Carmine Rimi, Product Manager AI and Kubernetes, Canonical – the company behind Ubuntu
Artificial Intelligence (AI) has witnessed extraordinary growth in business circles over the past few years. So much so that it feels an omnipresent buzzword in corporate technology conversations the world over. The advent of cloud computing and open source initiatives have supported the rapid expansion of these technologies, which are experiencing huge investment from both multinational businesses and smaller enterprises.
Deloitte’s Digital Disruption Index highlights that 85% of senior executives plan to invest in AI by 2020, while Stanford University’s AI Index reports a six fold rise in the annual investments from venture capitalists into AI start-ups since 2000, with significant quickening of that investment after 2010. Moreover, Gartner predicts that the business value resulting from AI initiatives will hit $3.9 trillion by 2022, rising from $1.2 trillion in 2018. These impressive figures serve to underline the huge potential AI boasts.
The trend towards the adoption of AI shows no signs of abating. In a software dominated world, AI acts as a trigger for business growth, innovation and the launching of new, advanced services for consumers. However, the deployment of AI technology is not a straightforward process, and no industry is immune from this complication. There are many considerations prior to a business being able to enjoy the advantages AI can deliver.
Business have had to discover that rolling out AI technology can be problematic. From concerns around integration with current systems, to a lack of understanding around how AI works, it’s clear that there are broad and complex challenges. A recent report from Databricks, for example, stated that 96% of organisations are experiencing data-related problems such as inconsistent datasets, while 80% reported a lack of collaboration between data engineers and data scientists. Then we come to the question of compute power. AI solutions tend to leverage large reserves of processing power, which will rise as data volumes rocket and the algorithms driving these systems grow increasingly more complex. This presents some large concerns around scalability.
It’s important to note that, from a practical point of view, AI technology is very much in its infancy but is developing rapidly. While the technology has been spoken about for some time, it is only during the last few years that deployments have started to take place and increase.
Challenges to AI deployment can even emerge before the roll-out begins. One of the major barriers for AI is IT teams and business leaders understanding how it can be used for everyday business problems; and also, how it can fit to the specific requirements of the company. As opposed to deploying AI for the sake of it, companies should look at where the technology can make the biggest mark, and what specific processes would benefit from being automated. This is easier said than done. AI requires people with knowledge who can seize the challenge of turning the theory into profitable outcomes. AI encompasses a range of processes and technologies, such as machine learning, data transformation, model creation, natural language processing and deep learning. To derive the most value from these it is essential to understand the differences between these innovations. So, what does it take to deal with these issues to realise the potential of AI?
Making the most of AI
Capitalising on the power of AI comes down to a few key factors. Primarily, it’s crucial that companies understand the importance of rolling out back-end infrastructure and systems which can support the compute-intensive tasks involved in AI and machine learning. Operating systems then have to be adjusted to these sophisticated workloads, which allows businesses to work with huge datasets, deploy applications at scale and manage the complexity that comes with that. Getting AI systems operational takes a lot of time, effort, expertise and resources – which are not always at the disposal of an organisation. AI platforms are only as good as the people who programme them. The industry skills shortage can impact businesses, so it’s important to partner with experts who are able to guide them and address any internal gaps. Enterprises need to be considered in the way they introduce AI, creating a long-term strategy and investing in the right people with the right set of skills and experience.
Harnessing the power of AI may be easier said than done, but no one doubts it presents a phenomenal opportunity. A laser-like focus on how AI can be leveraged to solve business challenges will help. AI has been growing in intelligence for some time – now businesses need to follow that lead and realise the true potential of this remarkable technology.
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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