Welcome to week two, day five.
The last day of week two where a lot is coming together.
I am so grateful that you're sticking with it, and I'm going to make it worth your while because today
is going to be really, really good fun.
I'm excited to get into this.
It's the big conclusion of the second week.
Again, I'm going to keep saying what you can do.
I think it's so important to celebrate your upskilling, you know, Transformers back to front.
You can code against the frontier APIs, you can build an AI assistant, and you can add tools to give
it expertise.
Today we introduce agents.
We talk about how agents can carry out more advanced sequential activities.
And then we do something super fun creating a multimodal AI assistant using agents and tools.
So what are agents?
Agents, I should say.
An agent I.
An agent.
It is one of these umbrella terms that people can use in different contexts.
So it is one of these things that, that that can mean different things to different people.
But generally speaking, most often people are talking about software entities that are autonomous.
They can perform tasks not just in the sense of taking an input prompt and generating text.
Um, typical characteristics.
Let's say they are autonomous.
They have some sort of agency, they are goal oriented, that they have some kind of thing that they're
setting out to do, and that they are task specific.
They are usually specialized on being good at one thing or another.
Um, and they're typically designed to be part of something called an agent framework, which is a sort
of environment in which agents can interact to solve more complex problems and potentially with limited
human involvement.
So it's not like it's just a sort of request response situation with a human.
But you can imagine this sort of environment where multiple software agents that could be combinations
of llms along with traditional software interacting in order to carry out tasks.
And so some of the features you might expect is the ability to have memory or persistence that sort
of goes beyond just a request response, the ability to have some sort of decision making and orchestration
about what does what are planning abilities.
And sometimes that is just a matter of the environment as some planning coded into it.
Sometimes you have an LLM which is responsible for planning.
It's a model that knows how to take complex problems and break it down into smaller problems for other
models to take care of.
And then use of tools is often also an example of a genetic AI.
This is where, of course, as you are now very familiar, we give models the ability to do things like
connect to databases or connect to the internet or whatever we want because we are providing it access
to functions and we know how that works behind under the hood.
Now we know that it's really just a fancy if statement, but it gives the effect that the Llms are able
to do this.
So we're about to do a few things.
Let me just quickly sort of set the scene for you.
We're going to first build a function that can generate images, a good multimodal use case.
We're going to have an LLM call that can do that.
And it's going to be a function that does it.
And you can think of that in its own right as being like an agent.
It's like a piece of software that is able to take this very specific, specialized instruction and
do it.
That will be an artist that we will create in code with the help of Dall-E three, the the image generation
model from uh, OpenAI.
Uh, and, you know, if you want to to quibble, you could argue that image generation is not in itself
an LM thing.
Uh, lm being language models, but these days, generally llms are used interchangeably with the broader
gen AI context.
And so one does tend to think of image generation and other kinds of multimodal generation as falling
within the LM engineer's, uh, toolkit.
So we're then going to look to, to make agents these sort of, uh, these, these functions that are
able to do things.
And we're going to add sound as well as images, and then we're going to have an agent framework in
that we are going to teach our AI assistant, the same airline assistant that we've been working on
how to speak and draw.
All right, without further ado, I hope that sounds fun to you.
I hope it sounds exciting because it's going to be it's going to be great.
Uh, I can't wait to do it.
Let's go and do it right now.