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5/17/2022
10 minutes

What's The Difference Between AI And Machine Learning?

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Team Copado
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In the business world, the terms artificial intelligence (AI) and machine learning (ML) are often used interchangeably to describe essentially the same thing – a highly sophisticated computer system that can take in, process, and learn from vast amounts of data, then apply that intelligence to business applications. AI sounds more impressive, which is why marketing teams tend to brand all machine learning applications as artificial intelligence.

Academia, on the other hand, prefers to define AI and ML as separate (but interconnected) concepts, though even they haven’t come to a consensus on where exactly to draw the line between the two. Essentially, AI is a machine’s ability to display human-like intelligence through behaviors like problem solving, planning, and adaptive learning. Machine learning is generally defined as a machine’s ability to take in large quantities of data and learn from it. Technically, you could view a machine’s ability to learn as an example of artificial intelligence, creating significant overlap between ML and AI even within the more rigid definitions.

An easier way to conceptualize the difference between AI and machine learning is with Lego. ML is the Lego blocks and AI is what you can build with those blocks. It's broadly accepted that AI always needs some form of machine learning to function, but machine learning can be used for purposes other than just AI. For example, machine learning is also used for things like email spam filters, search engines, and voice recognition. However, many of these other applications of machine learning could also, theoretically, be defined as AI as well – if your email filter uses ML data to get better at detecting spam over time, it’s displaying unsupervised learning, which is a characteristic of artificial intelligence.

Understanding the Difference Between AI and Machine Learning

Ultimately, it’s less important to define the exact difference between AI and machine learning than it is to understand how each can be used – together or separately – to benefit your business.

Business Applications for Machine Learning

Machine learning can process huge amounts of data very quickly. It uses algorithms that change over time, using past experiences and newly acquired information to get better and faster at processing that data. We’ve already mentioned how ML creates better email spam filters, but there are plenty of other AI and non-AI applications for machine learning.

For example, many firewall technologies use machine learning algorithms to scan network traffic and detect potential threats. The sophistication of ML-based intrusion detection and prevention applications varies. At a basic level in this example, machine learning uses signature-based detection, meaning it compares an incoming request to a database of known threats to determine whether or not it’s malicious. This level of machine learning doesn’t display true intelligence or decision-making, so it doesn’t necessarily fall into the AI category. On the other hand, some next-generation firewalls (NGFWs) use advanced machine learning methods like neural networks to analyze traffic in real time and identify breaches that don’t follow established attack vectors. In that case, the machine is recognizing patterns and making independent decisions using ML data, so it could be classified as artificial intelligence.

Machine learning is also used by sales and marketing teams for segmentation. Segmentation is the practice of categorizing current and potential clients, donors, end-users, etc. in a way that helps create more targeted marketing and sales messaging. Using data from sales software, a CRM, or other source, an ML algorithm can spot patterns and sort people into segments according to characteristics they share (such as age, location, or purchasing habits). Machine learning can also take things a step further, crossing into AI territory, by applying industry-specific knowledge and marketing best practices to the segmentation process. That means the ML application can recommend new segmentation criteria, suggest the best time to send out messages to individual segments, and perform other tasks that used to require a human with years of marketing experience in a particular industry.

Business Applications for AI

Artificial intelligence uses machine learning to, well, learn. While an ML application’s ability to improve over time, recognize patterns, and adapt to changes frequently pushes it into the AI category, there are some artificial intelligence capabilities that go far beyond this.

For example, modern business intelligence (BI) applications use AI to analyze data and predict future outcomes. AI-powered business intelligence systems can process data from many different sources in near-real-time and spot tiny indicators of upcoming industry trends or changes that a human would likely miss. AI can also perform predictive forecasting of the likely outcomes from certain business decisions, essentially allowing you to run potential changes through a simulation to see how beneficial (or harmful) they could be to your company.

AI is also used in robotic test automation for DevOps CI/CD software development. Traditional test automation involves writing and maintaining test scripts, which can perform tasks automatically but need to be frequently modified as testing parameters change. Robotic test automation uses AI to learn how end-users interact with an application as well as how that application interacts with other software and systems, then predict and adapt the automated testing parameters. Applications like Copado Robotic Testing can even use AI to detect broken or outdated test scripts and self-hea, changing scripts automatically when needed.

Putting AI and Machine Learning to Work for Your Organization

The main difference between AI and machine learning is that ML is the process by which an artificial intelligence learns. For many people outside of the data science community, the exact line between the two concepts is irrelevant. What’s more important is that you understand the business applications of AI and machine learning, and how this technology can work for your organization.

 

 

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