Have you ever used ChatGPT or Google Bard and wondered how machines can understand and generate human language? The key enabling technology is a type of artificial intelligence called a transformer.
Transformers are a type of neural network architecture that are well-suited for natural language processing (NLP) tasks, such as machine translation, text summarization, and question answering. Transformers are able to understand relationships between words and phrases even if they are are far apart in a sentence. These relationships allow the transformer to understand the overall meaning of a sentence.
For example, in the sentence “The cat sat on the mat.”, the word “the” refers to the cat and the phrase “sat on the mat” describes what the cat is doing. Transformers use an attention mechanism to focus on the different parts of an input text sequence, depending on the context. This allows them to learn complex relationships between words and phrases which is the key to conveying and understanding meaning.
Transformers are usually trained on huge datasets of text and code. This allows them to learn the relationships between words and phrases, even for terms that are uncommonly used. Transformers are the key to unlocking the full potential of artificial intelligence. Without transformers, tools like ChatGPT and Bard could not understand and generate human language anywhere near as well as they do today. This technology is at the heart of the massive disruption we’re seeing today across a diverse set of industries and applications.
How do transformers work?
You can imagine a transformer as a machine with two main parts: the encoder and the decoder.
Encoder: Think of the encoder like a sophisticated scanner. It looks at the input text and turns it into a series of numbers. These numbers, sometimes called the hidden state, capture the essential meaning of the text in a numeric representation.
An important characteristic of an encoder is a feature known as “self-attention”. Self-attention is what allows the encoder to decide the importance of each word in the input. For instance, in the sentence “The cat sat on the mat“, the word “mat” is more closely related to “sat” than “cat”. The self-attention feature helps the encoder see these connections, even though the words “cat” and “mat” are on opposite ends of the sentence.
Decoder: Now, the decoder is like a printer. It takes the numeric representation of the input sentence and turns it back into text. Just like the encoder, the decoder has the self-attention feature. But it also has an extra capability: the autoregressive layer. This tool helps the decoder predict the next word it should generate based on the words that it has already produced. So, if it’s printed “The cat sat on the…”, it knows the next word is likely “mat”.
Attention Mechanism: You’ve probably noticed we’ve talked a lot about “attention”. That’s because it’s so important to how transformers work. The attention mechanism lets the model decide which words in the text are most important at any given moment. It assigns weights to words based on their relevance. So, in our cat example, “mat” might get a high weight when the model is thinking about “sat” given the relationship between these two words (you can sit on a mat).
The magic behind it all is this attention mechanism. In essence, this is the capability that let’s the transformer pay attention to the words it has received as input and to select the word that makes the most sense to follow the words it has already generated.
So that’s how transformers work at a very high level, but what exactly can one do with a transformer? Well, the impressive capabilities of transformers are being applied in various innovative ways as we’ll see next.
Applications of transformers
Transformers aren’t just an interesting theoretical technology; they have practical applications that are being used to create all sorts of useful technologies and tools:
- Translators: One of the earliest examples of transformers was in language translation. Because transformers can grasp the context and nuances of sentences, they were able to do more than substitute the equivalent word from one language into another. They were able to find the phrase that most closely matches what a native speaker might use.
- Chatbots and Customer Support: Love them or hate them, customer support bots and voice assistants are here to stay. And almost certainly there’s a transformer powering them behind the scenes and making them sound more natural and human-like.
- Text Summarization: Need a quick summary of a long article or document? Transformers can read extensive texts and produce concise summaries, capturing the key points from the text and sparing you the need of reading superfluous details.
- Content Creation: One of the biggest applications for transformers is generating creative content. This can range from blog posts, articles, and press releases to songs and poetry.
- Sentiment Analysis: Companies increasingly use transformers to gauge public sentiment on social media. By looking at tweets and comments, companies can get an idea of the general attitudes towards their posts and their brand.
- Code Completion: Tools like Copilot from Github are making developers more productive than ever and with generated unit tests, improving overall quality and reliability along the way.
What’s really interesting is that we’re still in the very early infancy of these technologies. As research progresses and transformers become even more sophisticated, we can expect them to show up in pretty much every application we use across every industry.
Conclusion
Artificial intelligence is quickly becoming an integral part of the way we work and interact with technology. From making it easier to write docs to improving our experiences with digital interactions, AI is touching nearly every aspect of our lives and at heart of this transformation is one incredibly important technology: the transformer.
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