WhatsApp payment feature in testing stage, Indian users may get it in December

According to the report, WhatsApp is readying the in-chat payment option and may officially roll out the feature sometime around December. Indian users are likely to get the feature at the same time, i.e December. "They are likely to do some sort of an extended beta program for the feature in November and by December you can expect a full rollout," notes a source aware of the plan.

Earlier reports claimed that WhatsApp is talking with financial institutions for its upcoming payment feature. However, the Facebook-owned messaging application hasn't revealed any details about this feature as of yet. So if at WhatsApp is working on this payment feature - there's no doubt that it is going to be a big threat for other players in the Indian digital payments ecosystem.

The new report reveals that WhatsApp payment feature is in its final stage and almost nearing announcement. WhatsApp employees are seemingly testing the feature. Reports are also such that WhatsAp may conduct some sort of extended beta program for the payment feature in November before the final roll out - which is to happen in December.

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Top 10 machine learning frameworks

When delving into the world of machine learning (ML), choosing one framework from many alternatives can be an intimidating task. You might already be familiar with the names, but it’s useful to evaluate the options during the decision-making process. There are different frameworks, libraries, applications, toolkits, and datasets in the machine learning world that can be very confusing, especially if you’re a beginner. Being accustomed to the popular ML frameworks is necessary when it comes to choosing one to build your application. This is why we compiled a list of the top 10 machine learning frameworks.
1. Tensorflow
Tensorflow was developed by the Google Brain Team for different language understanding and perceptual tasks. This open source framework is being used for extensive research on deep neural networks and machine learning. Being the second machine learning framework by Google Brain, it is compatible with most new CPUs and GPUs. Many of the popular Google services that we use on a daily basis such as Gmail, Speech recognition, Google Photos and even Google Search are equipped with Tensorflow.
Tensorflow uses data flow graphs to perform complicated numerical tasks. The mathematical computations are elaborated using a directed graph containing edges and nodes. These nodes are used to implement the operations and can also act as the endpoints where data is fed. The edges also represent the input/output associations between different nodes.
2. Caffe
Caffe is a machine learning framework that was designed with better expression, speed, and modularity as the focus points. It was developed for computer vision/image classification by leveraging Convolutional Neural Networks(CNNs). Caffe is popular for its Model Zoo, which is a set of pre-trained models that doesn’t require any coding to implement.
It is better suited for building applications as opposed to Tensorflow which fares better at research and development. If you are dealing with applications with text, sound or time series data, note that Caffe is not intended for anything other than computer-vision. However, it can dynamically run on a host of hardware and does a good job at switching between CPU and GPU using just a single flag.
3. Amazon Machine learning
Amazon has developed their own machine learning service for developers called AML. It is a collection of tools and wizards that can be used for developing sophisticated, high-end, and intelligent learning models without actually tinkering with the code. Using AML, predictions needed for your applications can be derived via APIs that are easier to use. The technology behind AML is used by Amazon’s internal data scientists to power their Amazon Cloud Services and is highly scalable, dynamic and flexible. AML can connect to the data stored in Amazon S3, RDS or Redshift and carry out operations such as binary classification, regression or multi-class categorization to create new models.
4. Apache Singa
Apache Singa is primarily focused on distributed deep learning using model partitioning and parallelizing the training process.  It provides a simple and robust programming model that can work across a cluster of nodes. The main applications are in image recognition and natural language processing (NLP).
Singa was developed with an intuitive layer abstraction based programming model and supports an array of deep learning models. Since it is based on a very flexible architecture, it can run both synchronous and asynchronous and even hybrid training methods. The tech stack of Singa comprises of three important components: IO, Model and Core. The IO component contains classes used for reading/writing data to the network and disk. The core component handles tensor operations and memory management functions. Model houses algorithms and data structures used for machine learning models.
5. Microsoft CNTK
CNTK (Cognitive Toolkit) is Microsoft’s open-source machine-learning framework. Although it is more popular in the speech recognition arena, CNTK can also be used for text and image training. Having support for a wide variety of machine learning algorithms such AS CNN, LSTM, RNN, Sequence-to-Sequence and Feed Forward, it is one of the most dynamic machine learning frameworks out there. CNTK supports multiple hardware types, including various CPUs and GPUs.
Compatibility is one of the highlights of CNTK. It is also praised as the most expressive and easy to use machine learning architecture out there. On CNTK, you can work with languages like C++ and python and either use the built-in training models or build your own.
6. Torch
Torch could arguably be the simplest machine learning framework to set up and get going fast and easily, especially if you are using Ubuntu. Developed in 2002 at NYU, Torch is extensively used in big tech companies like Twitter and Facebook. Torch is coded in a language called Lua, which is uncommon but easy to read and understand. Some of the perks of Torch can be attributed to this friendly programming language with useful error messages, a huge repository of sample code, guides, and a helpful community.
7. Accord.NET
Accord.NET is an open source machine learning framework based on .NET and is ideal for scientific computing. It consists of different libraries that can be used for applications like pattern recognition, artificial neural networks, statistical data processing, linear algebra, image processing etc. The framework comprises of libraries that are available as installers, NuGet packages and source code. Accord.NET has a matrix library which facilitates code reusability and gradual algorithmic changes.
8. Apache Mahout
Being a free and open source project by the Apache Software Foundation, Apache Mahout was built with the goal of developing free distributed or scalable ML frameworks for applications like clustering, classification, and collaborative filtering. Java collections for different computational operations and Java libraries are also available in Mahout.
Apache Mahout is deployed on top of Hadoop using the MapReduce paradigm. One great application is to instantly turn data into insights. Once the stored Big Data on Hadoop is connected, Mahout can help the data science tools in finding meaningful patterns from the datasets.
9. Theano
Theano was developed in 2007 at the University of Montreal which is world renown for machine learning algorithms. Although regarded as a low-end machine learning framework, it is flexible and blazing fast. The error messages thrown by the framework are infamous for being unhelpful and cryptic. Leaving these aside, Theano is a platform more suited for research tasks and can be extremely helpful at that.
It is mostly used as a base platform for high-end abstraction systems which would send  API wrappers to Theano. Examples of some popular libraries are Lasagne, Blocks and Keras. One drawback of using Theano is that you will have to tinker with some workaround to have multi-GPU support.
10. Brainstorm
Brainstorm is one of the easiest machine learning frameworks to master considering its simplicity and flexibility. It makes working with neural networks faster and fun at the same time. Being written entirely in Python, Brainstorm was built to run smoothly on multiple backend systems.
Brainstorm provides two ‘handers’ or data APIs using Python- one for CPUs by Numpy library and the other one to leverage GPUs using CUDA. Most of the heavy lifting is done by Python scripting which means a rich front-end UI is almost absent.
Source: promptcloud.com

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5 ways to use artificial intelligence (AI) in human resources

Human Resources (HR) have been experiencing significant changes thanks to the evolution of information technologies in the last two decades. Today, Artificial Intelligence (AI) is reshaping the way that companies manage their workforce and make HR plans, which increases productivity and employee engagement in general.
Bearing in mind that employee engagement programs boost company revenue by 26%, it is clear that you should accept AI solutions to strengthen your team and gain some long-term benefits. In this article, we will present you 5 ways to use AI in human resources management.
How to Improve Human Resources Using AI
AI has the power to take your HR experience to the higher level. Just like Donald Southern, an HR specialist at Resumes Planet, explained: “AI can help you to handle recruiting, productivity, and retention more efficiently than traditional HR methods. At the same time, it also allows you to do it faster than ever before.” Let’s see here exactly how AI is doing that.

• Talent Acquisition

Using AI, you can remove tons of stressful and monotonous work from your HR managers. Namely, talent acquisition software can scan, read, and evaluate applicants and quickly eliminate 75% of them from the recruiting process.
This is a huge benefit as it allows the recruiter to spend more time analyzing and evaluating only a smaller group of eligible candidates. In such circumstances, HR units are drastically increasing the quality of hiring decisions. Additionally, companies save a lot of money this way because they don’t have to pay the cost of poor hiring decisions.

• Onboarding

Hiring the most promising talents is not the only concern of HR departments. Adaptation is the second step in the process as many prospects can’t fit in the new environment due to lack of onboarding procedures. Namely, new employees demand a lot of attention and it is often impossible to dedicate enough time to each one of them.
That’s where AI steps in – it determines customized onboarding procedures for every single position. This proved to be extremely productive in practice since new workers who went through well-planned onboarding programs had much higher retention rates than their peers who didn’t have the same opportunity.

• Training

With so many technological changes happening almost on a monthly basis, it is crucial for all employees to keep learning and improving professional skills. AI can successfully plan, organize, and coordinate training programs for all staff members.
Online courses and digital classrooms are the most common solutions in that regard. But this is not the only job of AI because it also determines the best timeframe for new courses and schedules lessons so as to fit the preferences of all employees individually.

• Performance analysis

Engagement and productivity are essential qualities of successful professionals. However, most companies are struggling to find individuals who have those traits. That’s why it is easier to monitor their behavior and analyze key performance indicators.
Using AI tools, HR managers are enabled to set concrete objectives and let all units work in smaller increments. This type of work is easier to follow and assess and it generates better overall results. Of course, it doesn’t only serve to improve productivity but also to detect team members who show lack of engagement continuously.

• Retention

As much as it is difficult to hire talented employees, it is as difficult to keep them in your team. This is why almost 60% of organizations consider employee retention their biggest problem. However, AI has the ability to analyze and predict the needs of staff members.
It can determine individual affinities and reveal who should get a raise or who might be dissatisfied with the life-work balance. Such analysis gives room to HR professionals to be proactive and solve the problem even before it actually occurs.
Conclusion
AI is everywhere these days – from simple calculators to flight controls and space operations. It also enables HR executives to improve results and monitor employees more efficiently. In this article, we explained you 5 ways to use AI in human resources. Make sure to use these suggestions in everyday business and share your thoughts about it in comments.

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How An IoT System Actually Works

Devices and objects with built in sensors are connected to an Internet of Things platform, which integrates data from the different devices and applies analytics to share the most valuable information with applications built to address specific needs.

These powerful IoT platforms can pinpoint exactly what information is useful and what can safely be ignored. This information can be used to detect patterns, make recommendations, and detect possible problems before they occur.

For example, if I own a car manufacturing business, I might want to know which optional components (leather seats or alloy wheels, for example) are the most popular. Using Internet of Things technology, I can:

• Use sensors to detect which areas in a showroom are the most popular, and where customers linger longest;
• Drill down into the available sales data to identify which components are selling fastest;
• Automatically align sales data with supply, so that popular items don’t go out of stock.

The information picked up by connected devices enables me to make smart decisions about which components to stock up on, based on real-time information, which helps me save time and money.

With the insight provided by advanced analytics comes the power to make processes more efficient. Smart objects and systems mean you can automate certain tasks, particularly when these are repetitive, mundane, time-consuming or even dangerous. Let’s look at some examples to see what this looks like in real life.

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Internet of Things (IoT)

The Internet of Things (IoT) is a system of interrelated computing devices, mechanical and digital machines, objects, animals or people that are provided with unique identifiers and the ability to transfer data over a network without requiring human-to-human or human-to-computer interaction.

 The IoT allows objects to be sensed or controlled remotely across existing network infrastructure, creating opportunities for more direct integration of the physical world into computer-based systems, and resulting in improved efficiency, accuracy and economic benefit in addition to reduced human intervention

"Things", in the IoT sense, can refer to a wide variety of devices such as heart monitoring implants, biochip, transponders on farm animals, cameras streaming live feeds of wild animals in coastal waters etc.

The Internet of Things refers to the ever-growing network of physical objects that feature an IP address for internet connectivity, and the communication that occurs between these objects and other Internet-enabled devices and systems.

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Machine Learning

Machine learning is a branch of science that deals with programming the systems in such a way that they automatically learn and improve with experience. Here, learning means recognizing and understanding the input data and making wise decisions based on the supplied data.

It is very difficult to cater to all the decisions based on all possible inputs. To tackle this problem, algorithms are developed. These algorithms build knowledge from specific data and past experience with the principles of statistics, probability theory, logic, combinatorial optimization, search, reinforcement learning, and control theory.
 
The developed algorithms form the basis of various applications such as:

• Vision processing
• Language processing
• Forecasting (e.g., stock market trends)
• Pattern recognition
• Games
• Data mining
• Expert systems
• Robotics

 Machine learning is a vast area and it is quite beyond the scope of this tutorial to cover all its features. There are several ways to implement machine learning techniques, however the most commonly used ones are supervised and unsupervised learning.

• Supervised learning: The computer is presented with example inputs and their desired outputs, given by a "teacher", and the goal is to learn a general rule that maps inputs to outputs. 
• Unsupervised learning: No labels are given to the learning algorithm, leaving it on its own to find structure in its input. Unsupervised learning can be a goal in itself (discovering hidden patterns in data) or a means towards an end

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