From Siri on our iPhones to even the junk mailbox (yes, it helps to filter out the spam mail we receive) in our e-mail folders, artificial intelligence plays a huge part in our daily lives – even if we’re unaware of it. And now, it’s making its way into the way our skincare is made. Cue: Dior’s refreshed Capture Totale C.E.L.L Energy line.
Slated to hit stores come Jan 1, this five-piece beauty regime consists of a Super Potent Serum, a Firming & Wrinkle-Correcting Creme, a Firming & Wrinkle-Correcting Eye Cream, a High-Performance Treatment Serum-Lotion and a High-Performance Gentle Cleanser. The reformulated range focuses on skin stem cell technology. Skin stem cells are what produces new skin cells, which helps keep skin looking youthful and healthy. Harvesting the prowess of a potent blend of four floral extracts – namely madagascan longoza, Chinese peony, Chinese Jasmine and white lily – the products re-energise skin stem cells to produce fresh ones. Read: your skin will look youthful and taut.
We sit down in an exclusive interview with the French beauty giant’s Executive Vice‐President of Product, Michael Pressigout, and its Scientific Director, Edouard Mauvais Jarvis, where they break down how this new line taps in on the power of Deep Learning (a subset of artificial intelligence that Dior uses) that powers the brand’s upcoming age-defying skincare line.
Can you explain how Deep Learning works for us in a few sentences?
Michael Pressigout (MP): “The term dates back to 2012, a time when artificial intelligence was
making enormous progress, both theoretical and also linked to the progress seen in
computers. We realised that a machine could benefit from automatic performance learning,
to recognise shapes for example: if you show it a lot of images of cats and dogs, it will
eventually be able to identify a dog and a cat. In fact, the software is a bit like the human
brain, with different levels of neurons. Deep Learning uses a type of neurons network and
adapts the connections between them in order to improve shapes recognition.”
What are the classical uses for this technology? Which types of organisations and sectors does Owkin (the AI startup) usually work with?
MP: “Anything that concerns sound, image and text analysis, for example, iPhone’s Siri, or Google’s Alexa. On social media, Facebook and Google use Deep Learning to identify your face; chatbots decipher words to understand sentences; automatic cars also use it in order to distinguish a pedestrian from a car or an animal. We work especially in the field of health, with hospitals, for medical imagery, looking at how you can predict a patient’s response to a treatment from looking at an image. Moreover, Owkin was created in 2016 by a doctor and a researcher in Artificial Intelligence. We’re based in France (Paris, Nantes) and in the US.”
How did Dior and Owkin come together?
Edouard Mauvais‐Jarvis (EMJ): “We have been working on neuroscience and the criteria for
perceiving beauty in particular, for Capture Totale. We were looking for another, different
way to approach the subject, and thought why not look at the latest technological methods
available? Today, the only way of measuring these criteria is to sit 30 people in front of a
face and to record their impressions – which we did, for many years. With Deep Learning,
we are taking it up a notch, because after all, behind of the subjective nature of perception,
a logic exists, that we don’t know how to explain.”
Do you mean that thanks to artificial intelligence we are capable of objectivising beauty?
EMJ: “Not beauty, no, which is linked to judgement and emotion. However when we look at someone, we immediately know if their face looks healthy or not – and that perception goes far beyond lines, pigmentation spots etc. In fact, our general impression of a face is linked to a database that has been recorded in our brains throughout our lives, and during our childhoods in particular.”
MP: “That’s why it is so difficult to guess how old someone from a different culture is! Man is a social animal, and the same goes for the software ‐ if you only show it Asian or Western faces, it won’t be able to evaluate other cultures and ethnicities.”