The AI glossary: 5 artificial intelligence terms you need to know — from by David Nield


Ah, the famous (or infamous) algorithm. Algorithms are sets of rules that computer programs can follow, so if one of your best friends posts a photo of you on Facebook, then the rules say that should go up at the top of your News Feed. Or if you need to get from A to B on Google Maps, an algorithm can help you work out the fastest route.

The rules are followed by computers but usually set by humans – so it’s the Facebook engineers who choose what makes a story important or which roads are fastest. Where AI starts to come in is in tweaking these algorithms using machine learning, so programs begin to adapt these rules for themselves. Google Maps might do this if it starts getting feedback data that a particular road is shut.

When image recognition systems get it wrong, for example, that’s an example of an algorithm or set of rules at work – the same rules have been applied but the wrong result has been reached, so you get a cat-like dog rather than an actual cat. In many ways, algorithms are the building blocks of machine learning.


Deep learning
Deep learning is a type or a subset of machine learning, which is why the two terms often get jumbled up, and can correctly be used to describe the same AI in a lot of cases. It’s machine learning but designed to be even more intelligent, with more nuance and more layers, and intended to work more like the human brain does.

Deep learning has been made possible by two key technological advances: more data and more powerful hardware. That’s why it’s only recently come into fashion, though its original roots go back decades. If you think about it as machine learning turned up to 11, you can understand why it’s getting smarter as computers get more powerful.

Deep learning often makes use of neural networks to add this extra layer of intelligence. For example, both deep learning and machine learning can recognize a cat in a picture by scanning a million cat images – but whereas machine learning needs to be told what features make up a cat, deep learning can work out what a cat looks like for itself, as long as there’s enough raw data to work from.