The Difference Between Machine Learning and Artificial Intelligence, Explained
What’s the difference between machine learning and artificial intelligence? These two terms are thrown around frequently in technology, but they’re often used interchangeably. In fact, artificial intelligence encompasses several different disciplines, including machine learning, natural language processing and computer vision, among others. This article will break down the difference between machine learning and artificial intelligence to help you understand both concepts better and improve your tech vocabulary overall. Let’s dive in!
How do we define intelligence?
If artificial intelligence is really just machine learning applied to complex tasks like reasoning or visual recognition, why is it something we’re so excited about right now? The answer boils down to how we define intelligence. Many researchers are focusing on emulating one type of intelligent system (like a mouse) or replicating one type of intelligent behavior (such as playing chess). That makes sense: If you want to study how humans learn to recognize faces or ride bikes, it’s useful to focus on humans. But if you want your software to do something more challenging — make millions of decisions with fewer instructions from us — you have a much larger design space and problem ahead of you.
Different Types of AI
There are multiple kinds of artificial intelligence (AI), but when most people talk about AI they’re referring to machine learning. And that’s good enough for now. AI has existed in some form or another since computers were invented, but today it’s used to power everything from social media algorithms to Netflix movie recommendations. The technology uses large datasets — really large sets of data — to find patterns. In order to do so, programmers teach a computer how to learn and make decisions on its own by feeding it thousands or millions of examples.
The Importance of Deep Learning
If you’ve heard of machine learning, but aren’t entirely sure what it is, don’t worry. It’s a broad term that encompasses many different tasks — from natural language processing to facial recognition to voice identification. Deep learning is a subset of machine learning that focuses on artificial neural networks (ANNs), which are systems meant to replicate our brains’ information processing techniques. They take in large amounts of data (i.e., images or audio) and automatically learn how to process it for a specific purpose. At their most basic level, ANNs can be used for clustering related pieces of data together; an example would be grouping pictures based on visual similarities so they’re easier to locate later or identifying speech patterns in text with similar sounds so as the words sound alike when spoken aloud.
Is There a Danger That AI will Become Super Intelligent and Take Over Humanity?
While you’ve probably heard about things like AI superintelligence (in science fiction), you may be wondering if there’s anything to worry about with that kind of thing actually happening. In short: yes. And no. The biggest issue isn’t necessarily a takeover as much as it is what happens when machines can make their own decisions — even if we don’t understand why they made those decisions or how they arrived at them.
How Will AI Change Our Lives in the Next 10 Years?
It’s difficult to know exactly how AI will evolve in 2018 and beyond. But what’s certain is that we’ll see a range of new tools implemented in daily life. Advanced voice recognition software like Siri and Alexa are already putting AI in our pockets — and Apple has even opened its platform to third-party developers. In fact, every major tech company — from Amazon to Microsoft — is currently working on improving their respective AI offerings. And if you’re an entrepreneur or small business owner? You can get in on the ground floor by partnering with them (or one of their competitors) to develop an AI product or service. It all comes down to answering these key questions: What do you want your customers’ experience with your brand to be?
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