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Digital twin is based on the fact that a digital information construct of a physical system can be built on its own. This digital construct is nothing but a twin of the actual physical system(i.e) the replica of the actual physical system. Both the physical system and its twin is linked throughout the lifecycle.
Digital Twin (DT) - set of virtual information constructs that fully describes a the physical system from micro atomic to macro geometric level. This is because the two system are linked throughout the lifecycle. So even a small change in original environment gets reflected in the twin or virtual environment. This new technology is an innovative concept came by integrating machine learning, artificial intelligence and software analytics.The concept of the Digital Twin dates back to a University of Michigan presentation to industry in 2002 for the formation of a Product Lifecycle Management (PLM) center. It was originated by Dr. Grieves, was simply called "Conceptual Ideal for PLM."
The elements required for digital twin is that a real space, virtual space, link for information flow and a link for data flow. The actual system resides in real space and the twin will be in virtual space. Data flow will be from real space to virtual space and information flow is from virtual to real space.
Based on phases involved it is classified as:
Digital Twin Prototype (DTP) describes the prototype of the physical system. First we design the physical system in virtual space so that we can have a virtual view of it and all who are engaged in the construction of physical system can have a better idea of what to be built. We built a virtual version and then produce a physical version. Digital Twin Instance (DTI) describes a specific corresponding physical product that an individual Digital Twin remains linked to throughout the life of that physical product. Digital Twin Aggregate (DTA)is the aggregation of all the DTIsIt may be a computing construct that has access to all DTIs and queries them either ad-hoc or proactively.
Digital Twin Environment is an integrated, multi-domain physics application space for operating on Digital Twins for a variety of purposes like
It has various advantages like
It is used in various fields like
Hence I conclude that Digital twin has been placed into 10 Strategic Technological Trends for the year 2017
Google lens is an application announced by Google during Google I/O 2017, designed to bring up relevant information using visual analysis. It was initially released in October 4,2017. It can be used both in Android and iOS.
When direction the phone's camera at an object, Google Lens will attempt to identify the object and show relevant search results and information. For example, when pointing the device's camera at aWi-Fi label containing the network name and password, it will automatically connect to Wi-Fi source that has been scanned. Lens is also integrated with the Google Photos and Google Assistant apps.
The service is similar to Google Googles, a previous app that functioned similarly but with lesser capability. Lens uses more advanced deep learning routines, similar to other apps like Bixby Vision, artificial neural networks are used to detect and identify objects, landmarks and to improve optical character recognition accuracy.
Google official launched Google Lens with the previews pre-installed into the Google Pixels. In November 2017, the feature began rolling out into the Google Assistant for Pixel and Pixel 2 phones. A preview of Lens has also been implemented into Google Photos app for Pixel phones. On March 5 2018, Google officially released Google Lens to Google Photos on non-pixel phones. Support for Lens in the iOS version of Google Photos was made on March 15, 2018. Beginning in May 2018, Google Lens was made available within Google Assistant on OnePlus devices, as well as being integrated into camera apps of various Android phones. A standalone Google Lens app was made available on Google Play in June 2018. Device support is limited , although it is not clear which devices are not supported or why. It requires Android Marshmallow or newer.
The distributed ledger technology, better known as blockchain, has the potential to eliminate huge amounts of record-keeping, save money and disrupt IT in ways not seen since the internet arrived.
Blockchain is poised to change IT in much the same way open-source software did a quarter of a century ago. And in the same way that Linux took more than a decade to become a cornerstone in modern application development, Blockchain will take years to become a lower cost, more efficient way to share information between open and private networks.
Because blockchain is based on a distributed, peer-to-peer topology where data can be stored globally on thousands of servers - and anyone on the network can see everyone else's entries in real-time - it's virtually impossible for one entity to gain control of or game the network.
In simple terms, blockchain isn't going to replace the corporate relational database, but it will create a new paradigm for transactional data within (and outside of) global enterprises.
Saying (or writing) the right words in the right sequence to convey a clear message that can be easily understood by the listener (or reader) can be a tricky business. For a machine, which processes information in an entirely different way than the human brain does, it can be trickier still.Solving this issue has been the key focus of the burgeoning field of Natural Language Generation (NLG) for years beyond counting. Natural language generation, a field which has made great strides of late, has begun to manifest in many areas of our lives. It is currently being used in customer service to generate reports and market summaries.
Natural language generation (NLG) is the process of artificial intelligence interpreting data and presenting or displaying the data in a digestible, easily understood manner. These tools are used when processing large data sets, structured or unstructured, to create business actions based on the data. The data is presented as a written report or a dashboard of charts and graphs, among other presentation types. The generation is entirely done by artificial intelligence and is best aligned with business needs of the specific user. NLG solutions can work as a tool for data scientists or analysts and in some cases even supplement the need for such positions. They can also be used by less technical job positions when attempting to apply meaning to data. Often these projects work in conjunction with a business intelligence solution or a big data analytics platform, among other software.
To qualify for inclusion in the Natural Language Generation category, a product must:
This article lets us know that more complex form of datas can be converted and let to us in an understandable manner and the eligibility criteria and process of inclusion in natural language generation is clearly understood.