If you've spent any time on Twitter lately, you may have seen a viral black-and-white image depicting Jar Jar Binks at the Nuremberg Trials, or a courtroom sketch of Snoop Dogg being sued by Snoopy. These surreal creations are the products of Dall-E Mini, a popular web app that creates images on demand. Type in a prompt, and it will rapidly produce a handful of cartoon images depicting whatever you've asked for. More than 200,000 people are now using Dall-E Mini every day, its creator says--a number that is only growing. A Twitter account called "Weird Dall-E Generations," created in February, has more than 890,000 followers at the time of publication.

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Recently, there have been more efforts and requests to make photo editing tools that can be used on devices with touch screens (DALL·E 2, Imagen and Co) Sketching is one of the easiest ways for people to show off their creative ideas and interact with apps because it is expressive and easy to change. Sketch-based image editing is a new area of AI art where the goal is to build models that can change the whole image or parts based on sketches drawn by the user. Sketching is an easy way to create a photo and can make more complex edits. In this video post, I explain the concept of making AI sketches, and how it could be used for editing photos. The dangers of this demo could be that it makes fake news easier to spread, and changes how people think about their bodies.

In addition, because you've gone to the cloud now, machine learning and data science were catching up with software architecture and what the software …

Recommender systems personalize content by recommending items to users. Item recommendation algorithms are evaluated by metrics that compare the positions of truly relevant items among the recommended items. To speed up the computation of metrics, recent work often uses sampled metrics where only a smaller set of random items and the relevant items are ranked. This paper investigates such sampled metrics in more detail and shows that they are inconsistent with their exact counterpart, in the sense that they do not persist relative statements, for example, recommender A is better than B, not even in expectation. Moreover, the smaller the sample size, the less difference there is between metrics, and for very small sample size, all metrics collapse to the AUC metric. We show that it is possible to improve the quality of the sampled metrics by applying a correction, obtained by minimizing different criteria. We conclude with an empirical evaluation of the naive sampled metrics and their corrected variants. To summarize, our work suggests that sampling should be avoided for metric calculation, however if an experimental study needs to sample, the proposed corrections can improve the quality of the estimate. Recommender systems are a key technology in online platforms for personalizing the selection of items that are shown to a user. Examples include recommending which products to buy, which videos to watch or which songs to play. Recommendations are typically user-dependent and often context-dependent. A key operation of recommender systems is to retrieve a ranked list of the best items for a user in a particular context.

With technology continuing to move on at a swift pace, there's been plenty of recent discussion as to whether digital renders can truly ever replace product photography. Taking this one step further, is it possible that one day, artificial intelligence could simply create images without needing any input from a photographer or digital artist at all? As photographers, we often marvel at how amazing modern technology can be, how magical that new "must-have" camera feature is, or how smart the image-processing software has become. I don't consider myself to be especially old, but when I think back to using a manual-focusing 35mm SLR (because that's all we had to use, not because I'm a hipster) and compare that experience to the incredible face detection or eye detection autofocus on modern mirrorless cameras, it's hard to believe these huge technological advances have happened within my lifetime. Even the act of sitting in my living room, controlling the lighting and home entertainment with my voice, or video-calling a friend in another country on an iPad are literally things that my child self would have considered science fiction.

Machine-learning algorithms aren't likely to put painters or singer-songwriters out of work anytime soon, to judge from their body of work to date. But Google Brain is developing tools that pair artists with deep-learning tools to develop novel artwork together, said Douglas Eck, senior staff scientist at the search giant's artificial-intelligence research division, during the MIT Technology Review's EmTech Digital conference on Tuesday. He hopes the platform, called Magenta, will allow people to produce completely new kinds of music and art, in much the way that keyboards, drum machines, and cameras did. Eck said that Magenta could serve a role analogous to that of Les Paul, who helped develop the modern electric guitar. But Eck said they want to keep artists in the loop to push the boundaries of the new tool in interesting ways, like a Jimi Hendrix who flips it upside down, bends the strings, and distorts the sound.

Deep neural networks are popularly used for object recognition, detection, and segmentation across different avenues. Of these, generative adversarial networks (GANs) are a superior class of neural networks whose performance exceeds that of conventional neural networks. They are meant to minimize the inconsistencies between real and fake data, and have proven successful for image detection, medical imaging, video prediction, 3D image reconstruction, and more. Despite their growth over the last few years, they are not devoid of limitations. Training conventional GANs is difficult and involves very high computational costs, making them unreliable for complex computer vision problems.

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