AI is everywhere at the moment, and it’s responsible for everything from the virtual assistants on our smartphones to the self-driving cars soon to be filling our roads to the cutting-edge image recognition systems reported on by yours truly.
Unless you’ve been living under a rock for the past decade, there’s good a chance you’ve heard of it before — and probably even used it. Right now, artificial intelligence is to Silicon Valley what One Direction is to 13-year-old girls: an omnipresent source of obsession to throw all your cash at, while daydreaming about getting married whenever Harry Styles is finally ready to settle down. (Okay, so we’re still working on the analogy!)
But what exactly is AI? — and can terms like “machine learning,” “artificial neural networks,” “artificial intelligence” and “Zayn Malik” (we’re still working on that analogy…) be used interchangeably?
To help you make sense of some of the buzzwords and jargon you’ll hear when people talk about AI, we put together this simple guide help you wrap your head around all the different flavors of artificial intelligence — If only so that you don’t make any faux pas when the machines finally take over.
We won’t delve too deeply into the history of AI here, but the important thing to note is that artificial intelligence is the tree that all the following terms are all branches on. For example, reinforcement learning is a type of machine learning, which is a subfield of artificial intelligence. However, artificial intelligence isn’t (necessarily) reinforcement learning.
There’s no official consensus agreement on what AI means (some people suggest it’s simply cool things computers can’t do yet), but most would agree that it’s about making computers perform actions which would be considered intelligent were they to be carried out by a person.
The term was first coined in 1956, at a summer workshop at Dartmouth College in New Hampshire. The big current distinction in AI is between current domain-specific Narrow AI and Artificial General Intelligence . So far, no-one has built a general intelligence. Once they do, all bets are off…
You don’t hear so much about Symbolic AI today. Also referred to as Good Old Fashioned AI, Symbolic AI is built around logical steps which can be given to a computer in a top-down manner. It entails providing lots and lots of rules to a computer (or a robot) on how it should deal with a specific scenario.
This led to a lot of early breakthroughs, but it turned out that these worked very well in labs, in which every variable could be perfectly controlled, but often less well in the messiness of everyday life. As one writer quipped about Symbolic AI, early AI systems were a little bit like the god of the Old Testament — with plenty of rules, but no mercy.
Today, researchers like Selmer Bringsjord are fighting to bring back a focus on logic-based Symbolic AI, built around the superiority of logical systems which can be understood by their creators.
If you hear about a big AI breakthrough these days, chances are that unless a big noise is made to suggest otherwise, you’re hearing about machine learning . As its name implies, machine learning is about making machines that, well, learn.
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