Artificial Intelligence vs. Machine Learning: Key Differences in Intelligent Technology
Often, the terms Artificial Intelligence and Machine Learning are used interchangeably. In reality, Machine Learning is actually a subset of the larger umbrella of AI.
In the same way that all squares are rectangles, but not all rectangles are squares, there are other varieties of Artificial Intelligence that aren’t necessarily Machine Learning.
To help clear up the confusion, we’ll explore both as individual practices of computer science, as well as the ways that they interact with each other. Let’s dive in.
In an article on the past and future of AI, computer scientist John McCarthy defined it in this way: “It is the science and engineering of making intelligent machines, especially intelligent computer programs. It is related to the similar task of using computers to understand human intelligence.”
When the idea of Artificial Intelligence first hit the tech scene at the 1965 Dartmouth Conference, McCarthy led the charge, fulling believing that computers could become capable of understanding abstract thought and reasoning in much the same way that humans are able to.
What we understand as AI today is teaching a computer anything that it wasn’t capable of doing before. By feeding in algorithms and rules, the computer can create, sort, and report new information based on the data it’s been given.
For that reason, many people don’t really consider AI as “intelligent” at all, saying that the concept of intelligence is uniquely human. Computers aren’t (yet) capable of performing processes that are entirely distinctive to the machine. They must work within the confines of the structure and data that we provide.
Under the overarching concept of AI is Machine Learning. With ML, your computer is able to adapt and enhance performance based on nuances in the user’s input.
A great example is Clippy, the old Microsoft Word mascot who would adapt its reactions to key parts of a document you were typing. If your first words were “To Whom It May Concern,” Clippy’s algorithm might suggest tips on writing a letter. If you were listing out job experience, it might offer you a resume template.
If you watch any streaming services, like Netflix or Hulu, use autocorrect on your phone, or let iTunes compile a list of songs you might like, you’re using machine learning. Based on your input, the algorithms provided to that particular service by the developers is able to offer you a more customized experience.
Why should businesses care about AI and ML?
Many companies can use both to provide themselves and their customers with an optimized interface or product. Loading demographic data into a trend-finding program can help you make better marketing decisions. Data on cyber attacks helps your security suite predict and eliminate potential threats. Even the ability to set up chat bots through your social media is a valuable product of technology’s advancements in computer intelligence.
At Verus, we work with AI to keep business networks secure.
WatchGuard Fireboxes use AI to detect zero day attacks at the network perimeter. It uses sophisticated Machine Learning to predict when, where, and how attacks are going to happen based on data about past attempts to breach the network.
Sophos Anti-Virus uses a deep learning neural network at the host level to protect users from threats. It is able to detect both known and unknown malware without relying on signatures, which can be unreliable in identifying possible threats.
Finding ways to implement AI successfully is becoming more and more necessary as customers seek a more personalized way to interact with their technology.