Image SEO used to be a huge aspect of content and site optimization, with the potential to drive tons of image search traffic. File names, alt tags, and image sitemaps were all super important. The "view image" button was added to Google Images in 2013. With the new implementation, sites saw an average decrease of 63 percent in image search traffic. While image optimization is still an extensive practice, it hasn't been effective for driving much traffic.

The craze of deep learning has brought about many challenges to the information status quo. For some use-cases, its success makes sense and seems inevitable. For others, like image processing, its bid to outshine hardened algorithms in compression and optimization seemed harder to predict, begging the question of what feats of computer engineering are safe from its grasp. Today we will only look at the ways machine learning is changing how we store, create and optimize images, but every corner of information science is seeing similar confrontations by deep learning. Last year, Google released RAISR, an algorithm combining traditional upsampling with deep learning in order to turn low-resolution images into convincing high-resolution counterparts.

The craze of deep learning has brought about many challenges to the information status quo. For some use cases, its success makes sense and seems inevitable. For others, like image processing, its bid to outshine hardened algorithms in compression and optimization seemed harder to predict, begging the question of what feats of computer engineering are safe from its grasp. Today, we will only look at the ways machine learning is changing how we store, create, and optimize images, but every corner of information science is seeing similar confrontations by deep learning. Last year, Google released RAISR, an algorithm combining traditional upsampling with deep learning in order to turn low-resolution images into convincing high-resolution counterparts.