Generative Deep Learning

Chapter 10. Conclusion

In this book, we have taken a journey through the last half-decade of generative modeling research, starting out with the basic ideas behind variational autoencoders, GANs, and recurrent neural networks and building upon these foundations to understand how state-of-the-art models such as the Transformer, advanced GAN architectures, and world models are now pushing the boundaries of what generative models are capable of achieving, across a variety of tasks.

I believe that in the future, generative modeling may be the key to a deeper form of artificial intelligence that transcends any one particular task and instead allows machines to organically formulate their own rewards, strategies, and ultimately awareness within their environment.

As babies, we are constantly exploring our surroundings, building up a mental model of possible futures with no apparent aim other than to develop a deeper understanding of the world. There are no labels on the data that we receive—a seemingly random stream of light and sound waves that bombard our senses from the moment we are born. Even when our mother or father points to an apple and says apple, there is no reason for our young brains to associate the two and learn that the way in which light entered our eye at that particular moment is in any way related to the way the sound waves entered our ear. There is no training set of sounds and images, no training set of smells and tastes, and no training set of actions and rewards. ...