Discriminative versus generative models

However, these latter examples of AI differ in an important way from the model that generated Théâtre D’opéra Spatial. In all of these other applications, the model is presented with a set of inputs—data such as English text, or X-ray images—that is paired with a target output, such as the next word in a translated sentence or the diagnostic classification of an X-ray. Indeed, this is probably the kind of AI model you are most familiar with from prior experiences in predictive modeling; they are broadly known as discriminative models, whose purpose is to 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 English language appears next in a translation), or a continuous outcome (such as the expected amount of money a customer will spend in an online store over the next 12 months).

However, this kind of model, in which data is “labeled” or “scored,” represents only half of the capabilities of modern machine learning. Another class of algorithms, such as the one that generated the winning entry in the Colorado State Art Fair, doesn’t compute a score or label from input variables but rather generates new data. Unlike discriminative models, the input variables are often vectors of numbers 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, which can produce complex outputs such as text, music, or images from random noise, is the topic of this book.

Even if you did not know it at the time, you have probably seen other instances of generative models mentioned in the news alongside the discriminative examples given previously. A prominent example is deepfakes—videos in which one person’s face has been systematically replaced with another’s by using a neural network to remap the pixels8 (Figure 1.2).

Figure 1.2: A deepfake image9

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 it could be used to create propaganda and misinformation online (Figure 1.3)11.

Figure 1.3: A chatbot dialogue created using GPT-210

In these and other applications—such as Google’s voice assistant Duplex, which can make a restaurant reservation by dynamically creating conservation with a human in real time12, or even software that can generate original musical compositions13—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 stylistic “filters” on pictures, 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?