Intelligence is the field of developing computers and robots that are capable of behaving in ways that both mimic and go beyond human capabilities. AI-enabled programs can analyze and contextualize data to provide information or automatically trigger actions without human interference.

Today, artificial intelligence is at the heart of many technologies we use, including smart devices and voice assistants such as Siri on Apple devices. Companies are incorporating techniques such as natural language processing and computer vision — the ability for computers to use human language and interpret images ­— to automate tasks, accelerate decision making, and enable customer conversations with chatbots.

What Is Machine Learning?

Machine learning is a pathway to artificial intelligence. This subcategory of AI uses algorithms to automatically learn insights and recognize patterns from data, applying that learning to make increasingly better decisions.

By studying and experimenting with machine learning, programmers test the limits of how much they can improve the perception, cognition, and action of a computer system.

Deep learning, an advanced method of machine learning, goes a step further. Deep learning models use large neural networks — networks that function like a human brain to logically analyze data — to learn complex patterns and make predictions independent of human input.

How Companies Use AI and Machine Learning

To be successful in nearly any industry, organizations must be able to transform their data into actionable insight. Artificial Intelligence and machine learning give organizations the advantage of automating a variety of manual processes involving data and decision making.

By incorporating AI and machine learning into their systems and strategic plans, leaders can understand and act on data-driven insights with greater speed and efficiency.

AI in the Manufacturing Industry

Efficiency is key to the success of an organization in the manufacturing industry. Artificial intelligence can help manufacturing leaders automate their business processes by applying data analytics and machine learning to applications such as the following:

• Identifying equipment errors before malfunctions occur, using the internet of things (IoT), analytics, and machine learning
• Using an AI application on a device, located within a factory, that monitors a production machine and predicts when to perform maintenance, so it doesn’t fail mid-shift
• Studying HVAC energy consumption patterns and using machine learning to adjust to optimal energy saving and comfort level

AI and Machine Learning in Banking

Data privacy and security are especially critical within the banking industry. Financial services leaders can keep customer data secure while increasing efficiencies using AI and machine learning in several ways:

• Using machine learning to detect and prevent fraud and cybersecurity attacks
• Integrating biometrics and computer vision to quickly authenticate user identities and process documents
• Incorporating smart technologies such as chatbots and voice assistants to automate basic customer service functions

AI Applications in Health Care

The health care field uses huge amounts of data and increasingly relies on informatics and analytics to provide accurate, efficient health services. AI tools can help improve patient outcomes, save time, and even help providers avoid burnout by:

• Analyzing data from users’ electronic health records through machine learning to provide clinical decision support and automated insights
• Integrating an AI system that predicts the outcomes of hospital visits to prevent readmissions and shorten the time patients are kept in hospitals
• Capturing and recording provider-patient interactions in exams or telehealth appointments using natural-language understanding

The difference between AI and machine learning

Artificial intelligence and machine learning are very closely related and connected. Because of this relationship, when you look into AI vs. machine learning, you’re really looking into their interconnection.

What is artificial intelligence (AI)?

Artificial intelligence is the capability of a computer system to mimic human cognitive functions such as learning and problem-solving. Through AI, a computer system uses math and logic to simulate the reasoning that people use to learn from new information and make decisions.

Are AI and machine learning the same?

While AI and machine learning are very closely connected, they’re not the same. Machine learning is considered a subset of AI.

What is machine learning?

Machine learning is an application of AI. It’s the process of using mathematical models of data to help a computer learn without direct instruction. This enables a computer system to continue learning and improving on its own, based on experience.

How are AI and machine learning connected?

An “intelligent” computer uses AI to think like a human and perform tasks on its own. Machine learning is how a computer system develops its intelligence.

One way to train a computer to mimic human reasoning is to use a neural network, which is a series of algorithms that are modeled after the human brain. The neural network helps the computer system achieve AI through deep learning. This close connection is why the idea of AI vs. machine learning is really about the ways that AI and machine learning work together.

How AI and machine learning work together

When you’re looking into the difference between artificial intelligence and machine learning, it’s helpful to see how they interact through their close connection. This is how AI and machine learning work together:

Step 1

An AI system is built using machine learning and other techniques.

Step 2

Machine learning models are created by studying patterns in the data.

Step 3

Data scientists optimize the machine learning models based on patterns in the data.

Step 4

The process repeats and is refined until the models’ accuracy is high enough for the tasks that need to be done.

Capabilities of AI and machine learning

Companies in almost every industry are discovering new opportunities through the connection between AI and machine learning. These are just a few capabilities that have become valuable in helping companies transform their processes and products:

Predictive analytics

This capability helps companies predict trends and behavioral patterns by discovering cause-and-effect relationships in data.

Recommendation engines

With recommendation engines, companies use data analysis to recommend products that someone might be interested in.

Speech recognition and natural language understanding

Speech recognition enables a computer system to identify words in spoken language, and natural language understanding recognizes meaning in written or spoken language.

Image and video processing

These capabilities make it possible to recognize faces, objects, and actions in images and videos, and implement functionalities such as visual search.

Sentiment analysis

A computer system uses sentiment analysis to identify and categorize positive, neutral, and negative attitudes that are expressed in text.

Benefits of AI and machine learning

The connection between artificial intelligence and machine learning offers powerful benefits for companies in almost every industry—with new possibilities emerging constantly. These are just a few of the top benefits that companies have already seen:

More sources of data input

AI and machine learning enable companies to discover valuable insights in a wider range of structured and unstructured data sources.

Better, faster decision-making

Companies use machine learning to improve data integrity and use AI to reduce human error—a combination that leads to better decisions based on better data.

Increased operational efficiency

With AI and machine learning, companies become more efficient through process automation, which reduces costs and frees up time and resources for other priorities.

Applications of AI and machine learning

Companies in several industries are building applications that take advantage of the connection between artificial intelligence and machine learning. These are just a few ways that AI and machine learning are helping companies transform their processes and products:

Retail

Retailers use AI and machine learning to optimize their inventories, build recommendation engines, and enhance the customer experience with visual search.

Healthcare

Health organizations put AI and machine learning to use in applications such as image processing for improved cancer detection and predictive analytics for genomics research.

Banking and finance

In financial contexts, AI and machine learning are valuable tools for purposes such as detecting fraud, predicting risk, and providing more proactive financial advice.

Sales and marketing

Sales and marketing teams use AI and machine learning for personalized offers, campaign optimization, sales forecasting, sentiment analysis, and prediction of customer churn.

Cybersecurity

AI and machine learning are powerful weapons for cybersecurity, helping organizations protect themselves and their customers by detecting anomalies.

Customer service

Companies in a wide range of industries use chatbots and cognitive search to answer questions, gauge customer intent, and provide virtual assistance.

Transportation

AI and machine learning are valuable in transportation applications, where they help companies improve the efficiency of their routes and use predictive analytics for purposes such as traffic forecasting.

Manufacturing

Manufacturing companies use AI and machine learning for predictive maintenance and to make their operations more efficient than ever.