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  • The Shared Benefits Of Connectivity Mean A Shared Effort Of Providing It
    by Vino Govender on January 20, 2021 at 3:21 pm

    As the need for connectivity becomes increasingly fundamental to our way of life, the need to deliver quality and cost-efficient connectivity and services by private communication services providers is of paramount importance. However, the cost of delivering quality services is relative to demand and use. Demand and volume of consumption of services play a significant role in determining the return profile of investments made by network operators in the infrastructure, platforms, product development, and services that are required to deliver quality connectivity and digital services. While a lot of efforts have been made on the supply side, including policy, the licensing of new entrants and the release of the spectrum, it requires equal or even more efforts on the demand side to enable the adoption and use of services that in turn feed a cycle of reinvestment. When looking at the South Africa Connect Broadband Policy, the pillars of Digital Readiness and Digital Future lean heavily toward the supply side, ensuring that policy alignment and network supply is geared toward delivering universally accessible coverage. The Pillars of Digital Development and Digital Opportunities deal more with the demand side, and while the former pillar is focused on aggregating and consolidating Government driven demand and anchor tenancy for network and infrastructure deployment, it has to be supplemented by the demand from Digital Opportunities to support a healthy and conducive investment climate. A study conducted by Accenture estimates that the use of digital technologies in five priority areas of South Africa’s government services could add more than R2 trillion in value over the next decade. An example of this is virtual reality (VR), Augmented Reality (AR), and broadband to extend the reach of education and enrich the content of education services through virtual classrooms, virtual labs, and e-learning or e-education. In addition to this, a significant surge in the digitalization of common citizen services would drive millions of South Africans to adopt digital platforms as they experience the benefits. Linked to adoption is digital literacy. The United Nations defines digital literacy as “the ability to access, manage, understand, integrate, communicate, evaluate, and create information safely and appropriately through digital technologies”. Earlier this year, Dignify, as part of a digital skills project, estimated 80% of South Africans to have little or no digital literacy. Continuous focus and alignment of efforts in the public and private sectors are required to change this picture change significantly. The COVID-19 pandemic, and the worldwide lockdowns that happened, as a result, triggered a migration to digital platforms on a scale that we have never seen before. Lockdown restrictions graphically illustrated the crucial role that networks and service platforms can play in our business and personal lives. For all strata of society, reliable connectivity became a necessity rather than a luxury. A research paper titled: COVID-19 and digital inequalities: Reciprocal impacts and mitigation strategies, found that while even lower-income earners were forced to use digital services at a higher level than ever before, higher-income earners found that their existing connection was no longer adequate. The increased demand required them to upgrade their class of service to meet their own changing needs and to mitigate the impact of increased internet traffic on connection speeds. All these developments were made possible by the increased need or connectivity which put pressure on both users and suppliers to overcome some of the hurdles in providing or accessing connectivity. Ultimately, it illustrated that demand is every bit as critical as a supply when it comes to bridging the gap in access to digital services, and it is a multipronged approach that will truly achieve the vision of affordable connectivity for all. As the pioneer of open-access connectivity in South Africa and an active industry participant, DFA supports collaboration and cooperation as a means of making connectivity – with its innumerable benefits for the economy and society – more widely available. Vino Govender is the Executive for Strategy, Mergers and Acquisitions, and Strategy at DFA The post The Shared Benefits Of Connectivity Mean A Shared Effort Of Providing It appeared first on CIO East Africa.

  • Huawei Helps Black Friday Banks Avoid Red Faces
    by Staff Writer on January 20, 2021 at 5:54 am

    The Black Friday 2020 rush was bigger than ever pushing banks’ systems to their limit and throwing the spotlight on those that had failed to update legacy storage systems. One un-named bank, cited in a report from Huawei, saw transaction volumes on Black Friday jump ten times over the previous day as consumers rushed to place orders on ecommerce sites. In the first few seconds after midnight, the bank was processing 30,000 transactions per second, 30 per cent more than the previous year’s Black Friday. Such a surge in input/output operations per second (IOPS) would quickly swamp the systems of payment providers and banks which rely on legacy storage platforms, Huawei said. At the same time, it would highlight any shortcomings in latency within systems. The combination of insufficient IOPS and stretching latency would quickly lead to time outs and even worse, payment failures, leaving customers frustrated, and retailers – and their payment service providers – missing out on revenue. Huawei likens the effect to driving on a motorway at midnight, then suddenly finding yourself in rush hour. This is why smart banks have been preemptively upgrading their storage to all-flash systems like Huawei’s OceanStor Dorado Series, the vendor said. Sticking with the motorway analogy, the OceanStor Dorado effectively expands the number of lanes transactions have access to, solving the traffic jam problem. At the same time, massively improved latency further speeds transactions through the system – the equivalent to replacing manual toll booths with an automated toll collection system. The OceanStor Dorado 18000 v6 recently took the top spot in the Storage Performance Council’s SPC-1 benchmark league table, turning in a record-breaking performance of 21 million IOPS, due in part to its minimum latency of 0.05ms. This level of performance meant banks who had planned in advance to upgrade their storage enjoyed the easiest Black Friday they have ever had, said Huawei. Get more Information / Free POC about Huawei All-Flash The post Huawei Helps Black Friday Banks Avoid Red Faces appeared first on CIO East Africa.

  • 3 Security Career Lessons From Back To The Future
    by Dan Lohrmann on January 18, 2021 at 12:29 pm

    The security industry had a terrible year in 2020—some even think the worst ever. You can point to failures in working from home after COVID-19 struck, various election narratives, the SolarWinds breach, foreign nation-state cyberattacks, new ransomware, the global lack of cyber talent, government leader mistakes or a long list of other items. My favourite quote that captures this “good riddance” sentiment is from Back to the Future when Doc warns Marty: “Whatever happens, don’t ever go to 2020!” Regardless of who you blame (or not) for 2020 failures, Bruce Schneier now thinks the best path forward after the SolarWinds breach is for the majority of Fortune 500 companies to burn down their networks and rebuild from scratch. But even if this radical approach is followed by the public- and private-sector organisations, this advice begs many questions. Do we rebuild the same network architecture? Will the same people, processes, and technology (presumably with known vulnerabilities patched) keep the bad actors out in the future? Can we keep doing the same things and expect a different result? Bottom line, have we learned anything from the past decade—or even the past year? Career lessons from Back to the Future Which brings me back to my second favourite trilogy of all time. There are several great lists of life lessons we can learn from the Back to the Future movies. As I pondered this topic over the recent holidays and watched the three fun movies one more time, I came up with my top three career lessons that cyber pros (and other tech enthusiasts) can learn from that masterful movie series that features a DeLorean time machine. 1. Surround yourself with experts who you trust and who believe in you. I love the multi-generational aspects of Back the Future, with both the Doc/Marty relationship and how the parents’ and grandparents’ character traits are passed down through the generations—even as their surroundings were very different in Hill Valley. No matter what circumstances arise in the trilogy, those trusted relationships are key. Understanding our past can help us understand the present and the future. It is easy to make assumptions about others and think that they made decisions because of who they are rather than the circumstances they experienced. When we learn more about the past, it can put their actions into context and enlighten us about how things came to be in the current situation. Knowing history well can also help us avoid making the same mistakes over and over. Tip: Ask trusted colleagues about the key decisions (good or bad) that they made, and how those decisions impacted their current situation. 2. Believe in yourself; don’t sweat it if people call you “chicken.” Throughout the trilogy, Marty McFly reacts strongly whenever anyone calls him “chicken.” However, at the end of the third movie, when it becomes clear that he could die from a duel with Buford “Mad Dog” Tannen, Marty realizes it doesn’t matter what Tannen (or his other adversaries) say about him. After Marty learns this lesson, he refuses to enter a car race in 1985. This decision saves him from getting into a car accident. We learned in the second movie that this car accident would have injured his wrist, stopped him from playing the guitar, and get him fired from his job in the future (2015). The questions that we all need to ask ourselves on a regular basis is: What are our career goals? Who are you trying to please? Why? As cyber pros, we need to believe in ourselves rather than focus on negative comments that are sure to come from industry competitors. As Mark Victor Hansen recommends, “By recording your dreams and goals on paper, you set in motion the process of becoming the person you most want to be. Put your future in good hands—your own.” Tip: Go over your goals and plans on a regular basis with a trusted mentor who can support your action plans. Also, becoming a life-long learner who is constantly reinventing your career and growing skillsets in different situations will enable you to succeed no matter what cyberspace throws at you. 3. Don’t stop thinking about tomorrow, Because past trends can teach us about tomorrow’s reality—especially in security. Predicting the future is hard in any area of life, but it’s especially difficult when it comes to technology and cybersecurity. That doesn’t mean we don’t try to our best to connect the dots regarding cyber trends, which is why I spend many hours digesting and writing about security industry predictions each year. True, no one saw a global COVID-19 pandemic coming in 2019, so our view of 2020 was fundamentally flawed in many respects. Nevertheless, prognosticators still got many things right. Five years ago, I wrote this article for CSO Magazine entitled: Why more security predictions and how can you benefit? I ended by saying: “Bottom line, the more the security and technology industries grow, the more predictions we will have. From the Internet of Things to new technologies to robots to self-driving cars, do you really think we will be talking about security and privacy less in 2020? I don’t. Predictions are not new, and they are not going away. In fact, they are just getting started. Congratulations security industry, and welcome to centre ring in this three-ring circus. Yes, it is a very big circus, but that’s where all the action is.” It turned out that I was right, and we now have more new security predictions than ever before. Tip: Take time to think about your future career in your area of expertise. Thinking about the movie trilogy, when we project ourselves into the future and consider all of our goals, it can help us gain perspective on the present situation and what to do next. Considering future options will open doors to insights about your present situation and what your current decisions might actually mean. One final thought: As Bill Gates said, “We always overestimate the change that will occur in the next two years and underestimate the change that will occur in the next ten.” Don’t let yourself be lulled into inaction. The post 3 Security Career Lessons From Back To The Future appeared first on CIO East Africa.

  • What That Digital Services Tax Means For You
    by Ndegwa Alex on January 18, 2021 at 12:03 pm

    In the context of the 2008 financial crisis, the Organisation for Economic Cooperation and Development (OECD) and the G20 set up the Base Erosion and Profit Shifting Project (the BEPS Project), to tackle tax avoidance by multinational companies. One of the action items of the BEPS Project was the need to address the tax challenges arising from the digitalisation of the economy. This is because, the traditional and current international tax framework which dates back to the 1920s, is/was based on brick-and-mortar businesses and taxation is/was tied to the physical presence of a business. However, the digitalisation of the global economy made it possible for multinationals to operate in multiple countries without having any physical presence in those countries. In addition, these companies no longer need to generate revenues/profits from tangible goods as their businesses are modelled on commercialising intangibles. Take, for example, a streaming service such as Netflix. It doesn’t need to have any physical office in Kenya in order for Kenyan users to stream their favourite shows. Whereas digitalisation has delivered tangible benefits to consumers, the new business models have also provided an avenue for these companies to derive income from the countries they operate in without paying their respective share of taxes, seeing how they have no physical presence in those countries. It is against this background that the OECD/G20, under the BEPS Project, have been working on finding consensus on an international tax framework/solution. However, pending global consensus, countries such as France, Japan, India, and Turkey began to impose unilateral digital taxes and Kenya has now joined the bandwagon. Expansive taxation plan It is against this background that Kenya introduced a Digital Services Tax (DST) to limit the shift of income by foreign digital service providers operating in Kenya and expand the tax base to the digital sector that had arguably been out of the purview of the taxman. A tax on digital services was initially contemplated in 2019 when Parliament passed amendments to the Income Tax Act to specifically provide that any income derived from or accrued in Kenya from a digital marketplace would be subject to income tax in Kenya. The amendments also introduced the definition of a digital market place to mean a platform that enables the direct interaction between buyers and sellers of goods and services through electronic means. However, the implementation was conditioned on the publication of the corresponding regulations by the Cabinet Secretary (CS) of the National Treasury. Months later, President Uhuru Kenyatta assented to the Finance Act 2020 which among other provisions, introduced a Digital Services Tax to be effective from 1 January 2021 at the rate of 1.5 per cent of the gross transaction value of services and payable on income earned from services performed on digital market places. However, it was not until recently when the CS published the Income Tax (Digital Services Tax) Regulations, 2020 that the DST became a reality for Kenya. Defining digital services DST will be chargeable on digital services that are provided to users located in Kenya. Digital services refer to any services that are performed on digital market places, which are platforms that enable the direct interaction between buyers and sellers of goods and services. This definition would include social media platforms such as WhatsApp, Twitter and Facebook. However, the regulations have expanded the scope beyond what would traditionally be considered as services by including items such as downloadable digital content and user data monetisation. I should note that the Kenya Revenue Authority (KRA) will have wide discretion in determining whether a particular service is a digital service as the regulations provide that any service performed on a digital market place will be treated as a digital service. Common examples of digital services subject to the DST are streaming services, website hosting, cloud storage services, lead generation services (ride-hailing apps) etc. Interestingly, online services that facilitate payments, lending, trading of commodities and foreign exchange done by licensed financial institutions and other approved institutions such as cooperative societies will be exempted from the DST. In addition, online services performed by government institutions are also exempt from DST and prices for government services are not expected to increase on account of the DST. Who is a user? A user of digital services will be deemed to be located in Kenya, if (1) they pay for the service from a financial institution in Kenya, (2) they access the service from an IP address in Kenya, (3) they access the service using a device in Kenya and (4) their billing or residential or business address is in Kenya. If a user meets any of the four proxies, then the income derived from the service provided to that user will be subject to DST in Kenya. The DST is calculated by computing 1.5 per cent of the amount paid to the digital services provider or the fee paid to the digital marketplace provider, but without including any Value Added Tax (VAT) payable on the transaction. The responsibility to pay DST to KRA is on the digital service or digital market place provider. The regulations distinguish between a provider of services and a provider of a market place. For context, the owner of an e-commerce site would be a digital market place provider and the provider of a streaming service would be a digital services provider. Digital service or a digital market place provider is required to pay the DST and file the corresponding returns on or before the 20th day following the month that the service was offered. However, for foreign digital service and market place providers, they have the option of registering for DST through a simplified registration framework, pay the respective DST and file returns directly; or they could appoint a tax representative to comply on their behalf. For digital sector players that are located in Kenya, the DST they pay during the year will be an advance tax of their total tax liability for the year. This means that they will be able to recover the DST they remit from their tax liability for the year. However, if the DST they pay during the year will be higher than their tax liability, they could claim a refund from KRA on account of the overpaid tax or carry forward the overpaid tax and use it as a credit against their future tax liability. For foreign digital service providers, the DST they pay to KRA will be a final tax and will not be recoverable. For local businesses, the DST will be an additional burden on their cash flows and for businesses that will be paying the new minimum tax, their cash flows are expected to take a bigger hit. The DST may also be a headache for technology startups that burn through cash in their first year’s operations as they will not be able to recover the DST they pay during their loss-making years until they break even and generate tax profits. On the other hand, consumers may end up paying higher prices for digital services as digital service providers gross-up prices to account for the impact of the DST. I expect that once the OECD/G20 find consensus on an international solution, the DST will be repealed or modified in favour of the globally agreed framework and as such the DST is likely a temporary measure for the exchequer to shore up tax revenue to bridge the worrying budget deficit. Lastly, considering that the KRA may not have visibility of digital transactions, enforcement will not be a walk in the park. We can only adopt a wait-and-see approach. By Ndegwa Alex, Corporate/Tax Lawyer ( The post What That Digital Services Tax Means For You appeared first on CIO East Africa.

  • 12 Dark Secrets Of ArtificiaI Intelligence
    by Peter Wayner on January 18, 2021 at 9:24 am

    Humanity has always dreamed of some omniscient, omnipotent genie that can shoulder its workloads. Now, thanks to the hard work of computer scientists in the labs, we have our answer in artificial intelligence, which if you buy into the hype can do just about anything your company needs doing — at least some of it, some of the time. Yes, AI innovations are amazing. Virtual helpers like Siri, Alexa, or Google Assistant would seem magical to a time traveller from as recently as 10 to 15 years ago. Your word is their command, and unlike voice recognition tools from the 1990s, they often come up with the right answer — if you avoid curveball questions like asking how many angels can dance on the head of a pin. But for all of their magic, AIs are still reliant on computer programming and that means they suffer from all of the limitations that hold back the more pedestrian code such as spreadsheets or word processors. They do a better job juggling the statistical vagaries of the world, but ultimately, they’re still just computers that make decisions by computing a function and determining whether some number is bigger or smaller than a threshold. Underneath all of the clever mystery and sophisticated algorithms is a set of transistors implementing an IF-THEN decision. Can we live with this? Do we have any choice? With the drumbeat for AI across all industries only getting louder, we must begin to learn to live with the following dark secrets of artificial intelligence. Much of what you find with AI is obvious The toughest job for an AI scientist is telling the boss that the AI has discovered what everyone already knew. Perhaps it examined 10 billion photographs and discovered the sky is blue. But if you forgot to put night-time photos in the training set, it won’t realize that it gets dark at night. But how can an AI avoid the obvious conclusions? The strongest signals in the data will be obvious to anyone working in the trenches and they’ll also be obvious to the computer algorithms digging through the numbers. They’ll be the first answer that the retriever will bring back and drop at your feet. At least the algorithms won’t expect a treat. Exploiting nuanced AI insights may not be worth it Of course, good AIs also lock on to small differences when the data is precise. But using these small insights can require deep strategic shifts to the company’s workflow. Some of the subtle distinctions will be too subtle to be worth chasing. And computers will still obsess over them. The problem is that big signals are obvious and small signals may yield small or even nonexistent gains. Mysterious computers are more threatening While early researchers hoped that the mathematical approach of a computer algorithm would lend an air of respectability to the final decision, many people in the world aren’t willing to surrender to the god of logic. If anything, the complexity and mystery of AI make it easier for anyone unhappy with the answer to attack the process. Was the algorithm biased? The more mystery and complexity under the hood, the more reasons for the world to be suspicious and angry. AI is mainly curve fitting Scientists have been plotting some noisy data and drawing lines through the points for hundreds of years. Many of the AI algorithms at the core of machine learning algorithms do just that. They take some data and draw a line through them. Much of the advancement has come from finding ways to break the problem into thousands, millions, or maybe even billions of little problems and then drawing lines through all of them. It’s not magic; it’s just an assembly line for how we’ve been doing science for centuries. People who don’t like AI and find it easy to poke holes in its decisions focus on the fact that there’s often no deep theory or philosophical scaffolding to lend credibility to the answer. It’s just a guesstimate for the slope of some line. Gathering data is the real job Everyone who’s started studying data science begins to realize that there’s not much time for science because finding the data is the real job. AI is a close cousin to data science and it has the same challenges. It’s 0.01% inspiration and 99.99% perspiring over file formats, missing data fields, and character codes. You need massive data to reach deeper conclusions Some answers are easy to find, but deeper, more complex answers often require more and more data. Sometimes the amount of data will rise exponentially. AI can leave you with an insatiable appetite for more and more bits. You’re stuck with the biases of your data Just like the inhabitants of Plato’s Cave, we’re all limited by what we can see and perceive. AIs are no different. They’re explicitly limited by their training set. If there are biases in the data — and there will be some — the AI will inherit them. If there are holes in the data, there will be holes in the AI’s understanding of the world. AI is a black hole for electricity Most good games have a final level or an ultimate goal. AIs, though, can keep getting more and more complex. As long as you’re willing to pay the electricity bill, they’ll keep churning out more complex models with more nodes, more levels, and more internal state. Maybe this extra complexity will be enough to make the model truly useful. Maybe some emergent sentient behaviour will come out of the next run. But maybe we’ll need an even larger collection of GPUs running through the night to really capture the effect. Explainable AI is just another turtle AI researchers have been devoting more time of late trying to explain just what the AI is doing. We can dig into the data and discover that the trained model relies heavily on these parameters that come from a particular corner of the data set. Often, though, the explanations are like those offered by magicians who explain one trick by performing another. Answering the question why is surprisingly hard. You can look at the simplest linear models and stare at the parameters, but often you’ll be left scratching your head. If the model says to multiply the number of miles driven each year by a factor of 0.043255, you might wonder why not 0.043256 or 0.7, or maybe something outrageously different like 411 or 10 billion. Once you’re using a continuum, all of the numbers along the axis might be right. It’s like the old model where the Earth was just sitting on a giant Turtle. And where did this turtle stand? On the back of another Turtle. And where does the next stand? It’s turtles all the way down. Trying to be fair is a challenge You could leave height out of the training set, but the odds are pretty good that your AI program will find some other proxy to flag the taller people and choose them for your basketball squad. Maybe it will be shoe size. Or perhaps reach. People have dreamed that asking a neutral AI to make an unbiased decision would make the world a fairer place, but sometimes the issues are deeply embedded in reality and the algorithms can’t do any better. Sometimes the fixes are even worse Is forcing an AI to be fair any real solution? Some try to insist that AIs generate results with certain preordained percentages. They put their thumb on the scale and rewrite the algorithms to change the output. But then people start to wonder why we bother with any training or data analysis if you’ve already decided the answer you want. Humans are the real problem We’re generally happy with AIs when the stakes are low. If you’ve got 10 million pictures to sort, you’re going to be happy if some AI will generate reasonably accurate results most of the time. Sure, there may be issues and mistakes. Some of the glitches might even reflect deep problems with the AI’s biases, issues that might be worthy of a 200-page hairsplitting thesis. But the AIs aren’t the problem. They will do what they’re told. If they get fussy and start generating error messages, we can hide those messages. If the training set doesn’t generate perfect results, we can put aside the whining result asking for more data. If the accuracy isn’t as high as possible, we can just file that result away. The AIs will go back to work and do the best they can. Humans, though, are a completely different animal. The AIs are their tools and the humans will be the ones who want to use them to find an advantage and profit from it. Some of these plans will be relatively innocent, but some will be driven by secret malice aforethought. Many times, when we run into a bad AI, it’s because it’s the puppet on the string for some human that’s profiting from the bad behaviour. The post 12 Dark Secrets Of ArtificiaI Intelligence appeared first on CIO East Africa.

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