Data Virtualization Market Receives a Rapid Boost in Economy due to High Emerging Demands by Regional Forecast 2019 – 2023


Data virtualization finds crucial application in the latest data architecture. It facilitates the formation of a single data layer by sourcing and procuring data from different sources. It is a new technology and is expected to gain quick mileage over the next couple of years. Market Research Future (MRFR)'s study states that the global data virtualization market is poised to expand at 15% CAGR over the forecast period 2017 to 2023. It is also highlighted in the study that the market is likely to earn revenues worth USD 278 Bn by the end of the evaluation period.

Data virtualization can be acknowledged as an advanced and advanced user-friendly version of data federation, which is enabling various functions to perform, such as data extraction, data transform, and data load in a very efficient way. These have been termed as the key factors that are influencing the market to expand at a rapid pace.

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6 Predictions About Data In 2020 And The Coming Decade

It’s difficult to make predictions, especially about the future. But one fairly safe prediction is that data will continue eating the world in 2020 and the coming decade. At the beginning of the last decade, IDC estimated that 1.2 zettabytes (1.2 trillion gigabytes) of new data were created in 2010, up from 0.8 zettabytes the year before. The amount of the newly created data in 2020 was predicted to grow 44x to reach 35 zettabytes (35 trillion gigabytes). Two years ago, we were already at 33 zettabytes, leading IDC to predict that in 2025, 175 zettabytes (175 trillion gigabytes) of new data will be created around the world.

The most important new tech development of the passing decade has been the practical success of deep learning (popularly known as “artificial intelligence” or “AI”), the sophisticated statistical analysis of lots and lots of data or what I have called Statistics on Steroids (SOS). In the coming decade, data will continue to beget data, to break boundaries, to drive innovation and profits, and create new challenges and concerns.
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Deep learning vs. machine learning: Understand the differences


Machine learning and deep learning are both forms of artificial intelligence. You can also say, correctly, that deep learning is a specific kind of machine learning. Both machine learning and deep learning start with training and test data and a model and go through an optimization process to find the weights that make the model best fit the data. Both can handle numeric (regression) and non-numeric (classification) problems, although there are several application areas, such as object recognition and language translation, where deep learning models tend to produce better fits than machine learning models. 

Machine learning explained: Machine learning algorithms are often divided into supervised (the training data are tagged with the answers) and unsupervised (any labels that may exist are not shown to the training algorithm). Supervised machine learning problems are further divided into classification (predicting non-numeric answers, such as the probability of a missed mortgage payment) and regression (predicting numeric answers, such as the number of widgets that will sell next month in your Manhattan store).

Unsupervised learning is further divided into clustering (finding groups of similar objects, such as running shoes, walking shoes, and dress shoes), association (finding common sequences of objects, such as coffee and cream), and dimensionality reduction (projection, feature selection, and feature extraction).

Deep Learning explained: Deep learning is a form of machine learning in which the model being trained has more than one hidden layer between the input and the output. In most discussions, deep learning means using deep neural networks. There are, however, a few algorithms that implement deep learning using other kinds of hidden layers besides neural networks.

The ideas for “artificial” neural networks go back to the 1940s. The essential concept is that a network of artificial neurons built out of interconnected threshold switches can learn to recognize patterns in the same way that an animal brain and nervous system (including the retina) does.

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Challenges to the Reproducibility of Machine Learning Models in Health Care

Reproducibility has been an important and intensely debated topic in science and medicine for the past few decades. As the scientific enterprise has grown in scope and complexity, concerns regarding how well new findings can be reproduced and validated across different scientific teams and study populations have emerged. In some instances, the failure to replicate numerous previous studies has added to the growing concern that science and biomedicine may be in the midst of a “reproducibility crisis.” Against this backdrop, high-capacity machine learning models are beginning to demonstrate early successes in clinical applications, and some have received approval from the US Food and Drug Administration. This new class of clinical prediction tools presents unique challenges and obstacles to reproducibility, which must be carefully considered to ensure that these techniques are valid and deployed safely and effectively.
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The Lingolet ONE Voice Translator for Translation and Interpreters

Medical professionals, lawyers, and others need certified interpreters to ensure correct translation. But on-call interpreters are $200-400 per hour, often with two- or three-hour minimum.

The Lingolet ONE language translator device connects them to more than 2000 registered, certified live human interpreters in 185 languages and +30 professional domains (medical, legal, etc.) worldwide 24/7.

Lingolet ONE also includes an AI real time translator which can be used by international conference attendees, law enforcement, and global travelers to communicate in any language any time.

Lingolet ONE offers:

  • Cloud-based AI voice translator in twelve languages translates with 98% accuracy.
  • Transcription with voice-to-text recording, transcribe and translate to twelve languages, export, and cloud backup
  • Interpretation-as-a-Service (IaaS) connects to +2000 registered live human interpreters in 185 languages and +30 domains for professional, dedicated service

“The combination of mobile devices, machine learning, and artificial intelligence creates challenges and opportunities for better real-time interpretation and translation,” said Jerry Song, founder and CEO of Lingolet. “Going beyond machine translation and AI, Lingolet built a platform for users to access the global resource of thousands of professional interpreters for live interpretation without constrains of time, location, or cost. The Lingolet ecosystem will level up the language services,” he added.

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