Understanding the biometric opportunity
Algorithms currently shape many of our decisions; just think suggested reading, recommended items or Twitter’s “who to follow”, for example. Developments in AI make our digital lives easier and in fact, AI is now so firmly embedded in our interactions that we take for granted the ease at which we can be quickly authenticated and our journeys personalised. Far from being apprehensive of developments that capture and use behavioural and preferential data, our appetite for technology that speeds up and enriches our interactions grows as quickly as the technology itself evolves. Much is written about biometric authentication in the consumer space, particularly in financial services, but this is a single use case in a vast landscape of potential applications.
The passive monitoring - of our interests, preferences, patterns or behaviours - permeates our lives; what we do, when we do it, where we do it and how we do it, both online and in the physical world. We may not even be aware of the continual, invisible monitoring to keep us in an authenticated state for whichever application we’re in. This is in addition to active biometric verification, namely facial or voice authentication, which can be captured in multiple scenarios and contexts to create richer profiles of a user. Whatever the approach, AI is enabling the rapid assembly and interpretation of vast amounts of data that is used to predict behaviours, enhance the experience or authenticate the individual.
Organisations are beginning to see the potential of not just deploying biometrics to gather valuable customer data or quickly and safely authenticate a user, but also the potential of integrating it within the workplace too. And the real value comes into play when you can deploy a model that spans any channel, so that whomever the user, in whichever channel they’re using, the experience is consistent, cohesive and built with security in mind.
Beyond the smartphone
One would be forgiven for assuming that biometrics are limited to smartphones, there is that much noise around mobiles. But many organisations today are moving to a server-side authentication model, so that the user may be authenticated regardless of the device that they use or channel in which they engage with their supplier. By moving the authentication process to the cloud, whereby a pseudonymised digital template is stored in the cloud and serves as the single true version upon which all other authentication requests are compared, means that regardless of the channel - web, mobile, call centre or physical location, the user can be authenticated against their template.
Put simply, a user - whether a customer, partner, employee or whomever the organisation needs to authenticate - can enrol for voice authentication for example using a mobile, but as the voice template is stored centrally, they can be authenticated through any channel using a microphone. This could be utilised across an organisation in myriad ways; such as deploying voice-activated physical access or requiring voice authentication to approve an internal workflow, authenticating customers calling a helpline or authorising access to a conference call of a confidential nature.
The same applies to facial authentication, whereby users enrol once with a selfie photo via one channel, and this digital face template is used to authenticate them across any channel with a camera, for whichever purpose the authentication is required. Again, the use cases reach far and wide; using facial authentication to access restricted physical areas, applying a ‘step-up’ selfie challenge to confirm changes to an external website or confidential document, or a webcam selfie for partners to access a portal, are just some of the examples.
And finally, behavioural authentication can also be deployed way beyond the smartphone. In fact, by storing a digital template of a user’s behaviour centrally rather than locally, this allows an organisation to monitor behaviour across any physical device that is connected - such as a mouse, keyboard, keypad, touchpad, manual entry system and more. Any deviation from their standard behaviour could invoke a secondary step-up authentication request - such as a selfie or voice challenge - for optimum safety and security. Similarly, if the behaviour is well within your comfort level, security can be stepped-down.
Enterprise-configured risk parameters
The benefit of deploying a centralised Biometric Identity as-a-Service (or BIDaaS) platform is that an organisation can configure individual risk thresholds depending on the nature of the transaction or process. Whereas passwords and PINs are binary yes/no decisions, an organisation is now at liberty to authenticate against the digital template but set its own risk levels and configurations. The more sensitive or high-risk the scenario, the higher the threshold can be set, and all requests return an in-session risk-based assessment of a user’s authenticity. The lower the risk, the more an organisation can apply a step-down approach to security.
Convenience, security, consistency
A BIDaaS approach ultimately means that your customers, partners, suppliers or employees can be authenticated however they interact with you, in a secure and consistent way. This omnichannel deployment is not only simpler and more familiar for the user (one enrolment for multiple channels) but is faster and more cost effective than delivering a channel-by-channel approach. More importantly, by placing your critical assets or those of your stakeholders under biometric lock and key, yesterday’s threats, such as stolen phones or laptops, are not the cause for concern that they once were.
We are moving towards a time where our physical characteristics are truly replacing the passwords and PINs of yesterday. By embracing a secure cloud authentication module, you can factor in the biometric technologies and channels not just of today, but on tomorrow’s horizon.
Andrius Sutas, CEO and Co-Founder, AimBrain
Google AI Designs Next-Gen Chips In Under 6 Hours
In a Google-Nature paper published on Wednesday, the company announced that AI will be able to design chips in less than six hours. Humans currently take months to design and layout the intricate chip wiring. Although the tech giant has been working in silence on the technology for years, this is the first time that AI-optimised chips have hit the mainstream—and that the company will sell the result as a commercial product.
“Our method has been used in production to design the next generation of Google TPU (tensor processing unit chips)”, the paper’s authors, Azalea Mirhoseini and Anna Goldie wrote. The TPU v4 chips are the fastest Google system ever launched. “If you’re trying to train a large AI/ML system, and you’re using Google’s TensorFlow, this will be a big deal”, said Jack Gold, President and Principal Analyst at J.Gold Associates.
Training the Algorithm
In a process called reinforcement learning, Google engineers used a set of 10,000 chip floor plans to train the AI. Each example chip was assigned a score of sorts based on its efficiency and power usage, which the algorithm then used to distinguish between “good” and “bad” layouts. The more layouts it examines, the better it can generate versions of its own.
Designing floor plans, or the optimal layouts for a chip’s sub-systems, takes intense human effort. Yet floorplanning is similar to an elaborate game. It has rules, patterns, and logic. In fact, just like chess or Go, it’s the ideal task for machine learning. Machines, after all, don’t follow the same constraints or in-built conditions that humans do; they follow logic, not preconception of what a chip should look like. And this has allowed AI to optimise the latest chips in a way we never could.
As a result, AI-generated layouts look quite different to what a human would design. Instead of being neat and ordered, they look slightly more haphazard. Blurred photos of the carefully guarded chip designs show a slightly more chaotic wiring layout—but no one is questioning its efficiency. In fact, Google is starting to evaluate how it could use AI in architecture exploration and other cognitively intense tasks.
Major Implications for the Semiconductor Sector
Part of what’s impressive about Google’s breakthrough is that it could throw Moore’s Law, the axion that the number of transistors on a chip doubles every five years, out the window. The physical difficulty of squeezing more CPUs, GPUs, and memory on tiny silicon die will still exist, but AI optimisation may help speed up chip performance.
Any chance that AI can help speed up current chip production is welcome news. Though the U.S. Senate recently passed a US$52bn bill to supercharge domestic semiconductor supply chains, its largest tech firms remain far behind. According to Holger Mueller, principal analyst at Constellation Research, “the faster and cheaper AI will win in business and government, including with the military”.
All in all, AI chip optimisation could allow Google to pull ahead of its competitors such as AWS and Microsoft. And if we can speed up workflows, design better chips, and use humans to solve more complex, fluid, wicked problems, that’s a win—for the tech world and for society.