Is beauty in the eye of the beholder? Data scientists aren’t so sure. Many consider the Turing test to be the ultimate judge of whether artificial intelligence is fully developed. Technology should be able to behave in such nuanced human ways that even humans recognize it as human. That’s one reason many scientists consider the concept of beauty an ultimate Turing test. Can machine learning and data understand beauty and, more importantly, can it make us more beautiful?

Beauty in Data Points

Computers can examine databases of photos and ratings to learn what humans find attractive, theoretically. Determining what it means to be beautiful is the crucial first step for AI in the beauty industry. It start with facial analysis and determining the various parts of the face and gathering information on certain data points. One of the most widely accepted trademarks of beauty is facial symmetry. Skin color and evenness is also generally accepted as a reliable indicator. Once information on vital data points is gathered, machines can rank a person on exactly how beautiful they are, turning something historically subjective into a completely objective task. Personal opinions and preferences, both conscious and unconscious, are entirely removed from the process and could be a great breakthrough for tearing down uneven standards.

Of course, this is also where the problems begin. Apart from symmetry and color evenness, what are the rules for judgment? The world’s first beauty contest judged by AI kicked off earlier this year, and they, interestingly, included wrinkles as one of their primary parameters for beauty. Data points like this are going to bring the entire concept of data-judged beauty under fire, as few people can get behind the idea that a few more wrinkles are the make or break between beautiful and not. This particular contest, however, wasn’t judging only beauty contestants, but also algorithms. This is where the concept actually gets interesting.

By connecting the dots between beauty and real health, robots will be able not only to judge appearance, but give a small glimpse into the connection between inside and outside health. Pinpointing people that are healthy inside and out could lead to real breakthroughs in the beauty industry and health. In this example, locating and analyzing contestants could lead to more insight on how to prevent wrinkles. The real challenge isn’t just judging whether a person is beautiful or not, but putting the analysis to use for consumers.

Applying Makeup by Numbers

Unfortunately, data is still being gathered, and it will be some time before scientists can turn judgment into serums or lifestyle suggestions. The switch from predictive to prescriptive analysis is, however, slowly happening in certain markets. Data is already being used to create better products and optimize formulas. Traditionally, a perfume might be physically tested, reviewed and compared before being deemed ready. Now, data can be used to optimize specific scent ratios to create the next big hit. Similarly, it will lead to better cosmetics. Leveraging data means better, longer-lasting formulas. Anyone whose ever used lipstick knows the science behind makeup is important. It’s not just about color or how nice the makeup looks in the case. It’s about how it really spreads and stays in real life.

The ability of AI to properly analyze the human face will also become incredibly handy for testing purposes. Thanks to services like Amazon, consumers are buying online as a way to save time and money. For beauty products, that doesn’t always work. It’s nearly impossible to know how a new eye shadow or face cream will actually look on the skin, and the result is a lot of heartache. That’s precisely why so many data scientists are looking to create AI that can properly understand the human face. Once mastered, the ability to test out new looks and products will become exceptionally easy and realistic.

The process would be simple enough: upload a selfie and pick a new hairstyle. The app then layers the new style onto your head. Hair style and makeup testing programs already exist today, but the quality is usually laughably low. Several companies are now getting on the bandwagon and creating increasingly better testing and matching apps. Sephora used worldwide tests and 1,000 different combinations of foundation to help customers find their perfect match using the ColorIQ app. The app records 27 color-corrected images in under two seconds, eight different light settings and one ultraviolet light to capture just what the skin really looks like. No more salespeople holding up different color swabs to your face and guessing. AI will know exactly what product will match your skin tone.

An Unexpected Future for Data and AI

Many scientists are also looking to use AI to transform the way beauty products are viewed in the lab altogether. By compiling data on skin and the aging process, AI will be able to imagine exactly what cosmetics, creams and even procedures will do. “Not tested on animals” labels may really become a thing of the past if technology can successful mimic the human body and its reactions to products. Furthermore, plastic surgery and facial reconstruction will also be getting a huge boost from AI. The ability to predict with near perfect accuracy what a person will look like post-surgery is vital not just for customer satisfaction but growing the entire field. In many countries, plastic surgery is incredibly common. In fact, rather dangerous procedures like the double-jaw surgery are increasingly common, and the opportunity to use data to keep patients safer and predict complications could prove invaluable for individuals who choose to undergo surgery.

There’s no doubt this technology is still in its infancy. There are loads of apps out there leveraging data to help customers find the perfect foundation, but most are far from realistic and thorough. While AI can tell a person just how symmetrical their face is, the usages of that information is incredibly limited. Instead, AI will focus on other questions, leading to a generation of entirely different styles of beautification. The only downside is having to wait for startups to catch up and create those exciting new products.

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