We've seen AI generate images from other images using GANs. Then, there were models able to generate questionable images using text. In early 2021, DALL-E was published, beating all previous attempts to generate images from text input using CLIP, a model that links images with text as a guide. A very similar task called image captioning may sound really simple but is, in fact, just as complex. It is the ability of a machine to generate a natural description of an image.

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Breakthrough US Army technology using artificial intelligence to find deepfakes. While they seem like they've always been there, the very first realistic deepfake didn't appear until 2017. It went from the first-ever resembling fake images automatically generated to today's identical copy of someone on videos, with sound. The reality is that we cannot see the difference between a real video or picture and a deepfake anymore. How can we tell what's real from what isn't?

Here are the 3 most interesting research papers of the month, in case you missed any of them. It is a curated list of the latest breakthroughs in AI and Data Science by release date with a clear video explanation, link to a more in-depth article, and code (if applicable). Enjoy the read, and let me know if I missed any important papers in the comments, or by contacting me directly on LinkedIn! Follow me on Medium to see this AI top 3 monthly! Properly relight any portrait based on the lighting of the new background you add.

This promising model called GANverse3D only needs an image to create a 3D figure that can be customized and animated! I will openly share everything about deep nets for vision applications, their successes, and the limitations we have to address. The next step for view synthesis: Perpetual View Generation, where the goal is to take an image to fly into it and explore the landscape! Colab demo you can try right now! With this AI-powered nerve interface, the amputee can control a neuroprosthetic hand with life-like dexterity and intuitiveness.

Odei Garcia-Garin et al. from the University of Barcelona have developed a deep learning-based algorithm able to detect and quantify floating garbage from aerial images. They also made a web-oriented application allowing users to identify these garbages, called floating marine macro-litter, or FMML, within images of the sea surface.