Exploring the Exciting World of Generative Artificial Intelligence

Hello all,

Today’s topic is a very exciting aspect of AI called generative artificial intelligence. In a few words, generative AI refers to algorithms that enable machines to create content using various forms of input, such as text, audio files, and images. In a previous post, I discussed Variational Autoencoders and how they are used to generate new images. I mentioned that they are part of a broader category known as generative models, and today, we will delve deeper into this fascinating field.

Discriminative vs. Generative Models

To understand generative AI, it’s essential to differentiate between two primary types of models: discriminative and generative. Discriminative models, such as convolutional and recurrent neural networks, focus on distinguishing patterns in data to categorize them into classes. Applications like image recognition, skin cancer diagnosis, and Ethereum prediction fall under this category.

On the other hand, generative models are capable of creating new patterns in data. This means they can produce new images, text, and even music. Mathematically, discriminative models estimate the posterior probability ( p(y|x) ), which represents the probability of an output sample (e.g., a handwritten digit) given an input sample (an image of that digit). In contrast, generative models estimate the joint probability ( p(x,y) ), which considers the likelihood of both the input and output samples being true simultaneously. Essentially, generative models aim to calculate the distribution of a set of classes rather than merely defining the boundaries between them.

The Possibilities of Generative AI

The potential applications of generative AI are vast and varied. Current advancements have led to the development of technologies that can generate text from images, create new molecules for oncology, discover new drugs, and even transfer the artistic styles of masters like Van Gogh to new images. You may have also heard about deepfakes, which involve superimposing celebrities’ faces onto videos. The realism of these creations is so advanced that distinguishing between real and fake can be nearly impossible.

At the heart of many of these applications lies a groundbreaking architecture known as Generative Adversarial Networks (GANs). While there are other models like Variational Autoencoders and Deep Boltzmann Machines, GANs have garnered significant attention and hype over the past few years.

What are Generative Adversarial Networks?

Introduced in 2016 by Ian Goodfellow, GANs represent one of the most promising advancements in AI. They are an unsupervised learning technique based on a simple yet powerful premise: to generate new data, you build two models that compete against each other.

The first model, known as the Generator, is tasked with producing fake data from random noise. The second model, the Discriminator, receives both real and fake data and learns to distinguish between the two. As these models compete, the Generator improves its ability to create realistic data, while the Discriminator becomes more adept at identifying fakes.

This dynamic can be likened to a forger (the Generator) trying to create convincing counterfeit documents while a detective (the Discriminator) works to uncover the fraud. This zero-sum game leads to both models enhancing their capabilities over time.

Training Generative Adversarial Networks

Training GANs is where things get complex. Unlike traditional neural networks that use gradient descent and a loss function, GANs involve two models competing against each other. This competition can lead to various challenges, making optimization one of the most active research areas in AI.

In essence, we can think of GAN training as a minimax game. The Discriminator aims to maximize its ability to correctly label real and fake data, while the Generator seeks to minimize the Discriminator’s success. This interplay can be mathematically represented through a minimax function, where the Discriminator performs gradient ascent on its objective function, and the Generator performs gradient descent.

The training process continues until the Discriminator can no longer maximize its function, and the Generator can no longer minimize it—this point is known as Nash equilibrium in game theory.

Example Code for GAN Training

Here’s a simplified example of how one might implement GAN training in Python:

def get_gan_network(discriminator, random_dim, generator, optimizer):
    gan_input = Input(shape=(random_dim,))
    x = generator(gan_input)
    gan_output = discriminator(x)
    gan = Model(inputs=gan_input, outputs=gan_output)
    gan.compile(loss='binary_crossentropy', optimizer=optimizer)
    return gan

def train(epochs=1, batch_size=128):
    x_train, y_train, x_test, y_test = load_minst_data()
    batch_count = x_train.shape[0] / batch_size
    adam = get_optimizer()
    generator = get_generator(adam)
    discriminator = get_discriminator(adam)
    gan = get_gan_network(discriminator, random_dim, generator, adam)

    for e in range(1, epochs + 1):
        for _ in range(batch_count):
            noise = np.random.normal(0, 1, size=[batch_size, random_dim])
            image_batch = x_train[np.random.randint(0, x_train.shape[0], size=batch_size)]
            generated_images = generator.predict(noise)
            X = np.concatenate([image_batch, generated_images])
            y_dis = np.zeros(2 * batch_size)
            y_dis[:batch_size] = 0.9
            discriminator.train_on_batch(X, y_dis)
            noise = np.random.normal(0, 1, size=[batch_size, random_dim])
            y_gen = np.ones(batch_size)
            gan.train_on_batch(noise, y_gen)

While this code provides a basic framework for GAN training, it’s important to note that there are numerous challenges to overcome, such as oscillation of model parameters, the Discriminator becoming too successful, and sensitivity to hyperparameters.

Conclusion

Generative artificial intelligence is a groundbreaking field with the potential to revolutionize various industries. The most prominent model in this domain, Generative Adversarial Networks, showcases the power of competition between two neural networks to create realistic data. While there are still challenges to address in GAN training, the progress made in recent years is promising.

If you’re interested in diving deeper into GANs, I highly recommend the fifth week of the Introduction to Deep Learning course offered by the University of Colorado Boulder.

Key Takeaways

• Generative artificial intelligence generates new data from existing data.
• The most notable model in this field is the Generative Adversarial Network (GAN).
• GANs consist of two neural networks that compete against each other, improving their capabilities over time.
• Ongoing research is focused on optimizing GAN training and addressing existing challenges.
• The real-time applications of GANs are vast and continue to expand.

Thank you for joining me on this exploration of generative AI. The future of this technology is indeed exciting, and I look forward to seeing how it evolves in the coming years. Finito!