What are AI tokens?

Tokenization in practice

In practice, tokenization plays a crucial role in various AI applications, including text generation, language translation, and sentiment analysis.

Text generation

Copilot and other AI models use tokens to create coherent and contextually relevant sentences. For instance, if you ask the model, “Find online recipes for orange chicken,” it will tokenize the query into [“Find”, “online”, “recipes”, “for”, “orange”, “chicken”], which allows it to understand that you want online orange chicken recipes. Based on this context, the AI model can respond appropriately with links to your desired recipes.

Language translation

Tokenization helps break down sentences into manageable units, even down to the character, allowing AI models to accurately translate each part. If you want to translate the sentence “I walked to the store” from English to Spanish, Copilot would tokenize it to [“I’, “walked”, “to”, “the”, “store”], and then translate each token, giving you the translated sentence “Yo caminé a la tienda.” Whether you’re studying for a German exam or want to brush up on your French before a trip, you can ask Copilot to translate sentences from English into your target language and vice versa.

Sentiment analysis

When it comes to businesses seeking insight on how their product is received by customers, product reviews are essential, and sentiment analysis AI systems help businesses see how they’re doing. By breaking down product review text into tokens, the AI can better understand the sentiment behind the text, whether it's positive, negative, or neutral. For example, the sentence “This product is cute, but the sizing is not accurate, and I had to return it for a different size” would be tokenized into [“This”, “product”, “is”, “cute”, “,” “but”, “the”, “sizing”, “is”, “not”, “accurate”, “,”, “and”, “I”, “had”, “to”, “return”, “it”, “for”, “a”, “different”, “size”]. The tokens “cute” and “not accurate” can then be processed by the sentiment model to assign mixed sentiment labels for the business.

The future of tokens in AI

As AI models continue to evolve, tokenization will play a critical role in improving the quality and relevance of generated text. These advancements will have a significant impact on AI-driven tools and applications, making them more efficient and effective. For instance, improved tokenization techniques could lead to better language translation, more accurate sentiment analysis, and more coherent text generation.

The building blocks of AI

From text generation to language translation to sentiment analysis, tokenization plays a huge role in how AI models interact with their users. Because of these building blocks, you can hold a consistent conversation with Copilot, and Copilot can offer more context-aware and relevant responses to your queries. Try Copilot today and open up a world of possibilities.