Peter Holmes wrote: ↑Mon Mar 27, 2023 2:49 pm
I look forward to seeing a civilised explanation of Nietzche's model of relationship between the things we call dogs, the word
dog and the supposed concept of a dog - for example, how and why the word
dog 'immediately becomes a concept'.
Yeah sure. Here's a demonstration.
I typed the English sentence "Give me a photo-realistic image of a dog" in the prompt of DALL-E 2.
This is what I got - a unique/original, never-seen-before work of art, It exists nowhere else except in this forum post.
- Photo-realistic image of a dog - 2371b0f0.png (217.53 KiB) Viewed 757 times
How did a dumb computer know to generate this specific image from my English sentence "Give me a photo-realistic image of a dog"?
How is it that a human recognises this image as a dog if no human has ever never seen this exact image before; and if this is exact image is not a photo of an actual dog, but a painting of a dog made by a computer.
How does it work?! Well, lets ask the very AI which generated the image above.
So I asked it again: How does a machine learning algorithm conceptualize a dog?
Here's what it said:
A machine learning algorithm conceptualizes dogs based on the data it has been trained on. Typically, a machine learning algorithm would be trained on a dataset of images of dogs and non-dogs. During the training process, the algorithm would analyze the features of the images, such as color, texture, and shape, and learn to recognize patterns that are associated with dogs.
Once the algorithm has been trained, it can be used to identify dogs in new images that it has not seen before. The algorithm does this by comparing the features of the new image with the patterns it has learned during training. If the features of the new image match the patterns associated with dogs, then the algorithm will classify the image as a dog.
So I asked it some more stuff: How does DALLE-2 generate images from linguistic descriptions?
DALLE-2 (short for "DAll-E 2") is a deep learning model developed by OpenAI that can generate high-quality images from textual descriptions. The name "DALL-E" is a combination of the artist Salvador Dali and the character WALL-E from the Pixar movie of the same name.
DALLE-2 is based on the transformer architecture, similar to the one used in GPT-3, and it uses a combination of unsupervised and supervised learning to generate images. It consists of an encoder that encodes the textual description into a vector representation and a decoder that decodes the vector into an image.
To generate an image, DALLE-2 takes a textual description as input and encodes it into a fixed-length vector representation using an encoder network. This encoding is then used to generate the image using a decoder network.
The decoder network is based on a generative adversarial network (GAN), which is a type of neural network that can generate realistic-looking images by learning from examples. The decoder network is trained to generate images that match the input textual description, while a discriminator network is trained to distinguish between real images and generated images.
During training, the model learns to generate images that match the input textual description by minimizing the difference between the generated image and the actual image using a loss function. The loss function is based on the difference between the pixel values of the generated image and the actual image.
DALLE-2 is capable of generating a wide variety of images, including objects, scenes, and abstract concepts, and it can also generate images with multiple objects and complex compositions.