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

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. 

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. 

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.

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.

• 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

• 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

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: 

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

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

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

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

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