Two Minute Papers takes a look at DeepMind’s recent paper on understanding 3D scenes. Watch the video to find out why this a big deal.
Here’s a great tutorial that uses deep learning to compose one image in the style of another image. If you’ve ever wished that you could paint like Picasso or Van Gogh. then this AI technique is your big chance.
Known as neural style transfer and the technique is outlined in A Neural Algorithm of Artistic Style, you can do this today with TensorFlow.
Neural style transfer is an optimization technique used to take two images—a content image and a style reference image (such as an artwork by a famous painter)—and blend them together so the output image looks like the content image, but “painted” in the style of the style reference image.
Lex Fridman interviews Ian Goodfellow, the author of the popular textbook on deep learning (simply titled “Deep Learning”). He coined the term Generative Adversarial Networks (GANs) and with his 2014 paper is responsible for launching the incredible growth of research on GANs.
It acts as a kind of game that anyone can play. Visitors to the site have a choice of two images, one of which is real and the other of which is a fake generated by StyleGAN.
As to what motivated them, here’s a quote from the article:
Our aim in this course is to teach you how to think critically about the data and models that constitute evidence in the social and natural sciences.
Artificial Intelligence (AI) is usually not associated with creativity. Typically, algorithms are used to automatize repetitive tasks or predict new outcomes based on previously seen examples. However, the rise of GANs (Generative Adversarial Networks) gives AI a touch of creative spark. Could this innovation automate the creative process?
Let’s take a classic creative marketing example: product naming. The moment a product is pushed out onto the market, the most creative minds of the company come together to generate a number of proposals for product names that must sound familiar to the customers and yet are new and fresh too. Of all those candidates, ultimately only some will survive and be adopted as the new product names. Not an easy task!
Soon, AI will make artists of us all — no matter how well (or how poorly) you can draw.
Check out this article on GauGAN.
The neural network isn’t simply replacing doodles and shapes with photorealistic images of rocks, mountains, skies, or water. In addition to taking into account the original shape of the drawing, GauGAN also takes into account other objects in the scene. Turn a patch of grass into a pond and it will create reflections on the surface based on what’s surrounding the new body of water.
If there ever was a case for AI, this could be a compelling one: Resume writing. It’s a task many job seekers see as a necessary evil and it seems as everyone has an opinion about how to carefully craft the perfect document.
A new website can write your résumé for you in just ten seconds — as long as you don’t mind sending employers a document of totally-made-up information and just a touch of gibberish.
YouTuber carykh explores GANs that create customized faces of imaginary celebrities.
Using neural networks can you generate generate new, never before seen Garfield comics? CodeParade explains how it works.