Big data and Artificial Intelligence are two inextricably linked technologies, to the point that we can talk about Big Data Intelligence. Artificial Intelligence has become ubiquitous in companies in all industries where decision making is transformed by intelligent machines. The need for smarter decisions and Big Data management are the criteria that drive this trend. The convergence between Big Data and AI seems inevitable as the automation of smart decision-making becomes the next evolution of Big Data. Rising agility, smarter business processes and higher productivity are the most likely benefits of this convergence. The evolution of data management did not go smoothly. Much of the data is now stored on a computer, but there is still a lot of information on paper, despite the possibility of scanning paper information and storing it on disks or in databases. You just have to go to a hospital, an administration, a doctor’s office or any business to realize that a lot of information about customers, vendors, or products is still stored on paper. However, it is impossible to store terabytes of data produced by streaming video, text and images on paper.
Effective narratives are defined by two characteristics. Firstly, they articulate the "why" - a higher purpose or common goal that helps actors overcome vested interests and form a shared identity. The first line in Satoshi Nakamoto’s eminent white paper that launched Bitcoin describes how "a purely peer-to-peer version of electronic cash would allow online payments to be sent directly from one party to another without going through a financial institution". Secondly, effective narratives establish cause-effect relationships that form the basis for working towards this goal. Chinese electric vehicle manufacturers and Tesla are rivals in retail markets, but also partners in propagating the idea that electric passenger vehicles are the best means for lowering carbon emissions. Narratives interact with the real world in that actors combine normative beliefs (the "why") and positive beliefs (the "how") into decisions which result in perceived outcomes that potentially trigger a change of the narrative itself. As such, narratives are categorically different from stories. Stories are self-contained, whereas narratives are open-ended.
The goal is to provide a simple platform to Microsoft researchers and collaborators to share datasets and related research technologies and tools. Microsoft Research Open Data is designed to simplify access to these datasets, facilitate collaboration between researchers using cloud-based resources and enable reproducibility of research. We will continue to shape and grow this repository and add features based on feedback from the community. We recognize that there are dozens of data repositories already in use by researchers and expect that the capabilities of this repository will augment existing efforts. ... Datasets in Microsoft Research Open Data are categorized by their primary research area, as shown in Figure 4. You can find links to research projects or publications with the dataset. You can browse available datasets and download them or copy them directly to an Azure subscription through an automated workflow. To the extent possible, the repository meets the highest standards for data sharing to ensure that datasets are findable, accessible, interoperable and reusable; the entire corpus does not contain personally identifiable information. The site will continue to evolve as we get feedback from users.
Virtual/Augmented reality (VR and AR) — using technology to see something that literally is not there — is coming to a manufacturing facility near you. It's actually already there, but according to PwC, more than one in three manufacturers will implement virtual or augmented reality in manufacturing processes in 2018. Perhaps it will be something relatively simple, like what logistics giant DHL recently accomplished by introducing "Vision Picking," pilot programs of workers wearing smart glasses with visual displays of order picking instructions along with information on where items are located and where they need to be placed on a cart. The smartglasses freed pickers' hands of paper instructions and allowed them to work more efficiently and comfortably. ... "Digitalization is not just a vision or program for us at DHL Supply Chain, it's a reality for us and our customers, and is adding value to our operations on the ground," says Markus Voss, Chief Information Officer & Chief Operating Officer, DHL Supply Chain.
Where ‘big data’ appears to be the skeleton key that will unlock everything and all you want to know about your business, there’s more than meets the eye when it comes to understanding your data. Yes, clean data will unlock incredible value for your enterprise; inaccurate records, on the other hand, are a significant burden on our productivity. This is why we all seek the “Golden Record”. The Golden Record is the ultimate prize in the data world. A fundamental concept within Master Data Management (MDM) defined as the single source of truth; one data point that captures all the necessary information we need to know about a member, a resource, or an item in our catalogue – assumed to be 100% accurate. Its power is undeniable. ... The complexity of implementing a Master Data Management solution stems from defining the workflow that will connect our disparate data sets. First, we have to identify every data source that feeds into the dataset. Then, we must consider which fields we find to be the most reliable depending on their source. Finally, we must define the criteria that will determine when the data from one source should overwrite conflicting data from a secondary source in our MDM system.
Some IoT experts, taking a practical view, think the only requirements at the end-points should be to deliver secure identity and no other complexity. Amir Haleem, CEO of Helium, which is building a decentralized network of wide-range wireless protocol gateways and a token to connect edge IoT devices, said adding complexity to end devices"is like a gigantic hurdle to people actually building things." Apart from anything else, there's the cost. "People get very sensitive about the bill of materials (BoM) when you start talking at a scale of millions or tens of millions," said Haleem. "You start proposing like a 60 cent addition to a BoM and all of a sudden that's a meaningful number." Haleem said it makes no sense for end devices, like sensors that track and monitor medicine or food supply chains, to actively participate in a blockchain because these have to be power-efficient and cheap in an IoT setting. But delivering strong identity in the form of hardware-secured keys is essential, particularly in the face of recurring widespread vulnerabilities, botnets etc.
“Generally, the idea that needs to be adopted by the industry is an ethical design right from the very start. So, it’s no longer useful just to have ethical approval of a system once it’s done and deployed – it has to be considered from the beginning and it has to be continuously considered.” It’s clear that the problem with intelligent machines is people. Without careful checks and balances, we could find ourselves using data that is inherently biased to feed machines which would themselves become biased. And without serious consideration and action, we might also find ourselves at the whim of corporations and governments. Francois Chollet, an artificial intelligence researcher in Google wrote in a recent blog post that AI poses a threat given the possibility of ‘highly effective, highly scalable manipulation of human behaviour.’ He also stated that continued digitization gives social media companies an ever-increasing insight into our minds, and ‘casts human behaviour as an optimization problem, as an AI problem: it becomes possible for [them] to iteratively tune their control vectors in order to achieve specific behaviours.’
When disruptive technologies are addressed in education, they are usually considered in isolation. I increasingly come across discussions about “artificial intelligence,” “blockchain,” or “robots.” But the world is revolving more and more around these technologies working together. Disruptive technologies are accelerating each other’s development, creating new societal, economic, legal and commercial realities. For instance, disruptive digital technologies (operating together) are transforming the way business works. Instead of hierarchical and asset-heavy companies, we see flatter organizations/platforms with fewer assets and employees. Coordination of the assets and workers isn’t done by traditional managers, but digital technologies, sensors, and data analytics. Some even predict the end of the firm. ... Disruptors create growth by redefining performance that either brings a simple, cheap solution to the low end of a traditional market or enables “non-consumers” to solve pressing problems in their everyday lives. Employing “old world” ideas seems unlikely to work when pursuing the new.
The machine learning paradigm is continuously evolving. The key is to shift towards developing machine learning models that run on mobile in order to make applications smarter and far more intelligent. Deep learning is what makes solving complex problems possible. As put in this article, Deep Learning is basically Machine Learning on steroids. There are multiple layers to process features, and generally, each layer extracts some piece of valuable information. Given that deep learning is the key to executing tasks of a higher level of sophistication – building and deploying them successfully proves to be quite the Herculean challenge for data scientists and data engineers across the globe. Today, we have a myriad of frameworks at our disposal that allows us to develop tools that can offer a better level of abstraction along with the simplification of difficult programming challenges. Each framework is built in a different manner for different purposes. Here, we look at the 8 deep learning frameworks to give you a better idea of which framework will be the perfect fit or come handy in solving your business challenges.
Trading is about transferring funds from one financial instrument to another and back again, in such a way as to have more of the first financial instrument when you've finished. In this case, the two financial instruments we will use are the pound sterling and the dollar. I'm going to start with a simulated £10,000 and trade in and out of the dollar. ... This data contains a date and the open, close, high and low exchange rates for each day. We're going to simulate trading at the close. In the file, this value is called "price". When you trade anything through an exchange, or use a high street foreign exchange, there are two sources of cost. There's normally a transaction fee and there's a "spread" which is an offset to the central rate. These are the sources of guaranteed profit to the brokers. Our simulation will have values for both of these. Simulating trading will require us to respond to a trading signal and to buy whichever currency we are told, to calculate and subtract charges, and to keep track of the value of our holding. In trading parlance, in currency trading, you can be "long" or "short" a particular currency. If we are holding sterling, we are long sterling and short the dollar, if we are holding dollars we are short sterling and long dollar.
Quote for the day:
"Leaders think and talk about the solutions. Followers think and talk about the problems." -- Brian Tracy