From our 2012 inception, Kairos has licensed much of its core technology from other innovative AI vendors. By adding our own sizzle to it, we saw impressive adoption from businesses and developers, all around the world. However, it was always our vision to create our own.
Since joining Kairos earlier this year, my team and I have completely redesigned Kairos’ algorithmic system and architecture.
Starting from scratch didn't come without its challenges, yet having the opportunity to design a new face recognition algorithm today was actually a blessing in disguise.
Over 6 years of doing business allowed us the time to witness the progression of deep learning techniques, the innovations of companies such as Apple (that little thing called ‘FaceID’), and society's reaction to an often divisive technology.
And, this year has really been the tipping point for the whole face recognition industry. Among the explosive growth, it’s also taken a bashing in the press; some of that criticism even coming from us— we all felt strongly about taking a stand against the misuse of the technology. And we all continue to feel strongly about this.
Melissa Doval, our interim CEO, told me what it means to her:
“As a first-generation daughter of Cuban immigrant parents, a woman and a minority—it’s my duty to continue to push Kairos to address the biases that exist in AI. Diversity is built into the Kairos DNA and our team will never abandon that mission.”
Powerful words. Which has struck me since I joined Kairos— everyone here cares deeply about how our technology impacts peoples lives. It’s always top of mind, and I love how we empower our employees, investors, partners, and customers to always be part of that conversation.
In this article I wanted to share some details about how we, at Kairos, are addressing the challenges of creating a truly diverse and inclusive facial recognition algorithm.
You'll notice some of our approaches are grounded in ‘well known’ machine learning practices, while others are far more experimental.
Combined I believe we have one of the strongest commercial strategies to tackling today’s AI biases; ensuring the future versions of our technology strive for the highest standards —it’s the right thing to do.
Beyond that, by sharing our own learnings and tactics, we hope we can inspire other face recognition companies looking to raise the bar.
Much of how AI actually works is an unknown— as mentioned above, it’s a metaphorical ‘black box’. So, when it comes to assessing something like bias in AI, visibility on how the AI is ‘making decisions’ can be what makes or breaks an algorithmic model. Traditionally, this has been a very hard thing to assess; it’s not easy to ‘look inside the box’. Despite this, I knew it was critically important we had an objective way of to guarantee our AI was making good decisions.
Enter Untangle— the Singapore based startup that helps companies like Kairos understand and audit their AI tech, ensuring [deep learning] models are right for the right reasons. We’re thrilled to be working with them to help us keep our algorithms accountable.
This video shows the output 'facial-relevance map' as generated but the Untangle platform. As we can see, the person's face in the middle is not detected by the algorithm, the relevance map shows that the model doesn’t recognise his face to be a true face as the relevance scores are much lower. By extending this method to understand where in the network the facial features are not activating we can modify the network to generalize better to faces of all diversity.
“Partnering with Kairos is great for us at Untangle as they truly see the need to understand AIs and remove any inherent bias in their models. It’s companies like Kairos who will truly drive innovation and wider adoption of AI”— Jimmy Moore, CEO, Untangle
Untangle wants to see algorithmic accountability as the standard. They fundamentally believe that to have truly useful human to AI interactions—even AI to AI interactions— they need to be able to explain themselves in intuitive, understandable ways. For the general public to have trust in AIs they need to be auditable.
It’s clear Untangle plans to drive the change that needs to happen in the AI space, and we’re totally here for it.
In my next article I’ll be sharing the results of the work mentioned above, future goals for our algorithms, and give you a deeper dive into some of our in-house R&D efforts— stay tuned!
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Google Next is Google’s annual conference focusing on their cloud computing offering, Google Cloud Platform. Thanks to our great Google Cloud account team in Miami, we were able to attend this year and learn about Google’s new announcements, network with experts and other peer companies, and get some in depth knowledge about GCP.
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Facial Recognition is in big demand with businesses all over the world—from preventing fraud to enabling more profitable customer experiences; it’s becoming the natural authority on identity. Which is why we’re excited to announce a renewed partnership between Kairos and RapidAPI—the leading API marketplace for software developers.
Last week I attended the 2nd Annual Biometric Summit in New York City. It was hosted at a cool co-working space called “Rise” that’s sponsored by Barclays Bank and TechStars incubator. It was one of the smaller summits I have attended, with a single track and about 100 attendees—ranging from biometric vendors and end-users to analysts (? Alan Goode) and investors.
As Kairos’ Director of Product Integration, I’m on the front lines when it comes to customer inquiries. From pet detection to weight detection, I’ve heard it all. While some ideas are more far-fetched than others, the common trends cannot be ignored—and these represent innovations that are happening NOW.
From getting the latest TechCrunch headlines on your phone, to booking a Lyft to your office—APIs are powering most of the products and services we all take for granted each day. They make the world move.
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