We all know and love Google translate, the website that can instantly translate between 100 different human languages as if by magic. It is even available on our phones and smartwatches:
The technology behind Google Translate is called Machine Translation. It has changed the world by allowing people to communicate when it wouldn’t otherwise be possible.
But we all know that high school students have been using Google Translate to… umm… assist with their Spanish homework for 15 years. Isn’t this old news?
But we all know that high school students have been using Google Translate to… umm… assist with their Spanish homework for 15 years. Isn’t this old news?
The technology behind this breakthrough is called sequence-to-sequence learning. It’s a very powerful technique that is used to solve many kinds of problems. After we see how it is used for translation, we’ll also learn how the exact same algorithm can be used to write AI chatbots and describe pictures.
TOPICS INCLUDE :
- Making Computers Translate
- Making Computers Translate Better Using Statistics
- Thinking in Probabilities
- Step 1: Break original sentence into chunks
- Step 2: Find all possible translations for each chunk
- Step 3: Generate all possible sentences and find the most likely one
- Statistical Machine Translation was a Huge Milestone
- The Limitations of Statistical Machine Translation
- Making Computers Translate Better — Without all those Expensive People
- Recurrent Neural Networks
A recurrent neural network (or RNN for short) is a slightly tweaked version of a neural network where the previous state of the neural network is one of the inputs to the next calculation. This means that previous calculations change the results of future calculations!
Update: This article is part of a series. Check out the full series: Part 1, Part 2, Part 3, Part 4, Part 5, Part 6, Part 7, and Part 8
www.dprg.co.in
