Let’s examine the differences between different generative ai models (discriminative and generative modeling) in order to comprehend the generative AI concept.

Existing data points are classified using discriminative modeling. It usually applies to activities requiring supervised machine learning.

The goal of generative modeling is to comprehend the dataset’s structure and provide analogous examples. It primarily pertains to machine learning activities that are unsupervised or semi-supervised.

The fields of discriminative and generative modeling are expanding as neural networks become more pervasive in our daily lives. Let’s get into more detail about each now.

Discriminative modeling

Predictions are typically made using machine learning algorithms. Given a set of attributes, discriminative algorithms attempt to categorize input data and predict a label or class to which a certain data example belongs.

For generative ai examples, let’s say that our training data includes a variety of pictures of tomatoes and cucumbers. They may also be known as samples. Each sample has output class labels and input characteristics (X) (Y). Additionally, we have a neural network that can analyze an image and determine if it is a tomato or a cucumber by focusing on the characteristics that set them apart.

In a way, this model essentially “recalls” what the object looks like based on what it has already seen after it has been taught and used to distinguish between tomatoes and cucumbers.

So, even if you show the model an image from a completely unrelated class, like an animal, it can probably recognize that it’s a tomato. In this instance, the expected output (y) from the training dataset is contrasted with the predicted output (). We can determine how and what needs to be modified in an ML pipeline to get more accurate outputs for specific classes based on the comparison.

In summary, the discriminative model compresses information on the distinctions between tomatoes and cucumbers without attempting to define either vegetable.

Generative modeling

Instead of predicting a label given to various features, generative algorithms attempt to predict features given a specific label. While generative models are concerned with how you get x, discriminative algorithms are concerned with the relationships between x and y.

Generative modeling enables us to quantify the likelihood that two events, x and y, will occur simultaneously. Instead of learning the border, it learns the distribution of distinct classes and traits.

Returning to our earlier example, generative models provide insight into the nature of the “tomato itself” or “cucumber itself.” Additionally, if the model is aware of the various varieties of tomatoes and cucumbers, then it is also aware of the variations between them.

An information-free holistic process modeling is the goal of a generative algorithm. Why do we even need discriminative algorithms, you might be asking. The truth is that a more targeted discriminative algorithm frequently provides a better solution to the issue than a broader generative one.

However, there is a large class of issues where generative modeling enables you to achieve outstanding outcomes. For instance, cutting-edge technology like transformer-based algorithms and GANs.