Google Translate’s system most likely works through extensive classification algorithms of all the languages that they support on their system. Classification functions as a type of algorithm that categorizes the features of data and stores it for both machine learning and retrieval once the application is in use.
It was stressed within the CrashCourse Machine Learning video that conceptualizing the process of machine learning and how fast AI truly computes machine learning translation is impossible due to how sophisticated it is. This is evident, based on how Google Translate’s user interface provides a response to application users in almost real time – much faster than having to whip out a translator book. As defined in the CrashCourse videos, machine learning is a set of techniques that sit inside the even more ambiguous goal of artificial intelligence. Those set of techniques are made up of various different components that contribute to both machine learning and intelligence for artificial intelligence. Users input a set of strings that consist of numbers, letters, or punctuations. This set is called an array, which is made up of binary numbers and stored amongst eachother so that once a command is made to access a certain string, it goes straight to that binary code. Structs are also characteristics of machine learning that consist of compound data structures beyond numbers and simplistic data. They store several pieces of data (think a group) and are then inputted into the AI system to then be outputted out. Google Translator’s system must function in queues, where it’s “first-in first-out” fashion, in comparison to stacks that works from top to bottom. Additionally, Google Translator utilizes artificial neural networks to take in what the user is typing and output it into the desired answer. What we don’t see/pay much mind to is the hidden layer in between the input and output that organizes the input, classifies it, and outputs it properly.
In Google’s Neural Network for Machine Translation article, it’s interesting to delve into their concept of phrased-based machine translation, and how that technology has developed into the “Google Neural Machine Translation system to be phrased based, however more colloquial than a solely phrased based system or human translation. The difference between the phrase based and google neural machine translation system is that the Google version scans and classifies each word being translated and then matches it to a weighted distribution over the most relevant words to the target language.