As we continue to accumulate digital photos on our devices, it can be challenging to keep them organized and easy to find. But artificial intelligence (AI) has made things easier by enabling a wide range of intelligent organization features. AI-powered photo organizers use machine learning algorithms to automatically tag, sort, and categorize photos based on their content, date, location, and other factors. These intelligent tools are becoming essential in the digital age, allowing us to quickly locate specific photos and share them with ease. One of the best AI-powered photo organizers on the market is PhotoPrism, an app that helps users manage and organize their digital photo collection more efficiently and effectively.

Powered by artificial intelligence, Imagga's Wordroom is a plug-in for Adobe Lightroom that'sees' images and recommends a list of up to 30 keywords based on attributes including objects, colors, shapes, emotions, timeframes, and events. With one click, keywords can be added to an image's metadata so that it's easily searchable. It was created for professional and hobbyist photographers who don't want to spend long hours assigning individual keywords to hundreds of thousands of images. Wordroom relies on machine learning algorithms that get smarter as more people use them. This means the more images it sees, the better it gets at accurately identifying keywords.

Automating the categorization of your images and videos can help democratize access to your organization's creative assets. Many teams throughout your organization have likely spent a lot of time and effort generating high-quality content, but it'd be all for naught if the content just ends up in some anonymous folder on somebody's hard drive or is randomly dropped into your cloud storage with no functional organizational strategy. So, how do you efficiently organize your digital assets, making them easier to find, use, or repurpose? AI content-recognition tagging is a great way to add intelligence and categorize your assets. While not perfect, machine learning is becoming more robust and more accurate every day. API-based Cloudinary can integrate with any AI tool.

Artificial intelligence has proven to be a dab hand at recognizing what's going on in photos and videos, but the datasets it's usually trained on are pretty genteel. Not so for Pornhub, which announced today that it's using machine learning to automatically catalog its videos. The site is starting small, deploying facial recognition software that will detect 10,000 individual porn stars and tag them in footage. It plans to scan all 5 million of its videos "within the next year," and then move onto more complicated territory: using the software to identify the specific categories videos belong to, like "public" and "blonde." In a press statement, Pornhub VP Corey Price said the company was joining the trend of firms using AI to "expedite antiquated processes."

If you already tried to upload some of your pictures to OneDrive, you may be aware that Microsoft's cloud storage service is able to automatically tag your photos and categorize them, group them by location, and more. By adding more data to user-generated content, Microsoft's artificial intelligence tools also make it easier for OneDrive users to find relevant pictures using OneDrive's search feature. But could artificial intelligence accomplish the same sort of magic with video content? That's exactly what Chia-Wen Lin and Min Sun, professors in the Electrical Engineering department of National Tsinghua University in Taiwan, are trying to do. In a new blog post on the Microsoft Research blog, the company explains that both professors partnered in 2015 with Dr. Tao Mei, lead researcher in multimedia at Microsoft Research Asia who worked on a new image recognition, segmentation, and captioning dataset called COCO (Common Objects in Context). Professor Sun created a video title generation method based on deep learning to automatically find the special moments--or highlights--in videos, and generate an accurate and interesting title for the highlights.