Cybersecurity is becoming impossible without AI – Capgemini report

Security is certainly a topic which is top of the agenda for almost everyone in the technology world, but it is quickly becoming apparent it will be impossible without AI.

The concept of 100% secure has now been rightfully banished and now more people are waking up to the idea any form of security is going to be impossible without artificial intelligence.

According to a new report from the Capgemini Research Institute, 69% of respondents believe they will not be able to respond to cyberattacks without the use of AI. Such is the velocity, volume and variety of threats thrown towards businesses nowadays, there will never be enough budget or hours in the day for humans to effectively deal with the problem in its entirety.

“Organizations are facing an unparalleled volume and complexity of cyber threats and have woken up to the importance of AI as the first line of defence,” said Geert van der Linden, Cybersecurity Business Lead at Capgemini Group.

“As cybersecurity analysts are overwhelmed, close to a quarter of them declaring they are not able to successfully investigate all identified incidents, it is critical for organizations to increase investment and focus on the business benefits that AI can bring in terms of bolstering their cybersecurity.”

The report follows another interesting bit of research from enterprise ISP Beaming earlier in the week. Beaming suggested the number of attacks levelled at British businesses during Q2 increased 179% year-on-year. These firms were effectively facing a threat every 50 seconds on average over the three-month period.

The Capgemini research suggests investments in security AI will increase dramatically over the next twelve months. During 2020, 48% of decision makers suggested investments in this area will increase by a third. 73% are currently testing use cases for AI in cybersecurity, while 63% intend to deploy AI security in 2020 to bolster defences.

And while it might seem like a grave conversation to have right now, the situation is only going to become worse. With the introduction of 5G, more products and services moving to the cloud, consumers adopting more connected devices and IOT set to boom over the next couple of years, the perimeter is expanding. Threats exist today, but exposure to the dark corners of the web is going to become much more apparent.

Europe publishes stance on AI ethics, but don’t expect much

The European Commission has revealed its latest white paper detailing guidelines on an ethical and trustworthy approach to AI, but whether it actually means anything remains to be seen.

The guidelines themselves are now open for public comment with the Gaggle of Red Tapers seeking feedback on how to make improvements and increase applicability to the world of today. However, the industry continues to operate under the semblance of oversight but in the reality of the digital wild-west.

Such is the top-line nature of the guidelines, you have to wonder whether there have been any real efforts to integrate the thinking into business. At the moment, the guidelines do not seem to have any substance to them, simply stating the obvious, or at least what you would hope is obvious to the developers creating the algorithms and applications. These guidelines would have been useful 2-3 years ago, but now it seems a bit of a redundant statement. AI regulation needs action not philosophical thinking.

After reading the guidelines, there is a sense of ‘so what’. What was the point in making this statement aside from cosmetically attracting headlines for the European Commission? There doesn’t seem to be anything new in there, just the European Commission making a statement for the sake of making a statement.

The seven guidelines are as follows:

  1. Humans should have oversight of AI at all times
  2. AI systems need to be resilient and secure
  3. Governance measures should be introduced to protect privacy
  4. Transparency should be ensured
  5. Bias should be removed
  6. AI should benefit all
  7. Accountability for AI should be introduced

Having the guidelines is all well-and-good, there needs to be a yard-stick, but we would expect at the least for some sort of accountability model. It seems a bit half-arsed at the moment as there are still numerous questions.

Firstly, how is the European Commission going to judge whether these guidelines are being followed by industry? What will the metrics be? What will be the punishments for not taking the principles into account or negligible behaviour? Where are the reporting mechanisms for ‘unethical’ behaviour and complaints?

The next steps for the Commission is to consult with industry and run various pilot programmes across the bloc. After these initiatives have been completed, another consultation period will be entered into before the Commission will review the assessment lists for the key requirements in early 2020. At some point in the ill-defined future, Europe might have some rules on AI.

Considering the posturing which has taken place over the last couple of months, Europe has promised it will lead the world on AI, this announced seems nothing but superficial. These generic comments and guidelines should have been put out years ago, now is a time for action and a time for rules.

AI is already in the world and having a fundamental impact on our day-to-day lives. We might not realise it all the time, but it is increasingly interwoven into the services and products which we use each day. Now is the time for action from regulators, not posturing and pondering.

The threat of Amazon is forcing supermarkets into drastic changes

UK supermarket giant Tesco is undergoing trials with Israeli AI surveillance business Trigo Vision to trial the concept of cashier-less stores, supposedly due to pressure from Amazon.

When Amazon first emerged, few could have imagined the revolution which would have been thrust upon the retailing industry. Even now, more than two decades after Amazon was founded, there are businesses which are still struggling to adapt to life in the digital epoch. The writing has been on the wall for a long-time in retail, and now it seems Tesco is attempting to get ahead of trends for the supermarket segment.

According to The Telegraph, Tesco is currently in trials with Trigo Vision to create a cashier-less store, a concept which Amazon has been playing with in the US for years. Don’t be fooled by the absurdity of the vision, it will soon enough be a presence on the High Street and once the benefits can be seen by all, it will become much more common place.

Take self-checkout tills as an example. When these first emerged everyone hated them, and in some regions, they still do. But you cannot walk into a Tesco or Sainsbury’s in an urban environment anymore without seeing them. And most importantly, no-one really cares anymore. The idea took some time to bed in, but once the bugs were worked out and people saw how much more efficient the system was, they accepted it. The same trend will most likely occur with cashier-less stores.

What is worth noting is that Tesco is not alone in pursuing the future. Sainsbury’s has also announced it is toying with the cashier-less idea, opening its first store in Holborn, Central London, in April this year.

Trigo Vision, the provider of the underlying technology, was founded in 2017 and has been through one round of funding thus far, attracting $7 million from Hetz Ventures and Vertex Ventures Israel. The team already has a partnership with Shufersal, Israel’s largest supermarket chain, to roll-out its automated retail platform in over 272 stores across the country.

The firm supplies both high-resolution RGB cameras, installed on ceilings and an on-premises processing unit that runs machine learning-powered tracking software. The algorithms are continuously honed by Trigo Vision through the data collected at various sites and the team can also help develop customisable apps and kiosks to improve experience.

The technology makes use of artificial intelligence and a dense series of surveillance cameras to track what items are being placed into a customer’s shopping trolley. Customers will be prompted to download an app and enter payment details, or an alternative for sceptics could be using a screen at the exit to complete the purchases.

As it stands, Amazon Go, the eCommerce’s cashier-less business, has launched in several cities across the US and has plans to open its first store in the UK at Oxford Circus in London. This will act as the flagship store for the UK though Amazon is reportedly on the hunt for more sites, 3,000 to 5,000 square feet in size, to expand the footprint.

The stores have been hailed as a success in the US and Amazon is reportedly targeting 3,000 locations within three years. Although this is far from proof the idea is profitable right now, the internet giants tend to run with unprofitable ideas they know will change the world, it should be viewed as a massive red flag for traditional supermarkets.

And while the bookstore segment did little until it was too late, Tesco is at least attempting to get ahead of trends. Another example of this is the ‘Scan Pay Go’ initiative. Here, customers can download an app and carry around a scanner to register products themselves as they wander the aisles, helping keep an eye on spending while also speeding up the check-out process at the end of the trip.

Many companies will state they want to disrupt themselves before being disrupted, though there is little evidence of this. The majority of the time there is an outside influence, a threat from a new player, to alter the status quo. This seems to be the case here, as Amazon is forcing the hand of Tesco, though future success of the Amazon Go business will depend on the ability of the traditional players to scale quickly.

Using machine learning as a stethoscope for 5G

Telecoms.com periodically invites third parties to share their views on the industry’s most pressing issues. In this article Yuval Stein, AVP Technologies at TEOCO looks at the use of machine learning to optimise the roll out of 5G

The invention of the stethoscope was thanks to shyness rather than a spark of genius. In 1816, French doctor René Laennec, felt that listening to a young woman’s heart by pressing his ear to her chest wasn’t appropriate. Instead, he rolled up some paper, and found that he was able to hear much better.

The earliest designs of stethoscopes were simple wooden tubes and it was many years before the instrument we see as emblematic of healthcare was created. But the stethoscope was more than just a useful tool, it led to a new way of doing medicine. Before, it was normal to treat symptoms rather than underlying causes—now, through this new device, doctors had insight into what was going on inside the body, and were better able to understand the diseases behind the symptoms.

This shift from treating symptoms to treating causes seems natural to us now, but at this time doctors would treat a fever with no real idea of what was causing it. A similar change is now necessary with mobile networks. Increased complexity means that we need a new way of looking at these networks, to find the root causes of faults, rather than only treating the symptoms.

Machine learning as a stethoscope

The sheer amount of data that a 5G network will produce is going to be overwhelming. More data is good, of course, because the more data we have on a network, the better we can understand the issues that may be causing problems for users. But all this data needs to be analysed. In the past, this was simple—more data meant hiring more people to analyse and formulate actionable conclusions from the data.

This is no longer tenable. But it’s also no longer possible to rely on simple forms of automation in order to react to regular and more obvious issues. 5G is different from previous network generations, in that many new technologies and architectural innovations are being introduced at the same time. These technologies include NFV/SDN, edge computing, new radio access technologies and more.

This new complexity means we need new tools to examine the network. And this is where machine learning becomes just like the stethoscope—not just a tool, but a shift in how things are done. The use of machine learning can identify patterns and reduce the need for human oversight—a vital means of increasing operational efficiency by reducing headcount. But the real change is shifting from fixing issues to detecting underlying issues—even those that don’t linger in the network for long.

Treating the causes, not the symptoms

The rise of virtualised, software-driven networks has meant that service assurance is more decentralised. This means more network alarms—even with automation it’s still often impossible to determine where the real problems reside. This is particularly an issue where faults are intermittent—the symptoms may last for far longer that the fault itself. Manually examining service alarms will give an engineer no real clue as to where they can start to fix the underlying problems.

Also, there is big difference between being reactive and being proactive in maintaining a level of network assurance. A simple example would be if network bandwidth was too low to provide a certain service, and an alarm is set for when this happens. Automation would mean that the fix for this happens without any intervention. But a step further would be to use statistical techniques, such as trend analysis and forecasting to detect abnormalities in the network. These tools would mean pre-empting a situation that would result in poor service in anticipation of a glitch,  rather than reacting when the issue actually arises. This isn’t about fixing problems, but preventing them before they ever happen, addressing the underlying symptoms before they have a chance to take root and cause havoc.

But machine learning can go further. Self-learning algorithms mean that operators can create a baseline profile that identifies when exceptions occur. Rather than determining a threshold for an alarm, this allows for the creation of adaptive thresholds. An example of this would be an area where many homes are being built—at some point there will be a lot more traffic in that area, but engineers don’t have the time to check how closely construction timelines are being followed. Instead, the network behavior should change to meet the demand automatically. While a hard-coded threshold would need to be reconfigured, machine learning means that thresholds are adjusted automatically.

These examples seem fairly straightforward—but millions of similar decisions need to be made every day based on an overwhelming amount of data. Operators have known that automation is necessary for some time, but machine learning is key to decision-making in a 5G network. Without it, operators will be reduced to guesswork, lacking the tools to make the most out of their new—and expensive—networks.

 

Yuval-SteinYuval Stein is the AVP of Product Management and Service Assurance Products at TEOCO. With more than 15 years of experience in the service assurance domain, Yuval has held key product management positions throughout his career. He brings his knowledge to the fault, performance and service domains, and uses his hands-on experience to adapt service assurance solutions to the industry challenges: digital services and network technologies.

UK government thinks AI sector isn’t diverse enough

A few million quid is being thrown at artificial intelligence and data science conversion degrees, with people from underrepresented groups eligible for scholarships.

The Department for Digital, Culture, Media and Sport had a rummage around the back of the sofa and came up with £13.5 million that is wants to spend on encouraging people to take ‘conversion degrees’ specialising in AI and data science. This seems to mean you already need a degree to qualify, but have decided the smart money is in AI.

The social engineering doesn’t stop there, however, with a significant chunk of that cash set aside to provide 1,000 scholarships for ‘under-represented groups… including women and people from minority ethnic backgrounds, or lower socio-economic backgrounds.’ Another 5 million quid is being set aside to encourage companies to come up with better online learning tools.

“Creating a more diverse future workforce will help with the design of new technology, including the fair and accurate development of algorithms, and tackle some of the greatest social challenges of our time – from protecting our environment, to transforming the way we live and work, and saving lives through diagnosing diseases earlier,” the announcement says.

“The UK has a long standing reputation for innovation, world-leading academic institutions and a business friendly environment and everyone, regardless of their background, should have the opportunity to build a successful career in our world-leading tech sector,” said Digital Secretary Jeremy Wright. “Through these new AI and Data Conversion courses and our modern Industrial Strategy, we are committed to working with the tech sector and academia to develop and maintain the best AI workforce in the world.”

Loads of other people were quoted as saying almost exactly the same thing. Try as it might the government couldn’t find a single voice of dissent. Apparently only 19 percent of UK tech workers are women, which is bad. No data was offered regarding ethnicity or socio-economic grouping. This initiative joins a Byzantine web of other schemes the government has in place to help people acquire some useful vocational skills. The first round of funding will be made available in September.

Microsoft and Sony join up on AI and cloud gaming

Microsoft and Sony have signed a memorandum of understanding to jointly develop cloud systems for game and content streaming, and to integrate Microsoft’s AI with Sony’s image sensors.

This is another step on Sony’s journey to transform from a console and title seller to a game streaming service platform. Microsoft’s leadership in both cloud computing, its Azure cloud platform, and the global footsteps of its datacentres makes it an ideal partner to Sony.

The collaboration will also cover semiconductors and AI. Sony has been a leader in image sensors (among its clients is the iPhone including the latest XS Max model), and the integration of Microsoft Azure AI will help improve both the imaging processing in the cloud and on device, what the companies called “a hybrid manner”. Microsoft’s AI will also be incorporated in Sony’s other consumer products to “provide highly intuitive and user-friendly AI experiences”, the companies said.

“Sony has always been a leader in both entertainment and technology, and the collaboration we announced today builds on this history of innovation,” said Satya Nadella, CEO of Microsoft, in a statement. “Our partnership brings the power of Azure and Azure AI to Sony to deliver new gaming and entertainment experiences for customers.”

Kenichiro Yoshida, president and CEO of Sony agreed. “I hope that in the areas of semiconductors and AI, leveraging each company’s cutting-edge technology in a mutually complementary way will lead to the creation of new value for society,” he said.

Looking to the future of the PlayStation platform, Yoshida said, “Our mission is to seamlessly evolve this platform as one that continues to deliver the best and most immersive entertainment experiences, together with a cloud environment that ensures the best possible experience, anytime, anywhere.”

Gaming is following the trend of video and music from one-off ownership selling to access streaming. But gamers are more sensitive to the visual quality and, above everything else, lagging. So to provide good experience to convert gamers to long-term streaming subscribers, the platform needs to guarantee superb connection. This is where Microsoft’s datacentre footsteps and the upcoming 5G networks will fit well with the “game” plan.

Another key success factor, similar to video streaming market, is the content. Gamers’ taste can be fast changing and frivolous. That is why the companies also stressed the importance to “collaborate closely with a multitude of content creators that capture the imagination of people around the world, and through our cutting-edge technology, we provide the tools to bring their dreams and vision to reality.”

No information on the size of investment or the number of staff involved in the collaboration is disclosed, but the companies promised to “share additional information when available”.

LG muscles in on competitive AI chip space

LG has unveiled has developed its own artificial intelligence chip in an attempt to muscle in on this increasingly competitive segment of the semiconductor market.

The AI market is proving to be rewarding for those who can prove their worth, and each day there seems to be a new ‘thought leader’ entering the fray. While there is a feeling AI could benefit application developers (Uber, Cruise, Waymo etc.) and internet companies (Amazon, Google, Microsoft etc.) more than the semiconductor giants, there will be winners and losers in this segment also.

“Our AI C​hip is designed to provide optimized artificial intelligence solutions for future LG products,” said IP Park, CTO of LG Electronics. “This will further enhance the three key pillars of our artificial intelligence strategy – evolve, connect and open – and provide customers with an improved experience for a better life.”

Nvidia might have made a run at this segment in the early days, though considering its experience lies in gaming applications, whether it can mount a serious challenge remains to be seen. Graphcore is one which has attracted investment from the likes of Dell, Microsoft and Samsung, while AMD, Intel, Huawei, Google and Qualcomm (as well as numerous others) are making this a very competitive space.

As with Intel in the PC-era and Qualcomm’s continued dominance in mobile, some might suspect there might be a clear leader in AI also.

LG has stated its chip will feature its proprietary LG Neural Engine to better mimic the neural network of the human brain. The aim is to distinguish space, location, objects and users, while hoping to improve the capabilities of the device by detecting physical and chemical changes in the environment. As with every AI plug, LG is also promoting the ability of on-device processing power.

Looking at the approach from LG, the team are targeting quite a niche aspect of the AI segment; the smart home. This makes sense, as while LG has a smartphone business, the brand is perhaps primarily known for its home appliances range.

During the last earnings call, the LG mobile business continued to struggle in a sluggish and cut-throat market, reporting a 29% year-on-year drop to $1.34 billion, though the home appliance market soared. Revenues and profits soared to record levels, accounting for more than 80% of the total profits for the business over the three months.

Future products, such as washing machines, refrigerators, and air conditioners will be fitted with the devices, as ‘intelligence’ and personalisation become more common themes in more generic and everyday products.

Maybe the smart toilet isn’t that far away after all.

UK Gov names members of AI Council

The UK government will be hoping its AI advisory board is a bit more successful than Google’s as it names the full line-up.

Bringing together experts from industry, academia and data rights organisations, the ambition is to provide a guiding light for the future of artificial intelligence. Tabitha Goldstaub, co-founder of CognitionX, will chair the council which will feature the likes of Ocado CTO Paul Clarke, Kriti Sharma, the founder of AI for Good and Deepmind’s co-founder Mustafa Suleyman.

The primary objective of the council will be to make the UK a leading name in the AI world.

Such is the promise of the technology in terms of productivity and the creation of new services, technologists will be keen to drive innovation forward, though the dangers are also high.

AI not only presents the risk of abuse through prejudice and unconscious bias, but the unknown risks should be considered as much of a danger. Such is the embryotic nature of AI, the full-potential, power and influence are anyone’s guess for the moment. This is an exciting prospect, but also should be approached with caution.

For example, back in July 2017, a Facebook AI application managed to invent its own language to speak to other applications meaning human overseers had no idea what was going on. This was a very simplistic and limited application so there was no real danger, but it was a lesson to the industry; more defined perimeters need to be created for more complex applications in the real world.

This council will aim to create a framework to take the UK into a leadership position in the AI world, but it will be critical the members do not forget about the importance of ethical and responsible development.

“Britain is already a leading authority in AI,” said Secretary of State for Digital, Culture, Media and Sport, Jeremy Wright. “We are home to some of the world’s finest academic institutions, landing record levels of investment to the sector and attracting the best global tech talent, but we must not be complacent.

“Through our AI Council we will continue this momentum by leveraging the knowledge of experts from a range of sectors to provide leadership on the best use and adoption of artificial intelligence across the economy.”

The full list of members:

  • Tabitha Goldstaub, Chair and Cofounder of Cognition X
  • Wendy Hall, Professor of Computer Science at the University of Southampton
  • Professor Adrian Smith. Institute Director and Chief Executive at the Alan Turing Institute
  • Alice Bentinck, Co-founder at Entrepreneur First
  • Alice Webb, Director for Children’s and Education at the BBC
  • Ann Cairns, Executive Vice Chair of Mastercard
  • Professor Chris Bishop, Microsoft Technical Fellow and Director of the Microsoft Research Lab in Cambridge
  • Dr Claire Craig, Chief Science Policy Officer at the Royal Society
  • Professor David Lane, Professor & Founding Director of the Edinburgh Centre for Robotics
  • Kriti Sharma, Founder of AI for Good
  • Marc Warner, CEO of Faculty
  • Professor Maire O’Neill, Professor at Queen’s University Belfast
  • Sir Mark Walport, Chief Executive of UKRI
  • Martin Tisne, Managing Director of Luminate
  • Mustafa Suleyman, Co-founder of Deepmind
  • Professor Neil Lawrence, Professor at the University of Sheffield and Director, IPC Machine Learning at Amazon
  • Professor Nick Jennings, Vice-Provost Research and Enterprise of Imperial College
  • Dame Patricia Hodgson, Member of the Independent Commission on Freedom of Information and Centre for Data Ethics and Innovation
  • Paul Clarke, CTO of Ocado
  • Professor Pete Burnap, Professor of Data Science & Cybersecurity at Cardiff University
  • Priya Lakhani, Founder of edtech AI platform Century Tech
  • Rachel Dunscombe, CEO of NHS Digital Academy

San Francisco puts the brakes on facial recognition surveillance

The City of San Francisco has passed new rules which will significantly curb the abilities of public sector organisations to purchase and utilise facial recognition technologies.

Opinions on newly emerging surveillance technologies have varied drastically, with some pointing to the benefits of safety and efficiency for intelligence and police forces, while others have bemoaned the crippling potential it could have on civil liberties and privacy.

The new rules in San Francisco do not necessarily ban surveillance technologies entirely, but barriers to demonstrate justification have been significantly increased.

“The success of San Francisco’s #FacialRecognition ban is owed to a vast grassroots coalition that has advocated for similar policies around the Bay Area for years,” said San Francisco Supervisor Aaron Peskin.

The legislation will come into effect in 30 days’ time. From that point, no city department or contracting officer will be able to purchase equipment unless the Board of Supervisors has appropriated funds for such acquisition. New processes will also be introduced including a surveillance technology policy for the department which meet the demands of the Board, as well as a surveillance impact report.

The department would also have to produce an in-depth annual report which would detail:

  • How the technology was used
  • Details of each instance data was shared outside the department
  • Crime statistics

The impact report will have to include a huge range of information including all the forward plans on logistics, experiences from other government departments, justification for the expenditure and potential impact on privacy. The department may also have to consult public opinion, while it will have to create concrete policies on data retention, storage, reporting and analysis.

City officials are making it as difficult as possible to make use of such technologies, and considering the impact or potential for abuse, quite rightly so. As mentioned before, this is not a ban on next-generation surveillance technologies, but an attempt to ensure deployment is absolutely necessary.

As mentioned before, the concerns surround privacy and potential violations of civil liberties, which were largely outlined in wide-sweeping privacy reforms set forward by California Governor Jerry Brown last year. The rules are intended to spur on an ‘informed public debate’ on the potential impacts on the rights guaranteed by the First, Fourth, and Fourteenth Amendments of the US Constitution.

Aside from the potential for abuse, it does appear City Official and privacy advocates are concerned over the impact on prejudices based on race, ethnicity, religion, national origin, income level, sexual orientation, or political perspective. Many analytical technologies are based on the most likely scenario, leaning on stereotypical beliefs and potentially increasing profiling techniques, effectively removing impartiality of viewing each case on its individual factors.

While the intelligence and policing community will most likely view such conditions as a bureaucratic mess, it should be absolutely be viewed as necessary. We’ve already seen the implementation of such technologies without public debate and scrutiny, a drastic step considering the potential consequences.

Although the technology is not necessarily new, think of border control at airports, perhaps the rollout in China has swayed opinion. When an authoritarian state like China, where political and societal values conflict that of the US, implements such technologies some will begin to ask what the nefarious impact of deployment actually is.

In February, a database emerged demonstrating China has used a full suite of AI tools to monitor its Uyghur population in the far west of the country. This could have been a catalyst for the rules.

That said, the technology is also far from perfect. Police forces across the UK has been trialling facial recognition and data analytics technologies with varied results. At least 53 UK local councils and 45 of the country’s police forces are heavily relying on computer algorithms to assess the risk level of crimes against children as well as people cheating on benefits.

In May last year, the South Wales Police Force has to defend its decision to trial NEC facial recognition software during the 2017 Champions League Final as it is revealed only 8% of the identifications proved to be accurate.

It might be viewed by some as bureaucracy for the sake of bureaucracy but considering the potential for abuse and damage to privacy rights, such administrative barriers are critical. More cities should take the same approach as San Francisco.

The private power of the edge

One of conundrums which has been quietly emerging over the last couple of months concerns how to maintain privacy when attempting to improve customer experience, but the power of the edge might save the day.

If telcos want to be able to improve customer experience, data needs to be collected and analysed. This might sound like a very obvious statement to make, but the growing privacy movement across the world, and the potential of new regulatory restraints, might make this more difficult.

This is where the edge could play a more significant role. One of the more prominent discussions from Mobile World Congress in Barcelona this year was the role of the edge, and it does appear this conversation has continued through to Light Reading’s Big 5G Event in Denver.

Some might say artificial intelligence and data analytics are solutions looking for a problem, but in this instance, there is a very real issue to address. Improving customer experience though analytics will only be successful if implemented quickly, some might suggest in real-time, therefore the models used to improve performance should be hosted on the edge. This is an example of where the latency business model can directly impact operations.

It also addresses another few issues, firstly, the cost of sending data back to a central data centre. As it was pointed out today, telcos cannot afford to send all customer data back to be analysed today, it is simply an unreasonable quantity, therefore the more insight which can be actioned on the edge, with only the genuinely important insight being sent back to train models, the more palatable customer experience management becomes.

Secondly, the privacy issue is partly addressed. The more which is actioned on the edge, as close to the customer as possible, the lesser the concerns of the privacy advocates. Yes, data is still being collected, analysed and (potentially) actioned upon, but as soon as the insight is realised the sooner it can be deleted.

There are still sceptics when it comes to the edge, the latency business case, artificial intelligence and data analytics, but slowly more cases are starting to emerge to add credibility.