Connect with us


Artificial Intelligence Art



Artificial intelligence art is the creation of artwork using machine learning and other artificial intelligence processes. It can be anything from visual artwork to audio, and it has its own distinct aesthetic and creative questions that artists must grapple with.

There have been a few historical precedents for this kind of art, including a portfolio of twelve images created by German mathematician Frieder Nake in 1967.


AARON is a high end digital radio with a few nifty features including a responsive web interface, advanced off-air metering, and of course the best part: it’s completely wireless. It also comes in the most convenient colors and shapes, allowing for an easy fit in any location.

Using artificial intelligence to determine the type of receiver, AARON automatically selects the most appropriate transmitter for its environment and application, delivering a superior experience that’s a joy to use. It’s even able to learn your favorite station’s frequency and display it on the front panel.

A seasoned employee benefits expert, Aaron has worked in account management of large employer groups for the past decade. His passion is in helping clients implement innovative and cost-effective benefits programs while maintaining a healthy bottom line. He enjoys educating his clients on the latest in medical and dental technology and alternative funding strategies. When he’s not working or wrangling with his adorable, but grumpy cocker spaniel, Aaron can be found playing with the kids, remodeling his mid-century modern home, or traveling to new and interesting places.


Nvidia’s GauGAN2 is a text-to-image generator that transforms various inputs, including sketches and text, into photorealistic images. Named after post-impressionist painter Paul Gauguin, this AI model combines segmented graphs, text-to-image generation and inpainting to create high-quality landscape images.

Simply enter a phrase, such as “sunset at a beach” or “snow-capped mountain range,” and the AI will instantly generate a scene. If you change the word “sunset” to “afternoon” or “rainy day,” the model will modify the image based on what are known as generative adversarial networks (GAN).

The tool is easy to use, as Nvidia has created an interface that is reminiscent of a drawing board. You can type a phrase, upload sketches or even sketch your own custom segmentation maps.

The tool is a great way to quickly convert ideas into finished artworks, but it’s not without its limitations. For instance, the tool’s output can look a bit bizarre if you draw stray lines and odd shapes.


GauGAN3, the latest version of the wildly popular AI painting demo by NVIDIA Research, allows anyone to channel their imagination into photorealistic pictures. Simply enter a phrase like “lake in front of mountain” and press a button, and a scene is instantly generated in real time.

The model is based on generative adversarial networks, or GANs, which are deep learning models that learn visual concepts by feeding them labeled images, texts, and sketches. These labels help the system recognize what it is creating, such as a fog, hill, or stone.

The system combines text-to-image generation, segmentation mapping, and message production in a single GAN framework to make it easier for users to turn their visions into high-quality AI-generated art. It also incorporates a discriminator that increases confidence in the results and produces an output between 0 and 1.


Nvidia has released a new version of GauGAN, a popular AI painting demo that channels the user’s imagination into photorealistic landscape images. The demo’s underlying algorithms are based on generative adversarial networks, which allow the AI to generate realistic scenes in real time.

Users can create a basic outline of the scene, select weather elements like snow and water, and then apply natural textures. They can also use style filters to mimic the work of a specific artist.

The system uses a cyclic training architecture to progressively add layers to the generator and discriminator, while adjusting coarse/structural image details in lower resolution layers, and fine details in higher resolution layers. This technique improves stability and generation.