Making AI Even Smarter: Cheat Sheets, Bigger Brains, and New Ways to Read

Imagine you have a super-smart computer that can understand and write like a human. That's basically what a "large language model" is. Now, there are a few ways to make these models even better:

Giving them a Cheat Sheet (RAG)

Think of RAG like giving the computer a cheat sheet with extra information. This cheat sheet can have the latest news, specific facts about a topic, or even secret company information. This helps the computer:

* Stay updated: So it doesn't give you outdated information.

* Be an expert: It can talk about specific topics with more detail.

* Avoid mistakes: By checking the cheat sheet, it can avoid making things up.

Want to learn more about RAG? Check out this link: [https://aws.amazon.com/what-is/retrieval-augmented-generation/](https://aws.amazon.com/what-is/retrieval-augmented-generation/)

A Bigger Brain (Large Context Windows)

Imagine increasing the computer's memory so it can remember a whole book at once! This is what a "large context window" does. It helps the computer:

* Understand better: It can see how different parts of a long text fit together.

* Need less help: Sometimes it won't even need the cheat sheet because it already remembers the important information.

* But... it needs more power: Remembering so much takes a lot of computer power.

Want to learn more about large context windows? Take a look at this: [https://arxiv.org/abs/2412.09871](https://arxiv.org/abs/2412.09871)

New Ways to Read (Tokenization Alternatives)

Normally, computers read by breaking words into smaller parts. But there are new ways to "read" that can:

* Understand any language: Without needing special rules for each one.

* Handle mistakes: Typos won't confuse it as much.

* Read in a more human way: By looking at the whole text instead of just parts.

There are two main types of new reading methods:

1. Byte-Level Models: These models read text like a stream of bytes, which is like the raw data of the text. This helps them understand different languages and handle mistakes better. Learn more: [https://arxiv.org/abs/2412.09871](https://arxiv.org/abs/2412.09871)

2. Tokenizer-Free Architectures: These models skip the breaking-down step and try to understand the whole text at once. This can help them avoid some mistakes and understand things in a more natural way. Learn more: [https://arxiv.org/abs/2404.14408](https://arxiv.org/abs/2404.14408)

Why use all three?

Each of these methods has its own strengths. It's like having a toolbox with different tools for different jobs.

* RAG is great for staying updated and having specific knowledge.

* Large context windows help with understanding long and complex texts.

* Tokenization alternatives make the computer a more flexible and accurate reader.

By combining these methods, we can create super-smart computers that are accurate, understand lots of information, and can be used for all sorts of tasks!

What do you think about these new ways to make AI even smarter? Let us know in the comments!