It turns out that data by itself is not protectable under the American intellectual property regime; however, data title rights are similar to the rights afforded by a copyright. Data title includes a bundle of usage rights that allow the titleholder to copy, distribute and create derivative works. Data within a database is like the words and images that compose a copyrighted book. The usage rights and title to the book are separable. The author of the novel retains title to the words and pictures that comprise the novel. The author also owns the ability to authorize a publisher to publish books and distribute them. However, he or she does not control each reader's usage rights of the content once they are accessed by readers. Similarly, an entity that holds title to data or a database holds the associated data ownership rights. If the data set is copied and transmitted elsewhere, the author relinquishes the usage rights.
Current solutions are not entirely secure as they will eventually be broken as hacking algorithms advance. For example, post-quantum cryptography organizes digital signatures in a unique way that makes it more complex to hack them. However, they are still vulnerable to the development of new algorithms and it is only a matter of time until someone creates a way to hack them. The quantum-safe blockchain developed by the Russian Quantum Center secures the blockchain by combining quantum key distribution (QKD) with post-quantum cryptography so that it is essentially un-hackable. The technology creates special blocks which are signed by quantum keys rather than the traditional digital signatures. These quantum keys are generated by a QKD network, which guarantees the privacy of the key using the laws of physics.
Infrastructure, Acosta said, "is what cities do. Cities need to start with their infrastructure to make sure they're ready to create alternative energy paths." The role of city officials, she said, is "making sure that their communities are prepared for this crazy scary new world we are entering. You have to create safe ways for them to be actually be able to engage. Not only by saving money, but we have to create a world where they are 'prosumers' not just consumers. If we can create a world where energy is created by an individual and sold on the market, which we're doing in California by creating the CCA's [Community Choice Aggregation], which are competitors to our incumbent utilities, we believe we can accelerate that world." Jain said there are three essential components to the infrastructure of a city that can survive throughout the centuries, and that is having the ability to provide emergency services, essential services and entertainment.
Banks' responses to fintech have not been uniform, however, in terms of how much investment they were willing to make and the level of integration they want between the new digital activities and their traditional operations. Some banks have adopted a "low integration" strategy, that is, an arms-length approach where they rely on contracting with fintech companies or investing in them. Others have taken a bolder "high integration" approach through partnership arrangements –- such as the small-business lending deal between JPMorgan Chase and OnDeck -- and integrating new technologies into their loan-application and decision-making processes. Less common among banks are those that choose to develop their own systems. This typically involves a more significant investment to automate underwriting processes, synchronize bank proprietary account data with new algorithms, and create a more customer-friendly design.
Predictive maintenance avoids both the extremes and maximizes the use of its resources. Predictive maintenance will detect the anomalies and failure patterns and provide early warnings. These warnings can enable efficient maintenance of those components. In this article we will explore how we can build a machine learning model to do predictive maintenance. The next section discusses machine learning techniques, while the following discusses a NASA data set that we will use as an example. Sections four and five discuss how to train the machine learning model. The Section “Running the Model with WSO2 CEP” covers how to use the model with real world data streams. To do predictive maintenance, first we add sensors to the system that will monitor and collect data about its operations. Data for predictive maintenance is time series data.
The most direct reason that Project Closeout phase is neglected is lack of resources, time and budget. Even though most of project-based organizations have a review process formally planned, most of the times “given the pressure of work, project team member found themselves being assigned to new projects as soon as a current project is completed” (Newell, 2004). Moreover, the senior management often considers the cost of project closeout unnecessary. Sowards (2005) implies this added cost as an effort “in planning, holding and documenting effective post project reviews”. He draws a parallel between reviews and investments because both require a start-up expenditure but they can also pay dividends in the future. Human nature avoids accountability for serious defects. Therefore, members of project teams and especially the project manager who has the overall responsibility, will unsurprisingly avoid such a critique of their work if they can.
Humans have no real definition of our own intelligence, in part because we didn’t need one. But one thing we’ve learned is that, even with the most powerful minds, one mind cannot do all mindful things perfectly well. A particular species of mind will be better in certain dimensions, but at a cost of lesser abilities in other dimensions. In the same way, the smartness that guides a self-driving truck will be a different species than the one that evaluates mortgages. The superbrain that predicts the weather accurately will be in a completely different kingdom of mind from the intelligence woven into your clothes. In my list I include only those kinds of minds that we might consider superior to us, and I’ve omitted the thousands of species of mild machine smartness, like the brains in a calculator, that will cognify the bulk of the Internet of Things.
For data engineers, the most important aspects of data storages are how they index, shard, and aggregate data. To compare these technologies, we’ll examine how they index, shard, and aggregate data. Each data indexing strategy improves certain queries while hindering others. Knowing which queries are used most often can influence which data store to adopt. Sharding, a methodology by which databases divide its data into chunks, determines how the infrastructure will grow as more data is ingested. Choosing one that matches our growth plan and budget is critical. Finally, these technologies each aggregate its data very differently. When we are dealing with gigabytes and terabytes of data, the wrong aggregation strategy can limit the types and performances of reports we can generate. As data engineers, we must consider all three aspects when evaluating different data storages.
Statistics, though a central set of tools for data science, are often overlooked in favor of more solidly technical skills like programming. Even machine learning learning algorithms, with their reliance on mathematical concepts such as algebra and calculus -- not to mention statistics! -- are often treated at a higher level than is required to appreciate the underlying math, leading, perhaps, to "data scientists" who lack a fundamental understanding of one of the key aspects of their profession. This post won't resolve the discrepancy between knowing and not knowing the absolute basics of statistics. However, if you are unable to fully understand the basic descriptive statistics terminology included herein, you are definitely lacking foundational knowledge that is needed to build a whole series of much more robust and useful professional concepts on top of.
Many of the surveyed executives are counting on blockchain to deliver competitive advantage -- along with developing a platform approach to innovation. As the study's authors put it: "Blockchains aren’t just new; they’re likely to radically change how organizations operate, generate revenues and respond to customers, partners and competitors alike. The new business models that result can evolve in unexpected ways." As anyone who has delved into such adventures knows, creating new platform business models is not for the faint of heart. In this survey, six in 10 executives admit they aren’t yet ready to build blockchain platforms that connect customers and partners across an ecosystem. The IBM authors suggest the new modes of disruptive thinking that can help realize the value of blockchain:
Quote for the day:
"Management is about arranging and telling. Leadership is about nurturing and enhancing." -- Tom Peters