Discriminative and generative models

 Theses other example of AI differ in an important way from the model that generated The Portrait of Edmond Belamy. In all of thes other applications, the model is presented with a set of inputs-data such as English text, imatges from X0rays, or the positions on a gameboard- that is paired with a target output, such as the next word in a translated sentence, the diagnostic classification of an X-ray, or the next move in a game. Indeed, this is probaly the kind of AI model you are most familiar with from prior expreiences of predictive modelling; they are broadly knows as discriminative models, shose purpose is th create a mapping between a set of input variables and a target output. The target output could be a set of discrete classes(such as which word in the Englkish language appears next in a translation), or a continuous outcome(such as the expected amount of money a cuatomer will spend in a online store over the next 12 months).

In should be noted that this kind of model, in which data is labeled or scored, represents only half the capabilities of modern machine learning. Another class of algorithms, such as the one that generated the artificial portrait sold at Christie's, don't compute a score or label from input variables, but rather generate new data. Unlike discriminative models, the input variables are often vectors of numgbers that aren't related to real-world values at all, and are often even randomly generated.

This kind of model-known as a generative model-can produce complex outputs such as text, music, or images from random noise, and is the topic of this book.

Even if you didn't know it at the time, you have probably seen other instances of generative models in the news alongside the discriminative example given earlier.

A prominent example is deep fakes, whcih are videos in which one person's face has been systematically replaced with another's by using a neural network to remap the pixels.

Maybe you have also seen stories about AI models that generate fake news, which scientists at the firm OpenAI were initially terrified to release to the public due to concerns they could be used to create propaganda and misinformation online.

In these and other applications, such as Google's voice assistant Duplex, which can make a restaurant reservation by dynamically creating a conversation with a human in real time, or software that can generate original musical compositions, we are surrounded by the outputs of generative AI algorithms.

These models are able to handle complex information in a variety of domains: creating photorealistic images or styleistic filters on pictures(Figure 1.4), synthetic sound, conversational text, and even rules for optimally playing video games. You might ask, where did these models come from? How can I implement them myself? 

We will discuss more on that in the next section.