AI company Optic has developed a web tool called AI or Not, which aims to combat misinformation spread through AI-generated images. The tool scans images and quickly determines whether they were generated by artificial intelligence or by humans. Optic claims that its algorithms provide highly accurate results with a precision rate of 95%. However, users may have concerns about privacy when uploading images to the tool. Optic states that uploaded images and URLs are not stored on its servers longer than necessary and that they adhere to data protection regulations. By analyzing images and detecting signs of AI generation, the company aims to improve its algorithms and machine learning techniques.
The article presents a method for creating word-as-image illustrations automatically, which involves creating a visualization of the meaning of a word while preserving its readability. The method relies on large pretrained language-vision models to distill textual concepts visually and optimize the outline of each letter to convey the desired concept, guided by a pretrained Stable Diffusion model. The approach uses a differentiable rasterizer and additional loss terms to ensure the legibility of the text and the preservation of the style of the font. The method can handle a large variety of semantic concepts and use any font while preserving the legibility of the text and the font's style. The article provides a detailed explanation of how the method works, including the optimization of parameters for each letter and the use of additional loss functions to preserve the original letter's shape and ensure legibility.
The article discusses the use of analogies in discussing and debating "AI" art, highlighting the usefulness and limitations of each analogy. The most common analogy is photography, which emphasizes the similarity to previous artistic technologies that seemed to automate art but came to be widely accepted. Other analogies include conceptual art, theft, appropriation, music recording, sampling and collage, lava lamps, and parrots. While analogies can be useful in identifying similarities between old and new things, they can also hide differences and reflect the speaker's agenda. The article argues that each analogy is useful in some ways but misleading in others.
A collection of GAN. GAN is a new generator architecture for generative adversarial networks that enables unsupervised separation of high-level attributes and stochastic variation in generated images. This generator improves the state-of-the-art in distribution quality metrics and disentangles latent factors of variation. The article also introduces two new automated methods to quantify interpolation quality and disentanglement, and a new dataset of human faces.
A book of 1000 paintings and illustrations of robots created by artificial intelligence. The author generated all of the images in this book by writing original prompts for DALL·E 2, OpenAI’s AI system that can create realistic images and art from a description in natural language. Upon generating the images, the author curated and arranged the images to their own liking and takes ultimate responsibility for the content of this publication.
Neural Cellular Automata (NCA) are capable of learning diverse behaviors and can solve complex tasks through massively parallel and inherently degenerate processes. The article focuses on applying NCA to the task of texture synthesis, reproducing the general appearance of a texture template rather than pixel-perfect copies. After training NCA models to reproduce textures, the article investigates their learned behaviors and observes surprising effects, suggesting that the cells learn distributed, local algorithms. The article employs NCA as a differentiable image parameterization to accomplish this.
A database project that aggregates tools and resources for artists, engineers, curators, and researchers interested in incorporating machine learning (ML) and other forms of artificial intelligence (AI) into their practice. The database contains resources from partners and networks and covers a broad spectrum of possibilities presented by the current advances in ML, enabling users to generate images, create interactive artworks, draft texts, or recognize objects. While most of the tools require coding skills, the database includes entries tagged as courses and encourages beginners to turn to RunwayML. The article notes that the database is not comprehensive and is a growing collection of research commissioned and collected by the Creative AI Lab, with new entries added regularly.
Literary analysts have long noticed the hand of another author in Shakespeare’s Henry VIII. Now a neural network has identified the specific scenes in question—and who actually wrote them.
For much of his life, William Shakespeare was the house playwright for an acting company called the King’s Men that performed his plays on the banks of the River Thames in London. When Shakespeare died in 1616, the company needed a replacement and turned to one of the most prolific and famous playwrights of the time, a man named John Fletcher.
Demonstration tutorial of retraining OpenAI’s GPT-2-small (a text-generating Transformer neural network) on a large public domain Project Gutenberg poetry corpus to generate high-quality English verse.
Humans of AI is an online exhibition that showcases three works based on the COCO image dataset. The exhibition aims to credit and applaud the photographers who made the technical achievement of machine learning algorithms possible. By showing the actual training pictures and giving credit where it's due, Humans of AI exposes the myth of magically intelligent machines and highlights the importance of acknowledging the hard work that goes into creating the datasets used to train these algorithms.
YOLO is a machine learning algorithm that detects objects in images and labels them with a single category. While it may seem like magic, computers recognize pixel formations statistically similar to previously learned data. The first piece in the Humans of AI series, Declassifier, processes images using YOLO and superimposes images from the training dataset COCO, exposing the myth of intelligent machines and highlighting the biases and glitches present in the dataset. Declassifier ultimately helps users understand how machines see by visualizing the data that conditioned a certain prediction.
Generative Adversarial Networks (GANs) are an exciting tool for artists, allowing them to create unique, unpredictable digital art. The GAN process simulates a game, with a Critic network and a generator network competing to create realistic images. The artist works as a curator, selecting the most interesting images produced by the generator. The author recommends using CycleGAN, a neural network architecture that transforms images from one dataset into the style of another, as it allows for high-resolution images and quick training. The author offers practical advice on using CycleGAN, such as fine-tuning models on smaller datasets and experimenting with different batch sizes. The author also emphasizes the importance of using unique, personal datasets for training. Overall, the author encourages artists to experiment with GANs and to let the unpredictability of the process inspire them to create something special.
Copying an element from a photo and pasting it into a painting is a challenging task. Applying photo compositing techniques in this context yields subpar results that look like a collage --- and existing painterly stylization algorithms, which are global, perform poorly when applied locally. We address these issues with a dedicated algorithm that carefully determines the local statistics to be transferred. We ensure both spatial and inter-scale statistical consistency and demonstrate that both aspects are key to generating quality results. To cope with the diversity of abstraction levels and types of paintings, we introduce a technique to adjust the parameters of the transfer depending on the painting. We show that our algorithm produces significantly better results than photo compositing or global stylization techniques and that it enables creative painterly edits that would be otherwise difficult to achieve.
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