Artificial intelligence is reshaping organizations and redefining the work culture. With Artificial intelligence (AI) emerged two more terms Generative AI and responsible AI. These two terms are closely linked to Artificial intelligence and address different aspects of AI. AI based solutions are deployed in high stake domains such as healthcare, hiring, criminal justice, education etc. which makes it more challenging to address issues related to undue discrimination against minority groups, biases, data manipulation etc. 

In today’s topic we will learn about Responsible AI and Generative AI, key principles of both, key features of both, and key differences. 

What is Responsible AI

Responsible AI refers to ethical and responsible development and use of artificial intelligent systems which emphasize on ensuring use of AI technologies in a way that it aligns to human values, privacy respect, promoting fairness, non-biases and avoidance of negative consequences. 

Ethical considerations are essential while dealing with AI and businesses can promote responsible AI usage with: 

• Establish data governance to ensure data accuracy, preventing bias, and protection of sensitive information 
• Algorithm transparency to foster trust among stakeholders
• Identifying and mitigating ethical risks associated in AI usage such as discrimination and bias
• Human expertise to monitor and validate AI output, alignment to business objectives and meeting regulatory requirements

What is Generative AI

Generative AI systems create any type of new content basis of patterns and existing content. Generative AI can reveal valuable insight but businesses need to be vigilant about bias and misleading outcomes. Generative AI is a subset of AI technologies which are capable of generating new data instances such as text, images, music etc. having resemblance to training data. These technologies leverage patterns learned from larger data sets and create content which is indistinguishable from what is produced by humans. 

Key Technologies in Generative AI

• Generative Adversarial Networks (GANs) involve two neutral networks having the generator and discriminator which compete against each other for generation of new, synthetic data instances which are indistinguishable from what is produced by humans. 
• Variational Autoencoders (VAEs) are meant to compress data into a latent space and reconstruct to allow generation of new data instances by sampling 
• Transformers are meant for natural language processing, and can also be used for generative tasks such as creation of coherent and contextually relevant text or content.

Uses of Generative AI

• Generative AI is used in content creation such as art, music and text 
• Data augmentation and machine models training 
• Modelling and simulation in scientific research 

Comparison: Responsible AI vs Generative AI

Download the comparison table: Responsible AI vs Generative AI

Nowadays you often hear buzz words such as artificial intelligence, machine learning and deep learning all related to the assumption that one day machines will think and act like humans. Many people think these words are interchangeable but that does not hold true. One of the popular google search requests goes as follows “are artificial intelligence and machine learning the same thing?”

What is Deep Learning

Deep learning is a subset of machine learning which makes use of neural networks to analyse various factors. Deep learning algorithms use complex multi-layered neural networks where the abstraction level gradually increases by non-linear transformations of data input. To train such neural networks a vast number of parameters have to be considered to ensure the end solution is accurate. Some examples of Deep learning systems are speech recognition systems such as Google Assistant and Amazon Alexa. 

What is Machine Learning (ML)

ML is a subset of artificial intelligence (AI) that focuses on making computers learn without the need to be programmed for certain tasks. To educate machines three components are required – datasets, features, and algorithms.

• Datasets are used to train machines on a special collection of samples. The samples include numbers, images, text, or any other form of data. Creating a good dataset is critical and takes a lot of time and effort. 
• Features are important pieces of data that work as the key to the solution of the specific task. They determine when machines need to pay attention and on what. During the learning process the program learns to get the right solution during supervised learning. In the case of an unsupervised learning machine it will learn to notice patterns by itself.
• Algorithm is a mathematical model mapping method to learn the patterns in datasets. It could be as simple as a decision tree, linear regression. 

Artificial Intelligence (AI)

AI is like a discipline such as Maths or Biology. It is the study of ways to build intelligent programs and machines which can solve problems , think like humans, and make decisions on their own. Artificial intelligence is expected to be a $3 billion industry by year 2024. When artificial intelligence and human capabilities are combined, they provide reasoning capability which is always thought as human prerogative.  The AI term was coined in 1956 at a computer science conference in Dartmouth. AI was described as an attempt to model how the human brain works and based on this know-how creating more advanced computers. 

Comparison: Deep Learning vs Machine Learning vs AI

Download the comparison table: Deep Learning vs Machine Learning vs AI

Today we look more in detail about two important terms, widely used data science and artificial intelligence and understand the difference between them, the purpose for which they are deployed and how they work etc.

What is Data Science?

Data science is the analysis and study of data. Data science is instrumental in bringing the 4th industrial revolution in the world today. This has resulted in data explosion and growing need for industries to rely on data to make informed decisions. Data science involves various fields like statistics, mathematics, and programming.

Data science involves various steps and procedures such as data extraction, manipulation, visualization and maintenance of data for forecasting future events occurrence. Industries require data scientists which help them to make informed decisions which are data driven. They help product development teams to tailor their products which appeal to customers by analysing their behaviours.

What is Artificial Intelligence?

Artificial Intelligence (AI) is a broad field and quite modern. However, some ideas do exist in older times and the discipline was born a way back in 1956 in a workshop at Dartmouth College. It is presented in contact with intelligence displayed by humans, and other animals. Artificial intelligence is modelled after natural intelligence and talks about intelligent systems. It makes use of algorithms to perform autonomous decisions and actions.

Traditional AI systems are goal driven however contemporary AI algorithms like deep learning understand the patterns and locate the goal embedded in data. It also makes use of several software engineering principles to develop solutions to existing problems. Major technology giants like Google, Amazon and Facebook are leveraging AI to develop autonomous systems using neural networks which are modelled after human neurons which learn over time and execute actions.

Comparison Table: Data Science vs Artificial Intelligence

Below table summarizes the differences between the two terms:

Parameter	Data Science	Artificial Intelligence
Definition	Comprehensive process which comprises of pre-processing, analysis, visualization and prediction It is a discipline which performs analysis of data	Implementation of a predictive model used in forecasting future events It is a tool which helps in creating better products and impart them with autonomy
Techniques	Various statistical techniques are used here	This is based on computer algorithms
Tools size	The tools subset is quite large	AI used a limited tool set
Purpose	Finding hidden patterns in data Building models which use statistical insights	Imparting autonomy to data model Building models that emulate cognitive ability and human like understanding
Processing	Not so much processing requirement	High degree of scientific processing requirements
Applicability	Applicable to wide range of business problems and issues	Applicable to replace humans in specific tasks and workflows only
Tools used	Python and R	TensorFlow, Kaffee, Scikit-learn

Download the comparison table: Data Science vs Artificial Intelligence

Where to use Data Science?

Data science should be used when:

• Identification of patterns and trends required
• Requirement for statistical insight
• Need for exploratory data analysis
• Requirement of fast mathematical processing
• Use of predictive analytics required

Where to use Artificial Intelligence?

Artificial intelligence should be used when:

• Precision is the requirement
• Fast decision making is needed
• Logical decision making without emotional intelligence is needed
• Repetitive tasks are required
• Need to perform risk analysis

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Artificial Intelligence vs Machine Learning

Top 10 Networking technology trends 

Automation has been among humans for a long time though Artificial Intelligence has been developed in recent years. In this article, we are going to see the difference between these two. Yes, though we consider both as the robots or machines that work on their own there is a pretty big difference between them.

So without ado let’s get started with the an introduction to automation and AI before discussing Automation vs Artificial Intelligence.

What is Automation?

Automation refers to a technique or process that makes a machine or system operate on its own or with minimum human inputs. Implementing automation in a process improves efficiency, reduces cost, and gives more reliability.

The history of automation starts from mechanization which is connected to the great industrial revolution. Now automation is everywhere in the modern economy.

Examples of Automation

The examples of automation are:

• Automatic payment system in your banks,
• automatic lights, and
• even automatic or self-driving cars.

To explain it technically, automation is software that acts according to the way it is pre-programmed to act in a given situation. For example, let’s take the example of copy-pasting or moving data from one place to another. Moving data from one place to another can be a tedious repetitive task for humans, but automation software makes it simple.

All you need to do is program the computer or machines how to transfer files from, and when to do it. After that, the machine itself will transfer or move files automatically from one place to another. In this way, automation saves both money and time spent on these monotonous, large tasks. The employees and human resources can be used in something more creative.

What is Artificial Intelligence?

Artificial Intelligence is the further advanced form of automation, where the machines or mostly systems mimic human thinking and make decisions of their own. AI is software that simulates human thinking and processing in machines.

Artificial Intelligence is achieved by combining various automation technologies like data analysis, data prediction, etc… In Artificial Intelligence you don’t need to write any program for a particular process…all you need to do is give the past data to the system, it will analyze the decisions made in the past and make decisions for the current problem like a human being.

As automation can only be applied for repetitive tasks, artificial intelligence has been invented to do more variable processes where there is a need for human decisions. It learns from experience and involves self-correction to give a proper solution to a problem.

Examples of Artificial Intelligence

Good examples of Artificial Intelligence are

• Chatbots,
• Digital assistants,
• Social media recommendations,
• Text or grammar editors,
• Facial detection,
• maps,
• navigation, etc…

Let’s explain it with maps and navigation, Google maps show you the quickest way to go to a place. As it is not a repetitive process the navigation software should adopt artificial intelligence and guide users in a way an ordinary human would do.

Comparison Table: Automation vs Artificial Intelligence

Now as you got the basic idea about what automation and artificial intelligence is let’s see the major difference between them i.e. Automation vs Artificial Intelligence:

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RPA – Robotic Process Automation

What is AIML (Artificial Intelligence Markup Language)

Today we look more in detail about Artificial intelligence or cognitive technologies and its types, and usage.

What is Artificial Intelligence?

The term Artificial intelligence term was coined in the year 1956 by John McCarthy. The definition of Artificial intelligence (AI) is ‘Science of engineering of making intelligent machines’. Artificial intelligence (AI) is also defined as development of systems which are capable of performing tasks which require human intelligence such as decision making, rational thinking, object detection,  solving complex problems and so on. 

Related: Artificial Intelligence vs Machine Learning

Artificial Intelligence Types

Artificial intelligence can be categorized into 3 main types based on its capabilities. 

Artificial Narrow Intelligence (ANI)  – Stage I Machine Learning 

It is also called weak AI/Narrow AI. It is able to perform dedicated tasks intelligently. The most commonly available AI is narrow AI. It cannot perform beyond its field as it is trained only to perform a specific task. One commonly used example of this AI is Apple Siri, Alexa and Google Assistant. 

Common use cases of narrow AI are playing chess, purchase decisions on e-commerce websites,  self-driving cars, speech, and image recognition. Narrow AI is also used in the medical field, for analyzing MRI or computed tomography images and in the manufacturing industry for car production or management of warehouses. 

Narrow AI is not able to reason independently or learn from new situations unlike humans or perform tasks which require creativity and intuition. 

Artificial General Intelligence (AGI) – Stage II Machine Intelligence

It can perform any intellectual task with human-like efficiency. There is no such system that could think and act like humans and perform tasks with perfection just like humans. It is a theoretical concept having human level cognitive function, across a wide variety of domains such as processing of languages, images, computational functioning, and reasoning. 

To have a system like this would require Artificial narrow systems working together, communicating with each other like human beings. Even the most advanced computing systems in the world such as IBM Watson takes approximately 40 minutes to simulate a single second of neuronal activity. 

Artificial Super Intelligence (ASI) – Stage I Machine Consciousness

It is one level ahead of human intelligence which means machines could perform tasks with more accuracy than humans with cognitive properties. It includes capabilities such as ability to think, reason, solve, make judgements. plan, learn and communicate on its own.

Super AI is a hypothetical concept and development of such systems in the real world is yet a dream come true. 

Comparison Table: ANI vs AGI vs ASI

Download the comparison table: ANI vs AGI vs ASI

Artificial Intelligence – Based on Functionality

In addition, based on functionality, the AI can be further divided as:

• Reactive Machines – basic types of artificial intelligence which do not store memories or past experiences for any future actions. They focus only on the current scenario and react as per possible best action. IBM Deep Blue and Google AlphaGo are examples of reactive machines.
• Limited Memory – Limited data and past experiences can be stored for a short period. These systems use stored data for a limited time only. Self-driving cars are one of the ideal examples of this type of systems which store recent speed of nearby cars, distance to other cars, speed limit etc.
• Theory of Mind – understanding of human emotions, people, beliefs and being able to interact socially with human beings. These machines are still in theory and not developed yet. 
• Self-Awareness – is the future of artificial intelligence. These machines will be super intelligent and will have their own consciousness, sentiments, and self-awareness and smarter than human beings. 

Every company is dealing with increasing volumes of unstructured data and information, which makes it difficult to automate processes. There are plenty of Robotic Process Automation RPA tools that have made it easy for businesses to tackle this complexity. Using RPA tools helps companies cut costs, accelerate time to market, and improve operational efficiency while reducing manual intervention. These RPA tools help businesses streamline their operations by enabling them to conduct tasks in a more automated manner than ever before. These software programs remove the need for manual tasks by identifying and repeating actions that can be codified as rules.

List of Top Robotic Process Automation RPA tools

Let’s take a look at some of the most popular RPA tools below:

Automation Anywhere

Automation Anywhere is a business process automation platform designed to help organizations improve their operational efficiency and transform their businesses.

The company’s RPA platform allows organizations to streamline business processes, increase operational efficiency, and operationalize their business. It uses a rules-based approach to perform tasks that are typically manual or repetitive, which can be codified as rules. Features include:

• a visual programming environment,
• a workflow engine, and
• a process analytics engine.

This RPA tool offers a number of benefits to its users. For example, it can help with process standardization, process compliance, process excellence, and process optimization. It also enables integration with existing systems and applications. Automation Anywhere is one of the most popular RPA tools in the market today.

Blue Prism

Blue Prism is an RPA platform that enables business transformation by helping organizations achieve high levels of automation while optimizing the investment in people. The company’s RPA solution enables organizations to change the way they do business by automating manual business processes.

It uses a rules-based approach to capture and automate routine manual tasks through a user-friendly graphical user interface.

Blue Prism offers a complete solution for organizations that want to automate their processes with minimum effort. It is one of the most well-known RPA tools in the market today. Some of the key features of this RPA solution include

• the ability to connect to any data source and
• real-time visibility into business processes.

UiPath

UiPath is an RPA tool that is used to automate business processes across industries. Its robust platform allows businesses to maximize their efficiency by automating the manual, repeatable tasks that have been a constraint for organizations for a long time.

• The platform efficiently manages the entire automation lifecycle, from design to run time.
• It also enables the creation of business rules, which can be applied across different processes.

UiPath is one of the most comprehensive RPA tools available in the market today. It enables IT, business analysts, and process owners to automate their manual tasks and processes. This RPA solution is used by large enterprises across various industries.

Kofax

Kofax is one of the leading providers of solutions for capturing, managing, and transforming information. It has a number of RPA tools that help organizations automate their operations and processes. With these tools, companies can achieve

• real-time visibility and operational efficiency,
• reduced cost, and
• improved customer experience.

This RPA solution allows businesses to digitize their operations by creating digital workflows and automating manual tasks.

It can be integrated with existing applications and systems to eliminate manual operations. Kofax is currently one of the top RPA tools in the market today.

NICE

NICE is a business operations management company that provides solutions that enable organizations to optimize their internal processes. A few of its solutions include Automated Workforce Management, Collaborative Business Process Management, and Automated Intelligent Real-time Root Cause Analysis.

NICE’s Automated Workforce Management solution enables organizations to automate their workforce and gain real-time visibility into their business processes.

• This RPA solution allows companies to streamline their manual business processes and scale their operations.
• It also enables real-time visibility and operational efficiency for a lower cost.

NICE is one of the most popular RPA tools in the market today.

Keysight’s Eggplant

Keysight’s Eggplant is a business process automation solution that enables organizations to achieve operational excellence.

• It uses a rules-based approach to digitize manual business processes and execute them in a predictable manner.
• Eggplant can be used to automate both structured and unstructured data.
• It also allows users to build and test their processes before actually implementing them in the live environment.

This RPA tool is currently one of the most popular RPA tools.

Pega

Pega is a business transformation platform that enables businesses to achieve operational excellence. The company’s RPA tool is used to automate business processes and integrate operations. It uses a rules-based approach to capture and execute manual business processes.

Pega is one of the most comprehensive RPA tools available in the market today. It has plenty of features that make it easy for organizations to automate their operations. It has a visual programming builder that enables users to create their automation without writing a single line of code.

Kryon

Kryon is a visual programming language that can be used to automate business processes. It helps organizations reduce the time and effort required to create automation by up to 90%.

• This RPA solution enables businesses to create visual workflows using a drag-and-drop interface.
• It provides an easy way to create automation without writing code.
• Its simple drag-and-drop interface makes it easy for business analysts and non-technical users to create automation.

Kryon  is currently one of the most popular RPA tools.

Inflectra Rapise

Rapise is a business process automation solution that enables organizations to achieve operational excellence.

• It uses a rules-based approach to capture and execute manual business processes.
• Rapise can be used to automate both structured and unstructured data.
• It also allows users to build and test their processes before actually implementing them in the live environment.

This RPA tool is currently one of the most popular RPA tools.

Rocketbot

Rocketbot is a business process automation solution that enables organizations to achieve real-time visibility and operational efficiency.

• It uses a rules-based approach to capture and automate manual business processes. Rocketbot can be used to automate both structured and unstructured data.
• It also allows users to build and test their processes before actually implementing them in the live environment.

This RPA tool is currently one of the most popular RPA tools.

Summing up

Using an RPA tool can help any organization automate its operations and processes. However, you should know that not all RPA tools are created equal. To find the best RPA tools, you should consider factors such as cost, ease of use, scalability, and integrations with other systems and applications.

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RPA – Robotic Process Automation

Automation vs Artificial Intelligence: Understand the difference

As per Gartner prediction 72% of the enterprises will be working with Robotic process automation (RPA) in next two years and Digital process automation (DPA) is identified as major component for digital transformation with DPA market worth $6.76 billion and expected to rise to $12.61 billion by year 2023. 

So, what is this buzz about RPA and DPA? Process automation has always been a key driver to run business efficiently with simplification of complex manual tasks to speed up operations. It has three major functions namely streamlining processes, centralizing information, and reduction in human touch points. 

Today we look more in detail about Robotic process automation (RPA) and Digital process automation (DPA) concepts, how they differ from each other, what are the advantages of both and use cases. 

What is RPA (Robotic Process Automation)?

The use of software which mimics human behaviour and carry out repetitive high volume basic administrative tasks which are time consuming. The monotonous tasks taken over by RPA which frees employees to focus on more high value activities, including the ones which require emotional intelligence and logical reasoning. It can be used to automate queries and calculations as well as maintaining records and transactions. It is easy to deploy over existing applications. 

Benefits 

• Effective use of staff resources
• Enhanced customer interactions
• Reduction in costs
• Improvement in accuracy
• Elimination of human errors
• Completion of automated tasks faster with less effort 

Use cases

• Automating service order management, quality reporting etc.
• Automating reports management and healthcare systems reconciliation
• Automation of claim processing in insurance
• Automation of bills of materials generation
• Automation of account setup and validation of meter readings in energy and utility field
• Automation of hiring process, payroll, employee data management 
• Automation of general ledger, account receivables and payables etc.
• Automation of requisition to issue purchase order, invoice processing etc. 
• Automation of customer services activities 
• Building, testing, and deploying infrastructure such as PaaS 
• Mass email generation, archival and extraction
• Conversion of data formats and graphics

What is DPA (Digital Process Automation)?

DPA automates processes that can span across applications. It has more to do with Business process management (BPM). It takes the entire infrastructure of an enterprise business processes and streamlines them to improve efficiency and cost reduction. It evolved out of the need of enterprises to automate business processes to achieve digital transformation.

Its aim is to extend the business process to partners, customers, and suppliers to offer a better experience. DPA are usually used to automate tasks like customer onboarding, purchase orders, credit approvals and many other similar business processes. 

Benefits 

• Time savings
• Cost savings
• Efficiency gains
• Improved customer experiences

Use cases

• Customer onboarding including auto checks, data entry across multiple applications, login credentials generation, setting up accounts and sending welcome email 
• Procurement functions such as copying data between ERP and ordering systems, data entry into tracking systems, auto invoice post order placement etc.
• Order fulfilment – automate various back-end tasks associated with order fulfilment of new products, estimation of fulfilment and delivery times, local taxes calculations, shipping manifest generation, order status tracking and receipt of package by customer 

Robotic Process Automation vs Digital Process Automation

Below table summarizes the difference between RPA and DPA:

Download the comparison table: RPA vs DPA

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RPA – Robotic Process Automation

10 Most Popular Robotic Process Automation RPA Tools

In today’s article, we would look more in detail about Machine learning (ML) enabled NGFW, their advantages, use cases etc. 

ML Powered NGFW 

Attackers use different methods of existing ones and modify them to get into traditional signature-based protection systems. NGFW uses heuristics for detection of modified malware, Victim zero (o) is first person or enterprise to experience attacks. Signature modifications do not help security systems to solve problems, alternative methods of analysing every bit of traffic or every file is slow and cumbersome.

NGFW enabled ML algorithms directly into firewalls core and enforce results in real time. NGFW’s inspect files which are getting downloaded and block anything which looks malicious before the download gets over. Single pass inspection as it is called with inline prevention. NGFW prevents infections without the need for cloud or offline analysis, avoids false positives and reduces potential infection to zero. 

NGFWs leverage inline ML based prevention to prevent threats such as file less attacks, malicious scripts, phishing attempts, and portable executables.

Advantages of ML Powered NGFW

• Provides protection against sophisticated and complex threats which require detection mechanism which relies on accurate and timely signatures
• Zero delay signatures enabled every ML powered NGFW in seconds 
• ML powered NGFW can classify all IoT and OT devices in network 
• ML powered NGFWs can use cloud scale for protection and management of devices

Limitations of ML Powered NGFW

• ML powered NGFWs analyse large amounts of telemetry data and can recommend security policies based on organizational network analysis
• ML based firewalls do not cover every file format so it alone could not be sufficient to provide complete protection and there is a need for cloud-based analysis to support threat detection

Security services by ML NGFWs 

Advanced threat protection is there with intrusion prevention systems (IPS) having offline and online security analysis using cloud compute for AI and deep learning techniques without compromising the performance. It can detect unknown and targeted command and control (C2) attacks as well as evasive attacks from tools like Cobalt Strike

• ALOps – uses machine learning to predict up to 51% of disruptions to NGFW before impacting firewalls with telemetry of over 6000 deployments. 
• DNS security – extends protection for latest DNS based attack techniques inclusive of strategically aged domains with 40% coverage of DNS based threat coverage
• Advanced URL filtering – Prevention of new and highly evasive phishing attacks, ransomware and web-based attacks via deep learning powered analysis of web traffic including live web content in real time 
• IoT Security – IoT devices visibility and policy creation automation across seen and unseen devices using machine learning capabilities

Quick tips!

The Next-Generation Firewall Market expected to grow from $2.39 billion in 2017 to $4.27 billion by 2023.

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Artificial Intelligence vs Machine Learning

Firewall Serving as Egress Gateway: Networking Scenario

What is Zigbee Protocol?

Zigbee is a wireless protocol that enables smart devices to communicate with each other over a Personal Area Network (PAN). It is designed to be a low-cost, low-power solution for home automation and control. Zigbee operates in the 2.4GHz frequency band, the same as WiFi, and uses the IEEE 802.15.4 standard for physical and media access control.

Zigbee Stack

The Zigbee protocol stack is comprised of four layers: the physical layer, the MAC layer, the network layer, and the application layer. The physical layer is tasked with transmitting and receiving data through wireless signals, while the MAC layer manages access control and data framing. The Zigbee network layer takes care of routing data between devices within the network, and the application layer defines the device’s specific functions and features.

Zigbee Network Components

A typical Zigbee network consists of three main components: the Zigbee Coordinator (ZC), Zigbee Router (ZR), and Zigbee End Device (ZED).

• The Zigbee Coordinator is the central hub of the network and is responsible for setting up and maintaining the network, adding devices, and managing communications between devices. There can only be one Coordinator in a Zigbee network, and it must be permanently powered.
• Zigbee Routers are AC mains-powered devices that act as intermediate devices in the network. They provide the backbone of the Zigbee network by routing communications between devices to create a reliable and efficient network.
• Zigbee End Devices are typically battery-powered devices that can only send or receive data. They cannot perform routing tasks and can only communicate with Routers or directly with the Coordinator. Examples of Zigbee End Devices include motion sensors, door sensors, temperature sensors, and door locks.

How Does Zigbee Work?

Zigbee builds upon the physical layer and media access control defined in the IEEE 802.15.4 standard. It uses a mesh networking topology, where devices communicate with each other through intermediate devices called Routers. This allows for multiple communication paths and increased network coverage.

Zigbee devices are designed to be simple and focused on specific tasks, such as motion sensing or dimming lights. This simplicity allows for better performance and reliability. The Zigbee network is self-configuring and self-healing, meaning that devices can automatically join and leave the network, and the network will adapt and reroute communication when needed.

Benefits of using Zigbee

There are several reasons why Zigbee is a popular choice for home automation systems:

• Lighting Options: Zigbee has a wide range of lighting options, including LED bulbs, color-changing LED strips, light switches, and dimmer modules. This makes it a versatile choice for creating different lighting scenes and effects in your home.
• Power Efficiency: Zigbee devices are designed to be energy-efficient, with some devices boasting up to 10 years of battery life. This makes Zigbee ideal for battery-powered devices such as motion sensors and door sensors.
• Optimized for Battery Devices: Zigbee is optimized for battery-powered devices by using a sleep mode when the devices are not in use. This helps to conserve battery power and extend the lifespan of the devices.
• Network Stability: Zigbee uses a mesh networking topology, which means that devices can communicate with each other through multiple paths. If one device goes offline, the network will automatically reroute the communication through another device, ensuring a stable and reliable connection.
• Security Features: Zigbee employs 128-bit AES encryption, the same level of security used in online banking services. This ensures that the communication between devices is secure and protected from unauthorized access.
• Firmware Updates: Zigbee supports over-the-air firmware updates, allowing devices to receive software updates without the need for manual intervention. This ensures that your devices are always up to date with the latest features and security patches.
• Scalability and Affordability: Zigbee networks can support thousands of devices in a single network, making it suitable for both small and large-scale home automation systems. Additionally, Zigbee devices are often more affordable compared to other technologies such as Z-Wave and WiFi.

Zigbee vs WiFi

Zigbee and WiFi are both wireless technologies, but they have different strengths and use cases. WiFi is well-suited for high-bandwidth tasks such as video streaming and online gaming, while Zigbee is designed for low-power, low-bandwidth applications.

WiFi networks are typically limited in terms of the number of devices they can support, usually between 32 and 64 devices. Zigbee, on the other hand, supports thousands of devices in a single network, making it ideal for home automation systems with a large number of devices.

When it comes to smart home applications, Zigbee is more power-efficient and can provide longer battery life for devices. Additionally, Zigbee’s mesh networking topology ensures better network coverage and stability compared to WiFi.

Zigbee vs Z-Wave

Zigbee and Z-Wave are two popular wireless protocols used in home automation systems. Both protocols have their own advantages and considerations.

Zigbee has a wider range of device options, especially for lighting applications. There are more Zigbee-based devices available in the market, including LED bulbs, light switches, and dimmer modules. Zigbee also supports a larger number of devices in a single network, making it suitable for larger homes or commercial applications.

Z-Wave, on the other hand, is known for its reliability and compatibility. Z-Wave devices typically have better range and signal penetration, making them suitable for larger homes with multiple floors or thick walls. Z-Wave also uses a different frequency band, which means it is less prone to interference from other wireless devices.

In general, it is recommended to choose a smart home controller that supports both Zigbee and Z-Wave to have the flexibility to choose devices from both protocols.

Conclusion

Zigbee is a wireless protocol that offers a range of benefits for home automation systems. With its extensive device options, power efficiency, scalability, and security features, Zigbee is a popular choice for creating a smart home ecosystem.

With Zigbee’s open standard and widespread adoption by major companies, it’s clear that Zigbee is here to stay and will continue to play a significant role in the future of smart homes.

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Top 10 wireless technology trends

What is Wireless Mesh Technology?

So, how is artificial intelligence being integrated into education? It comes with benefits, of course. But with risks too. To understand more about how artificial intelligence is shaping the educational landscape, continue reading this article. Let’s explore together both the opportunities and challenges it brings to education. 

Artificial Intelligence in Education: Opportunities 

• Personalized learning 
• Efficiency and automation 
• Quality education 

Personalized Learning 

As briefly mentioned above, artificial intelligence is already being integrated into education. And one of the greatest opportunities it comes with is personalized learning. Education is most effective when it is tailored to the needs of every student, their learning style, and abilities. 

So, with the help of AI, as a teacher, you can adapt the pace of every lesson. You can design homework and an assignment to help students put to practice and learn especially the information that they might need more time to integrate. It is a great resource for students, as it can offer the university homework help they need. Having a personalized learning experience is exactly what students need and are looking for these days. They want an attractive and engaging one, and AI can easily do this. 

Efficiency and Automation 

But getting an education is not only about going to classes and completing assignments. It is also about taking tests and exams. This is something educators have to deal with constantly. And sometimes, it can take them a great deal of time. The time that they would otherwise spend preparing the lessons and learning more about alternative and engaging teaching methods. 

So, AI can help automate some of these tasks. Grading or record keeping are just some of the tasks that can be automated by AI. This reduces the burden on teachers and improves the overall quality of education. 

Quality Education 

Another opportunity AI comes with is access to quality education. And here we can discuss more topics. For example, AI-powered platforms can provide education to remote or underserved areas. The world population would be helped by remote education. Which increases access to learning resources globally. 

On top of this, AI offers insights driven by data. Educators can tap into discovering more about the strengths and weaknesses of students. Which, of course, helps them tailor their courses and lessons to address exactly these weaknesses and contribute to improving education. Which will lead to improved learning outcomes too. But there is another way AI is increasing the quality of education. AI-driven tools, such as chatbots or virtual assistants can engage students in interactive and immersing learning experiences. And education will be more enjoyable and effective. 

Artificial Intelligence in Education: Risks 

• Privacy concerns 
• Depersonalization 
• Bias and fairness
• Job displacement 

Privacy Concerns 

Just as with any other technological advancement, AI comes with some concerns for the public. The collection of student data for AI analysis raises these privacy concerns. This is why it is essential to ensure that this data is used and collected ethically. Breaches and misuse have to be avoided at all costs. Student data has to be kept safe and not made public, as it is personal and sensitive information. 

Depersonalization 

Even though AI can provide educators with data insights that turn out to be incredibly useful, overreliance on AI can lead to depersonalization. Indeed, as an educator, you can use the data provided by AI to tailor the information you deliver in class. But this does not mean that human-to-human interactions have no value anymore. 

Bias and Fairness

We talk about technology, but this does not mean that we should not talk about bias and fairness. AI algorithms can inherit biases from the data they are trained on. For example, if historical data is presented, AI might unfairly disadvantage certain groups of students. Which will do nothing more than perpetuate inequalities in education. We live in a modern world where colleges and universities strive to offer students equal educational opportunities. No one says AI should not be used or integrated into education. However, we have to keep an eye out for these risks. 

Job Displacement 

This has been a hot topic in the last few years since AI tools and platforms have gained more and more momentum. As it becomes more and more powerful and performant, AI might indeed lead to job displacement. It could replace some educational roles, such as administrative tasks or basic tutoring. 

Final Thoughts 

AI is really powerful. Integrating it into education comes with both opportunities and risks. However, a few measures can be taken to harness its benefits and mitigate the risks it comes with. Implement algorithms that are designed to minimize bias. Make sure these algorithms are transparent and subject to auditing. Develop training for educators so that they learn how to better integrate it into education. 

And maybe one of the most essential parts is to develop ethical guidelines and standards for AI in education. Emphasize responsible use and ethical decision-making. AI is helpful and powerful. Use it wisely.

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Automation vs Artificial Intelligence

Data Science vs Artificial Intelligence

Understanding Data Annotation

Data annotation labels unprocessed data to offer factual data to train AI models. AI models can learn from labeled data and generalize patterns to make precise predictions thanks to several data annotation techniques, including image, text, audio, and video annotations. AI models can recognize and comprehend ways by receiving annotated data, which improves performance and decision-making abilities. The reliability of the training data is ensured through high-quality data annotation, which is essential for creating effective and reliable AI models.

Impact of Quality Data Annotation

The effectiveness or failure of AI applications can be affected significantly by high-quality data annotation’s effects on model operations. The main ideas highlighting this impact are as follows:

Improved Model Performance

When trained on well-annotated data, AI models display improved skills to recognize patterns, make precise predictions, and effectively perform complicated tasks. AI models can serve better and produce better outcomes. This is where a data annotation company provides accurate and trustworthy solutions. Models can better generalize to new data when the noise and biases are reduced through good annotation. As a result, model performance is improved, errors are reduced, and AI applications are used more effectively overall.

Accuracy and Reliability

In developing AI models, accuracy and reliability are crucial characteristics. The accuracy of a forecast is the assurance that the model’s outputs correspond to the labels found in the real world. For practical applications, trustworthy and dependable AI models constantly deliver accurate results. High accuracy and reliability are attained by quality data annotation, which gives AI models the essential framework to successfully learn patterns, reduce errors, and make precise conclusions.

Generalization and Adaptability

When AI models are trained on thoroughly annotated data, they may efficiently generalize their knowledge to handle brand-new, untainted material. This capability makes AI models more robust and versatile by enabling them to perform effectively outside the training data in real-world circumstances. Quality annotations also help AI models adapt to shifting surroundings, allowing them to constantly advance and provide precise predictions in ever-changing and dynamic contexts.

Reduced Bias and Fairness

High-quality data annotation is essential to minimize bias and advance fairness in AI model operations. Developers can reduce unfair depictions of particular groups or demographics by carefully selecting and annotating data. Ethical data annotation procedures guarantee that AI models treat every person fairly, regardless of background. As a result, users are more likely to trust and accept AI models that are just and equal and make objective judgments. AI technologies that have a beneficial social impact and avoid perpetuating harmful preconceptions or discrimination have less bias and more fairness.

Efficient Learning

High-quality data annotation enables efficient learning by allowing AI models to recognize patterns and correlations in the annotated data quickly. Accurate annotations give the model clear and instructive signals that help it learn from instances. Fewer training iterations are needed for AI models with well-annotated data, which lowers training costs and speeds up learning. The model performs better overall in various applications and can produce accurate predictions due to efficient learning.

Minimizing Overfitting and Underfitting

In the training of AI models, high-quality data annotation reduces overfitting and underfitting. A model can learn relevant patterns using accurate image annotation services without memorizing the training data (overfitting) or missing important marks (underfitting). It guarantees that the AI model generalizes well to fresh, unexplored data, enhancing its dependability and utility in practical applications.

Effective Problem Solving

 AI models can recognize and comprehend complicated patterns in the data by being given accurate and trustworthy annotations, which enables them to make wise decisions when presented with real-world problems. The ability of the model to solve problems is improved by having well-annotated data, which allows the model to provide pertinent and insightful solutions across various areas, making AI an essential tool for tackling complicated issues and fostering creativity.

Conclusion

In conclusion, there is no disputing the influence of high-quality data annotation on AI model operations. Annotations that are accurate and trustworthy improve model performance, increase generalization, and reduce biases, making AI models more responsible, efficient, and equitable. Utilizing the potential of high-quality data annotation opens the path for AI’s revolutionary effects in various industries.

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Artificial Intelligence vs Machine Learning

Automation vs Artificial Intelligence: Understand the difference

Today we look more in detail about two important technologies which are changing the way we look and perceive things and revolutionize the entire paradigm of industries, not just IT. We look at artificial intelligence and Machine learning and understand the difference between them , the purpose for which they are deployed and how they work etc.

About Artificial Intelligence  

Artificial intelligence is part of computer science which mimics human intelligence. And as its name suggests it means human made thinking power. Artificial intelligence is a technology which can help us to create intelligent systems which can simulate human intelligence. These systems are not pre-programmed but they use such algorithms such as reinforcement learning and deep learning neural networks. 

IBM Deep Blue which beat chess grandmaster Garry Kasparov in 1996 and Google DeepMind’s AlphaGo , which beat Sedol at Go in 2016 are all examples of narrow AI – skilled at one specific task. Based on its capabilities AI can be classified into below types – Artificial Narrow intelligence (ANI) or Weak AI, Artificial General intelligence (AGI) or General AI and Artificial Super Intelligence (ASI) or strong AI. Currently we are working on weak and General AI. The future of AI is strong AI which is going to be more intelligent than humans.

Applications of Artificial Intelligence 

• Map services
• Recommendation engines such as Amazon, Spotify , Netflix etc. 
• Robotics such as Drones, Sophia the robot
• Health care industry such as medical diagnosis, prognosis, precision surgery
• Autonomous systems such as autopilot, self-driving cars
• Research – drug discovery
• Financials – Stock market predictions 

About Machine Learning   

Machine learning is a subset of Artificial intelligence, in very simple words machines take data and learn for themselves. It is the most wanted and promising tool in the AI domain. ML systems can apply knowledge and training from large data sets , speech recognition, object recognition, facial recognition and many such tasks. ML allows systems to learn and recognize patterns and make predictions instead of hardcoding instructions for tasks completion.

In simple terms Machine learning can be defined as a subset of artificial intelligence which enables systems to learn from past data or experiences without being pre-coded with a specific set of instructions.  Machine learning requires the use of massive amount of structured and semi-structured data to perform predictions based on that data. ML can be divided into three types – supervised learning, reinforcement learning and unsupervised learning. ML is used at various places such as online recommendation systems for google search algorithm, email spam filtering, Facebook auto friend tagging suggestion etc. 

Applications of Machine Learning 

• Regression (Prediction)
• Classification (lesser number of classes , with less data)
• Control systems – Drones

Comparison Table: Artificial Intelligence vs Machine Learning

Below table summarizes the differences between the two terms:

FUNCTION	ARTIFICIAL INTELLIGENCE	MACHINE LEARNING
Definition	Artificial intelligence technology enables machine to simulate human behaviour	It is a subset of AI which let a machine to automatically learn from past data without any pre-coded instructions
Origin	Origin around year 1950	Origin around year 1960
Purpose	Make smart computer systems to solve complex problems like human beings	ML is used to allow systems to learn from data so that we can get accurate output without manual intervention
	It focuses on maximizing chances of success	It focuses on accuracy and patterns
Objective	Learning, reasoning and self-correction	Learning and self-correction when new data is introduced
Components	Artificial intelligence is subset of data science	Machine learning is subset of artificial intelligence and data science
Scope	Wide range of scope	Limited scope
Applications	Siri, customer support using catboats, expert system, online game playing, intelligent humanoid robot etc.	Online recommendation system, Google search algorithms, Facebook Auto friend suggestion, optical character recognition, web security, imitation learning etc.
Data types	Deals with structured, semi structured and unstructured data	Deals with structured and semi-structured data
Examples of algorithm	Q Learning, Actor critic methods, REINFORCE etc.	Linear regression, Logistics regression, K means clustering, Decision trees etc.

Download the comparison table: Artificial Intelligence vs Machine Learning

Conclusion

AI and ML are often confused terms but AI is a simulation of natural intelligence at par with humans and ML is an application of AI to give systems the ability to learn and understand things without any hard coded programming instructions. They evolve as they learn.

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Supercomputer vs Minicomputer

Top 10 Networking technology trends 

What is Big Data Analytics?

Big data analytics refers to the process of collecting, organizing, and analyzing large and complex datasets to uncover valuable insights and trends. This data can come from a variety of sources such as customer behavior, sensor data, and social media. The use of big data analytics enables organizations to better understand their customers, optimize their products and services, and gain a competitive advantage in their respective markets.

Big data analytics is different from traditional data analytics in that it requires the use of specialized tools and techniques to handle large and complex datasets. These tools and techniques enable organizations to process large amounts of data quickly and accurately. Furthermore, big data analytics also requires the use of machine learning algorithms to uncover valuable insights from the data.

Why is Big Data Analytics Important?

• Big data analytics is becoming increasingly important for organizations in today’s world. This is because organizations are now able to collect large amounts of data from a variety of sources and analyze this data to gain valuable insights into their customers, products, and services.
• By using big data analytics, organizations can uncover patterns and trends in the data that would otherwise be impossible to detect. This helps organizations optimize their products and services and gain a competitive advantage in their respective markets.
• It can also help organizations make better decisions by providing them with real-time insights into their customers, products, and services. This helps organizations reduce costs, increase efficiency, and improve customer satisfaction.

Types of Big Data Analytics

There are a number of different types of big data analytics that can be used by organizations. These include descriptive analytics, predictive analytics, prescriptive analytics, and machine learning.

• Descriptive analytics is the process of analyzing the data to uncover historical patterns and trends. This type of analytics can help organizations gain insights into their customers, products, and services.
• Predictive analytics is the process of using historical data to make predictions about the future. This type of analytics can help organizations make better decisions by providing them with real-time insights.
• Prescriptive analytics is the process of using data to suggest actions that can be taken to achieve a desired outcome. This type of analytics can help organizations optimize their processes and operations.
• Machine learning is the process of using algorithms to uncover patterns and trends in the data. This type of analytics can help organizations gain a deeper understanding of their customers, products, and services.

Lifecycle Phases of Big Data Analytics

The big data analytics process involves a number of different lifecycle phases. These include data collection, data cleansing, data integration, data mining, and data visualization.

• Data collection is the process of gathering data from a variety of sources such as customer behavior, sensor data, and social media. This data is then stored in a data warehouse.
• Data cleansing is the process of filtering and transforming the data to ensure that it is clean and ready for analysis.
• Data integration is the process of combining different datasets to create a single source of truth. This allows organizations to gain a holistic view of their data.
• Data mining is the process of extracting useful information from the data. This can be done using a variety of techniques such as clustering, regression analysis, and decision trees.
• Data visualization is the process of presenting the data in a graphical format such as charts and graphs. This helps organizations to quickly and easily understand the data and uncover valuable insights.

Related: Data Mining vs Data Analytics

Benefits of Big Data Analytics

Big data analytics provides a number of benefits to organizations. These include cost savings, improved efficiency, and better decision-making.

• Cost savings: Organizations can save money by using big data analytics to optimize their processes and operations. By analyzing the data, organizations can identify areas where they can reduce costs and improve efficiency.
• Improved efficiency: Organizations can improve their efficiency by using big data analytics to uncover hidden patterns and trends in the data. This can help organizations make better decisions and optimize their processes and operations.
• Better decision-making: Organizations can make better decisions by using big data analytics to gain real-time insights into their customers, products, and services.

Big Data Analytics Tools

Several different big data analytics tools can be used by organizations. These tools include Apache Hadoop, Apache Spark, and Apache Kafka.

• Apache Hadoop is an open-source platform for storing and processing large amounts of data. It can be used to store and analyze data in a distributed and scalable manner.
• Apache Spark is an open-source data processing engine. It can be used to process large amounts of data quickly and efficiently.
• Apache Kafka is an open-source distributed streaming platform. It can be used to process real-time data streams and publish them to other systems.

Big Data Analytics Use Cases

Big data analytics can be used in a variety of different use cases. These use cases include customer segmentation, fraud detection, market analysis, and predictive maintenance.

• Customer segmentation: Organizations can use big data analytics to segment their customers into different groups based on their behavior. This can help organizations target their marketing efforts more effectively.
• Fraud detection: Organizations can use big data analytics to detect fraudulent activities such as credit card fraud and money laundering.
• Market analysis: Organizations can use big data analytics to analyze the market and gain insights into consumer trends. This can help organizations make better decisions and gain a competitive advantage.
• Predictive maintenance: Organizations can use big data analytics to predict when equipment or machinery is likely to fail. This can help organizations reduce downtime and improve efficiency.

Conclusion

Big data analytics is becoming increasingly important for organizations in today’s world. By understanding big data analytics and utilizing the right tools and techniques, organizations can gain a competitive advantage in their respective markets.

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What is Data Mining?

What is the Difference Between Big Data and Cloud Computing?

What is Mixed Reality?

Mixed reality (MR) is a combination of virtual reality (VR) and augmented reality (AR). It is an immersive technology that combines the real world with virtual elements. It is a way of interacting with and manipulating digital objects in an environment that is realistic and interactive. MR is the next evolution of AR and VR and is often referred to as the “ultimate” reality.

MR is a technology that allows you to interact with digital objects in a way that is realistic and immersive. It combines elements of the real world with virtual elements, creating a hybrid environment. It is also a way of merging the physical and digital worlds. For example, you could use MR to create a virtual version of a real-world object and manipulate it in a way that is not possible with just physical objects.

MR is also a way of enhancing everyday experiences. For instance, you could use MR to create a virtual version of an event or a place and interact with it in an immersive way. This could be used to create an interactive educational experience or to explore and experience a new place virtually.

Types of MR Technologies

There are several types of MR technologies. The most commonly used types are:

• Augmented Reality (AR): AR overlays digital objects and information onto the real world. It is used to enhance the real world with digital elements.
• Virtual Reality (VR): VR is an immersive technology that creates a fully-fledged virtual environment. It is used to replicate real-world environments and experiences.
• Mixed Reality (MR): MR is a combination of AR and VR. It is used to create an interactive environment that combines the physical and digital worlds.
• Simulated Reality (SR): SR is an immersive technology that creates a virtual environment that is indistinguishable from the real world. It is used to create an experience that is as close to reality as possible.

Applications of Mixed Reality

• MR has a wide range of applications in different industries. It can be used to create an immersive experience, such as interactive educational experiences or virtual tours. It can also be used to create simulations, such as for training in hazardous environments or product testing.
• MR is also used for entertainment, such as gaming, virtual reality movies, and virtual reality concerts. MR is also used for marketing and advertising, as it allows companies to create engaging and immersive experiences for potential customers.
• MR is also used for data visualization, such as for viewing complex data sets or for creating interactive 3D models. It is also used for medical applications, such as for medical training and surgery simulations.

Mixed Reality Devices

There are several types of MR devices available. These include:

• Head-mounted displays (HMD): HMDs are devices that are worn on the head and display digital content. They are used to create an immersive experience.
• Smartphones and tablets: Smartphones and tablets are portable devices that are used to display digital content. They are used to create an interactive and immersive experience.
• Wearable devices: Wearable devices are devices that are worn on the body and are used to display digital content. They are used to create an interactive and immersive experience.
• Gesture-based devices: Gesture-based devices are devices that use hand or body gestures to interact with digital content. They are used to create an interactive experience.
• Haptic feedback devices: Haptic feedback devices are devices that use vibrations or other sensations to create an immersive experience. They are used to create an immersive experience.

The Future of Mixed Reality

MR has the potential to revolutionize the way we interact with technology. As MR technology advances, it will become more widely used in different industries, such as entertainment, education, marketing, and healthcare.

MR has the potential to create a more immersive and interactive experience. It could be used to create virtual tours of places, interactive virtual classrooms, and simulations for product testing or medical training.

MR also has the potential to create entirely new experiences. For example, it could be used to create virtual theme parks, virtual events, and digital versions of real-world objects.

Mixed Reality vs Virtual Reality

MR is often compared to VR, as they are both immersive technologies. However, there are several key differences between the two.

• VR is an immersive technology that creates a fully-fledged virtual environment. It is used to replicate real-world environments and experiences. MR, on the other hand, is a combination of AR and VR. It is used to create an interactive environment that combines the physical and digital worlds.
• VR is used to create an immersive and interactive experience, while MR is used to create a hybrid environment that combines the real world with virtual elements. MR is more versatile than VR, as it can be used to create more realistic experiences and simulations.

Conclusion

If you’re looking for a way to enhance your life and experience the world in a new way, then mixed reality is definitely worth exploring. With its versatile applications, MR can help you create immersive experiences and explore new possibilities.

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What is Virtual Reality (VR)?

Top 10 Networking technology trends

What is Virtual Reality?

VR is a computer-generated environment that simulates a realistic experience. It is an immersive and interactive experience that replicates real-world scenarios. It is like being present in the environment that you are interacting with, such as a game, a movie, or a virtual world.

VR is different from other forms of entertainment, such as 3D movies, because it creates an interactive 3D environment. It is also different from augmented reality (AR), which is a technology that overlays digital objects in the real world.

The technology behind VR is complex. It involves creating a 3D environment with the help of computer graphics, sensors, and software. The user interacts with the environment through a headset, which is connected to a computer. The headset is equipped with a display that produces 3D images, and the user can manipulate the environment by moving their head.

What Is the Need for Virtual Reality?

VR is becoming increasingly popular because it offers a unique, immersive experience. It is being used in a variety of industries, such as gaming, entertainment, education, healthcare, and more.

VR allows users to interact with a 3D environment, which can be used for training and education. For example, students can use VR to explore a virtual world, such as the inside of a human body. VR can also be used for rehabilitation and therapy, such as physical and cognitive therapy.

VR also has a lot of potential in gaming. It allows players to immerse themselves in the game and interact with the environment. This can make the game more engaging and immersive, and it can offer a unique gaming experience that is not possible with traditional gaming.

Types of Virtual Reality

VR can be divided into two main categories:

• Non-immersive VR: This type of VR is used mainly for gaming and entertainment. It uses a headset and a controller to interact with the environment.
• Immersive VR: This type of VR is used mainly for training and simulation. It is a more realistic experience, and it uses a headset, sensors, and controllers to interact with the environment.

Benefits of Virtual Reality

VR offers many benefits, such as:

• Immersive experience: VR provides an immersive experience that allows users to feel as if they are present in the environment they are interacting with.
• Cost-effective: VR is a cost-effective way to create immersive experiences without having to invest in expensive hardware or software.
• Versatility: VR can be used for a variety of applications, such as gaming, entertainment, education, healthcare, and more.
• Enhances creativity: VR can be used for creative purposes, such as creating art and music.

Challenges of Virtual Reality

VR is not without its challenges. Some of the challenges include:

• Health risks: One of the biggest challenges is the potential health risks associated with VR. The use of VR can cause dizziness, nausea, and eye strain.
• Expensive hardware: VR requires expensive hardware, such as headsets and computers, which can be expensive.
• Limited content: There is a limited amount of content available for VR, which can be a challenge for developers.

Applications of Virtual Reality

VR is being used in a variety of industries and applications, such as:

• Gaming: VR is being used in gaming to create immersive experiences.
• Education: VR is being used in education to create virtual environments for learning.
• Healthcare: VR is being used in healthcare to create simulations for training and rehabilitation.
• Entertainment: VR is being used in entertainment to create immersive experiences, such as virtual concerts and movies.

Difference Between VR and AR

VR and AR are two technologies that are often confused. While both technologies are used to create immersive experiences, there are some key differences:

• VR creates a completely immersive experience, while AR overlays digital objects in the real world.
• VR is used mainly for gaming and entertainment, while AR is used mainly for navigation and visualization.

Future of Virtual Reality

The future of VR is bright. It is becoming increasingly popular, and it is being used in a variety of industries and applications. It is also becoming more affordable, which is making it more accessible to the masses.

Some of the potential applications of VR include creating virtual events, such as concerts and conferences, and exploring virtual worlds. It can also be used in healthcare, such as for therapy and training.

Conclusion

VR is a technology that is rapidly becoming a part of our lives. The future of VR is bright, and it is only going to become more popular and accessible.

If you’re looking to explore the world of virtual reality, there are several resources available, such as VR headsets and tutorials. So, what are you waiting for? Get started with VR today and explore the world of possibilities.

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Top 10 Networking technology trends

Top 10 Trends of Software Development

New technology trends arise every day, but not all of them remain relevant. The same goes for the cloud computing industry. There are some technologies that have solid potential while others will disappear sooner than later.

Today, we take a look at the top 10 cloud computing trends of 2023 based on abundant market research and expert opinions. Cloud computing is a broad concept that covers many services, deployment models, and technologies. Its rapid adoption has led to new innovations and existing solutions optimised for this environment in almost every industry vertical. There are various Cloud Computing courses available to keep yourself updated with the ever emerging cloud technologies.

The following article details why cloud usage will continue to grow in the coming years, how businesses can capitalise on this trend to streamline their operations, and which areas of the cloud computing space will see the most innovation by 2023.

List of Top 10 Cloud Computing Trends

Multi-Cloud Solutions

The number one trend that will affect the entire cloud computing industry is the growing adoption of multi-cloud solutions. According to a recent report, more than 70% of companies use more than one cloud provider, and 23% use three or more different cloud vendors.

The reason behind this is that companies need to take advantage of the best features and pricing of each cloud vendor to optimise their IT organisations. However, this isn’t an easy task given that cloud services come with different price structures, payment models, SLAs, and feature sets.

Multi-cloud is likely to become the default choice for most organisations as it maximises their return on investment and helps them avoid vendor lock-ins.

AI and ML-Powered Cloud

Artificial intelligence and Machine learning have been on the rise in recent years, and they’re expected to reach new heights in the coming five years. AI and ML are used in many areas of business, and the cloud is no different.

AI-powered cloud services can help organisations with everything from cyber security to predictive maintenance. One of the most prominent AI and ML trends for the cloud is AI-powered image recognition.

This technology can help enterprises analyse images and identify objects within them using AI algorithms. A great example of this is Google Cloud’s image recognition solution. It can help you categorise images and detect objects in them with a few clicks.

Another trend is natural language processing. This technology allows you to analyse text and identify topics, mood, sentiment, and much more.

Cloud Security

As businesses increasingly embrace cloud-based solutions, they will require robust security solutions to keep sensitive data safe. This is where multi-factor authentication and risk-based authentication come into play.

Based on user behaviour, risk-based authentication flags and suspicious behaviour prompts users to provide additional authentication factors such as one-time tokens, passcodes sent to your smartphone, or biometrics such as voice and face recognition.

Multi-factor authentication, on the other hand, requires users to confirm their identity using multiple identifiers such as a username, password, and an authenticator app.

Another interesting trend that has emerged recently is machine learning-driven cyber security. This technology uses machine learning algorithms to predict and prevent cyber attacks, detect malware, and analyse data patterns.

Cloud Backup and Disaster Recovery

The next trend is the growing adoption of backup and disaster recovery services in the cloud. Cloud-based disaster recovery solutions are becoming increasingly popular because they’re easy to set up and manage.

Moreover, they’re cheaper than on-premise DR environments and allow organisations to achieve DR compliance easier.

Another trend we’re likely to see is the shift towards hybrid DR. Hybrid DR is the combination of cloud-based DR and on-premise DR. It’s a more cost-effective solution than on-premise DR alone, but it comes with challenges such as increased complexity.

Edge Computing

The next trend is edge computing, which is expected to become even more widespread in the coming years. Edge computing enables you to offload certain tasks from the cloud and process them at the network edge. This helps reduce network latency and improve the user experience.

Moreover, it can help businesses reduce their network costs by using cheaper equipment and removing the need for expensive WAN links. Some of the most common edge computing use cases are IoT applications, voice-over-IP communications, and authentication.

IoT Platform

The Internet of Things is a technology that’s likely to see exponential adoption in the next five years. With IoT, organisations can collect and analyse data from sensors and devices that are connected to the internet. This data can then be used to automate tasks and improve operational efficiency.

There are many different IoT platforms available on the market that can help businesses deploy IoT services quickly and efficiently. One of the top cloud computing trends for 2023 is the growing adoption of hybrid IoT platforms. These hybrid IoT platforms combine on-premise and cloud-based solutions to provide businesses with a more cost-effective and flexible solution.

DevSecOps

The next trend in the cloud computing industry is DevSecOps. This is an application of a more mature culture in the software development process, with the focus being on the security of the end product. A key difference between DevOps and DevSecOps is that the latter places more emphasis on security. This is because the software development process has become more mature, and organisations have a better understanding of their security weaknesses.

This is one of the most prominent trends in cloud computing because it can help organisations to achieve compliance with ease and less effort.

Serverless Architecture

Another promising trend is serverless architecture, which will become more relevant in the future as its adoption increases. Serverless architecture is an application architecture that has no dedicated servers or there is no concept of servers.

Instead, serverless architectures use software tools and APIs to run applications. You can host a serverless application on any cloud provider and pay only for what you use. This makes serverless architecture a very cost-effective solution. The most common serverless application areas are big data, IoT, and artificial intelligence.

Open-Source Cloud Computing

The final trend on our list is the adoption of open-source cloud computing. Open-source cloud computing is an approach that leverages open-source software and standardised resources to host applications.

With open-source cloud computing, businesses can reduce their spending on software and hardware, as well as enjoy better flexibility and scalability. Open-source cloud computing is a great choice for startups and small businesses that need to keep costs low.

Moreover, it’s a secure option for large enterprises that want more control over their infrastructure.

Service Mesh

A service mesh is a critical component of any cloud platform. It’s important to ensure that these platforms have secure and fast communication environments. Using a service mesh, you can provide customers with a dedicated S2S (service to service) communication layer. This will result in a highly secure and dynamic cloud ecosystem. Cloud platforms are still developing and adapting to new user demands. A service mesh fills these new demands and allows access to multiple policies in your cloud environment.

Conclusion

The cloud computing industry has come a long way since Amazon Web Services first launched in 2006. The potential of cloud computing is still far from being fully explored, and we’re likely to see many more innovations as the years pass.

The trends described above are likely to become more prominent in the next five years. However, it’s important to note that nothing is set in stone. New technologies and innovations may emerge that could change the cloud computing landscape as we know it.

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What is Multi Cloud Network Architecture – Aviatrix ?

Serverless Architecture vs Traditional Architecture

Technologies like virtual reality which was coined sometime in 1957 has been quickly taken over by its variation augmented reality which was an enhanced version of real-world environment which blended interactive digital elements into physical objects for better user experience. 

In today’s article we would venture more in detail about augmented reality technology, who coined this term, why it is getting so popular, its use cases and future of augmented reality technology etc.

What is Augmented Reality?

The term ‘Augmented Reality’ and first true device of its kind was created in 1990 by Boeing researcher Tom Caudell and his colleague David Mizell. These two scientists used this as a solution to simplify the jobs of workers in manufacturing units. The rudimentary device created a see-through display to be worn on the head that superimposed computerized images of the airplane schematics to guide workers during the assembly process of wiring for 777 jetliners. 

Two years after this Louis Rosenberg created Virtual Fixtures, the first AR system for use by the US air force. Since then, Augmented technology has taken a big leap in terms of performance and usability. 

It is an essentially interactive experience of a real-world environment where objects are improved upon using computer generated perceptual information across multiple sensory faculties such as visual, auditory, haptic, somatosensory or olfactory. Digital elements are infused / used to enhance the real world. 

Features of Augmented Reality

• Increase in engagement and interaction to provide rich user experience.
• Increase in perceived value of brands and products.
• Inexpensive alternative to other media platforms as no specific media is required.
• Detailed analytics to enable their audience.

Use cases 

• Automobile industry – AR in car dashboards to provide wide range of technical and travel information
• Virtual instructor for everyday maintenance such as oil change, tyre pressure etc.
• Marketing – Product sales via activation of additional brand content such as music videos, TV footage etc.
• Virtual product demos
• Banking – AR activated bank accounts to check account details such as balance, latest transactions etc.
• Hospitality – Virtual tour guides for specific city tours
• Healthcare – Practising surgery by medical students in controlled environment 

Challenges of Augmented reality

• Data overload due to creation and data sharing is all time high
• Can cause perception impairment
• Privacy threats 
• Cyber security risks 

Augmented Reality Working 

On using a device or application which is AR enabled, 

• The hardware of the device or application clicks an image of the object and shares it with a computer vision program. 
• Computer vision program processes the image and collects all information pertaining to it such as object measurements, its surroundings, distance to other objects. 
• Post applying these insights the AR enabled device builds up virtual information which is superimposed over real objects to have a unique user experience.
• In AR the user sees both natural and artificial light with a layering effect. 
• The real-world acts as the first layer. 
• The camera recognizes the target and processes the image and then augments digital assets onto their image. 

Types of Augmented Reality

There are four types of Augmented reality each having its own unique function and application:

1. Marker based 

It is also known as image recognition & uses a smartphone camera and visual marker which produces an augmented reality on sensing by camera. The marker can be a QR code or a specific image such as a movie poster, having distinct visual points. 

Markers can be used to bring still images to life or provide customers additional details. Such as for an upcoming concert one can market it by creating a poster that on scanning shows set list and plays music from the line-up.

2. Market less 

Market less augmented reality uses GPS, digital compass, or accelerometer to provide data based on location and speed to a device. Useful in showing physical objects in relation to other objects such as sizing furniture for houses on IKEA application.

3. Projection Based 

Projection based augmented reality or spatial augmented reality (SAR) projects artificial light onto a real surface. Usually done on a larger scale for a conference or event can be interactive using sensors and 3D.  helps to showcase large objects like cars

4. Superimposition Based 

Superimposition based augmented reality uses object recognition. The original image is replaced by augmented image partially or fully. Majorly used in the medical field to superimpose an x-ray onto a patient body.

Quick facts ! 

The Augmented Reality market is expected to grow to $340 billion by 2028. 

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Top 10 Networking technology trends

Top 10 Trends of Software Development

So, do you want to know how automation affected the IT sector and its benefits? Then you are in the right place. Here, in this article, you will get to know about the automation of the IT sector. Let’s get started. 

What is IT Automation? 

IT Automation, like any other automation, focuses on minimizing the manual work of the IT professional by giving a set of pre-programmed instructions to machines. It ranges from a single action process enhancement to multi-platform complex IT infrastructure. 

And sometimes, the words IT automation and IT orchestration are used synonymously. Though they vary a little bit, automation is the execution of a task without the need for manual work, whereas orchestration is where you coordinate various automated processes. 

So let’s see how it gets done,

It is very simple and uses software tools and frameworks or algorithms that instruct the machines or systems on how to execute a repetitive task. IT automation has numerous applications but it is mostly used for these three purposes. 

• Application Deployment
• Security and compliance
• Incident management

Benefits of Automation for the IT Industry

Okay now, let’s see how the automation of the IT Industry has helped this sector to grow- 

i) Low Operating costs 

As in all the cases of automation, the first and big benefit we get from automation is the low cost of the process. It’s because most of the IT development or management which can be related to software or hardware costs more to an organization, whereas the automated machine can get it done in a few hours. 

ii) Accuracy

Before automation, most software is prone to human mistakes and errors which is inevitable, if you are working for a long time on a project. On the other hand, automation not only reduces the time of the project but also gives more reliable and accurate results. 

iii) Boosts the efficiency of the organization

Human minds are meant to have new innovative ideas and find solutions for existing problems, it’s never meant to deal with the repetitive and voluminous tasks. Thus by introducing automation, human resources and efficiency are used more constructively.

iv) Quicker and higher Return On Investment 

Though adopting automation in the IT sector is considered a big investment, many organizations started to adopt them because of their higher return on investment. As productivity increases due to the increase in efficiency, there is also a quick return on investment. 

v) Flexibility and constant delivery and quality

Automation makes the production or development process more flexible, you can easily handle increasing demand, by running more automotive machines or systems. Whereas in the traditional way you need to outsource to the outsiders, which costs you. 

It offers real-time communication and can be easily reconfigured according to the new requirements. And the Delivery and quality of the products or software stay the same irrespective of the workload. 

Challenges of Automation 

The above said automation process is very useful when we see the IT field as a manufacturing or production place, but it gets tricky when we consider the service side of the IT sector. Many players offer IT services like Infrastructure as a Service, Product as a service, etc… 

In that case, adoption automation is hard as the process involves many variables and needs human thinking to solve the problems. And there is also a need for automation testers or IT professionals to over watch the automation process of the IT field. 

However, automation is indeed the key to future IT digital transformation. The advancement of Machine learning and Artificial Intelligence can soon help to resolve these challenges faced by the Automation of IT sectors. It is estimated around 40% of the DevOps and Infrastructure team will start using AI-powered solutions by 2023. 

If you have further questions please leave them in the comment section below. 

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NetBrain: Network Automation Software

Apstra (Intent Based Networking): Data Center Automation

Wireless technology has grown tremendously over the years. New and emerging technologies such as robots, drones, self-driven vehicles, new medical devices are coming into existence which is the need of Internet of Things (IOT) which will be cornerstone for the development of these technologies.

In this article we will look at some Wireless technology trends which made their place in top 10 and changed the way people and organizations communicate in future.  These trends may raise from organizations need to be more agile to market and customer demands, data security concerns , Internet of things and so on.

Top 10 Wireless Technology Trends 

1. Wi-Fi

Wi-Fi continued to stay for long time and will remain primary choice in high performance network technology for homes as well as offices. Wi Fi will have new domains to support such as radar systems, means for two factor authentications etc.

2. 5G

5G Cellular had acted as a supplement to Wi Fi technology as more cost effective and high-speed data networking in larger sites such as ports, airports, and factories.  However , 5G is still in development stage and full deployment would take five to eight years as of now most of the providers focused on selling 5G as high speed broadband only but eventually 5G will bring in improved in the areas of Internet of Things (IOT) or in case of low latency applications.

3. Vehicle-to-everything (V2X) wireless

Conventional car driving and self-driving cars both need to communicate with each other and with road infrastructure. This integration will be enabled by V2X wireless systems. V2X wireless systems will provide an additional band of other services such as safety capabilities, navigation support and infotainment.

V2X wireless systems would eventually be the legal requirements for automobile industry however to V2X systems would need 5G network to get best out of it.

4. Long range wireless power

The limitations in terms of distance still exists slightly better than cable connectivity for devices in wireless but the new technologies can charge devices at ranges up to one meter or over a table or a desk area. Long range wireless would eventually eliminate the need of power cables from laptops, display monitors, kitchen appliances, home utility systems such as vacuum cleaners and so on.

5. Low power wide-area (LPWA) networks

For low bandwidth connectivity for IoT applications in more power efficient mode to support a longer battery life. The Low power wide area networks such as Narrowband IoT (NB-IoT), long term evolution for machines (LTE-M), LoRa and Sigfox cover large areas such as big cities or even countries. Relatively inexpensive modules let IoT manufacturers enable small, low cost, battery powered devices like sensors and trackers.

6. Wireless sensing

The purpose of sensing is absorption and reflection of wireless signals. This sensing technology can be used such as indoor radar systems for robots and drones. Virtual assistants can use radar tracking system to improve their performance when several people are, they’re in one room and talking. Data from sensor is fuel for IoT systems and used in several applications such as medical diagnostics, object recognition and smart home interactions.

7. Enhanced wireless location tracking

High precision tracking of devices in wireless arena is enabled by IEEE 802.11az standard a feature of 5G networks. Location is key data point required in several business areas such as consumer marketing, supply chains, and IoT applications.

8. Millimetre wave wireless

They operate at frequencies ranging from 30 to 300 gigahertz with wavelengths in the range of 1 to 10 millimetres. This technology will be used by wireless systems such as Wi Fi, short range 5G, high bandwidth communications such as 4K and 8K video streaming.

9. Backscatter networking

Data can be sent at very lower power consumption technology in Backscatter networking.  This is ideal for small networking devices, and especially important for applications where already wireless signals are in saturated state and there is a need for simple IoT devices such as sensors and trackers in small offices and smart homes.

10. Software defined radio (SDR)

Majority of signal processing in radio systems would shift from chips into software using SDR technology. SDR technology enables radio to support wider range of frequencies and protocols. This technology is not new and available for quite some time but never taken off as it is expensive in comparison to using chips. SDR will enable support for legacy protocols and new protocols will be enabled via software upgrades.

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Top 10 Networking technology trends

Wifi6 vs Wifi5 vs Wifi4

Fog computing, popularly known as fogging is a concept that was released by Cisco in 2014. It was solely designed to connect the internet to devices at the periphery of the network. The main objective of fogging was to minimize the latency that beset cloud computing.

As we can see in the above scenario diagram, where we don’t have Fog computing setup,  the data is sent from IoT (internet of things) devices to the cloud directly.

While Fog computing uses nodes to evaluate information on the edge of the network without transferring it back to the Cloud. The idea behind fog computing is to perform as much localized processing as possible using fog computing systems, which are much nearer to the data-generating devices. This assures that only processed or optimized information is forwarded to Cloud rather than raw data and hence the bandwidth requirements are reduced.

Therefore, we can call Fog computing as a decentralized computing structure located between the cloud and devices producing the data. Currently, fog computing is establishing its foundations, and its market is expected to be worth $6.4 billion in coming years. We can call Fog as the Sum of Cloud and IOT.

Watch this video for better understanding:

(or continue reading)

How it works?

Fogging functions by building nodes all through a network. Fog nodes can be devices such as switches, cameras, routers, and controllers. The nodes can be placed in target locales, including offices and vehicles. Thus, when an IoT device generates information, it is processed in one of the nodes without being sent back to the cloud. Fog computing offers decentralized local access, which majorly differentiates it from cloud computing, which provides centralized access.   Data in fog computing follows the steps below:

• From device to automation controller
• Automation controller sends data via the protocol gateway or OPC server
• OPC server then converts the Data into an internet-based protocol
• Data is sent to the fog node for further analysis

Importance of Fog computing

Fog computing speeds up responses and awareness to events. Quick answers can boost service levels, increase safety and improve output in industries such as mining, public sector, transportation, oil and gas, manufacturing as well as utilities.  It also creates new business opportunities, such as vehicle insurance and pay-as-you-drive. The significant benefits of fog computing include lower operating cost; elevated business alertness; more in-depth insight into privacy control and better security.

The concepts help the cloud to handle two Exabytes of information generated every day from the internet. Analyzing information close to where it is needed and produced helps solve issues of excess data volume, velocity, and variety. Fog computing catalyzes awareness by eliminating the round trip of data to the cloud. By offloading gigabytes from the leading network; it reduces costly bandwidth additions and protects sensitive data. Companies that use fog computing have better business agility, improved safety levels, and customer service. It keeps up with the growth of IoT devices, which has proved impossible through cloud computing.

Advantages of Fog Computing

1. Low latency and drastic reduction in amount of data that is sent to the cloud.
2. Since the distance to be traveled by the data is reduced, it results in saving network bandwidth.
3. Improves scope for real time application and reduces the response time of the system. This results in enhancing user experience
4. System security is improved in addition to better privacy since the data resides close to the host.
5. Fog computing can provide better reliability due to reduction of data transmission burden.

Some of use cases of Fog Computing

1. Smart cities equipped with fog computing will allow traffic regulation with smart traffic signals and road barriers.
2. Smart System at home which have smart lighting, programmable shades and sprinklers and infact intelligent alarm systems.
3. In Healthcare also, doctors can consider smart choices during a case of emergency, while being secured and with reduced delays in comparison to a cloud-based application.
4. Fog computing has also been embraced by many other industries like agriculture, retail, oil & gas, Transportation and Energy.

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 Cloud Computing

Hybrid Cloud vs Multi Cloud

Talking about the virtualization concept, hypervisors technology is quite a well-known concept. A hypervisor is used by a person who would wish to merge the server space or run a number of other independent machines with the host server. Hypervisors add a touch of virtualization in a way that the data is controlled and managed centrally.

With the role of hypervisors expanding, the storage hypervisors are being used to form a centralized storage pond. Along with storage, networks are also being played with in a way that they are being formed, managed, manipulated or destroyed without even physically tampering the network devices. This is how network virtualization is being broadened.

Drilling deeper into the kinds come XenServer and KVM (Kernel-based Virtual Machine) as Type 1 hypervisors existing in the market. Since both being Type 1 Hypervisors, the question lies, which one is better? So let’s dive into the comparison part:-

XENSERVER:

An open source hypervisor recognized for its almost native performance, Xen hypervisor directly runs on the hardware of host. Xen allows formation, implementation and management of a number of virtual machines with a single host computer. Bought by Citrix Systems in 2007, XenSource created Xen. Commercial versions of Xen also exist.

Being a Type 1 hypervisor, Xen can be lodged directly on hardware of the computer without any requirement of a host operating system. Xen supports Windows and Linux operating systems. Xen can also be put to use on IA-32, ARM and x86 processors. Xen software is customizable because it has a unique structure getting virtualization everywhere.

XenServer is the first choice for hyper scale clouds of the industry like Alibaba, Amazon and Oracle Cloud and IBM Softlayer as it is easy to use with a flexible structure. An approach of detection and multilayered protection is used which makes Xen a secure option for usage. Xen’s architecture has advanced security features making it a leading choice in security related environments.

This hypervisor partitions the memory and also provides controlled execution for each virtual machine since the processing environment is commonly shared. This virtual solution is available in 64-bit hypervisor platform. Xen runs three virtual machines. A guest operating system and applications are run on each virtual machine thereby splitting the resources.

KVM (Kernel-based Virtual Machine):

A Linux inherent technology specifically converts Linux into a hypervisor that enables the host computer to operate a number of independent virtual systems also recognized as Virtual machines or guests. Initially disclosed in 2006, it merged with the mainline Linux kernel versions the following year. This open source virtual solution benefits from up to date features of Linux without the need of any additional skilful arrangement.

Being of any kind, hypervisors require some components of the level of an operating system to operate virtual machines like Input/output (I/O) stack, memory manager, process scheduler, security manager, network stack, device drivers and many more. All these components are contained by KVM since it is a part of Linux kernel. Linux gets converted into a native hypervisor through KVM and every machine is executed as a regular process of Linux organized by Linux Scheduler with committed virtual hardwares such as memory, disks, CPUs, network card, graphics adapter, etc.

To cut the explanation of its working short, you just need to install a version of Linux released after 2007 on X86 hardware which is capable of supporting virtualization. Then 2 modules, host kernel module and processor-specific module needs to be loaded along with emulators and helpful drivers which will run other systems.

Putting KVM into action on Linux based technologies like Red Hat Enterprise Linux- extends KVM’s capabilities like swapping resources, splitting shared libraries and more.

KVM is embedded in Linux so what Linux contains, KVM has it too. KVM is preferable because of its features like hardware support, security, storage, memory management, performance and scalability, live migration, scheduling and resource control, higher prioritization and lower latency.

To answer the question raised above, Xen is better than KVM in terms of virtual storage support, high availability, enhanced security, virtual network support, power management, fault tolerance, real-time support, and virtual CPU scalability. KVM is technologically stellar and contains some high quality uses but is still inferior to XEN.

Below list enumerates difference between XEN and KVM:

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Hypervisor in Cloud Computing

Type-1 vs Type-2 Hypervisors

Top 5 Type-1 Hypervisors

Financial technology or Fintech referred to back-end technology used to function traditional financial services however in today’s scenario the term has broadened to incorporate new innovations in technology in finance sector such as crypto currencies, block chain, robo-advising, crowd funding etc. 

In this article we will learn more about Fintech technology, its history, span in current times, its functions, advantages and use cases etc.

Adoption of Fintech 

Technology played a key role in every sector including the financial sector. It has come a long way and changed but what was the starting point when we adopted financial infrastructure?

Year 1887 – 1950 was an era when we started using technologies such as telegraph, railroads and steamships which allowed for the first-time rapid transmission of financial information across borders. 

Year 1950s bought credit cards, 1960 bought us ATMs and 1970s bought us electronic stock trading, in 1980s bank mainframe computers and more sophisticated data record keeping systems, 1990s bought internet and e-commerce business 

In the 21st century we are using mobile phones, wallets, payment applications, equity crowd funding, robo advisors, crypto currency and many other financial technologies which have changed the face of banking services.

Fintech Technology

In today’s digital era the traditional services once provided by financial institutions have lost their relevance and no longer meet the demands of tech savvy customers. Consumers have become used to digital experience and ease of functions as provided to them by global giants like Apple, Microsoft, Facebook etc. where by a simple click or swipe on smartphone can make tasks easier for end customers. As per the 2019 Global Fintech report the industry raised $24.6 billion with funding topping to $8.9 billion in the 3rd quarter of the financial year.

FinTech refers to technology and innovation which aims to compete with financial services to create new and better service experiences for consumers of banking, asset management, wealth management, investments, insurance and mortgage sectors. With the financial industry some of the technologies used include artificial intelligence, big data, robotic process automation (RPA), and blockchain. Artificial intelligence is used in various forms , AI algorithms are used to predict changes in the stock market and provide insight into the economy. Customer spending habits can be charted. Chat bots are used to help customers with their services. 

Artificial intelligence works best with the combination of big data and management solutions. AI analyses the performance of financial institutions, creates insights and automates essential organization processes such as documentation, client communication etc. Machine learning (ML) is key component of AI and widely used in many areas of banking sector such as:

• Fraud prevention – ML tools analyse existing fraudulent cases, detect common patterns, and evaluate and predict possible frauds and uncover discrepancies
• Risk management software analyses organization performance and detect potential threat patterns
• Fund development prediction is performed by scanning investment records, an ML powered tool can define most probable future developments
• Customer service enhancement by analysing customer data and build a smart consumer profile

Pros and Cons of FinTech

PROS

• Increase in accessibility and approachability to large section of people
• Speed up the rate of approval of finance or insurance. 
• Greater convenience to its customers by enablement of services over mobile devices, tablets or laptops from anywhere
• Low operating costs as  companies are not required to invest in physical infrastructure such as branch network
• Investments in major security to keep customer data safe and secure using technologies like biometrics and encryption 

CONS

• Limited access to soft information
• Different standards, procedures including business activities which are different then traditional banks. Have to pay higher charges imposed by OCC. 

Benefits of FinTech 

• Speed and convenience with FinTech products as products and services are delivered online in easier and quick manner
• Great choice of products and services as they can be bought remotely irrespective of location
• More personalized products by collecting and storing more and more information about customers so as to able to offer consumers more personalized products and services as per their requirements or buying pattern

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Artificial Intelligence vs Machine Learning

Data Science vs Artificial Intelligence

However, humans prefer to use natural language as a medium of communication hence research has been made regarding development of natural interfaces , usage of chatter bots or systems designed to simulate a conversation with humans require moderation hence AIML ( Artificial Intelligence Markup Language) was created.

In this article we will learn more about AIML language, how it works and where it is used etc. 

About AIML

AIML was created by Wallace in collaboration with six software developers communities from 1995 to 2000 based on the concept of Pattern recognition, matching pattern technique. It is applied to natural language modelling for dialogue between humans and chatbots which follow a stimulus response approach. A set of possible user input is modelled and for each of stimuli , pre programmed answers were built to be displayed to the user.

The ALICE (Artificial linguistic internet computer entity) chatbot was first to use AIML language and interpreter. In ALICE , the AIML technology was responsible for pattern matching and to relate user input with a response chatbot knowledge base (KB). 

The AIML language purpose is to make the task of dialogue modelling easier as per stimulus-based approach. It is an XML based markup language and tag based. Tags are identifiers which are responsible to make code snippets and insert commands in the chatterbot. AIML defines a data object class called AIML which is responsible for modelling patterns of conversation.

Syntax

The general form of an AIML object/command / tag has the following syntax.

<command> ListOfParameters </command>

An AIML command comprises a start tag <command> , a closing tag </command> and a text (ListOfParameters) which contain the command’s parameter list. AIML is interpreted language and as such each statement is read, interpreted and executed by a software known as interpreter. AIML is based on basic units of dialogue, formed by user input patterns and chatbot responses.

These basis units are known as categories, and a set of all categories make chatbot KB. The most noted tags in AIML are : category, pattern and template. The category tag defines the unit of knowledge of the KB, pattern tag defines possible user input and template tag sets the chatbot response for a specific user input.

The AIML vocabulary comprises of words , spaces and special characters “*” and “_” which are wildcard characters. Wildcards are used to replace a string (Word or sentences) . The AIML interpreter gives high priority to categories having patterns which use wildcard “_” and “*” and they are analysed first. Each object/tag has to follow XML standard hence an object name cannot start with a number and blanks are not allowed.

AIML Tags

Each AIML file begins with <aim1> tag and is closed by a </aim1> tag. This tag contains the version and encoding attributes. The version attribute identifies the AIML version used in KB. The encoding attribute identifies the type of character encoding which will be used in the document. 

Example of AIML code

<aim1 version = “1.01” encoding=”UTF-8” 

Basic units of AIML dialogue are called categories. Each category is a fundamental unit of knowledge contained in chatbot KB. A category consist of a user input in the form of a sentence and a response to user input presented by chatbot and an optional context

A KB written in AIML is formed using a set of categories. The categories are organized using subjects and stored in files with .aim1 extension. Category modelling is made using the <category> and </category> tag. 

<category>

<pattern> hello Bot </pattern>

</template> 

</category> 

The pattern tag contains a possible user input. In each category there is a single pattern and it must be the first element to be set. Words are separated by single spaces and wildcards can replace parts of a sentence. 

The template tag contains possible chatbot answers to the user. It must be within scope of a <category> tag and placed after the <pattern> tag. Most of the chatbot information is bound to this element. This tag can save data and activate other programs or responses.

AIML use cases

• Virtual agents in the form of chatbots as customer service agents to interact to humans and answer their queries
• Deriving meaningful information from digital images, videos and other visual inputs
• Self-driving cars powered by sensors which aides in mapping out immediate environment of the vehicle
• Enable emotions expressed by humans to be read and interpreted using advanced image processing or audio data processing
• Space exploration 
• Robotics process automation 
• Biometrics recognition and measurement to foster organic interactions between machines and humans 

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Artificial Intelligence vs Machine Learning

Data Science vs Artificial Intelligence

Convergence of the physical world and digital world has changed how businesses are interacting with customers nowadays. Robotic Process Automation can be tracked back in early 2000 , it has progressed and developed over a number of years. RPA comprises of three major technologies – Screen scraping (ability to capture data from legacy application interface and display using Morden user interface) , workflow automation (ability to remove manual process of data entry) and Artificial Intelligence (ability to learn from perceived behaviour and perform actions which require human intervention) 

Today we look some more in depth about what RPA is ? What are the benefits and how this evolved over the years and so on . 

What is Robotic Process Automation

RPA uses software with Artificial Intelligence (AI) and machine learning capabilities to perform high volume, repetitive and redundant tasks which required earlier involvement of human intervention and consumed loads of human resources and involved huge labour costs and prone to errors due to their nature of repetitiveness.

RPA comprises software robots commonly called ‘Bots’ which can mimic a human behaviour and perform actions like data entry, calculations and maintenance of transactions or records.

Categories – RPA 

RPA has the capability to learn from actions and situations; it is capable of learning and adapting to changes in circumstances, exceptions, and new situations; it captures and interprets actions of specific processes and applications deployed in the environment. Based on the perceived learning it would trigger responses, manipulate data, take new actions, and communicate to other systems as per the need.

RPA improves efficiency, boosts productivity, and saves costs by replacing manual, routine, and often error-prone processing jobs due to their repetitive nature and still performed manually by human labour in many organizations. 

• Probots – perform simple and repetitive tasks to process data
• Knowbots – perform search on Internet and store user specific information 
• Chatbots  – handle customer queries in real time.

Benefits of RPA 

Table 1: Pros and Cons of RPA 

RPA Pros	RPA Cons
Reduction in long hours – repetitive tasks can be handled for long hours which other be left to humans to perform	Resistance to change could be one of factor in RPA deployment . People are habitual to a routine and any change could cause stress for employees being part of new technology implementation and change in routine work culture and taking up new responsibilities
Enhancement in employee productivity as repetitive tasks are handled by RPA and employee can do some more productive task	Long turn sustaining could be a concern using RPA where tendency is more on quick fixes rather than doing things correctly from start in the long run
RPA performs tasks with zero defect especially in case of repetitive tasks as they are prone to errors when performed by humans	Initial implementation require more precision which involve time and money.
Speed up repetitive tasks and perform it in more efficient manner where humans get tired and bored	RPAs cant detect obvious errors which humans can detect such as data errors
Enhancement in customer experience such as reduction in waiting time while waiting for response on a query, 24*7 support without human intervention	RPA may not be a good fit where precision requirements are very high such as managing purchase invoices where data accuracy is critical
Data gaps between multiple sources of information is removed and actions performed by RPA Bots are logged / stored	RPA are made custom fit as per business requirements hence they require ongoing maintenance and minor change in business setup could lead to major disruption in RPA.

Use and Applications 

Many businesses are using RPA significantly however it is an ideal fit for some specific applications as under : –

• Customer service – Automation of customer call center tasks is one of the primary usage areas for RPA. Helpdesk support, verification of e-signatures, scanned documents upload into systems and verification of details uploaded for automatic approvals and rejections.
• Accounting services – General accounting , operational accounting , transactional reporting, and preparation of budget.
• Financial services – Opening new bank account, closure of account, processing insurance claims, Foreign exchange payments, handling audit requests.
• Healthcare services – managing patient records, claim processing, customer handling and queries, billing , reporting etc.
• Human resources services – Payroll services, managing employee records, Onboarding and offboarding of persons, timesheet submission and maintenance, leave records etc.
• Supply Chain Management – Procurement , automation of processing and payments, inventory management , shipment tracking , order tracking, dispatch etc.

Some top RPA vendors are Automation Anywhere Inc. Blue Prism , EdgeVerve Limited, HelpSystems, Workfusion,  UiPath and so on. The range of services start from digital workforce platforms to perform HR, claims management, back office processes , customer services, IT, and business processes automation etc.

Factors to consider while choosing – RPA 

Table 2: Factors to be considered while choosing RPA for Organization 

Functions	Features
Scalability	Centrally managed and scalable
Speed	Design and test new robotics quickly and optimize them as per need
Reliability	Tools with inbuilt monitoring and analytics so as to enable to monitor health of systems
Simplicity	Easy to use and able to handle various kinds of work
Intelligence	Advanced learning feature to improve automation
Enterprise Class	Scalable, reliable, and manageable in nature
Used cases	IVR, Factory Floor, Batch jobs, Customer help-desk ,

Way Ahead – 

The RPA industry would reach USD 3.11 billion by 2025 as per latest study conducted by Grand View Research Inc.  As per a report published by Deloitte consulting 50% of the tasks performed by employees are considered mundane , repetitive, and labour intensive and RPA technology will replace 16% of repetitive tasks by Year 2025. 

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Artificial Intelligence vs Machine Learning

Data Science vs Artificial Intelligence

Today we look more in detail about two important terminologies Business Intelligence and Data warehouses which are helping businesses to take more informed and better decisions to shape up the right direction for their businesses and place themselves ahead over their competitors in the market. This article will give insight into the major differences between the two, its purpose for which they are deployed and use cases. 

About Business Intelligence

Business intelligence is a set of tools and methods which are used by organizations for exploration and accessing data from multiple varied sources to better understand how their businesses are performing and let them make better and more informed strategic decisions for the betterment of the organization as a whole and also bring in new business opportunities. 

Business intelligence gives insight into what is happening or happened in business and gives actual description of the situation currently more like a real time scenario. Data is stored in Data Warehouse (DDs, cubes) and Business intelligence systems make use of Data Warehouse data and you can apply metrics of your choice to huge unstructured data sets and query or perform mining , online analytical processing and generate reports as well as business performance monitoring, predictive and prescriptive analytics.  

Applications of Business Intelligence 

• Sales intelligence
• Visualization
• Reporting
• Performance management

About Data Warehouse

Data Warehouse is consolidation of data from multiple sources which actually gives foundation to derive business intelligence from it. It helps organizations in making better strategic and tactical decisions. We can say in other words Data Warehouse is the backbone for business intelligence. Databases store data of different sources , types in a common format and Warehouse is like a Godown for data where many things can be stored but with intelligent algorithms like indexing, pattern matching it makes it easier to locate and retrieve data faster. 

Data Warehouse is like a relational database which is aimed at querying and analysing the data instead of transaction processing. It contains historical data derived from transactional data but might have data from various data sources. Data Warehouses hold data in Fact tables and dimensions. 

Applications of Data Warehouse 

• Used by varied industries such as Banking, finance, education, Healthcare, manufacturing and distribution , retail , insurance etc.
• High performance queries on large data volumes 
• In depth data exploration
• Historical data collection across organizations 
• Easy interface for business analysts and ready data for them  

Comparison Table: Business Intelligence vs Data Warehouse

Below table summarizes the differences between the two terms:

FUNCTION	BUSINESS INTELLIGENCE	DATA WAREHOUSE
Definition	Strategy and technology used by businesses for analysis of data to get insights into businesses.	Historical data and current data repository collected by an enterprise from various operational systems.
Source	Business intelligence uses data from Data Warehouse  or data marts as a source to perform analytics.	Data Warehouse may obtain data from multiple sources.
Outcome	Business reports, charts graphs, data visualization , dashboards etc.	Fact and dimension tables storing data which is ingested into business applications and tools.
Used by	C-level executives , managers and data analysts	Data engineers, data analysts, business analysts, Backend developers.
Tools used	MSBI, QlikView, Cognos , Clear Analytics, SAP Business intelligence Oracle Hyperion system Pentaho BI etc.	Ab Initio Software, Amazon Redshift, Informatica etc.
Characteristics	Helps to identify , develop and create new business strategies and opportunities.	Helps to store data in a centralized repository for analysis.

Download the Comparison table: BI vs Data Warehouse

Conclusion

Business intelligence and Data Warehouse are two important columns of support for any enterprise business.  Data is critical in today’s business scenario and working with data can be tough as data sets are huge and scattered all around. Big data sets and managing them is a tedious task and its meaningful analytics can give business new heights and help businesses to grow in diverse areas and be ahead of their competitors in the market. 

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Business Intelligence vs Data Science

Data Science vs Artificial Intelligence

Today we look more in detail about two important technologies Business intelligence and data science , understand the difference between them, the purpose for which they are deployed and how use cases etc.

About Business Intelligence (BI) 

Business intelligence is a set of technologies, applications, and processes which are used by organizations in business data analysis. It is used for conversion of raw data into meaningful information which will be used to make informed business decisions which are profitable. It may deal with structured or unstructured data sets and supports decision making based on actual facts derived from data sets and not on assumptions. It enhances the organization’s chances to enter new markets , mergers and acquisitions etc. 

BI tools are used for analysis and reporting. They are also used to produce graphs, dashboards, summaries and charts to help in better decision making. BI data is stored in data warehouses and also supports real time data which is generated from services hence it helps in strategic decision making. 

Uses of Business Intelligence

• Measurement of performance and quantify progress towards business goals
• Quantitative analysis via predictive analytics and modelling
• Data visualization and storage in data warehouses for further processing in OLAP
• Use of knowledge management programs to derive effective strategy to gain insight into learning management and raising compliance issues

About Data Science    

In the data science field information and knowledge can be extracted from the data using several scientific methods, algorithms, and processes. It is a combination of mathematical tools, algorithms, statistics, and machine learning techniques and identifies hidden patterns and insights into data which helps in facilitating decision making processes. Data science deals both with structured as well as unstructured data. It deals with both data mining and big data. It also involves historical trends study and use conclusions to redesign present trends and define future trends.

Data science focuses on generation of insight into the data which are resulting from complex predictive analytics and output is not presented as a report but as a data model. You can opt for a Data Science Online Course to get yourself acquainted with the Data Science technology.

Comparison Table: Business Intelligence vs Data Science

Below table summarizes the differences between the two terms:

FUNCTION	BUSINESS INTELLIGENCE	DATA SCIENCE
Definition	BI is accumulation of processes and technologies which takes raw data and derive something meaningful out of it	DS is all about gathering of raw data and analyse that data with statistical techniques and algorithms to bring out insight and conclusions
Scope	Analyses historical data to discover patterns and trends so business can take more informed decisions	It uses both historical and present data and uses it to create as much as impact possible onto business
Approach	Takes analytical approach to build decision making applications	Takes predictive and prescriptive analysis to generate insights into data and more complex in nature
Deals with	Deals with what has happened?	Deals with what will happen and what if
Type	Handles both structured and unstructured data sets which is static in nature (tabular data sets , relational models)	Handles both structured and unstructured data sets which are dynamic (text, audio, images, IoT signals etc.)
Focus on	Descriptive analytics: what happened why did it happen, what is the learning ?	Predictive and Prescriptive analytics : what will happen in future, how we prepare ourselves ?
Storage	Data is stored in data warehouses	Data is distributed in real time clusters
Procedure	Help organizations to solve questions	Used by data scientists (questions are curated and solved)
Goal	Identification of patterns and trends to turn raw data into insights	Test hypothesis using experimentation and iteration
Presentation	Findings are presented in the form of charts, dashboards, graphs, summaries etc	Findings are presented as algorithms and statistical models
Tools	MS Excel, SAS BI, Sisense, MicroStrategy, Cyfe, TIBCO Sportfire, Klipfolio etc	Python, R, Hadoop/ Spark, SAS, TensorFlow, BigML, MATLAB etc.

Download the Comparison table: Business Intelligence vs Data Science

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Data Science vs Artificial Intelligence

Artificial Intelligence vs Machine Learning

Are you interested to know more about Artificial Intelligence? And the recent developments and achievements in it? Then you are in the right place. 

Here in this article, you will get to know about Artificial Intelligence, types, uses, future, and Pros and cons. Okay without further ado let’s start the article. 

What is Artificial Intelligence? 

There are various definitions and explanations for the term Artificial Intelligence (AI). John McCarthy, the founder of Artificial Intelligent Discipline says- 

“It is the science and engineering of making intelligent machines, especially intelligent computer programs. It is related to the similar task of using computers to understand human intelligence, but AI does not have to confine itself to biologically observable methods”. 

In simple words, it is the integration of multiple technologies to build a machine, which performs the task that needs human intelligence or a human-like approach. That’s the system that acts like humans – Rational thinking and acting. 

The famous examples of Artificial Intelligence are – Siri, Alexa, Google Assistant, etc… 

How does it work? 

Artificial Intelligence works based on various concepts like machine learning, deep learning, Natural Language Processing, etc… But in simple words the AI works based on the Data. It combines large data with iterative processing and intelligent algorithms, which helps it to find a pattern or features.

Machine learning helps AI to imitate human intelligence. For example, it learns large data showing decisions taken by humans in a particular situation, and when the situation arises it does the same. 

Types of Artificial Intelligence 

The AI technology has been classified into two types based on their focus here, are they – 

i) Weak or Narrow AI 

Artificial Narrow Intelligence (ANI) is trained to perform tasks that assist humans. For example the AI Assistants in the mobile phone and autonomous vehicles etc… This AI has often focused on performing the same task again and again. 

Though they seem intelligent they operate under the limited space and have various limitations than ordinary human intelligence. They are formed using Machine learning and Deep learning techniques (using large data resources – Big Data) 

ii) Strong or General AI

These are the AI we see in the movies and fiction. They share the same intelligence as human beings, it can apply that intelligence to solve any problem. However, the quest for Strong AI is still at the starting point. 

Applications or Uses of the AI: 

Artificial Intelligence has been put into use in different fields, here are some of them – 

• Healthcare – AI is playing a very important role in the healthcare sector. One of the best-Known healthcare is IBM Watson. It understands the natural language and responds or diagnoses faster than humans. 
• Education – AI has been used for grading and checking the student’s answer sheets. And the recent development of AI tutors plays additional support to the students. 
• Manufacturing – Introduction of AI in the Industries and factories reduce the waste of cost in repetitive works. Automation, multi-tasking is made available because of AI. 
• Finance and Banking – AI virtual assistants and chatbots in the banks help their customers to be aware of the services. And the AI is also used for decision-making for loans, credit limits, and KYC purposes. 
• Transportation – AI technologies are used in Flights and traffic lights. Other than these NAI is used to manage the records of flight booking, shipments, and other transportation. 

Other than these the AI technology is used in almost every technical advancements made in recent years. 

Pros of Artificial Intelligence 

• Best choice for detailed-oriented jobs
• Consistent delivery 
• There is a good scope for virtual available 
• Fast in processing data-heavy tasks

Cons of Artificial Intelligence 

• Expensive 
• Need of deep technical expertise
• Only knows what is in the data
• May create unemployment in certain sectors 

Though the future is unpredictable, there is a good future for AI-based technology. If you have any more questions regarding AI and its nature please leave them in the comment section below. 

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Top 10 Networking technology trends 

Supercomputer vs Minicomputer

Organizations need to support digital infrastructures as infrastructure trends now focusing on new technologies such as Artificial intelligence (AI), Edge computing to support rapidly growing IT infrastructure and support agile business needs parallelly.

In this article, we will look at some infrastructure and operations trends in which shaped how support for digital infrastructure will grow over the years. Business continuity and disaster recovery are the prime focus for organizations had bought trends like colocation, cloud services, edge locations and more and more focus are on uptime guarantee.

Top 10 Trends Impacting Infrastructure & Operations 

Automation Strategy/Rethink

With growing automation maturity level in organizations and multi cloud adoption with new flourishing solutions providing a rich ground for poor tool choices made in the past leading to messy implementations and lack of scalability. Multiple vendors are coming up in market with new tools risking organizations to pick up or deploy duplicate tools leading to process overheads and hidden costs which cumulatively form a situation where scaling of infrastructure can’t be achieved as per business expectations. Top management is expected to employ a dedicated role to oversee automation forward and invent to build a proper automation strategy.

Hybrid IT versus Disaster Recovery (DR) choice 

Hybrid IT with on premises data centres, edge locations and cloud services are disrupting disaster recovery planning. Organizations are potentially exposing themselves when their traditional disaster recovery plans are not reviewed with hybrid infrastructure as resiliency requirements required to be evaluated at every stage of design right from the beginning and not just after the deployment.

A redundancy strategy which seamlessly responds to all infrastructure needs be it hybrid or cloud and address all IT abnormalities and interdependencies to support quick resumption of normal IT operations is the need of the hour.  Best way to achieve redundancy is to have a copy of your data which can be accessed onsite immediately as well as copy which is located off-site. Hybrid cloud helps in providing the secondary offsite backup location.

Scaling DevOps agility

Enterprise production teams are working in DevOps environments increasingly so the challenges of scaling DevOps must be addressed and ensure necessary skills are available to scale self-service platform services. A shared self-service platform services will provide a digital toolbox of I&O capabilities to help multiple DevOps teams to create, release and manage products along with ensuring consistency and streamlining of efforts at scale.

Infrastructure is everywhere and so is data

Infrastructure is everywhere as business needs it. Planning for explosive data growth is vital to business as technologies like Artificial intelligence and machine learning are being used as competitive differentiator more and more. The increased focus is on same workloads running across multiple locations and making data harder to secure. Availability and integrity of critical applications and data in the event of disaster is achieved via continuous replication and it captures all changes which occur considering real time replication strategy, write order fidelity and any point in time recovery.

Overwhelming impact of IoT

Far reaching and transforming effects of IoT projects and their intrinsic complexity can be a challenge for team to understand each piece of IoT puzzle. I&O must be involved at early stages in IoT planning discussions to understand proposed services model and its scale.

Distributed Cloud 

As more and more businesses are moving to cloud in a distributed model where public cloud services are available in different geographical locations while the provider remains responsible for operations, governance, updates, and evolution of the provided services. This model is likely to appeal organizations which are constrained by location of cloud services previously.

This trend is still in its early stages and operational leaders need to analyse if distributed cloud is right choice for their needs based on some questions such as how life cycle of hardware will be managed? What SLA would be needed? And how SLAs will be met? etc.

Immersive Experience

Operational leaders’ expectations are on rise, which are influenced majorly by wide and rich experience provided by consumer technology from digital giants. The functions considered value adds earlier and now forming the baseline expectations. User expects seamless integration, immediate feedback, and very high level of availability.

IT Democratization

Less code and no code platforms enable users to build at fast pace applications using minimal coding approaches. This has enabled citizen development to save resources and time. But a poorly developed approach risks increasing the complexity of IT group. Operational leaders need to build governance and support offerings which are not tough but easy for users.

Networking

Networking has evolved to delivery highly available networks and it is often achieved via careful change management. Some of the challenges which network teams are going to face going forward such as risk avoidance, technical debt, and vendor lock-in all mean more challenges and tough road ahead for network teams.

Multi-protocol lab switching technologies (MPLS) allow carriers to focus on developing IP networks which would support both public Internet and private WANs across IT infrastructure. In SD-WAN technologies there is a steep decline and focus is shifted towards Internet. Advances in machine learning and low costs high performance processors in today routers allow SD-WAN algorithms in making routing decisions. Performance data collected about each link will help businesses to make better purchasing decisions with internet carriers.

Hybrid Digital infrastructure management (HDM)

Operational leaders need to look at tools that challenge silos of visibility. Some vendors are already trying to address this however there are emerging tools which are not able to answer all challenges imposed by hybrid digital infrastructure management so I & O leaders need to do a careful examination to evaluate functionality promised and anticipate their own teams may be forced to fill gaps by integration of tools and growing their baseline.

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Top 10 trends in Automation Testing Tools

Top 10 Cybersecurity trends

In this article we will look at some automation testing tools which made their place in top 10 in 2021.  Continuous integration (CI) and Continuous development (CD) is the need of the hour and focus is shifted from shortening testing times to effective usage and better coverage of testing tools.

Top 10 trends in Automation Testing Tools

Here is the list of top 10 automation testing tools which are trusted to address the automation challenges. Tools selection is based on Support for APIs and services testing, AI/ML capabilities, Maturity, and popularity.

Tabular Comparison: Automation Testing Tools

Selenium –

It is considered a benchmark to use Selenium for user interface testing of web applications. Users can create / write scripts in different languages (such as Java, Python, C#, PHP, Ruby and Perl) which can run on multiple platforms such as Windows, MAC and Linux and supported browsers are Chrome, Firefox, Internet explorer and Headless browsers). The first alpha version of Selenium was released in 2019. To use this product advanced programming skills are required.

Katalon Studio–

It is a very comprehensive and powerful tool for testing API, Web, mobile, and desktop application testing. It has multitude of features and supports multiple platforms such as Windows, MacOS and Linux. Katalon Studio provides a unique integrated environment for testers to integrate and deploy different framework and libraries to use Selenium and Appium. It has a comprehensive set of automation of API/Web services, and mobile applications.

It has hundreds of built in keywords for creation of test cases, supports BDD Cucumber to express tests cases in natural languages, meant to be used both for manual and exploratory testing, test capabilities can be extended via plugins on Katalon Store.

UFT One –

Known as UFT is a commercial tool to test web, desktop, mobile and RPA applications. It is being extended to include a set of capabilities for API testing. It supports multiple platforms for target applications under test (AUT), UFT provides advanced capabilities for smart object detection, image-based object detection and correction.

The updated version of UFT V1.5.0 offers features that streamline testing processes, improve testing efficiencies, and sustenance of quality and reduction in testing time. It has intuitive user interface for creating, executing, and reporting API tests, supports generating API tests for WADL documents, Visualizing in diagrams for tests actions, activities, and parameters.

TestComplete –

TestComplete supports web, mobile and desktop applications testing. Testing scripts can be written by testers using JavaScript, VBscript, Python, C++ script. It has an object recognition engine which can detect dynamic user interface elements precisely. Suitable for applications having dynamic and frequently changing user interface.

Its version 14.4 released in April 2020 includes integration with Jira and cross platform testing permit users to record and create manual web tests for supported browsers. Self-healing function leverages the AI based algorithms.  It has playback feature like Katalon studio. Checkpoints can be inserted into test steps for verification of results.

SoapUI –

It is a tool of choice for testing API and services. It is especially designed for API testing. SoapUI supports REST and SOAP services. API testers can use open source or pro version. Pro edition has user friendly interface and many advanced features such as assertion wizard, form editor and SQL query builder.

This tool offers many advanced capabilities such as: – Test generation using easy drag and drop, point, and click feature, Asynchronous testing, reusable scripts, and mock services creation with RESTful mocking. It also allows developers and testers to debug API responses. The latest version 5.5 released in February 2019 added the endpoint explorer dialogue to help users in sending exploratory requests and analyse responses without creating projects.

IBM Rational Function Tester (RFT) –

It is a test automation tool meant for testing applications which are developed using different languages and technologies exploratory requests such as Web, .Net, Java, Visual Basic, Siebel, SAP, PowerBuilder, Adobe Flex, and Dojo Toolkit. It is a data driven test platform for regression and function testing.

Some of the features of RFT are visual editing via screenshots to have a storyboard format to represent test actions which allow users to address user interface changes and reduction in maintenance overheads, Advanced ScriptAssure technology, early data detection, choose between Java or Visual Basic .NET, Storyboard testing which helps to edit and visualize tests using application screenshots and natural language. It can integrate with other IBM applications lifecycle management tools such as IBM Rational Team Concert and Rational quality manager.

Tricentis Tosca –

It is a continuous testing platform which provides comprehensive toolset to support all testing activities from test design to test automation. This tool has many features such as dashboards, analytics, integration, and distributed executions to support continuous integration and DevOps practises. It has a user-friendly interface and has feature set for designing, implementing, executing, managing, and optimizing API tests.

It can be easily integrated with DevOps processes, API tests across browsers, mobile devices and platforms is supported, supports multiple standards and protocols  – HTTP(s), JMS, AMQP, Rabbit MQ, TIBCO EMS, SOAP, REST and IBM MQ , it has good set of reporting and analytical capabilities. It supports API security configuration in API connection manager and allow users to use signature security option to sign in into multiple parts of messages.

Ranorex–

It is being around for quite some time and provides a comprehensive set of features for web, mobile, desktop, and API testing. It leverages experience of desktop-based test automation, UI element identification, editing, and management. It has user friendly GUI interface, record/playback, and script generation. It can be integrated with Selenium grid to enable distributed testing with parallel test executions. Version 9.3 has enhanced Jira and TestRail reporting.

Postman –

It is another automation tool designed for API testing. Users can use this tool as a browser extension or a desktop application on Windows, Linux, and Mac OS platforms. It is popular among developers equally who use this tool to develop and test APIs. It has comprehensive feature set for designing, debugging, and testing, documenting, publishing APIs, it has user friendly interface, and supports both automated and exploratory testing, it accepts Swagger and RAML API formats.

Release 7.2 in June 2019 extended its support for GraphQL request and schemas, GraphQL query auto-completion function, and GraphQL variables.

Apache JMeter –

It is an open source tool for testing test load and performance. The tool is used for API and services testing, it is lightweight and easy to use user interface, test results can be replayed, supports .csv files to set API parameters values, supports integration with CI tools such as Jenkins. Version 5.2 released in November 2019 supports JMESPath extractor, JDBC improvements, StringtoFile, HTTP Samplers.

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Top 10 wireless technology trends

Top 10 Networking technology trends

Now the big companies like Google and Amazon included Green Computing as an important objective in their plans. So what does this Green Computing mean? Why it is important?

In this article, you will get the following detailed explanation about Green computing –

What is Green Computing?

Green Computing refers to the usage of computers and their resources in an Eco-friendly and responsible way. The term Green computing means a lot. It involves the study of designing environment-friendly and reusable computer or hardware architecture. 

The Goal of Green computing is the same as other Green Evolution concepts, to reduce the hazardous materials in the environment and increase the efficiency and viability of the process. The Green Computing process is very important and should be practiced by everyone. It covers from the large multi-national organization’s servers to the single personal usage computers. 

Many Corporate companies are spending huge investments in making their IT process environment friendly. Here is how the idea of Green Computing started. 

History of Green Computing

It all started when the U.S. Environmental production agency launched the Energy Star program in 1992. The Energy Star program measures and labels the energy efficiency of the monitors, climate equipment, etc… This started the creation of sleep mode and other energy consumption techniques in consumer electronics. 

All these Green Talks started when people realized that the earth is not an unlimited source of resources. The resources like minerals, foods, and other life support are limited in the earth so the energy. People realized that e-waste is becoming a large threat to human survival. So the idea of Green Computing started. 

Objectives of Green Computing

The major objective of Green Computing is to achieve optimal computing efficiency without affecting the existing resources (environment). The following can be considered as the objectives of green computing – 

• Minimizing the e-waste disposal
• Using energy created in an environmentally friendly way (solar energy). 
• Minimizing or optimizing energy consumption. 
• Reducing the printer ink (carbon) and paper usage. 

Green Computing Approaches

To achieve the final goal of Eco-friendly designs, the following four principal methodologies are used.

i) Green Use

It focuses mainly on reducing the PC or server’s power consumption. It is important to consider the life cycle of the system before inducing these green use methods. The system should be durable for a long time at need low power. The PC management methods like the sleep mode, power-saving mode, etc… are created to achieve the green use

ii) Green Disposal

This methodology focuses mostly on the reuse or disposal methods of computers. As the technologies develop and change rapidly, one system will become outdated soon. Instead of throwing the old system, you can replace the old hardware with a new one. 

And it advised recycling the old computers. Certain parts of the computers can be reused and help a lot in the reduction of e-waste. 

iii) Green design

The production of systems that are both effective and efficient and at the same time doesn’t affect the environment is the main goal of this methodology.  There is not much progress in the methodologies but still, we are doing large research in this category. The Energy Star values the Environment friendliness of all the existing systems. And recommending the big business adopt them. 

iv)  Green Manufacturing 

The main problem with electronic systems is they are not easily degradable. And this creates large e-waste. Green Manufacturing focuses on using bio items in computer manufacturing and eliminates the hazardous or poisonous substances in electrical appliances. 

Conclusion

Though the world realized the need for Green computing a little late, it’s not too late to adopt them. And it is not only for the large organizations, even common people can help the final goal of Green computing in the following ways – 

• Power down your CPU peripheral during an extended inactivity period. 
• Use the power management features like sleep mode, display brightness, etc…
• Dispose of e-waste according to the government regulations. 
• Use green energy sources (solar energy etc…)

If you want to know any more about green computing please leave them in the comment section.

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What is Fog computing?

Introduction to Edge Computing