Birthday Submit! Haha! Welcoming 33 with a damaged coronary heart! Yep, I hate every thing about every thing. Simply drained and burned out. Effectively, cheers to that, proper!? We’re preventing for freedom despite the fact that we had been born free! This capitalistic, sadistic world has made it so arduous for younger individuals. We are able to’t afford something.
There’s no different progress we will management apart from our personal private psychological progress. That’s all it’s. Simply holding and defending our personal sanity.
Transferring on…..!!
In a world the place expertise continually blurs the strains between actuality and fiction, the emergence of hyperrealistic avatars raises important questions on the way forward for media and private id.
These avatars, created by AI startup Synthesia, are so lifelike that they problem our means to tell apart between what’s actual and what’s not.
The journey begins with a go to to Synthesia’s studio, the place the creator undergoes a course of of knowledge assortment.
This includes capturing facial options, mannerisms, and even voice samples to create a digital double.
The expertise behind these avatars represents a big leap ahead in generative AI, promising to beat the uncanny valley — the discomfort individuals really feel when confronted with almost-but-not-quite human replicas.
Nonetheless, because the creator explores the implications of this expertise, deeper questions emerge. Whereas Synthesia emphasizes the benign functions of artificial media, considerations linger about its potential for misuse. The convenience with which AI can generate convincing however pretend content material raises problems with belief and authenticity in an more and more digitized world.
Furthermore, the article highlights the significance of consent and accountable use of AI-generated content material.
Not like many deepfakes created with out consent, Synthesia requires specific permission from people earlier than creating their avatars. Y
et, even with stringent moderation measures in place, the potential for abuse stays a urgent concern.
Because the expertise evolves, so too should our understanding of its affect on society. Whereas the attract of hyperrealistic avatars is plain, it prompts us to mirror on the moral implications of blurring the boundaries between actuality and fiction. In a world the place fact is more and more elusive, navigating the digital panorama requires a important eye and a cautious method.
Finally, the journey into the realm of hyperrealistic avatars serves as a reminder of the advanced interaction between expertise and humanity.
Whereas the long run could maintain thrilling prospects, it additionally calls for vigilance and duty to make sure that innovation serves the larger good.
First, guarantee you could have TensorFlow and Keras put in:
pip set up tensorflow keras
Now, let’s write the code:
import numpy as np
import tensorflow as tf
from tensorflow.keras.layers import Enter, Conv2D, Flatten, Dense, Reshape, Conv2DTranspose
from tensorflow.keras.fashions import Mannequin
from tensorflow.keras.datasets import mnist
import matplotlib.pyplot as plt# Load the MNIST dataset
(x_train, _), (x_test, _) = mnist.load_data()
# Preprocess the info
x_train = x_train.astype('float32') / 255.
x_test = x_test.astype('float32') / 255.
x_train = np.reshape(x_train, (len(x_train), 28, 28, 1))
x_test = np.reshape(x_test, (len(x_test), 28, 28, 1))
# Outline the generator mannequin
def build_generator(latent_dim):
generator_input = Enter(form=(latent_dim,))
x = Dense(7 * 7 * 64, activation='relu')(generator_input)
x = Reshape((7, 7, 64))(x)
x = Conv2DTranspose(64, kernel_size=3, strides=2, padding='identical', activation='relu')(x)
x = Conv2DTranspose(32, kernel_size=3, strides=2, padding='identical', activation='relu')(x)
generator_output = Conv2D(1, kernel_size=3, padding='identical', activation='sigmoid')(x)
generator = Mannequin(generator_input, generator_output)
return generator
# Outline the discriminator mannequin
def build_discriminator():
discriminator_input = Enter(form=(28, 28, 1))
x = Conv2D(64, kernel_size=3, strides=2, padding='identical', activation='relu')(discriminator_input)
x = Conv2D(128, kernel_size=3, strides=2, padding='identical', activation='relu')(x)
x = Flatten()(x)
x = Dense(1, activation='sigmoid')(x)
discriminator = Mannequin(discriminator_input, x)
return discriminator
# Outline the GAN (Generative Adversarial Community)
def build_gan(generator, discriminator):
discriminator.trainable = False
gan_input = Enter(form=(100,))
gan_output = discriminator(generator(gan_input))
gan = Mannequin(gan_input, gan_output)
gan.compile(loss='binary_crossentropy', optimizer='adam')
return gan
# Mix generator and discriminator to type GAN
latent_dim = 100
generator = build_generator(latent_dim)
discriminator = build_discriminator()
gan = build_gan(generator, discriminator)
# Practice the GAN
batch_size = 128
epochs = 50
for epoch in vary(epochs):
for batch in vary(x_train.form[0] // batch_size):
noise = np.random.regular(0, 1, (batch_size, latent_dim))
fake_images = generator.predict(noise)
real_images = x_train[np.random.randint(0, x_train.shape[0], batch_size)]
combined_images = np.concatenate([real_images, fake_images])
labels = np.concatenate([np.ones((batch_size, 1)), np.zeros((batch_size, 1))])
discriminator_loss = discriminator.train_on_batch(combined_images, labels)
noise = np.random.regular(0, 1, (batch_size, latent_dim))
misleading_targets = np.ones((batch_size, 1))
gan_loss = gan.train_on_batch(noise, misleading_targets)
print(f'Epoch: {epoch+1}, Batch: {batch+1}, Discriminator Loss: {discriminator_loss}, GAN Loss: {gan_loss}')
# Generate and show some pretend photos
noise = np.random.regular(0, 1, (10, latent_dim))
fake_images = generator.predict(noise)
for i in vary(10):
plt.subplot(2, 5, i+1)
plt.imshow(fake_images[i].reshape(28, 28), cmap='grey')
plt.axis('off')
plt.present()
This code units up a easy Generative Adversarial Community (GAN) to generate pretend photos resembling handwritten digits from the MNIST dataset. The generator tries to create sensible photos, whereas the discriminator tries to tell apart between actual and faux photos. Over time, each fashions study and enhance their efficiency. Lastly, the generator is ready to produce convincing pretend photos.
This instance solely scratches the floor of deepfake era, which may contain rather more advanced architectures and strategies, particularly when coping with high-resolution photos or movies. Moreover, it’s essential to recollect the moral concerns and potential misuse of deepfake expertise.
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