A guide to machine learning in search: Key terms, concepts and algorithms

Want to understand how machine learning impacts search? Learn how Google uses machine learning models and algorithms in search.

When it comes to machine learning, there are some broad concepts and terms that everyone in search should know. We should all know where machine learning is used, and the different types of machine learning that exist.

Read on to gain a better grasp of how machine learning impacts search, what the search engines are doing and how to recognize machine learning at work. Let’s start with a few definitions. Then we’ll get into machine learning algorithms and models.

Machine learning terms

What follows are definitions of some important machine learning terms, most of which will be discussed at some point in the article. This is not intended to be a comprehensive glossary of every machine learning term. If you want that, Google provides a good one here.

Algorithm: A mathematical process run on data to produce an output. There are different types of algorithms for different machine learning problems.
Artificial Intelligence (AI): A field of computer science focused on equipping computers with skills or abilities that replicate or are inspired by human intelligence.
Corpus: A collection of written text. Usually organized in some way.
Entity: A thing or concept that is unique, singular, well-defined and distinguishable. You can loosely think of it as a noun, though it’s a bit broader than that. A specific hue of red would be an entity. Is it unique and singular in that nothing else is exactly like it, it is well defined (think hex code) and it is distinguishable in that you can tell it apart from any other color.
Machine Learning: A field of artificial intelligence, focused on the creation of algorithms, models and systems to perform tasks and generally to improve upon themselves in performing that task without being explicitly programmed.
Model: A model is often confused with an algorithm. The distinction can get blurry (unless you’re a machine learning engineer). Essentially, the difference is that where an algorithm is simply a formula that produces an output value, a model is the representation of what that algorithm has produced after being trained for a specific task. So, when we say “BERT model” we are referring to the BERT that has been trained for a specific NLP task (which task and model size will dictate which specific BERT model).
Natural Language Processing (NLP): A general term to describe the field of work in processing language-based information to complete a task.
Neural Network: A model architecture that, taking inspiration from the brain, include an input layer (where the signals enter – in a human you might think of it as the signal sent to the brain when an object is touched)), a number of hidden layers (providing a number of different paths the input can be adjusted to produce an output), and the output layer. The signals enter, test multiple different “paths” to produce the output layer, and are programmed to gravitate towards ever-better output conditions.

Visually it can be represented by:

Artificial intelligence vs. machine learning: What’s the difference?

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Want to understand how machine learning impacts search? Learn how Google uses machine learning models and algorithms in search.

Machine learning terms

Artificial intelligence vs. machine learning: What’s the difference?

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