Artificial Intelligence vs Machine Learning: What’s the Difference?

Artificial intelligence vs machine learning: What’s the difference?

These terms and their acronyms (AI and ML) are often used interchangeably, particularly in discussions about digital transformation, big data, and analytics. However, while the concepts are closely related, artificial intelligence and machine learning aren’t the same.

They work together in many use cases but have different objectives, purposes, and applications. As artificial intelligence and machine learning continue to transform the world around us, it’s important to identify their differences.

Here, we’ll break down exactly how AI and machine learning work, how they’re similar, and how they differ.

What is Artificial Intelligence (AI)?

Artificial intelligence (AI) is a broad term that refers to machines that can simulate human intelligence and cognitive functions. For instance, AI solutions can see, understand, and respond to written or spoken language, analyze data, and solve complex problems.

While AI is often referred to as a “system”, it’s actually a broad range of technologies implemented into a system which allows it to learn, reason, and act in certain ways. How AI works, and what it can do, depends on the types of technologies implemented into the solution.

There are various subfields involved in artificial intelligence too, such as:

• Machine learning: The subset of AI in which algorithms are trained with data sets and enabled to improve themselves and their operations over time.
• Deep learning: A subset of machine learning in which artificial neural networks are used to mimic the human brain and perform complex reasoning tasks.
• Natural language processing: A subset of computer science, ML, AI, and linguistics, NLP focuses on creating software that can interpret human communication.
• Robotics: A subset of computer science, AI, and engineering, robotics focuses on creating machines capable of performing tasks and learning in real-world environments.

What is Machine Learning (ML)?

Machine learning, as mentioned above, is a subset of artificial intelligence that enables a system to learn and improve based on access to data and experience. Rather than using explicit programming, machine learning relies on algorithms to analyze large volumes of data, learn from the patterns within that data, and make informed decisions.

Machine learning algorithms organically improve their performance over time as they’re exposed to more data. The more data they’re given, the more advanced the machine learning model becomes.

Machine learning has become an extremely important component of AI in recent years. The chances are you’ve already interacted with systems built with machine learning models. For instance, companies like Netflix and Amazon use machine learning to deliver personalized content and product recommendations to customers.

Machine Learning vs Deep Learning

Deep learning is a subset of machine learning. The biggest difference between the two technologies is how each algorithm learns and processes data. With deep learning, most of the feature extraction component of the learning process is automated.

Additionally, deep learning allows for the use of much larger data sets for training, allowing developers to create highly scalable systems. Observing patterns in larger data sets allows deep learning models to cluster and organize inputs more effectively.

Typically, enterprises use deep learning solutions to enable the automation of more complex tasks. They’re used to create things like advanced virtual assistants, or systems that can detect fraud and risks in various business settings.

Artificial Intelligence vs Machine Learning: The Connection

While artificial intelligence and machine learning aren’t the same, they are closely connected. The best way to think about the two solutions is to remember that AI is the broader concept of empowering a machine to mimic the cognitive processes of a human.

Machine learning, on the other hand, allows machines to extract information from data, learn from it autonomously, and improve their functionality over time.

Both artificial intelligence and machine learning also:

• Enable intelligent problem-solving: AI and machine learning are both used to help companies solve complex problems. An ML algorithm, for instance, could use knowledge of previous sales prices in real estate to suggest prices for new homes. An AI system, on the other hand, could use computer vision to recognize objects in a self-driving car, allowing the machine to navigate a vehicle safely and effectively.
• Computer science: Both AI and ML are fields of computer science focused on creating software that can analyze and comprehend data in complicated ways. Scientists within these fields are focused on developing computer systems that can perform complex tasks, often faster and more efficiently than human beings.
• Versatility: There are applications for both AI and ML in all industries. Machine learning solutions can enable predictive maintenance scheduling, automated fraud detection and demand forecasting in retail. AI can help optimize supply chains, personalize product recommendations, and improve agricultural outcomes.

Artificial Intelligence vs Machine Learning: The Differences

Now you understand what both artificial intelligence and machine learning are, and how they’re connected, let’s take a closer look at the key differences. While AI revolves around the broad idea that machines can simulate human intelligence, machine learning focuses on teaching a machine to perform a specific task, by identifying patterns in data.

As an example, if you wanted to learn more about the weather forecast for tomorrow, you could ask an AI assistant to tell you what the forecast will be. It will perform the task of researching and surfacing data for you. A machine learning model wouldn’t just draw information from the internet; it would look at previous data and inputs to attempt to “predict” what the weather will be like.

Let’s break down the key differences between artificial intelligence vs machine learning.

The Objectives

The goal of an AI system is to allow a machine to complete a task human beings would usually have to do manually, such as solving a problem or answering a question. The goal of machine learning is to have a machine analyze large amounts of data. The machine uses statistical models to identify patterns in that data to help human beings complete a task.

The Methods

The varied field of artificial intelligence involves using a range of methods to solve diverse problems. AI systems use generic algorithms, deep learning, neural networks, rule-based systems, and machine learning models. Within ML, methods are generally separated into two categories: supervised and unsupervised learning.

Supervised models learn to solve problems using labeled data values. Unsupervised learning models attempt to discover hidden patterns in unlabeled data.

Implementation

The process of developing and implementing a machine learning solution involves two key tasks. Developers need to collect and prepare a data set, then choose a ML model or strategy, such as a decision tree or linear regression. Data scientists feed specific data into a system for training, and may continuously refine the data set with error checking and updated information.

Developing and implementing an AI model is often more complex. Most developers select pre-built AI solutions to achieve specific goals. These solutions may be pre-trained to perform various tasks, such as identifying images or responding to natural language.

Requirements

Machine learning solutions require huge data sets for training, as well as a significant amount of computational power. However, depending on your needs and use cases, a single server might be enough to run a machine learning model.

Other AI systems may have varying requirements for infrastructure, based on the task you want to complete. Some use cases will require several thousands of machines to work together to achieve complex goals.

Artificial Intelligence vs Machine Learning: Quick Overview

Bringing Artificial Intelligence and Machine Learning Together

Notably, when it comes to analyzing artificial intelligence vs machine learning, the goal isn’t usually to determine which solution is better than the other. These solutions are more effective when they’re combined into a single AI system.

Combining AI and machine learning means companies can analyze and leverage a larger amount of unstructured and structured data. Together, these technologies can enable faster, more accurate decision-making, improve operational efficiency, and solve a range of problems.

As mentioned above, AI and machine learning can be applied to various use cases in many industries. Some of the most common examples of AI and machine learning working together in different sectors include:

Healthcare and Life Sciences

Health and life sciences companies use AI and machine learning in various ways. These resources can be used for image processing, accelerating the detection of diseases and symptoms. They can accelerate drug development, augment and enhance diagnostics, and even support advanced patient monitoring practices.

Manufacturing

In the manufacturing industry, artificial intelligence and machine learning can help companies to identify equipment errors before malfunctions happen, with IoT, analytics, and machine learning. AI applications can monitor machines in real-time and alert professionals to faults. Intelligent tools can even help monitor energy consumption patterns to improve sustainability.

Banking and Financial Services

Machine learning is excellent at helping to detect and minimize fraud, as well as cybersecurity attacks in the financial landscape. AI systems with computer vision and biometrics capabilities can also help to authenticate user identity and process documents. Plus, AI can automate trading, risk assessment and analysis, and customer service.

Ecommerce and Retail

In the ecommerce and retail space, AI and machine learning can support supply chain optimization, enable demand forecasting, and augment search processes. It can help companies deliver personalized offers and experiences to customers with recommendation engines. Plus, it can support sales and marketing teams with sales forecasting, predictive analytics, and sentiment analysis.

Customer Service

In customer service, companies in a wide range of industries use both artificial intelligence and machine learning in tandem. Chatbots and virtual assistants with AI and machine learning capabilities can understand customer intent, offer 24/7 support, and answer questions. With machine learning, these systems can effectively solve more customer problems over time.

Cybersecurity

Although developments in AI have led to some new challenges in cybersecurity, both artificial intelligence and machine learning can also improve security strategies. Companies can use these tools to rapidly identify and detect anomalies, forecast risks, and minimize threats.

Transportation

In the transportation sector, AI and machine learning are working together to power autonomous vehicles and drones. They can also help companies improve the efficiency of their routes and use predictive analytics for traffic forecasting. Plus, they can support drivers with real-time analysis of traffic levels and potential risks.

Artificial Intelligence vs Machine Learning: Final Thoughts

Overall, artificial intelligence and machine learning are different technologies with different objectives. AI is a broader concept, covering all types of machines and systems designed to mimic human cognition. Machine learning focuses more on using algorithms and statistical models to understand data and discover patterns in information.

However, while these solutions are very different, they’re also closely connected. In many industries, artificial intelligence and machine learning work side by side to help companies accomplish countless tasks, solve problems, and improve operational efficiency.