Google Brain is an engineering team focused on deep learning research and applications. One growing area of interest within Google Brain is that of generative models. A generative model uses neural networks and a large data set to create new data similar to the ones that the network has seen before.

One approach to making use of generative models is GANs: generative adversarial networks. GANs can use a generative model (which creates new examples) together with a discriminator model (which can classify examples).

As an example, let’s take the task of generating new pictures of cats. We want an artificial cat picture generator. First, we train a discriminator by feeding it billions of example pictures of cats. We now have a model that can tell what a cat is. Next, we make a model that generates completely random images. We feed those randomly generated images to the discriminator. The discriminator outputs a “loss” for these random images. Loss is a metric we can use to represent how far off a given image is from being something that the discriminator would recognize as a cat. Finally, you can feed this “loss” back into the generative model, so that the generative model will adjust its weights in a way that will reduce loss. Over time, the generator gets better and better at reducing loss, until the discriminator starts believing that some of these semi-random images are actually cats.

Generative model systems have produced useful applications, such as object detection, image editing, and text-to-image generation. Today’s guest Doug Eck works on the Magenta team at Google Brain. Magenta uses applications of deep learning to produce tools and experiments around music, art, and creativity.

In a previous show, Doug described his vision for humans and computers to work together on creative tasks such as music. Today, we dive into some of the core machine learning building blocks that make machine creativity possible.

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