Mediaclip AI Roundup; a series of blogs reviewing AI, Machine Learning, and various other algorithms that can be used in photography for making complex photo personalization.
First let’s position Artificial Intelligence (AI), Machine Learning, Deep Neural Networks…often, those terms are interchangeably used, but in essence, they’re like building blocks. AI is the umbrella term for any computer software that does something “intelligent,” as its name suggests. Machine Learning is a subset of AI where you define success criteria that then enable the machine to create and complete tasks. Deeper still (pun intended!) is Deep Neural Networks (or deep learning), which is a subset of Machine Learning.
For Photographic Imaging, these networks are a set of algorithms that have set new records in accuracy for many concerns like image recognition, editing, and processing. In principle, AI means multiple things and is usually more of a means to an end, rather than an end in itself.
Today, AI technology is making splashes in many sectors, including consumer goods and retail. Largely, most sectors report use cases of AI in their business to be linked to customer care and support – building connectivity and becoming efficient in responding to customer needs.
When it comes to the creation of photo products, there are a few areas where AI is indeed an excellent tool to achieve value and enhance the customer experience and other areas where AI still requires major funding and intricate development considerations. Here are some examples of AI’s value in our domain:
- Context extraction and face/content recognition allow identifying people, finding out what is in the photo, how people feel, detect zones of interest for auto-cropping. Google Photo is a great example of machine learning at work
- Quality and pertinence of photos and automatic image improvement for automatic corrective suggestions after uploading photos. Products like Perfectly Clear by EyeQ or Adobe Lightroom reflect this.
- Grouping of related photos allows for better storytelling by avoiding putting photos from two different events on the same page (Important to note: this is usually better served and cheaper by date taken and delta grouping, rather than AI)
- Geo-tagging images by analyzing the content of photos (i.e. the pixels, not the metadata), even without built-in GPS systems, to easily compose travel projects.
- Filters like automatic “beautification”, artsy effects, etc. are now offered by most manufacturers in camera software and many apps
- Chatbots and automated support for providing help based on frequent questions.
- Automatic project creation such as automatic layout, though in this case, AI is not yet the winning candidate when compared to existing coded scripts or a human designer, except in regards to the simplest cases where traditional algorithms can often do an efficient analysis job at much lower costs.
In summary, currently in photography, AI performance is optimized for image creation and editing; the algorithms are principally designed to simplify and enhance the image quality and editing process, per image or as a group/category.
At present, Mediaclip’s Research & Development team is investigating credible and economical approaches for selecting large sets of images from multiple devices (phones, cameras) and archival environments (Google Photo, Facebook, computer) and creating logical complex content, like a photobook. Our investigations focus on uncovering how AI can create real, measurable value for business owners and consumers alike, by improving the photo selection process and by providing a better context for automatic product creation, while still being driven by expert design decisions and variety. AI still has significant financial and environmental costs, and the most important measures to track are how it affects conversion rates and user satisfaction.
In the few next articles, we’ll explore and discuss our ideas/ findings in a quest to understand how AI – current and future – can be used to enhance the shopping experience and storytelling for consumers while generating positive returns on investment for the business.
