The global text analytics market size is estimated to grow from $3.97 billion in 2017 to $8.79 billion by 2022, at a Compound Annual Growth Rate (CAGR) of 17.2%. The customer experience management (CEM) is expected to hold the largest market share during the forecast period. Among the various applications in the text analytics market, the CEM application is expected to hold the largest market share during the forecast period. Text mining is the most traditional application in customer service and is frequently utilized to improve customer experience through various information sources. Today, text analytics is implemented to offer quick, computerized feedback to the clients, which significantly reduces dependency on executives for resolving issues.

Data preprocessing is a fundamental part of any machine learning project and often more time is spent on the data preparation than on the actual machine learning. While some preprocessing tasks are problem specific many others such as partitioning data into training and test folds, stratifying samples or building mini-batches are generic. The following Canonical Pipeline shows the processing steps common for deep-learning in vision. A Reader reads sample data stored in text files, Excel or Pandas tables. The Splitter then partitions data into training, validation and test folds and performs stratification if needed. Usually not all image data can be loaded into memory and a Loader loads images on demand. These images are often processed by a Transformer, for resizing, cropping or other adjustments. Furthermore, to increase the training set additional images are synthesized by randomly augmenting (flipping, rotating, …) images using an Augmenter. Efficient, GPU-based machine learning demands that image and label data are grouped in mini-batches via a Batcher before passed on to the Network for training or inference. Finally, to keep track of the training progress, usually a Logger is employed to write training losses or accuracies to a log file. Some machine learning frameworks such as Keras provide (some of) these preprocessing components hidden behind an API that considerably simplify network training if it fits the task at hand. See the following excerpt of a Keras example to train a model with augmentation. However, what if an image format, augmentation or other preprocessing capability is needed that is not provided by the API? Extending a library such as Keras or others is not trivial and the common approach is to (re)implement the required functionality – often in a quick-and-dirty fashion. But implementing a robust data pipeline that loads, transforms, augments and processes images on demand is challenging and time consuming. The following excerpt from a nuts-ml example shows a pipeline for network training, where the operator defines the flow of data. In the example above training images are augmented, pixel values re-ranged, and the samples shuffled before building batches for network training. Finally, the mean over the batch-wise training losses is computed and printed. The resulting code is more readable and can readily be modified to experiment with different preprocessing schemes. Task-specific functions can easily be implement as nuts and added to the data flow. Any machine learning library that accepts mini-batches of Numpy arrays for training or inference is compatible. For more information about nuts-ml see the Introduction and have a look at the Tutorial. Bio: Stefan Maetschke (PhD) is a research scientist at IBM Research Australia where he develops machine learning infrastructure and models for medical image analysis.

Data preprocessing is a fundamental part of any machine learning project and often more time is spent on the data preparation than on the actual machine learning. While some preprocessing tasks are problem specific many others such as partitioning data into training and test folds, stratifying samples or building mini-batches are generic. The following Canonical Pipeline shows the processing steps common for deep-learning in vision. A Reader reads sample data stored in text files, Excel or Pandas tables. The Splitter then partitions data into training, validation and test folds and performs stratification if needed. Usually not all image data can be loaded into memory and a Loader loads images on demand. These images are often processed by a Transformer, for resizing, cropping or other adjustments. Furthermore, to increase the training set additional images are synthesized by randomly augmenting (flipping, rotating, …) images using an Augmenter. Efficient, GPU-based machine learning demands that image and label data are grouped in mini-batches via a Batcher before passed on to the Network for training or inference. Finally, to keep track of the training progress, usually a Logger is employed to write training losses or accuracies to a log file. Some machine learning frameworks such as Keras provide (some of) these preprocessing components hidden behind an API that considerably simplify network training if it fits the task at hand. See the following excerpt of a Keras example to train a model with augmentation. However, what if an image format, augmentation or other preprocessing capability is needed that is not provided by the API? Extending a library such as Keras or others is not trivial and the common approach is to (re)implement the required functionality – often in a quick-and-dirty fashion. But implementing a robust data pipeline that loads, transforms, augments and processes images on demand is challenging and time consuming. The following excerpt from a nuts-ml example shows a pipeline for network training, where the operator defines the flow of data. In the example above training images are augmented, pixel values re-ranged, and the samples shuffled before building batches for network training. Finally, the mean over the batch-wise training losses is computed and printed. The resulting code is more readable and can readily be modified to experiment with different preprocessing schemes. Task-specific functions can easily be implement as nuts and added to the data flow. Any machine learning library that accepts mini-batches of Numpy arrays for training or inference is compatible. For more information about nuts-ml see the Introduction and have a look at the Tutorial. Bio: Stefan Maetschke (PhD) is a research scientist at IBM Research Australia where he develops machine learning infrastructure and models for medical image analysis.

The tech giant has announced that it will improve the security level of its email service, Gmail, by introducing a couple of new features. Again, Google is tapping from the ability of machine learning to secure Gmail services. Some of the measures the company took in such situations include rejecting such messages and informing the sender of the presence of virus in an email, and preventing users from sending messages with attachments that have been infected. In the case of infected attachments, Google prevented them from being downloaded.

SYS-CON Events announced today that EARP Integration will exhibit at SYS-CON's 20th International Cloud Expo, which will take place on June 6-8, 2017, at the Javits Center in New York City, NY. EARP Integration is a passionate software house. Since its inception in 2009 the company successfully delivers smart solutions for cities and factories that start their digital transformation. EARP provides bespoke solutions like, for example, advanced enterprise portals, business intelligence systems and mobile applications for international enterprises across different sectors such as Energy and Utilities, GreenTech, MedTech, FinTech, Facility Management and Housing, Automotive Manufacturing, and Sport. EARP also cooperates with international software houses by providing them with highly qualified and well-selected, multilingual teams for bigger projects.

SYS-CON Events announced today that Tappest will exhibit MooseFS at SYS-CON's 20th International Cloud Expo, which will take place on June 6-8, 2017, at the Javits Center in New York City, NY. MooseFS is a breakthrough concept in the storage industry. It allows you to secure stored data with either duplication or erasure coding using any server. The newest - 4.0 version of the software enables users to maintain the redundancy level with even 50% less hard drive space required. The software functions on all major operating systems and is used around the world by businesses, universities and NGOs.

AI-powered intelligent assistant Alexa is now able to create reminders, Amazon announced today. Now anyone with an Alexa-enabled device can say "Alexa, remind me to stay woke in 10 minutes" or "Alexa, remind me to tell my dad I love him on June 18." Amazon also announced today that Echo or Echo Dot users can create countdown timers with custom names. All scheduled reminders and timers can be viewed in the Alerts and Alarms section section of Alexa app. The ability to create reminders is a longtime requested feature from Alexa fans, like adding calendar events used to be for Google Assistant on Google Home users until Google finally added that feature in May.

Over half (54%) of the almost 200 insurance executives surveyed said that their organization was using Machine Learning for predictive analytical modelling. Of those deploying the technology, 70% said they were using it for risk modelling; followed by demand models (45%) and fraud detection (36%). Although nascent, most companies using Machine Learning have realized measurable benefits. Over half of the respondents (57%) said that Machine Learning has made their analytical models far more accurate, which has led to better risk assessments, and ultimately better decisions. The survey found that the main barrier to wider adoption is a lack of knowledge and expertise within organizations.

Kathy Griffin is set to explain the reasoning behind her controversial photo shoot with a bloodied mask of President Trump and respond to alleged bullying from the Trump family on Friday, her attorney announced. Griffin and attorney Lisa Bloom said in a joint news release they will hold a press conference in Woodland Hills, Calif. at 9 a.m. It will be the first comments Griffin has made since she was relieved of her duties as CNN's New Year's Eve host. Proud to announce that I represent Kathy Griffin. We will be holding a press conference tomorrow morning.

You may want to rethink wearing sweats for your next flight because it might be caught on camera. JetBlue is integrating facial recognition software with the boarding process to create a "self-boarding process." The idea is that customers who use this option won't have to keep out their paper boarding pass or their phones. They'll be able to navigate to their flights easily after checking in. The new feature will launch at Boston Logan Airport on flights headed to Aruba's Queen Beatrix International Airport beginning in June, a press release from the company said. Customers using this new feature will opt in when they check into their flight.