We’re probably not aware of the moment when artificial intelligence sneaked into our lives and became a part of our everyday existence.
Was it the first movie recommendation we got from Netflix? Or the item we’ve purchased using Amazon’s suggestions?
AI is now all around us, and it’s holding tremendous potential, with new application possibilities being discovered every day.
We’ve gathered six very practical examples of AI, to show just how widespread this technology is today, and how prevalent it is yet to become.
The use of AI in healthcare is already bringing many benefits – at the beginning of the year, we’ve witnessed AI outperforming six radiologists in reading mammograms and identifying breast cancer more accurately and quickly. A computer algorithm can now analyze images in just a few seconds, significantly improving the speed of diagnosis.
The first AI-designed drug molecule, by Excienta, is currently being tested on humans. While it usually takes three to four years for traditional research to reach this stage, it just took 12 months for the algorithm to make it possible.
As for the current COVID-19 crisis, the Chinese technology giant Alibaba has recently developed an algorithm that can detect coronavirus in seconds, with 96% accuracy. The algorithm that analyzes CT images of patients’ chests is used by more than 100 healthcare facilities to distinguish the disease from other viral pneumonia cases.
2. Virtual Personal Assistants
Alexa, Siri, Cortana, and Google Assistant are just some of the most notable examples of artificial intelligence we’re all familiar with.
We interact with our PAs regularly – ask them for directions, information about the weather and the news. They allow us to finish various tasks easily, without having to use our hands – we can stream podcasts, play music, make to-do lists, set alarms, schedule our meetings, or order pizza.
These voice assistants use machine learning technology and natural language processing so that they can get smarter and more capable of understanding voice queries. In other words, with the help of AI and its subsets, voice assistants learn from your previous searches and preferences and use all the information they collect to offer you better search results and service.
3. Customer Service
Even if you’re still not interacting with your devices using your voice assistant, you’ve surely communicated with AI at least once, most probably in the form of a chatbot.
There is hardly any good online store that doesn’t offer at least some kind of AI-powered customer support.
The most common are conversational chatbots that are now able to answer 80% of most common customers’ queries in an accurate and timely manner, without any need for additional human intervention.
With data AI chatbots gather on the customers, they are now able to facilitate human-like interaction and personalize it more than their human counterparts.
Conversational AI algorithms can now give offers and recommendations that are more likely to fit customers’ needs and interests, boosting both their satisfaction and retailers’ revenue.
The banking and finance sector has recognized the possible advantages of AI early on and is now successfully implementing the technology for various purposes.
The finance industry relies heavily on large amounts of data and accurate real-time information. With the use of AI, it’s now much easier to detect frauds, money-laundering, or any other suspicious behavior. Some of the financial advisors’ tasks can now be automated too, as AI-powered advisors can quickly scan the market data, and predict the best portfolio or stock.
One of the best examples of how useful AI in the banking industry can be, is Erica, an AI employee of the National Bank of America. This digital financial assistant has already served over 7 million customers and handled over 50 million queries. Apart from managing many other different tasks, Erica helps customers with their transactions and budgeting, tracks their spending habits, monitors duplicate charges, and gives useful advice.
5. Smart Vehicles and Delivery
Cars and drones are also shifting towards the use of AI. Even though artificial intelligence is widely used in car manufacturing, its use in the automotive industry is most commonly related to the use of self-driving vehicles, that are leveraging machine learning and vision to find their way through the traffic safely.
Autox is, for example, currently testing their autonomous grocery delivery within San Jose. Their vehicles use AI software, real-time cameras, and sensors to navigate within a geofenced zone, with plans for gradual expansion.
Amazon and Walmart are already investing large amounts of money into drone delivery programs. Amazon’s goal for its Prime Air service is to create fully electric drones that can deliver packages lighter than 5 pounds, to their customers located within 15 miles, in less than 30 minutes.
6. Smart Homes
One of the finest examples is Nest, the thermostat algorithm, which uses an intelligent machine learning process to learn about your behavior and the temperatures you like. It then anticipates and adjusts your home or your office to your temperature needs, at the same time saving significant energy resources.
Google acquired Nest back in 2014 for $3.2 billion and now aims to create a smart and helpful home. The tech giant is trying to connect devices such as thermostats, cameras, alarm systems, doorbells, and locks under the same roof, offering an easy to use smart home solution.
However, for this to work seamlessly, technological advancement in the field of IoT and 5G needs to be utilized. Near-zero latency is what allows for smart homes to be remotely controlled.
As you can see, AI is impacting and improving every aspect of our lives and our society. It will revolutionize the way we do different things, from driving our cars to receiving medical treatment. Although this technology is still in its infancy, it has already managed to disrupt the above-mentioned areas and offer us a sneak peek into the future.
WHY DIGITAL TRANSFORMATION IN FINANCIAL SERVICES IS ABOUT CULTURE FIRST, TECH SECOND
Stuart Templeton, Head of UK at Slack
In today’s world, there’s no such thing as a ‘non-tech fin’. Every financial services company needs to consider itself a fintech in order to bring about the innovation, speed, and transparency that customers expect, and that’s why most are pumping significant investment into their digital transformation efforts.
Part of the challenge faced by traditional incumbent banks is that they rely on legacy core systems that stifle the speed of change. These core systems were not built in an API first era. The good news of course is that the obligations of PSD2 and open banking have gone some way to facilitate future innovation.
While legacy banking platforms do continue to present a technical challenge, the human one can be even greater. Traditional institutions are often faced with the prospect of rebuilding their culture from scratch in the pursuit of becoming digital-first. Like many industries, the fundamental challenge is one of coordination: the creation and maintenance of alignment over time.
Couple this with the fact that the expectations of today’s workforce are changing, then companies in the industry have a real job on their hands. A growing percentage are digital natives, and millenials – who greatly value trust and transparency – make up the largest proportion of the workforce today. So how have businesses in the industry historically ingrained culture, and how does this need to change?
Old ways of working – Team A, and Team B
Traditionally, the culture within large financial organisations has been separated by two distinct teams: operations, and tech. They are driven by seemingly opposing forces – one by GANTT charts and lofty business goals, the other by agile software delivery and customer obsession. Often, the two don’t even speak the same language, let alone collaborate and share ideas. Of course there are digital projects, but they aren’t the embodiment of the business, and often tech teams find themselves battling to get buy-in from internal stakeholders who are somewhat removed from those that drive innovation.
Part of the problem is even the notion of having digital transformation projects – there is no such thing in today’s environment – as digital is an overarching movement, and financial services institutions must think of themselves as ‘digital factories’ in order to see a marked change. It is no longer enough to deliver tech updates both internally and externally once every few months, with speed diminished by layers of bureaucracy.
What needs to happen, then, is that these two business segments need to find a way to blend that helps the old incumbents forget their binary ideas of teamship from time gone by and instead let them come together to become one unit. Flattening the established hierarchy so that workers from across all lines of the business can communicate, share ideas and identify problems in real-time is, after all, the key to addressing the transformation gap. They need to think on their feet and iterate as they go: it’s agile thinking, but permeating outside of just the software delivery cycle.
Eating the elephant – one bite at a time
The solution, in theory, is relatively simple: companies need to break open the silos of information created by technologies like email and ensure anyone within a business has access to the knowledge and skills they need to make their projects a success. But of course, in practicality, this can present a seemingly insurmountable task.
Using technology to create an agile and transparent working environment that fosters collaboration is key for many financial services organisations that want to see real tangible results from their investments. Digital natives such as TransferWise and Starling Bank are getting this right by prioritising a decentralised business model, one that empowers collaborative working and knowledge sharing that in turn has a positive impact on employee satisfaction and retention.
They do this through collaboration hubs that provide a rich, permanent, searchable record of knowledge for everyone in the organisation.
Looking ahead: Team ‘us’
Predictions are very difficult, but in five years’ time we can expect to see a greatly altered perception of the financial services industry. We can expect that digital communications tools will continue to play an integral role in the evolution of their workforce culture, helping to bring the right people together internally within the business, as well as strengthening relationships externally with partners and customers alike.
Ultimately, in order to keep learning and improving, banks need to ask questions of themselves as competition and customer demand becomes more fierce: “Why are we doing this?” “What’s the benefit here, and who are we considering in the pursuit of this goal?”
To answer these things, a culture of collaboration and openness is key – underpinned, of course, by the tools that empower it.
DISPELLING BIOMETRIC MYTHS AND MISCONCEPTIONS
By Lina Andolf-Orup, Head of Marketing at Fingerprints
Gangsters cutting off enemies’ fingers to access secret locations and spies lifting fingerprints from martini glasses – the imagination of the entertainment world has been running wild ever since biometrics entered the scene.
Couple that with the limitations of some early biometric solutions from fifteen years ago, still anchored in the minds of many consumers, and you have the perfect recipe for an apprehensive and uncertain public.
Thawing lukewarm attitudes with a biometric touch
The biometrics industry has made great strides in the last few years – something particularly true for smartphones. Fingerprint authentication has replaced PINs and passwords as the most popular way to authenticate on mobile, with 70% of shipped smartphones now featuring biometrics.
And it doesn’t end there. Many adjacent markets are now eager to benefit from the secure and convenient authentication solutions that biometrics offer. Take the payments industry, for example, where biometrics payment cards are currently gathering real momentum.
However, some consumers are still uneasy about accepting biometrics. A recent study found that 56% of US and EU consumers are concerned about the switch to biometrics as it’s not enough understood to be trusted.
Although attitudes are shifting for the better, stats like this demonstrate there is still some work to do to disprove common biometric myths and showcase just how smart today’s solutions really are.
Dispel, adopt, repeat
The evolution in consumer biometrics in the last two decades has been phenomenal. And today’s solutions are far more advanced and safe than many may think.
To help bring an end to the myths, let’s expose some of the most common misconceptions around biometrics.
Myth: Biometric data is stored as images in easy-to-hack databases.
A leading myth about biometrics is that when a fingerprint is registered to a device, it is stored as an image of the actual fingerprint. This image can then be stolen and used across applications. In reality, the biometric data is stored as a template in binary code – put simply, encrypted 0s and 1s. Storing a mathematical representation rather than an image makes hacking considerably more challenging. In most consumer applications, this template is also not stored in a cloud-based location, its securely hosted in hardware on the device itself for example in the smartphone, in the payment card. Thus, it stays privately with its owner.
Myth: Fingerprints can be easily replicated to ‘trick’ devices.
The internet is full of articles and videos that claim it is possible to use materials from cello tape to gummy bears to craft fingerprint spoofs and access biometric systems. Although there may have been a time where gummy bear spoofing was the go-to party trick, todays’ consumer biometric authentication solutions have too many technological defences, such as improved image quality and matching algorithms, to simply ‘trick’ devices. Plus, on top this, the criminal needs to have access to the person’s device where this fingerprint is enrolled e.g. smartphone, payment card, before he/she notices and blocks it. This is not scalable nor common, in comparison to gaining access to someone’s PIN code or skimming a contactless card.
Myth: Physical change will prohibit access to my device.
Although our irises don’t change as we age, our fingerprints can and our faces will. Does that mean we have to update our biometric devices every few months to capture these changes? Not quite! Unless there are drastic, sudden changes, the ‘self-learning’ algorithms in modern-day biometric systems are able to keep up with our developing looks.
Who you gonna call? Mythbusters!
These are just some of the common biometric myths and misunderstandings perpetuating in consumer mindsets. Thankfully, though, while we’re working hard to rid the world of the myths, belief in the value of biometrics is only expected to grow. But as solutions expand and diversify, the myth-busting fight will continue.
Fingerprints has been a leader of innovation in biometrics for the last two decades. We’re proud of the expertise and R&D we’ve been able to pour into our biometrics solutions to deliver stronger security and a better user-experience. To learn more about the most common biometric misconceptions and the modern-day technology that allows us to dispel them, download our eBook here.
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