Embracing Generative AI: A Tool in Service of Humanity
Generative AI is revolutionizing the way we create, offering a paradigm shift that empowers humans to focus on the essence of their work. Pinar Seyhan Demirdag, the co-founder and AI director of Seyhan Lee, highlights the transformative impact of generative AI in a LinkedIn Learning course. As she eloquently states, “Generative AI is changing how we create. For the first time, humans are supervising and machines are generating.”
Empowering Human Creativity: Generative AI has the potential to alleviate humans from mundane and laborious tasks, allowing us to concentrate on the core aspects of our work—vision, ideas, and purpose. This shift not only redefines the future of jobs but also opens up new avenues for creativity and innovation.
The Role of Generative AI: By leveraging generative AI, individuals can harness its capabilities to drive the creation of their intentions. This transformative tool empowers users to explore new creative frontiers and unlock the potential of advanced technologies.
The Promise of Creative Revolution: Pinar Seyhan Demirdag’s course offers an exciting opportunity to delve into the new creative revolution facilitated by generative AI. It provides a platform to understand and embrace the profound impact of this technology, offering insights into how each of us can find our place in this age of advanced technologies.
Example Code:
# Example code for training a simple generative adversarial network (GAN) using TensorFlow
import tensorflow as tf
from tensorflow.keras import layers, models
import numpy as np
# Define the generator model
def build_generator(latent_dim):
model = models.Sequential()
model.add(layers.Dense(128, input_dim=latent_dim))
model.add(layers.LeakyReLU(alpha=0.2))
model.add(layers.Dense(784, activation='tanh'))
model.add(layers.Reshape((28, 28, 1)))
return model
# Define the discriminator model
def build_discriminator(input_shape=(28, 28, 1)):
model = models.Sequential()
model.add(layers.Flatten(input_shape=input_shape))
model.add(layers.Dense(128))
model.add(layers.LeakyReLU(alpha=0.2))
model.add(layers.Dense(1, activation='sigmoid'))
return model
# Create a GAN by connecting the generator and discriminator
def build_gan(generator, discriminator):
discriminator.trainable = False
model = models.Sequential()
model.add(generator)
model.add(discriminator)
return model
# Define the training loop for the GAN
def train_gan(gan, discriminator, generator, epochs, batch_size, latent_dim, real_data):
for epoch in range(epochs):
# Generate random noise as input for the generator
random_latent_vectors = np.random.normal(size=(batch_size, latent_dim))
# Generate fake images using the generator
generated_images = generator.predict(random_latent_vectors)
# Combine real and fake images
combined_images = np.concatenate([real_data, generated_images])
# Labels for real and fake images
labels = np.concatenate([np.ones((batch_size, 1)), np.zeros((batch_size, 1))])
# Train the discriminator
d_loss = discriminator.train_on_batch(combined_images, labels)
# Generate random noise as input for the generator
random_latent_vectors = np.random.normal(size=(batch_size, latent_dim))
# Assemble labels that say "all real images"
misleading_targets = np.zeros((batch_size, 1))
# Train the generator (via the GAN model, where the discriminator weights are frozen)
a_loss = gan.train_on_batch(random_latent_vectors, misleading_targets)
This example code demonstrates the training process of a simple Generative Adversarial Network (GAN) using TensorFlow, showcasing the potential of generative AI in creating new and realistic data.
Generative AI is indeed a powerful tool that holds the potential to elevate human creativity and redefine the way we approach creation and innovation. Pinar Seyhan Demirdag’s course offers a compelling exploration of this transformative technology, providing a platform for individuals to embrace and leverage generative AI in their creative pursuits.