AI vs Machine Learning: What’s the Difference?

As the systems we use to power our businesses create more and more data, companies need help dealing with it, extracting insight from it, and using it to drive competitive advantage. Big data keeps getting bigger. In a 2019 IDG Marketpulse survey, enterprise companies revealed that they are seeing data volumes grow an average of 63 percent per month, and even as much as 100 percent per month.

That’s one of the reasons why we’re hearing so much about AI and machine learning (as well as AI vs machine learning). While there’s enormous business value hidden in the vast volumes of information in our systems, finding the valuable stuff is becoming more and more like finding a needle in a haystack.

AI and machine learning can help. But there’s a lot of confusion when it comes to AI vs machine learning. How are they different? When do you use one over another? It’s important to understand the difference between the two terms and how they can help address some of the challenges that businesses face with their growing stores of data.

What is Artificial Intelligence (AI)?

Artificial intelligence (AI) isn’t a new concept: the term has been around since 1950, when Alan Turing first explored whether machines could think in his paper, Computing Machinery and Intelligence.¹ Over the decades, AI has often appeared in popular culture as a terrifying example of technology getting out of control, like Hal from 2001: A Space Odyssey or Skynet in the Terminator movies. But in reality, AI is much more benign—and helpful—than science fiction has led us to believe.

So what is artificial intelligence, and what is the distinction between AI vs machine learning? AI is a broad term that refers to smart computers that are capable of performing tasks that would normally require human involvement. With machine learning, algorithms are learning from your data to make better predictions. The difference between AI and machine learning is that AI then uses those learnings to make a system act more human by applying what it has learned via automation.

AI is often divided into two main categories: general and narrow. General AI can intelligently solve multiple types of problems, whereas narrow AI refers to an AI that performs one or more specific tasks very well. Narrow AI is extremely common, whereas general AI is rarer.

So what is artificial intelligence helping us accomplish? A few examples of AI include:

• Self-driving cars
• Map applications that predict traffic times
• Autopilot for commercial planes

What is Machine Learning?

Although machine learning has become a buzzword recently, the term was originally coined in 1959 by Arthur Samuel, a computer scientist at IBM. Machine learning is a type of AI in which a system can “learn” from data over time, rather than relying solely on its original programming. It’s essentially a process that involves creating and then training algorithms to handle data better and more accurately. Machine learning needs data to process and learn from, so as always, it’s important to make sure your data maintenance processes are up to speed. Learn more about how to get your data ready for machine learning here.

One common example of machine learning is those product recommendation algorithms that seem to know what you like or need. Let’s say you’re shopping online and you put a gardening tool in your shopping cart. The site then recommends other gardening products you might be interested in, like gardening gloves. In the background, machine learning algorithms have “learned” that people who buy that gardening tool frequently buy gloves with it.

So why is machine learning so popular at the moment? In short, data and computation power are both abundant and cheap, so machine learning’s time has come. Machine learning can be used in a variety of scenarios today, such as:

• Fraud detection for banking and credit card companies
• Suggesting social media pages or profiles that align with a user’s interests
• Spam detection by email providers

What Are the Top Differences Between AI and Machine Learning?

When we’re talking about AI vs machine learning, the main thing to remember is that machine learning is really just a type of AI. Machine learning is simply a specific application of the broader AI concepts. It’s important to understand the definition of artificial intelligence vs machine learning. But in the end, the concepts are actually similar in a lot of ways. Both AI and machine learning require high-quality data: as the saying goes, garbage in, garbage out. If we’re going to rely on AI or machine learning to help make business decisions, we need to know that those decisions are backed by reliable, high-quality information. The following table shows the key differences between AI and machine learning.

Want to Learn More About AI or Machine Learning?

Now that we have examined AI vs machine learning, you may want to consider whether either approach is right for your business. AI and machine learning can help your business process and understand data insights faster – empowering data-driven decisions across your organization. For AI and machine learning to be successful, however, your models and algorithms need access to high-quality data. As the quality of your data increases, you can expect the quality of our insights to increase as well. Transforming data for analysis can be challenging based on the growing volume, variety, and velocity of big data. Your organization will need to overcome this challenge to unlock the potential of your data and mobilize to move faster and outpace competitors. When you are ready for AI and machine learning, Matillion’s purpose-built data transformation solutions for cloud data warehouses can help you increase the ROI on your data, transforming large volumes of data, quickly, to prepare it  for AI and machine learning.

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Download our ebook, the User Guide to Machine Learning and Data Transformation in the Cloud, to understand more about these technologies and the role that data transformation plays in each one.