“One of the most challenging AI technologies for security teams is a very new class of algorithms called generative adversarial networks (GANs). In a nutshell, GANs can imitate or simulate any distribution of data, including biometric data.
To oversimplify how GANs work, they involve pitting one neural network against a second neural network in a kind of game. One neural net, the generator, tries to simulate a specific kind of data and the other, the discriminator, judges the first one’s attempts against real data — then informs the generator about the quality of its simulated data. As this progresses, both neural networks learn. The generator gets better at simulating data, and the discriminator gets better at judging the quality of that data. The product of this “contest” is a large amount of fake data produced by the generator that can pass as the real thing.
GANs are best known as the foundational technology behind those deep fake videos that convincingly show people doing or saying things they never did or said. Applied to hacking consumer security systems, GANs have been demonstrated — at least, in theory — to be keys that can unlock a range of biometric security controls.”
“The Seattle company today announced that team ChaNJestimate — whose members include Chahhou Mohamed, Jordan Meyer, and Nima Shahbazi, hailing from Morocco, the U.S., and Canada, respectively — will take home the $1 million prize for a model that bested the Zillow “Zestimate” benchmark by approximately 13 percent. (The Zestimate’s nationwide error rate is 4.5 percent; the team’s work pushes it to below 4 percent.)
To achieve this new level of accuracy, team ChaNJestimate leveraged deep neural networks — layers of mathematical models modeled after neurons in the brain — and other machine learning techniques to “directly” estimate home values
Currently, the Zestimate is within $10,000 of a given home’s sale price, and Zillow expects the improvements could bring it $1,300 closer to the actual price.”
“Retailers can use AI to reduce losses resulting from error, faulty processes and intentional fraud. According to the 2018 National Retail Security Survey from the National Retail Federation, more than 50% of retail shrink (the difference between actual on-hand inventory and the inventory level recorded in the computer system) was due to employee theft or paperwork errors. These types of losses leave a data trail that can be detected through data analytics. Typically, retailers use an exception reporting system to detect shrink, but I believe using a system with AI models built in could yield far better results.”
“To date, Android phone makers who’ve wanted to include face recognition have had to craft their own secure solutions or else use basic face detection that you can fool with a photo. Soon, however, it might be relatively commonplace. Sleuthers at XDA and 9to5Google have discovered code in an early Android Q version that hints at native support for hardware face recognition. It wouldn’t just be used for signing into your phone, either, as it could also authorize purchases and sign into apps. It would largely be a parallel to the Face ID system found in Apple’s more recent iPhones, just with more flexibility.”
eCommerce/vending machine capability enables users to buy and sell solutions and services on the Digital Exchange (DX) making it a true eCommerce site. Technology partners, developers and anyone who has a best of breed AI or cognitive solution can join the Blue Prism DX community to market and sell on it.
Blue Prism Digital Exchange (DX) 2.0: A growing online community that provides business leaders with drag and drop access to AI, machine learning, cognitive and disruptive technologies. Since launching late last year, the DX has managed more than 1,000 registered users, 300 registered companies and more than 1,000 downloads of top assets enabling access to the technologies of strategic partners IBM, Microsoft and Google. The DX has new search capabilities as well as giving users the ability to rate and comment on assets.