- The last year, we've seen some pretty big leaps
in artificial intelligence.
That's why we're adding these three new videos
to the course.
In this chapter,
I'll explain how many of the concepts in this course apply
to newer generative AI.
So far in this course,
you've seen that artificial intelligence can be used
to play chess, direct robots,
and identify very complex patterns.
But to most people,
these systems still acted very much like machines.
These were complex tasks, but it didn't feel very human.
In the last few years,
you've seen the rise in generative artificial intelligence.
Instead of just analyzing existing data,
these new systems can generate new data.
They can write articles, produce images, develop videos,
and create complex chatbots.
For many people,
these new generative AI systems
now start to look a lot like a true thinking machine,
even though they're still based
on the same technology that you've already seen.
Newer generative AI systems still use a form
of machine learning.
They typically use
a deep learning artificial neural network,
but there are a few key differences
that help these systems generate content.
One of the biggest challenges
is that these new systems need to be able
to train on massive data sets.
So if you want a generative AI system to write a new poem,
it needs to understand all the poems
that have already been written.
If you want to write a poem about a cheese sandwich,
then it needs to know about all the different cheeses
and all the qualities they have in common.
Then it needs to combine these two massive data sets
into one common output to generate a cheese sandwich poem.
The challenge is that most current AI systems
have only been designed for one task.
So you might have one neural network
that checks credit card transactions for fraud.
Then you'll have another neural network
that plays video games.
But you couldn't use the same neural network to do both.
But to generate content, that's exactly what you need to do.
That's where two new technologies come in.
The first is using deep learning artificial neural networks
to create a foundation model.
A foundation model allows neural networks
to be able to handle more than one task without retraining.
With a foundation model,
you could create an artificial neural network
that both plays video games and looks for credit card fraud.
The challenge with these foundation models
is that they have an enormous appetite for data.
Think about all the different poems
that have ever been written,
or all the different cheeses and their qualities.
And that's just one type of poem.
People might want to write poems about their cats, their cars,
or even old television shows.
That means that the foundation model
will have to include all of this and even more.
To train these systems,
you'll have to use something
called self-supervised learning.
As you'll see later, this is a mixture
of both unsupervised and supervised learning.
It's a way to train these foundation models
without depending on humans to organize all that data.