Thursday, June 28, 2018

Bahamas plans to launch digital currency – Bahamas, Caribbean
Cyberattacks could cause banks US$100bn annual losses – IMF – Regional
How Artificial Intelligence is revolutionizing IoT – Regional
LTE reaches 32% of LatAm’s mobile connections – Regional
Latin America e-waste forecast to grow 10% annually – Brazil, Regional
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Bahamas plans to launch digital currency – Bahamas, Caribbean

The Bahamian central bank unveiled plans to pilot a blockchain-based digital currency, as the Bahamas joins a growing list of governments experimenting with their own cryptocurrencies, including Ecuador, Venezuela, Singapore and China.
In contrast to other governments developing their own fiat blockchain currencies, the Bahamas pegs its local currency, the Bahamian dollar, 1:1 to the US dollar, meaning the creation of a digital currency might allow for a truly native dollar.
Speaking at the recent 2018 Bahamas Blockchain and Cryptocurrency Conference, Bahamas deputy prime minister K. Peter Turnquest briefed attendees on the plans, expressing his hopes that Grand Bahama becomes the “digital paradise” of the region, according to state news agency Bahamas Information Services (BIS).
“The production of a modern fully digital payment service is the way forward for this era of governance. A digital Bahamian currency is especially important for the many family islands as they have seen many commercial banks downsize and pull out of their communities, leaving them without banking services,” said the minister. “As an island nation, where transportation can be an inconvenience for many, especially the elderly, and costly, we must offer financial services digitally and securely.”
“Digitization of our government and financial services complements both our ease of doing business initiatives and our digital Bahamas framework. As a first step in our ease of doing business initiative, we would have moved to a new online interface for start-up companies registering their business for the first time in the Bahamas,” added Turnquest.
According to Turnquest, the government would soon begin a blockchain certification process in coordination with the National Training Agency.
“Even though it is at a very preliminary form, the government is looking to see other ways in which certificates such as business licenses, passports, national insurance can make use of blockchain technology to enable persons to maintain their data and share it in a secure and verifiable way.”
Turnquest added the government sought to employ blockchain-type technology to be more efficient, lower costs and root out government corruption.
Crypto community reaction
The announcement by the central bank churned the debate within the crypto community over whether governments developing their own blockchain currencies qualify as true cryptocurrencies, in the spirit of the so-called blockchain revolution.
Some valuable points to consider found on Reddit include those that describe such programs as electronic ledgers of fiat, bringing with it many of the problems that non-fiat cryptos were created to escape – specifically taxation, volatility, seizure of assets, surveillance, transaction fees, seigniorage and the potential for politically motivated government manipulation.
Counterarguments suggest there is room for both fiat and non-fiat blockchain currencies in the future, with governments using homegrown digital coins to cut costs, increase fund transfer speeds (particularly cross-border), improve efficiency and transparency and potentially better manage volatility.
Cyberattacks could cause banks US$100bn annual losses – IMF – Regional

Global banks could face losses of up to US$100bn every year due to cyberattacks, or around 9% of global net income, but a more serious scenario in which such attacks double could send the figure skyrocketing to US$270-350bn a year, IMF managing director Christine Lagarde said in a recent blog.
The problem, Lagarde explained, is compounded by a very small market of insurance against cyber risks, with premiums of about US$3bn in 2017. “Most financial institutions do not even carry cyber insurance. Coverage is limited, and insurers face challenges in evaluating risk,” she said.
Mexican banks were hit by an attack on the country’s electronic interbank transfer system SPEI between April and May that allowed cyber criminals to steal around US$15mn and exposed the vulnerability of the country’s financial system . Also in May, Chilean commercial lender Banco de Chile was hit by hackers that stole US$10mn by apparently exploiting vulnerabilities in certain terminals running Windows.
Cyber risks, Lagarde said, have been the top concern for risk managers and financial executives since at least 2016, outpacing geopolitical and regulatory risks, with close to 80% of executives surveyed mentioning them in a 2017 poll.
“A successful cyberattack on one institution could spread rapidly through the highly interconnected financial system. Many institutions still use older systems that might not be resilient to cyberattacks. And a successful cyberattack can have direct material consequences through financial losses as well as indirect costs such as diminished reputation,” she added.
Among potential solutions to the risk is more data collection by government regarding the impact and frequency of cyberattacks, Lagarde said. “Further work is needed also to understand how to strengthen the resilience of financial institutions and infrastructures, both to reduce the odds of a successful cyberattack but also to facilitate smooth and rapid recovery. There is also a need to build capacity in the official sector in many parts of the world to monitor and regulate such risks.”
How Artificial Intelligence is revolutionizing IoT – Regional

By Wojciech Martyniak, M2M/IoT Product Manager at Comarch
In Hollywood, artificial intelligence is often portrayed as something to be feared. It is synonymous with a world dominated by machines, in which humans are an endangered species fighting for survival. The truth, though, is far more benign. In the Internet of Things – a global network of connected devices in our homes, workplaces, cars and cities – artificial intelligence and machine learning processes operate, often unnoticeably, smoothing the path of everyday life and facilitating business in every industry.
The reason for the stark contrast between Hollywood’s doomsday narratives and reality is simple: artificial intelligence operates according to goals set by humans, and, while machines are capable of learning, they do so within the framework pre-defined by these goals.
Data – the foundation of artificial intelligence
The foundation of artificial intelligence is data analysis. For example, organizations such as Google, Netflix, Facebook and Amazon all use algorithms that gather and examine data on user behavior, on which they base certain aspects of content delivery. Our banks and mobile network providers do something very similar – and in these cases, their systems are set up to compare data with established patterns and take certain actions if anomalies appear. In addition, consider the robot vacuum cleaners that have seen spikes in popularity, and even the advances in self-driving cars – in both instances, data are being gathered and analyzed, so that lessons can be learned and operations can become more efficient and safer.

IoT sensors and the power of AI for predictive maintenance

Where we have taken a huge leap forward recently is in harnessing the power of artificial intelligence not just to automate processes related to past and current events, but also to predict future activity for physical and virtual devices. For example, companies in various industries commonly deploy solutions that use information obtained from automated, in-depth analysis of network data over time and over vast geographical areas. These companies use devices, such as sensors connected to the Internet of Things (IoT), for predictive maintenance. Based on data analysis, the sensors predict the conditions under which a device is likely to malfunction – or simply wear out – and can then trigger actions automatically or issue an alert that gets an engineer on site before a problem arises.
Such solutions can also uncover interesting patterns in equipment data. For instance, by using a variety of sensors in the marine shipping sector, companies find means to correlate information from fuel meter readings and the amount of power used by on-board refrigerated containers. Data obtained in such a way can be used to optimize generator output, which led, for one company, , to savings of $30 per hour – or $6.5 million over the course of a year.
Similar IoT-based sensor solutions can be employed on production lines to monitor and predict potential issues. Healthcare providers are already using remote monitoring for patients, via devices that give early warning of life-threatening changes in vital signs. Transport and logistics operators keep track of goods and vehicles with solutions that can also learn routes, personnel availability and more. Even entire cities are becoming smart, with applications such as IoT-based and AI-powered smart-camera systems that can count vehicles or recognize license plate numbers.
The limitations of IoT-AI integration
The complex integration of IoT and AI via data analytics also has some limitations. Often, using self-learning AI to extrapolate all the required steps to achieve a certain goal is simply counterproductive – the process can be extremely time-consuming, especially in cases of new or large data sets.
In these instances, human intervention is required to optimize learning mechanisms and direct the solutions toward the required operational capabilities. It is quicker, easier and more cost-effective to give a device or network a pre-defined, algorithm-based course of action, which still automates and optimizes operations but saves money by cutting the time required for the machine to examine its rules, and subsequently to self-learn and act accordingly.
Consider an example of a logistics company that needs to account for changes of home locations: in large data sets, having AI figure out something like this may take a significant amount of time and would affect the quality of services delivered. For humans, this fact is so self-evident that it immediately springs to mind when considering logistical issues. Applying a simple algorithm – stipulating that going back to one location three times and staying there for more than two days updates the home location – is the correct course of action in such cases.
Comarch IoT Connect
Being able to manage all of these aspects – data analytics, IoT devices, AI and meaningful human intervention when necessary – is what will set key players ahead of their competition across many sectors. At Comarch, we implement this philosophy through Comarch IoT Connect, where we test the feasibility of various AI approaches (DBSCAN, K-Means and Linear Regression) within its framework. Results show the AI approach to be very effective in several areas, including service quality assurance and predictive maintenance – but particularly so in the anomaly detection field, where AI is trained to detect unexpectedly high (or low) device activity in a specific location. However, in many other instances – such as the case of changing home locations described above – we settle for a simpler algorithmic approach. In these instances, implementing AI takes much longer, costs more, and yields negligible benefits.
A platform designed in such a way offers the optimal solution to facilitate the business strategies of IoT service providers, effectively tailored for the needs of any given organization/industry. Its architecture is not only flexible and scalable, but also integrates smoothly in multi-national, multi-level and multi-operator environments. An award-winning solution respected by industry experts at Berg Insight and Gartner, Comarch IoT Connect has already been implemented by major players such as Telekom Austria Group and Saudi Telecom Company, to optimize IoT connectivity management and assist in generating offers to verticals in the automotive, consumer electronics, retail, energy, finance, healthcare, manufacturing, transport and security sectors.
Such solutions clearly have the capacity to revolutionize the way we work, do business and live in our everyday environments. And while machines can certainly learn, AI still relies heavily on a strong foundation of real, human intelligence – which is why we’re unlikely to see the Hollywood interpretation of the AI world any time in the near future. Then again, it wasn’t so long ago that mobile telecommunications – let alone 5G and the IoT – were unimaginable …
LTE reaches 32% of LatAm’s mobile connections – Regional

4G LTE technology reached a 31.5% market share in Latin America and the Caribbean at the end of the first quarter, up from 29% a year before, with 219mn connections out of 694mn mobile wireless accesses in total, according to 5G Americas.
In countries such as Brazil, Puerto Rico and Uruguay, 4G LTE reaches over 90% of the population, 5G Americas said in a press release on Friday.
5G Americas estimates that LTE will reach 258mn connections (including M2M) by the end of the year and become the most widely used cellular technology in the region by the end of 2019.
By the end of 2022, LTE is forecast to reach half a billion connections in the region, M2M included.
“Operators’ investment in the technology continues as LTE-A proliferates in the region and a higher penetration of smartphone allows a greater number of individuals to have access to mobile broadband technologies,” 5G Americas LatAm director José Otero said.
“5G trials continue to take place in the region with operators announcing the launch of the first 5G commercial network during the next 12 to 18 months.”
Worldwide, LTE achieved a 38.5% market share in Q1, with North America leading with 76%. Oceania, Eastern and South Eastern Asia had a 59% rate and Western Europe 46%.
Latin America e-waste forecast to grow 10% annually – Brazil, Regional

The amount of electronic waste (e-waste) generated in Latin America is expected to grow 10% a year at least until 2020, the GSM Association (GSMA) reported, citing UN data.
The forecast is part of the study ‘Technology for Climate Action in Latin America and the Caribbean – How ICT and Mobile Solutions Contribute to a Sustainable, Low-Carbon Future’.
In 2017, Latin American e-waste reached 4,400 kilotons (kt), or 9% of the world’s total, and around 46kt of the total generated in the region was directly associated with mobile phones.
Brazil generates the most e-waste in Latin America, with 1,500Kt per year according to 2017 data from electric-electronics industry association Abinee, ranking 7th in the world and tied with France.
The country’s first national waste management legislation (PNRS), setting reverse logistics principles, came into force at the end of 2010 following 20 years of debate. In July last year, Green Eletron, a logistics company created by Abinee for recycling electronics products, launched a pilot project in São Paulo for the disposal of electrical products, including mobile phones, notebooks and tablets.
According to GSMA, Latin American and Caribbean governments should those improve those initiatives and incentives for recycling and reusing electrical and electronic equipment. “Such practices decrease energy used in the production of electronic equipment as it reduces the demand of virgin materials, the sourcing of which is very energy intensive,” GSMA wrote.
The result of this would be a reduction of greenhouse gas emissions, as well as less e-waste going to landfills, said the GSMA.
The association recommended that regulators in the region implement additional policies directed at the ICT sector, namely extended producer responsibility, to move further towards the circular economy.
Further details of the study are available here.
Copyright 2017 Business News Americas
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