In this lesson, we are going to talk about LMS and about three things around LMS that are important
the size, the precision and the cost of the LM.
These three things are important about LMS, especially at this point, because some of you may have
heard that the thing to do at this point is to create your own LM, and this is not accurate.
A and just think about the size of what we call the foundational LMS, which are what we call LMS in
this program.
So a typical person reads between 0 and 700 books in their lifetime.
A book has eight 80,000 words.
So ChatGPT has been trained with the text equivalent to 10 million books.
That is 800,000 million words.
Remember, LMS have been made possible thanks to deep learning, neural networks, transformers and
GPUs.
So just think about the computational power you need to train ChatGPT for.
With the amount of data.
We are talking about.
Like 10 million books.
Let's think about the precision of LMS.
The accuracy of LMS depends on the quality of the data it has been trained on and the amount of data
it has been trained on.
So.
It depends on the quality and quantity of the data it has been trained on.
The position is nowadays the most important feature of any LM.
And that's why during 2023, the most used LM was ChatGPT.
We will talk more about that.
Remember, LM applications are not LMS.
LM applications are applications we build on top of LMS like ChatGPT.
In order to create ChatGPT or any other foundational LMS as they are called, you need huge tons of
data and you need a very refined way of doing things in order to get the precision level that ChatGPT
has got.
And if you think it's not enough, then you may think about the cost of training ChatGPT for the CEO
of OpenAI.
OpenAI said recently that training ChatGPT for costed more than $100 million.
So.
We understand now that LMS because of its size.
The precision and the cost they require.
Excuse me, are very difficult things to achieve, to build.
And this is not what you should do.
Focus your attention on building an LM.
We are going to build LM applications on top of LMS.
In the next lesson we will understand the meaning of foundation LM models.