In this lesson, we are going to talk about the evolution of artificial intelligence engineering, and
also about the training of the new AI engineers.
Remember that you will have detailed information about this topic in the attached materials, especially
in the chapter three of the book, Keys to Artificial Intelligence.
So remember at this point, the internet revolution as well, and see that one central new role, new
job that the internet revolution created was the what we call the full stack app engineer.
So the internet brought a different kind of applications, a what we today call full stack applications,
with the back end side and the front end side, etc., etc. and you have a different teams working on
each of these sides of the full stack apps.
But you also have what we call the full stack app engineer, which is a person with the knowledge to
understand what is happening in both sides of a full stack application.
And this full stack application engineer is the right person to become a CTO.
Why?
Because you have a global understanding of what is happening in your application.
If you are a front end engineer or a back end engineer, you know a lot of just one side of the product.
But the full stack engineer has a broader, uh, view and understanding of, uh, what is happening.
So what?
Happening at the internet, in the internet, during the internet revolution is happening as well in
this artificial intelligence revolution.
So what has been happening in the engineering side?
Building artificial intelligence applications in a company.
Used to require skills from different professionals.
Remember I told you that a years ago you needed at least three kinds of, uh, professionals the machine
learning engineer, the software engineer, and the product manager.
And these skills were learned in separate programs for different professionals.
But now integrated programs are emerging to train what we call the new AI engineers.
This is one of these programs.
So before we needed a team of different professionals, now with just one professional, we can build
the AI application.
Remember that AI applications can be of various types.
We have artificial intelligence applications based on images, videos, audio numbers, but the universalization
of artificial intelligence in the company comes mainly from what we call LM applications.
So artificial intelligence applications based on language.
Which will soon evolve to become multi-modal applications.
So.
We are right now in a very exciting moment around artificial intelligence engineering, because we are
seeing the birth of a new kind of engineer that is going to be central and also the birth of a new.
A kind of application that is going to be central.
The Elm application is the most promising kind of application in the artificial intelligence world,
and is the best suited to evolve into multimodal applications.
So right now, how are.
The new artificial intelligence engineers train right now.
These AI engineers are a kind of hybrid between machine learning and engineers, software engineers
and product managers.
And you are seeing today the emergence of a new kind of training programs.
99% of the universities still don't have the AI engineer training the AI engineer education in their
curriculum, but companies like our company AI accelerator have developed this new integral programs.
As you will see in the part two of this program.
This program is a very different kind of program.
A and you will see that it includes.
Use cases for LM applications.
Basic concepts for LMS architecture of LM applications.
Basic concepts of Rag and other techniques.
We will study orchestration frameworks like long chain Lambda index OpenAI.
We will talk about new tools like Lang, Smith, lang, Cerf, lang, chain expression language, lang
lang chain templates.
We will talk about programming.
We will talk about how to understand programming within the LM app development.
We will talk about development environment.
We will talk about different levels of applications from the toy demo to the professional application.
We will call this level one, level two and level three applications.
We will talk about full stack applications.
We will talk about speed cost.
Lamb chops, etc. etc. etc. so if you look at the detailed curriculum that we have for the part two
of this program, you will see how the new AI engineers are being trained.
So in this lesson, we have been talking a little bit about the evolution of AI engineering and the
evolution of the training of the new AI engineers.
You will see many more details about that in the book you have in the attached materials.
And in the next lesson, we are going to start talking about the changes that Artificial intelligence
is introducing in the business area.
You will see that many of these changes we have been talking about in the employment area, in the jobs
area, are going to impact in business as well.
So let's see what is going to happen or what is happening already in the business side.