Bitcoin Mining Is More Popular — And More Destructive — Than Ever

I had been considering the cybersecurity impacts of cryptocurrency mining for several weeks when I saw the recent episode of HBO’s Silicon Valley, where it served as a laugh-out-loud recurring gag.

Throughout the episode, the character Gilfoyle — a gruff, deadpan systems architect — keeps unnerving his coworkers with a jarring blast of the two-second song “You Suffer” by British extreme metal band Napalm Death.

“It’s an alert,” Gilfoyle explains. “Whenever the price of bitcoin dips below a certain value, it is no longer efficient to mine. When it comes back up, it is. I need to know when it breaks that threshold so I can remotely toggle my rig at home … bitcoin is very volatile.”

Clearly, mining bitcoin is now decidedly mainstream, and not just for comedy gold. For many organizations, bitcoin mining gets less amusing as it increasingly forms the motivation for malware and cyberattacks.

When episode writer Carrie Kemper uses the word “volatile,” though, it is an understatement. In April 2015, the value of bitcoin hovered around $230. Last December, it hit a high of nearly $20,000. The following February, it was down to $7,000.

When its value was a couple hundred dollars, bitcoin “mining” wasn’t cost-efficient. It requires a fast internet connection, creates electricity costs through power consumption and cooling, depletes storage space and takes time. When the value of a single bitcoin began to skyrocket, so did the profitability of bitcoin mining — and investment in it.

Couple that rising value with increased competition and a depleting supply of bitcoins and the result is an increasingly serious cybersecurity problem.

Read More Here

Article Credit: Forbes

Go to Source

Digital Transformation 3.0: Enter The IoT Blind Spot

As I look around my office, I see my laptop, an iPad, several smartphones and an Amazon Echo, as well as wireless mouse, keyboard and smart speakers. The conference room next door has a wireless voice over IP phone used for conference calls with a tablet to control the video conference system, a smart TV and webcams, and there’s a printer in the hallway and another smart TV in the reception area. All of these are connected to the internet, many are wirelessly connected and some are connected to each other via Bluetooth. This is just one office environment, but connected devices are everywhere — in businesses, hospitals, manufacturing plants, power stations, airplanes and government buildings.

The internet of things (IoT) is taking us through the biggest digital transformation the world has ever seen — bigger than both the PC and mobile revolutions combined. Currently, there are an estimated 8.4 billion connected devices in use worldwide this year, and that number is expected to reach at least 20.4 billion by 2020, according to Gartner. To compare, Gartner projects smartphone shipments will rise slightly in 2018 to 1.9 billion and that PC and laptop shipments will decline more than 5% to 193 million. It’s time to meet the new endpoint.

IoT is exploding fast because it brings extraordinary capabilities like improved efficiency, better collaboration and better quality in manufacturing, production, energy and all other sectors. The explosion of connected devices transmitting information all the time allows doctors to receive real-time reports on a patient’s pulse, temperature and other health indicators from afar —  and cars can drive themselves.

The inherent connectivity of all these different devices and the fast growth of the IoT industry have created significant security issues that put companies at great risk. During the PC revolution, security vendors developed firewalls, antivirus and other solutions to protect computers from outside threats. Corporations rely on mobile device management and other software for securing mobile devices. However, things are more complex when it comes to the IoT, which creates a big market opportunity. Gartner forecasts that worldwide spending on IoT security will increase to $1.5 billion in 2018, up 28% from a $1.2 billion spend in 2017 and rise 158% to $3.1 billion in 2021.

Why? The proliferation of IoT throughout the enterprise has created a new device that requires a fresh approach to security and more robust technology for defense. These are the new “endpoints” in businesses. Here are three key issues enterprise information technology (IT) teams need to keep in mind as their environments undergo this IoT transformation:

IoT Is Under The Radar

For global Fortune 1000 companies, the sheer amount of smart devices in use is causing an enormous problem due to a lack of visibility. IT teams are often focused on the traditional devices that people use to do their work such as desktops, laptops and servers because there is an established system for tracking and securing them. They can’t pay attention to all the nontraditional connected devices, such as the new connected business devices beyond the computer. Our research shows that enterprises cannot see 40% or more of these connected devices in their environments, and if they can’t see them, they can’t control or secure them.

Read More Here

Article Credit: Forbes

Go to Source

The Most Important Benefits and Challenges of Industrial IoT

By 2025, the global industrial IoT market is expected to reach 933.62 billion dollars. The scope of cloud applications and scalability are two of the factors that will drive the additional growth across all industries within it.

The sectors that benefit from industrial IoT solutions the most right now include manufacturing, healthcare, energy and power production, logistics and transportation, oil and gas, and agriculture. Over the coming few years, the manufacturing sector is expected to emerge as the dominant one. According to analytical reports, the industrial internet of things will reinvent many sectors that account for approximately two thirds of the global economic output, driving economic gains of 14.2 trillion dollars by 2030.

What are the reasons for the prominence of industrial IoT solutions? Some of them have been touched upon in the intro but let’s take a more thorough look.

Industrial Internet of Things: Key Benefits

Industrial IoT solutions allow for more efficient, affordable and easy way to maintain processes.

Currently, most companies adopt a very simple modus operandi – if something breaks, they fix it. The use of smart sensors and the right software, however, will allow for the correct prediction of upcoming failures. Equipment can be replaced and maintained in a much more effective way, reducing the risk of breakdowns and industrial processes coming to a halt.

The range of IoT applications is quite big and diverse. Currently, IoT solutions can be utilized to accomplish all of the following:

  • Facility management: Condition-based maintenance is an effortless task and so sensors can be used to increase the effectiveness of facility management. Manufacturing equipment is prone to wear and tear. It’s also susceptible to specific conditions. Sensors can monitor temperature, vibrations and other factors that could be leading to less than optimal operational conditions.
  • Inventory management: IoT solutions can also be quite beneficial when it comes to reducing the risk of inventory management errors. Events will be effortless to monitor across the supply chain, giving companies a comprehensive view of inventory. Estimates of available materials and supplies are accurate, which prevents slowdowns.

Read More Here

Article Credit: IOT For All

Go to Source

Big data in educational science: The story of meta-analysis

In this insightful analysis, two seasoned experts from University of Zurich (Switzerland) explore the exciting world of big data in educational science, focusing on the story of meta-analysis

By way of an introduction, big data is a currently a hot topic, not only in educational science but in science more generally, as Esther Kaufmann and Professor Dr Katharina Maag Merki explain. The authors add that recently, meta-analysis was mentioned as “the grandmother of the ‘big data’ and ‘open science’ movements” (Gurevitch, Koricheva, Nakagawa & Stewart, 2018). Considering this important development, the authors take the time to introduce the story of meta-analysis – to understand in detail the relevance of meta-analysis in relation to big data.

To present the complete story of meta-analysis and to understand the value and challenges of it in the context of big data, the authors introduce the origins of meta-analysis and how it spread into the world of educational science. Finally, they detail the pros and cons of meta-analysis and link it to their insightful summary and outlook for the future of big data analysis.

Looking ahead, the authors stress that more and more data coming from different sources can now be seen, but furthermore, they tell us that individual data is accumulating. They develop this point to us in their own words: “Hence, we see IPD meta-analysis as a grandchild of classical meta-analysis—with which it is possible to check for any aggregation bias—and IPD meta-analysis might also be an analysis tool for big data.”

If you are interested in this absorbing research from the University of Zurich (Switzerland), Esther Kaufmann, PhD and Professor Dr Katharina Maag Merki would warmly welcome your emails, should you require additional information. You can contact them at and  They also would like to draw your attention to their previously published work: Kaufmann, E., & Maag Merki, K. (2017). Big data in educational science: Meta-analysis as an analysis tool.

Read More Here

Article Credit: Open Access Government

Go to Source


Research conclusions can appear obvious in retrospect. But until the data is actually crunched we can’t know, and it is the knowing that is crucial for generating evidence-informed policy.

Indigenous Australians aged 45 and up report high levels of psychological distressat more than twice the rate of other Australians. A staggering 20 per cent of middle aged and older Indigenous Australians are in high psychological distress compared to 7.5 per cent broadly.

We’ve known this for a while. But now for the first time this data has been analysedto better understand what is driving the distress.

The researchers found that distress is strongly correlated with chronic illness and disability. In other words, Indigenous Australians are more distressed possibly because they are more unwell.

It seems obvious, but it is a clear policy message.


“In health, there are many things that we think we know but it isn’t until you gather the data and get the hard figures that we can be sure,” says lead researcher Dr Bridgette McNamara of the University of Melbourne, who was at the Baker Heart and Diabetes Institute when the research was done last year.

America to be hit by Europe’s big data law on Friday

Though the longer term effects of countries’ implementation of GDPR remain a question, though the law does inject a little order into an internet world that has been lacking in a bill of rights and includes provisions allowing people to ask to have their data deleted, “purpose limitation” saying that data can only be used for what it was collected for, and strict rules on getting consent to use data.

Go to Source

10 Charts That Will Change Your Perspective Of Big Data’s Growth

Sales and Marketing, Research & Development (R&D), Supply Chain Management (SCM) including distribution, Workplace Management and Operations are where advanced analytics including Big Data are making the greatest contributions to revenue growth today. McKinsey Analytics’ study Analytics Comes of Age, published in January 2018 (PDF, 100 pp., no opt-in) is a comprehensive overview of how analytics technologies and Big Data are enabling entirely new ecosystems, serving as a foundational technology for Artificial Intelligence (AI). McKinsey finds that analytics and Big Data are making the most valuable contributions in the Basic Materials and High Tech industries. The first chart in the following series of ten is from the McKinsey Analytics study, highlighting how analytics and Big Data are revolutionizing many of the foundational business processes of Sales and Marketing.

The following ten charts provide insights into Big Data’s growth:

  • Nearly 50% of respondents to a recent McKinsey Analytics survey say analytics and Big Data have fundamentally changed business practices in their sales and marketing functions. Also, more than 30% say the same about R&D across industries, with respondents in High Tech and Basic Materials & Energy report the greatest number of functions being transformed by analytics and Big Data. Source: Analytics Comes of Age, published in January 2018 (PDF, 100 pp., no opt-in).
  • Worldwide Big Data market revenues for software and services are projected to increase from $42B in 2018 to $103B in 2027, attaining a Compound Annual Growth Rate (CAGR) of 10.48%. As part of this forecast, Wikibon estimates the worldwide Big Data market is growing at an 11.4% CAGR between 2017 and 2027, growing from $35B to $103B. Source: Wikibon and reported by Statista.
  • According to NewVantage Venture Partners, Big Data is delivering the most value to enterprises by decreasing expenses (49.2%) and creating new avenues for innovation and disruption (44.3%). Discovering new opportunities to reduce costs by combining advanced analytics and Big Data delivers the most measurable results, further leading to this category being the most prevalent in the study. 69.4% have started using Big Data to create a data-driven culture, with 27.9% reporting results. Source: NewVantage Venture Partners, Big Data Executive Survey 2017(PDF, 16 pp.)

Read More Here

Article Credit: Forbes

Related Articles

Go to Source

Tips For Implementing an ERP Solution Successfully

As a business grows and the environment within which it operates becomes more complex with the demand for extensive analytics to stay ahead of the game and the need to share data and monitor several business process under one roof, there comes a need for an ERP (Enterprise Resource Planning) system to offer these solutions.

However, sourcing for a suitable ERP system can be downright confusing with so many vendors in the market, each promising their piece of heaven. The result is many manufacturing business spending money on systems that are not tailored to their specific requirements.

erp implementation

Here are tips for implementing a ERP solution that will actually work for you.

Define your needs

It’s practically impossible to get an ERP solution that is tailored for your business if you haven’t properly defined what your needs are.

A common approach among businesses is to simply automate the existing processes with an ERP system without having a review of their impact on service delivery. This doesn’t solve any of your business problems.

The better way towards realizing a successful ERP system implementation is to clearly define the problems you seek to solve and have a clear scope of the requirements you expect of your system. This way your vendor can then know how best to tailor your system to handle your specific needs as opposed to one that simply automates everything, whether they are effective towards realizing your business goals or not.

Think about the future

A customized system is important but you also have to think about the future when implementing an ERP system. Your system should be able to be seamlessly upgraded to accommodate growth in your business.

Your business might grow in a few years and you might want to introduce new products like Preparedness Mama Cream of Tartar in your manufacturing line but it will be impossible to accommodate these kind of new developments if you over-customized your system.

Choose a deployment model

You need to consider number of factors such as cost, support, reliability, etc., before you settle on a suitable deployment model.

There are three implementation models to choose from, namely on-premise ERP, cloud-based ERP and SaaS, each with its pros and cons.

On-premise and cloud-based solutions offer the best in terms of customization and support but those come at a high cost unlike SaaS which is much cheaper but doesn’t much in terms of flexibility options.


Even with the best planning, it’s difficult to predict the outcome of an ERP system implementation so it’s important to allow sufficient time for the change to be smooth. Do not skip through some of the implementation phases in a bid to save on cost or hasten the outcomes of the system.

More importantly, you need to properly manage the uptake of the new system across the whole spectrum of your business from the staff and the processes to the technology. You need to train all the staff in all the departments to be on board with new ERP system.

Go to Source

What animals is A.I. currently smarter than?

he world is teeming with intelligence, from little wormy grubs in the garden to physicists poring over equations in university offices. In the past few years we’ve also come to view our virtual assistants as possessing some kind of intelligence—imperfect and sometimes downright creepy, but intelligence nonetheless. A.I. has come a long way since Microsoft’s Clippy.

Whether we’re talking to Siri like a friend or asking our dogs for advice, humans love to imagine other animals’ intelligence. As we enter into the infancy of A.I., it’s fun to speculate how some existing lifeforms stack up to our best A.I so far. Scientifically, it’s hard to get a read on how they compare, but there are some interesting comparisons to be made.

Intelligence and consciousness is still a widely debated topic amongst scientists and philosophers alike. There is no exact consensus on what makes a human or animal, let alone an A.I. software program or robot, have intelligence. One recent idea of determining general intelligence comes from Robert Sternberg who put forth the Triarchic Theory of Intelligence. He argued that intelligence cannot be solely derived from IQ tests but instead can be broken down into analytic, creative, and practical.

Today we view animal cognition as something worthy of increased study. It’s possible that many different kinds of animals have a much richer inner lifethan we ever imagined. Understanding animal intelligence can help us change and evolve our views on creating A.I. systems, as research scientist Heather Roff writes for The Conversation, “Instead of thinking about A.I. as something superhuman or alien, it’s easier to analogize them to animals, intelligent nonhumans we have experience training.” Roff continues: “The analogy works at a deeper level too. I’m not expecting the sitting dog to understand complex concepts like “love” or “good.” I’m expecting him to learn a behavior. Just as we can get dogs to sit, stay and roll over, we can get A.I. systems to move cars around public roads. But it’s too much to expect the car to “solve” the ethical problems that can arise in driving emergencies.”

Will we ever have A.I. that understands feelings and ethics, and how far have we come on the road to creating intelligence?

How to benchmark artificial intelligence

Even the most advanced A.I. today is, in a way, just intelligence mimicry. While Google search can spit out billions of queries in mere seconds, it’s not fully encompassing sentient intelligence. It’s taking a human skill like browsing and putting it into overdrive. Watson might have had an excellent jeopardy performance, but it doesn’t mean that it really understood any of the questions it was asked in the way a human might.

Read More Here

Article Credit: BT

Go to Source