A consensus algorithm, like bitcoin's proof of work, does two things: it ensures that the next block in a blockchain is the one and only version of the truth, and it keeps powerful adversaries from derailing the system and successfully forking the chain. In proof of work, miners compete to add the next block (a set of transactions) in the chain by racing to solve a extremely difficult cryptographic puzzle. The first to solve the puzzle, wins the lottery. As a reward for his or her efforts, the miner receives 12.5 newly minted bitcoins – and a small transaction fee. Yet, although a masterpiece in its own right, bitcoin's proof of work isn't quite perfect. Common criticisms include that it requires enormous amounts of computational energy, that it does not scale well and that the majority of mining is centralized in areas of the world where electricity is cheap.
The Open Vulnerability Assessment System (OpenVAS) is a set of tools for vulnerability scanning and management. OpenVAS can scan systems for thousands of known vulnerabilities. It's incredibly powerful and should be considered a must have for anyone who serious about their network and system security. I'll walk you through the process of installing this powerhouse security admin tool on Ubuntu 16.04. The process is a bit time consuming, but what you gain in the end is worth every second. OpenVAS is an outstanding way to test machines you own/service/administer for vulnerabilities. Do not use this tool on systems outside of your purview.
The value of DLT to industry players is multifaceted. The top value driver noted by the report is operational simplification, whereby DLT reduces or eliminates manual efforts required to perform reconciliation and resolve disputes. The second key driver stems from improved regulatory efficiency, as DLT allows regulators real-time monitoring access to financial activity between regulatory entities across borders. The technology also allows for counterparty risk reduction propositions, reduce the clearing and settlement time, reduce locked-in capital requirements and boost liquidity as well as minimise fraud, by creating a full transparent and a practically immutable transaction history.
It's not just pulling in the information, either. The car also gives you access to all of those connected devices from the interior of the Instinct Concept. We're talking about temperature information from your Nest, what you like to watch from your smart TV or details from your virtual assistant on a gadget like Amazon Echo. Speaking of Amazon, a number of other automakers have already enlisted Alexa to power AI inside their vehicles, Peugeot decided instead to go with Samsung's cloud platform to collect all of the info and data science company Sentience analyzes the details for what's relevant to the system. The car has it's own AI that passengers can interact with via spoken cues. The Instinct Concept also features four modes that tailor the ride to you. There are two driving modes -- Drive Boost and Drive Relax -- for performance or more every day driving scenarios.
So where is smart money going? We crunched the data to identify where smart money VCs are investing in early-stage companies in recent years and how that investment focus has shifted. Using CB Insights’ natural language processing, we identified the most common words used in company descriptions among those companies that received early-stage investment from a smart money VC between 2010-2016. We then looked at which words have trended up and down among this cohort over the years. In addition, we identified the categories and industries that are seeing the most smart money early-stage investment. Through this lens, we can see where top investors see the most potential.
Whilst cloud removes old legacy systems, blockchain removes the middleman within such systems. Why then would you want to deploy your shiny new blockchain project on an old restrictive, expensive and possibly less than safe on-premise system? Cloud also opens up the bank to immense scale as we are now seeing with Black Friday, Cyber Monday and Singles Day where on one day $17 billion in sales occurred. Imagine the supply chain finance activities needed to support that single day’s activities. The traditional legacy system was to build more capacity by buying more computers, more software and hiring more IT people. The cloud provides cyber security and pay as you go so you can scale in safety. A second generation of banking is coming. We’re already on the cusp of it, and banks are running out of time before they become completely marginalised.
The trouble is that right now it's almost impossible to tell for regular users if a robot has been hacked or not, so it's a good target for APT attacks. So just how 'real world' is the robot hacking threat according to other security industry experts? Mike Pittenger, vice president of security strategy at Black Duck Software, is in no doubt that we will have already seen the consequences. "Drones (unmanned aerial vehicles) are a form of robot," he explains, "and an attractive target for our adversaries. Taking control of a drone would certainly disrupt a military mission, and could possibly turn a military's weapons on itself." ... Deral Heiland, research lead at Rapid7, agrees that the problem is both real and current. "On the personal level, the boom in IoT technology that we are now seeing has led to robots in various forms becoming part of our daily life," Heiland says.
Artificial Intelligence is the hottest buzzword in computing and business at the moment, and Machine Learning is the cutting edge. If you’re looking to expand your horizons as an IT professional or harness technology to move your business forward, an understanding of how it works will be a huge advantage in the next few years. I’ve written a basic introduction to the terms AI and ML here, and this article is for those who want to look into the subject a little bit more deeply. There are already a large number of well-supported frameworks available which allow anyone to jump in at the deep-end and by process of trial and error, learn how to use machine learning to solve real-world problems. These platforms highlighted below vary in complexity and beginner-friendliness. Some of them are fully fledged “as a service” cloud offerings from big players, while some are extensions of existing toolkits like Spark and Python.
CIOs have always been told to get closer to the business -- but now their very survival may depend upon it. New executive titles such as "Chief Data Officer" are proliferating, and Gartner says there are two different types of CIOs emerging: the "Chief Innovation Officers" who spearhead the technology-led business models of the future -- and the "Chief Infrastructure Officers" who are relegated to looking after the IT plumbing. IDC's research shows that digital business has thus far relied on a culture of experimentation and innovation driven primarily by the business and shadow IT -- and this is set to continue. For example, Gartner says that in 2017 -- for the first time ever -- the average Chief Marketing Officer will spend more on technology than the average CIO. These funds are being used to create "islands of innovation" outside the realm of core IT
Quote for the day:
"Don’t look for your dreams to become true; look to become true to your dreams." -- Michael Bernard Beckwith