Name: Introduction to Generative AI
Uploaded: 2025-07-09T05:00:45.000Z
Duration: 22 min 8 s
Description: Thousands of YouTube videos with English-Chinese subtitles! Now you can learn to understand native speakers, expand your vocabulary, and improve your pronunciation...

Hello, and welcome to Introduction to Generative AI.

My name is Dr. Gwendolyn Stripling, and I am the Artificial Intelligence Technical Curriculum Developer here at Google Cloud.

In this course, you learn to define generative AI, explain how generative AI works, describe generative AI model types, and describe generative AI applications.

Generative AI is a type of artificial intelligence technology that can produce various types of content, including text, imagery, audio, and synthetic data.

Well, since we are going to explore generative artificial intelligence, let's provide a bit of context.

So two very common questions asked are, what is artificial intelligence?

And what is the difference between AI and machine learning?

One way to think about it is that AI is a discipline, like physics, for example.

AI is a branch of computer science that deals with the creation of intelligent agents, which are systems that can reason and learn and act autonomously.

Essentially, AI has to do with the theory and methods to build machines that think and act like humans.

In this discipline, we have machine learning, which is a subfield of AI.

It is a program or system that trains a model from input data.

The trained model can make useful predictions from new or never-before-seen data drawn from the same one used to train the model.

Machine learning gives the computer the ability to learn without explicit programming.

Two of the most common classes of machine learning models are unsupervised and supervised ML models.

The key difference between the two is that with supervised models, we have labels.

Labeled data is data that comes with a tag, like a name, a type, or a number.

Unlabeled data is data that comes with no tag.

This graph is an example of the sort of problem that a supervised model might try to solve.

For example, let's say you are the owner of a restaurant.

You have historical data of the bill amount and how much different people tipped based on order type, whether it was picked up or delivered.

In supervised learning, the model learns from past examples to predict future values, and in this case, tips.

So here, the model uses the total bill amount to predict the future tip amount based on whether an order was picked up or delivered.

This is an example of the sort of problem that an unsupervised model might try to solve.

So here, you want to look at tenure and income, and then group or cluster employees to see whether someone is on the fast track.

Unsupervised problems are all about discovery, about looking at the raw data and seeing if it naturally falls into groups.

Let's get a little deeper and show this graphically, as understanding these concepts are the foundation for your understanding of generative AI.

In supervised learning, testing data values, or x, are input into the model.

The model outputs a prediction and compares that prediction to the training data used to train the model.

If the predicted test data values and actual training data values are far apart, that's called error.

And the model tries to reduce this error until the predicted and actual values are closer together.

Now that we've explored the difference between artificial intelligence and machine learning and supervised and unsupervised learning, let's briefly explore where deep learning fits as a subset of machine learning methods.

While machine learning is a broad field that encompasses many different techniques, deep learning is a type of machine learning that uses artificial neural networks, allowing them to process more complex patterns than machine learning.

Artificial neural networks are inspired by the human brain.

They are made up of many interconnected nodes, or neurons, that can learn to perform tasks by processing data and making predictions.

Deep learning models typically have many layers of neurons, which allows them to learn more complex patterns than traditional machine learning models.

And neural networks can use both labeled and unlabeled data.

In semi-supervised learning, a neural network is trained on a small amount of labeled data and a large amount of unlabeled data.

The labeled data helps the neural network to learn the basic concepts of the task, while the unlabeled data helps the neural network to generalize to new examples.

Now, we finally get to where generative AI fits into this AI discipline.

Gen AI is a subset of deep learning, which means it uses artificial neural networks, can process both labeled and unlabeled data using supervised, unsupervised, and semi-supervised methods.

Large language models are also a subset of deep learning.

Deep learning models, or machine learning models in general, can be divided into two types, generative and discriminative.

A discriminative model is a type of model that is used to classify or predict labels for data points.

Discriminative models are typically trained on a data set of labeled data points, and they learn the relationship between the features of the data points and the labels.

Once a discriminative model is trained, it can be used to predict the label for new data points.

A generative model generates new data instances based on a learned probability distribution of existing data.

Thus, generative models generate new content.

Here, the discriminative model learns the conditional probability distribution, or the probability of Y, our output, given X, our input, that this is a dog, and classifies it as a dog and not a cat.

The generative model learns the joint probability distribution, or the probability of X and Y, and predicts the conditional probability that this is a dog, and can then generate a picture of a dog.

So to summarize, generative models can generate new data instances, while discriminative models discriminate between different kinds of data instances.

The top image shows a traditional machine learning model, which attempts to learn the relationship between the data and the label, or what you want to predict.

The bottom image shows a generative AI model, which attempts to learn patterns on content so that it can generate new content.

A good way to distinguish what is gen AI and what is not is shown in this illustration.

It is not gen AI when the output, or Y, or label, is a number, or a class, for example, spam or not spam, or a probability.

It is gen AI when the output is natural language, like speech or text, an image, or audio, for example.

Visualizing this mathematically would look like this.

If you haven't seen this for a while, the Y is equal to f of X equation calculates the dependent output of a process given different inputs.

The Y stands for the model output, the F embodies the function used in the calculation, and the X represents the input, or inputs used for the formula.

So the model output is a function of all the inputs.

If the Y is a number, like predicted sales, it is not gen AI.

If Y is a sentence, like defined sales, it is generative, as the question would elicit a text response.

The response would be based on all the massive large data the model was already trained on.

To summarize, at a high level, the traditional classical supervised and unsupervised learning process takes training code and label data to build a model.

Depending on the use case or problem, the model can give you a prediction, it can classify something, or cluster something.

We use this example to show you how much more robust the gen AI process is.

The gen AI process can take training code, label data, and unlabeled data of all data types and build a foundation model.

The foundation model can then generate new content.

For example, text, code, images, audio, video, et cetera.

We've come a long way from traditional programming to neural networks, to generative models.

In traditional programming, we used to have to hard code the rules for distinguishing a cat.

In the wave of neural networks, we could give the network pictures of cats and dogs and ask, is this a cat?

In the generative wave, we, as users, can generate our own content.

Whether it be text, images, audio, video, et cetera.

For example, models like POM, or Pathways Language Model, or Lambda, Language Model for Dialogue Applications, ingest very, very large data from the multiple sources across the internet and build foundation language models we can use simply by asking a question.

Whether typing it into a prompt or verbally talking into the prompt itself.

It can give you everything it has learned about a cat.

Generative AI is a type of artificial intelligence that creates new content based on what it has learned from existing content.

The process of learning from existing content is called training and results in the creation of a statistical model.

When given a prompt, gene AI uses the model to predict what an expected response might be.

Essentially, it learns the underlying structure of the data and can then generate new samples that are similar to the data it was trained on.

As previously mentioned, a generative language model can take what it has learned from the example it's been shown and create something entirely new based on that information.

Large language models are one type of generative AI since they generate novel combinations of text in the form of natural sounding language.

A generative image model takes an image as input and can output text, another image or video.

For example, under the output text, you can get visual question answering while under output image, an image completion is generated and under output video, animation is generated.

A generative language model takes text as input and can output more text, an image, audio or decisions.

For example, under the output text, question answering is generated and under output image, a video is generated.