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)

Unlocking Potential: The Role of AI in Modern Business Operations

AI enablement is no longer just a trend but a necessity for businesses looking to stay competitive. By leveraging AI-driven systems, companies can process vast amounts of data, recognize patterns, and gain actionable insights that improve accuracy and reduce human error. Technologies like machine learning ensure that AI-driven operations continuously evolve, allowing businesses to anticipate challenges and adapt to market changes proactively.

Beyond efficiency, AI enhances scalability and workforce productivity by automating repetitive tasks, freeing employees to focus on strategic and creative work. Whether streamlining customer service through chatbots or optimizing logistics, AI seamlessly integrates into various business functions, fostering a balance between human expertise and intelligent automation.

Streamlining Process Efficiency with AI-Driven Automation

AI-driven automation is revolutionizing business processes by automating complex tasks that once required extensive human intervention. These systems operate around the clock, reducing process completion times and increasing productivity. AI algorithms can predict market trends, automate restocking orders, and schedule shipments, minimizing inventory costs and ensuring timely deliveries. This not only increases efficiency but also reduces overhead costs.

AI-powered chatbots and virtual assistants provide real-time customer support, enhancing customer satisfaction and freeing up human resources for more complex interactions. This level of support not only enhances customer satisfaction but also frees up resources for more complex interactions. AI-driven automation is also crucial for robust cybersecurity defenses, as it can detect and respond to security incidents more rapidly than human systems, protecting sensitive data and assets from potential breaches.

Leveraging AI for Enhanced Data Analysis and Decision Making

AI plays a crucial role in modern business strategy by sifting through vast amounts of data to uncover actionable insights. This enhanced data analysis allows companies to make informed decisions, staying ahead of market trends and customer preferences. AI-driven data analysis also aids in personalizing marketing efforts, creating a more engaging customer experience and increasing loyalty and sales.

Predictive analytics can forecast future trends, allowing businesses to prepare strategies in advance. AI systems also help make complex decisions under uncertainty, assisting leadership in understanding potential outcomes and risks. In financial decision-making, AI’s analytical prowess in credit scoring, fraud detection, and algorithmic trading improves investment and credit decisions, boosting financial morale and stability.

Transforming Customer Experiences with AI Integrations

AI integration in customer service operations is revolutionizing the customer experience by enhancing satisfaction and fostering loyalty. AI-powered algorithms guide every aspect of the customer journey, from product recommendations to post-sale service, ensuring a personalized experience. Companies can predict customer needs and suggest products based on past behavior, increasing sales.

AI-driven personalization extends beyond sales to customer support, providing relevant solutions based on specific interactions and purchase history. AI also extracts valuable insights from customer feedback and social media interactions, enabling businesses to gauge public perception and influence product development and marketing strategies. The goal is to deliver a personalized and efficient customer experience, streamlining service and product discovery processes, and continuously learning from customer interactions.

AI-Powered Innovation: Fostering Competitive Advantages in Various Industries

AI is revolutionizing various industries, including healthcare, automotive, agriculture, finance, manufacturing, retail, and retail. Healthcare uses AI-powered diagnostic tools and personalized treatment plans to improve patient outcomes and reduce costs. Automotive uses AI for self-driving technology and intelligent navigation systems, while agriculture uses precision farming techniques.

Financial sector is receptive to AI innovations, fostering secure, efficient, and customer-friendly fintech solutions. Manufacturing uses AI for predictive maintenance and optimized production lines, reducing downtime and improving output. Retail uses AI to manage inventory and customize shopping experiences, leading to improved sales and customer satisfaction. This comprehensive application of AI is giving businesses an edge in the global marketplace, allowing for growth and profitability.

Altogether, AI enablement is not just revolutionizing business operations but is also setting a new groundwork for innovation across industries. As artificial intelligence continues to evolve, its potential to drive growth, shape customer experiences, and enhance decision-making will remain a cornerstone for businesses seeking longevity and success in the digital age.

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3 Different Types of Artificial Intelligence – ANI, AGI and ASI

Artificial Intelligence vs Machine Learning

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

In today’s topic we will learn about the role of neural networks in artificial intelligence, why neural network is important? types of neural networks, use cases for neural networks. 

What are Neural Networks?

The very first neural network was conceived in 1943 by Warren McCulloch and Walter Pitts. They wrote a paper on how neurons work and created a simple neural network using electrical circuits. This advancement in the domain of neural networks paved the way to further enhance research in two areas: human brain Biological processes and application of neural networks in artificial intelligence (AI). 

Quick acceleration of artificial intelligence (AI) research happened with Kunihiko Fukushima who developed the first multi-layered neural network in 1975. The original goal of neural network approach was creation of a computation system which could solve problems as the human brain does but later the focus is shifted to perform specific tasks such as speech recognition, computer vision, machine-based learning and translation, games and in the area of medical diagnosis. 

What is the importance of Neural Networks?

Neural networks help people to solve complex problems in real life situations. Neural networks can learn and model the relationship between input/outputs which are non-linear and complex, make generalization and inferences, disclose hidden relationships, predictions and patterns, model highly volatile data such as time series data and variances required in prediction of rare events (Such as frauds) and can improve decision making in areas such as: 

• Credit card and fraud detection in claim processing in healthcare 
• Logistics optimization in transportation
• Disease diagnosis
• Targeted marketing 
• Financial predictions related to stocks, currency, options, 
• Robotics systems

And many more areas. 

How Neural Networks work?

A neural network is a network of artificial neurons in a software. They try to simulate the human brain and it has many layers of neurons. The first layer of neuron receives inputs such as images, voice, video, sound, text etc. This input goes through all layers and output of one layer is fed to the next layer. For example, suppose you have a neural network that is trained to identify shapes square and circle.

The first layer of neuron will break up shape into areas of light and dark. This data will be fed to the next layer to recognize the edges of shape. The next layer would try to recognize the shape formed by a combination of edges. The data will pass through several layers to finally recognize the shape according to data it is being trained on. 

Types of Neural Networks 

There are different types of deep neural networks. Let’s look at them more in depth. 

• Convolutional Neural Networks (CNNs) have five types of layers – input, convolution, pooling, output and fully connected. Each layer is meant for a specific purpose such as summarization, connecting and activation. Convolutional neural networks are used in natural language processing, image classification and object detection.
• Recurrent Neural Networks (RNNs) – make use of sequential information from sensors such as time stamped data. All inputs to this type of neural networks are not independent of each other, output of each element depends on preceding elements computation. They are used in forecasting and time series applications, sentiment analysis and text-based applications
• Feedforward Neural Networks – each perception is connected to every perception in the next layer and there are no feedback loops here. Information feeding happens in forward direction only.
• Autoencoder Neural Networks – used for creation of abstractions called encoders with a given set of inputs. Autoencoders desensitize irrelevant and sensitize the relevant. These can be used by linear or non-linear classifiers.

Uses of Neural Networks

• biomedical imaging and for monitoring of health 
• Energy and manufacturing companies use them for supply chain optimization, automated defect detection and forecasting energy needs
• Banks use neural networks for fraud detection, conducting credit analysis and automation of financial advisory services
• Public sector enterprises use them to support smart cities, facial recognition and security intelligence
• Retail and consumer industry use them to power chatbots, analyse customer preferences 

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Data Science vs Artificial Intelligence

Automation vs Artificial Intelligence: Understand the difference

Today, the electronics industry is responsible for up to 4% of all greenhouse gas emissions. This calls for sustainable innovations to help reduce the environmental footprint. With innovative processing methods and material choices, the environmental impact of producing integrated circuits (ICs) and printed circuit boards (PCBs) can be reduced easily. 

Some of the strategies that could be adopted include utilizing novel approaches, reusing and recycling materials, eliminating superfluous materials, and adopting low-temperature processing. The effective utilization of these approaches can significantly help in reducing the environmental footprint of the electronics industry. 

A Shift to Sustainable Electronics Manufacturing

There is currently so much emphasis on sustainability today thanks to the efforts of eco-conscious investments and governmental directives. Most businesses and industries are rapidly embracing various environmental regulations to further protect our planet. Before now, compliance with these regulations was considered a burden, but it has now evolved to an opportunity everyone seeks to utilize. 

In the electronics industry today, some of the smartest financial decisions to make are implementing strategies for recovery and recycling and embracing low-emission production processes. Companies and manufacturers are also rapidly positioning themselves so that they can maximize the trend of social and environmental investments. 

An Era of Intelligent Manufacturing

Innovation is an unstoppable force in sustainable electronics manufacturing. The integration of the Internet of Things and artificial intelligence has further helped in redefining cost-effectiveness, efficiency and waste reduction. There are also many intelligent digital manufacturing strategies that are utilized today. 

These technologies streamline manufacturing processes and automate operations. With the current growing need for sustainable practices, the need for the electronic industry to eliminate practices that cause climate change cannot be overemphasized. This also includes the shift to the use of low-emission and low-toxicity chemicals. 

Reducing Carbon Footprint

In an advent to shift to sustainable electronic manufacturing, there’s also a need to mitigate the industry’s carbon footprint. This need is especially for manufacturers that still make use of traditional manufacturing processes and methods. Different governments are now enacting stringent rules on emissions, which further increases public scrutiny. With that, it becomes essential for manufacturers to become accountable. 

Manufacturers should switch to the use of renewable energy sources. Even though this might be initially expensive, it offers more benefits in the long run. Fortunately, the growing demand for recycled materials makes things a lot easier for manufacturers. Today, recycled materials can be utilized for other applications. 

Even though product cost increases with the use of sustainable, innovative designs, we live in an era where everyone is ecologically conscious. Therefore, investing in these sustainable, innovative approaches is worthwhile. 

Challenges in Sustainable Electronics Manufacturing

Even though there are so many innovations for sustainable electronics manufacturing, there are also a few challenges. There’s a need for an all-out transformation to reduce the large carbon footprint of the industry. Even though the adoption of the use of renewable energy is increasing by the day, it requires massive upfront investment, which can discourage some manufacturers.

Another challenge is that adopting efficient processes for manufacturing can cause alterations to workflows that have been in use for many years. An ongoing challenge is the need for recycled materials to meet the ever-growing demand for producing electronic components. 

In addition, it’s worth noting that designing and manufacturing sustainable products increases the cost of production. This can be challenging for manufacturers, especially those that are yet to create a balance between planetary responsibility and profitability. 

Sustainable Electronic Manufacturing: What the Future Holds

Despite the challenges above, there are many opportunities in sustainable electronic manufacturing. The growing demand for sustainable electrical components is a driving force among both businesses and consumers. It influences the choices they make when they buy semiconductors and other electronic components. 

Before now, the concept of renewable energy didn’t make any sense, but technological advancements have changed that. There’s currently a surge in the market for recycled materials, which is promising for sustainable production. 

Manufacturers are also widely accepting the use of intelligent manufacturing, which is good for the future of sustainable electronic manufacturing. This will further help revolutionize waste reduction and efficiency. 

Incentives for Sustainability

Green investment initiatives and government mandates are two factors that drive sustainability in the electronics and semiconductor industries. Today, more people are consciously making choices to purchase from manufacturers that prioritize sustainability. 

Manufacturers that implement processes that minimize emissions, recovery schemes, and material recycling enjoy long-term benefits. They also further reduce the cost of superfluous steps, energy consumption, and waste treatment. In addition, prioritizing sustainable practices allows companies to stay ahead, especially now that legislation and regulations are becoming more stringent. 

Digitization for Sustainable Manufacturing

As mentioned earlier, there are many opportunities in sustainable electronics manufacturing. This is with respect to reducing waste, improving cost-effectiveness, and increasing efficiency. One of the key things to expect in the near future is the full integration of the Internet of Things and artificial intelligence into sustainable manufacturing processes. 

Conclusion

The electronic manufacturing industry is currently at a crossroads that can influence many things. Today, the growing demand for sustainability is further influenced by the change in customer’s preferences and environmental concerns. This further influences the conventional approaches that have been used in manufacturing processes for decades. 

There are also many challenges associated with sustainable manufacturing processes. However, all of these challenges are surmountable, and the main thing to understand is that the rewards are many. They include the creation of a cleaner planet and financial gains. The world is rapidly becoming ecologically conscious, which makes sustainable electronic manufacturing to be more than just a trend. It is now an important and vital moral obligation that shows great promise for the future. 

In the wireless world, Wi-Fi is the term that is similar in general to access of wireless. Although the fact is that this specific trademark is owned by Wi-Fi Alliance. This dedicated group takes care of Wi-Fi products certification when they meet the wireless standards of 802.11 set of IEEE. It is a bit complex in getting used to the standards naming scheme of IEEE and to make the process of understanding it easier, some simpler names haves been introduced by Wi-Fi Alliance. Under this convention of naming,

• Wi-Fi 4 is the name given to 802.11n
• Wi-Fi 5 is for 802.11ac
• Wi-Fi 6 is for 802.11ax.

Wi-Fi 4 (802.11n)

802.11n is the first standard in which MIMO is specified and usage is allowed by this in two frequencies- 5GHz and 2.4GHz having speeds ranging to 600Mbps. When the term dual band is used by vendors of wireless LAN, it reflects the ability of data delivering athwart the two frequencies. Wi-Fi 4 is the successor of Wi-Fi 3 i.e. IEEE 802.11g.

Introduction of MIMO has taken place in this Wi-Fi standard along with beamforming. However, testing of interoperability has not been done yet. Previous versions legacy fallbacks was also supported by it and bandwidths that is support are 40 MHz and 20 MHz It is also possible to achieve upto 150Mbps data rates due to higher bandwidth and use of MIMO.

70 meters is the range in indoor that could be supported by devices of WiFi-4 and in outdoor environments, it reaches about 250 meters. Wi-Fi 4 devices that offer support to MIMO configurations include 4T4R and 2T3R. The modulation schemes used in this are QPSK, BPSK, 64QAM and 16QAM.

 Wi-Fi 5 (802.11ac)

The wireless routers that are used in many homes at present are likely to have 802.1ac compliance operating in the frequency space of 5GHz. The data rates supported by this standard are up to 3.46Gbps with MIMO where speed is boosted and error is reduced by multiple antennas on the receiving and sending devices. This Wi-Fi standard stands as the first one in which feature of multi-user MIMO has been introduced.

On account of multi-user MIMO, higher bandwidths addition, more modulation schemes and spatial streams, higher throughput is supported by WiFi-5. 5GHz is its operating frequency and it offers support to modulation schemes of single carrier (CCK, DSSS), multi-carrier and types of baseband modulation (QPSK, BPSK, 256QAM and 64QAM).

Bandwidths of several channels are supported including 160 MHz, 20 MHz, 80 MHZ and 40 MHz Maximum data rate of 6.93 Gbps is also supported by Wi-Fi 5 along with approx. 80m of coverage range and 3 antennas. Multi-user as well as single user transmissions are also supported by this Wi-Fi standard.

 Wi-Fi 6 (802.11ax)

802.11ax is also termed as WLAN of high efficiency since it aims at enhancing WLAN deployments performance in the scenarios that are dense such as airports and sports stadiums while it still operates in the spectrum of 5GHz and 2.4GHz. About 4X of improvement is targeted by the group in throughput in comparison to 802.11ac and 802.11n via utilization of spectrum that is more efficient. In comparison to the legacy Wi-Fi networks including Wi-Fi 3, Wi-Fi 4, Wi-Fi 5 etc. it offers greater range of coverage and higher speed. In both downlink and uplink directions in Wi-Fi 6, introduction of concept of OFDMA has taken place.

Beamforming, MU-MIMO, OFDM symbol of longer size, 1024-QAM, more spatial streams, scheduling of uplink resources with no contention etc. are the features introduced in this Wi-Fi generation. BSS coloring is the other specific feature present in this Wi-Fi generation. It is also referred as High frequency WLAN (HEW) on account of performance of high efficiency. Wi-Fi 6 generation offers better network capacity, efficiency, user experience and performance at reduced latency.

Related –  Wi-Fi 6 Technology  

Comparison : Wifi6 vs Wifi5 vs Wifi4

The Email of Yesteryears

Historically, email platforms were static, serving as mere digital postboxes. While functional, they lacked the dynamism and personalization that today’s users crave.

AI: The Catalyst of Change

The introduction of AI into email systems has been nothing short of revolutionary. By learning and adapting to user behaviors, AI offers a tailored and efficient email experience, addressing many of the limitations of older platforms.

Unpacking AI’s Toolbox in Email Communication

• Learning Algorithms: These algorithms adapt to user habits, ensuring that email platforms evolve with their users.
• Language Interpretation: Modern platforms can now comprehend human language nuances, thanks to Natural Language Processing (NLP).
• Data Insights: Continuous data evaluation refines user experiences, making them more intuitive and relevant.

AI’s Offerings: More Than Just Features

• Intelligent Organization: Emails are now sorted with precision, ensuring users see what’s most relevant.
• Swift Composition: Predictive text and suggestions expedite the email drafting process.
• Timely Reminders: Automated prompts ensure important emails don’t slip through the cracks.

Advanced Capabilities: AI’s Extended Arm

• Mood Analysis: Modern platforms can gauge the sentiment behind emails, aiding in response prioritization.
• Robust Filtering: Unwanted emails are efficiently sidelined, thanks to advanced pattern recognition.
• Tailored Suggestions: Actionable insights are provided based on email content, streamlining tasks.

User-Centric AI Innovations

In the realm of email communication, the user remains paramount. Recognizing this, modern email platforms are leveraging AI to craft experiences that are not just efficient but also deeply personalized. SuperHuman Mail has been a trailblazer in this domain, setting a high standard with its speed and AI-driven features.

However, for those exploring alternatives, platforms like CanaryMail have emerged as strong contenders, offering a suite of innovative features that rival, and in some cases, surpass those of SuperHuman. For a deeper dive into how these platforms measure up, check out this comprehensive guide on Superhuman alternatives.

• Adaptive Learning: Platforms are designed to learn from user interactions over time. Whether it’s the type of emails a user frequently reads or the times they’re most active, AI algorithms adapt to these patterns, ensuring that the email experience is tailored to individual preferences.

• Contextual Responses: Gone are the days of generic auto-responses. Modern email platforms can now craft responses based on the context of the email received. For instance, if a user receives an email asking for a meeting, the AI might suggest potential time slots based on the user’s calendar.
• Visual Preferences: Recognizing that aesthetics play a crucial role in user experience, some AI-driven email platforms now adjust visual elements based on user preferences. This could range from changing themes to suit the time of day to adjusting font sizes based on user reading habits.
• Behavioral Predictions: By analyzing user behavior, AI can predict future actions. For instance, if a user frequently moves promotional emails to a specific folder, the AI will start doing this automatically, saving the user time and effort.
• Integrated Task Management: Understanding that emails often come with tasks, modern platforms are integrating AI-driven task managers. If an email mentions a deadline or a to-do, the AI can automatically create a reminder or a task, ensuring that the user stays on top of their responsibilities.
• Feedback Loops: User feedback is invaluable. Some platforms now use AI to solicit and analyze user feedback in real-time. This continuous feedback loop ensures that the platform is always evolving in line with user needs and preferences.

The focus on the user is evident in these innovations. By placing the user at the heart of every AI-driven feature, email platforms are ensuring that the digital communication experience is as intuitive, efficient, and personalized as possible.

The Expanding Horizons of AI in Email

• Adaptive Interfaces: Future email platforms might offer real-time interface adaptations based on user mood or tasks.
• Holistic Assistance: Beyond management, AI could anticipate user needs, automating tasks like flight check-ins from email notifications.
• Security Reinforcements: As cyber threats evolve, AI will play a crucial role in fortifying email security.

The Road Ahead: AI’s Expanding Influence

• Immersive Experiences: Integrations with AR and VR could redefine how we interact with emails.
• Voice Activations: Voice commands might become the norm, offering hands-free email management.
• Emotion Sensing: Enhanced sentiment detection could provide deeper insights into email communications.

Conclusion

The fusion of AI and email is ushering in a new era of digital communication. As we look to the future, it’s clear that the boundaries of what’s possible are expanding, promising an even more intuitive and personalized email experience.

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Automation vs Artificial Intelligence: Understand the difference

Artificial Intelligence vs Machine Learning

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

ChatGPT: What is it, and How is it changing the world?

OpenAI’s newest AI-powered chatbot, ChatGPT, is designed to facilitate conversation between humans. Generative Pre-trained Transformer (GPT) refers to a type of artificial intelligence (AI) engine that can generate new material by applying the knowledge it acquired during training.

The chatbot is built upon a machine learning framework that has been trained on more than 45 terabytes of text data to provide the most likely response to a question or prompt. This made it possible for ChatGPT to interpret text data for patterns and combinations that would prompt useful responses.

Is creating a ChatGPT-based chatbot profitable enough

A chatbot like ChatGPT can help you save money on overhead or bring in new members, depending on your business’s needs. There is little doubt about the value for money:

1. Instant scalability: Chatbots can be programmed to be available 24 hours a day, seven days a week, to assist customers whenever they need it.
2. 24/7 Availability: Businesses can save money thanks to chatbots because they can handle a high volume of queries from customers at once.
3. Increased Efficiency: Chatbots can provide instant, precise responses to consumer inquiries, allowing human agents to focus on higher-level issues.

We hope that this section will give you insight and motivate you to learn how to create a chatbot like ChatGPT (if you want deeper insights, check Topflight research to read about this topic in more detail).

Estimated costs of developing ChatGPT-like chatbot

The first step in any computational process is gathering relevant private and publicly available data. However, gathering information takes time and money. As an enterprise, you should arrange and organize the large availability of unstructured data to extend your line of business.  The already high price of obtaining proprietary data sources is an additional drawback.

Aside from data collection, data storage is an essential component of any computing system. The majority of companies that specialize in creating chatbots rely on cloud services for data storage, typically AWS, Azure, or Google Cloud. The ChatGPT app’s user interface is hosted on Microsoft Azure’s cloud. Using these cloud-based services can result in a hefty charge. Also, the history of ChatGPT’s development began in 2018 when GPT was first offered.

So, it’s reasonable to assume that developing a similar artificial intelligence chatbot like ChatGPT could take several months. When all these considerations are taken into account, the cost of creating an artificial intelligence chatbot like ChatGPT is estimated to be around $90,000-$450,000.

How to build ChatGPT-like bot: Step-by-Step Guide

You may be wondering how to create a ChatGPT-like bot. Well, in order to make your own ChatGPT, we must first go through a series of steps. Let’s discuss each step in detail:

Discovering and Analyzing the Market

Market research and analysis are the starting point for developing a bot with ChatGPT-like features. Finding your niche requires knowing who you’re selling to, what they value, and how your competitors stack up. Using this data, one can better comprehend what skills and features your bot has to offer to appeal to your intended audience and set itself apart from the competitors.

Towards Prototyping

You can begin developing a prototype of your ChatGPT-like once you have a solid understanding of the market. This necessitates the development of the bot’s basic functionality. You can put your ideas to the test and gather feedback from potential customers by creating a prototype.

Developing Proof of Concept

The next step to develop ChatGPT-like chatbot involves creating a prototype to build a proof of concept. This involves adjustments to the prototype and adding new features and functionality based on input from potential consumers. At this point, you’ll need to show that your ChatGPT clone bot can succeed where others have failed.

Designing & Coding

You can begin planning and developing your ChatGPT clone once you have a working proof of concept. Building a bot requires designing its user interface, developing its features and capabilities, and integrating it with existing infrastructure.

Troubleshooting & Testing

You should perform a lot of testing to find and fix any problems with your ChatGPT-like bot after you’ve designed and programmed it. This includes both functional testing, to make sure the bot does what it’s supposed to, and user testing to get real-world input from people who might use the bot.

Deploying & Maintenance

When your ChatGPT-like bot has undergone extensive testing and is ready for deployment, you can introduce it to your intended users. If you want your bot to keep up with the competition once it’s been released to users, it needs to undergo frequent maintenance and updates.

Final Verdict

You can make a powerful and user-friendly ChatGPT-like bot that caters to the needs of your target audience by following the steps outlined in this blog, such as conducting market research and analysis, prototyping, developing a proof of concept, designing and coding, troubleshooting, and testing, and deploying and maintaining your bot. You can make a powerful and simple-to-operate chatbot with some planning and effort. We hope this guide will clear your confusion about how to build ChatGPT-like bot.

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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

Networking technology have evolved significantly over the years as demands on Ethernet and Wi FI have tremendous increased. Apart from supporting a range of devices Local area networks require to manage traffic getting generated from many other sources such as live streaming video, Network attached storage (NAS), Voice over IP (VoIP) , virtualization , Cloud and IoT devices and services have generated demand for additional bandwidth.

In this article we will look at some Networking technology trends which made their place in top 10 in 2021. The need of high-speed internet, cloud and edge computing models and need for migration of data between servers have resulted in shift towards need for high bandwidth and low latency network technologies.

Top 10 Networking Technology Trends 

1. 5G and Wi Fi 6 technology –

5G or fifth generation cellular technology. It is characterized by increased speed, reduced latency and improve flexibility in wireless services. It helps organizations to mobilize workforces, extend automation, supporting new applications with increased network capacity and high data rates. 5G gives seamless open roaming capabilities between cellular and Wi FI access. 5G would solve the issue of many wireless devices connected at once – and IoT makes it worse by slowing wireless network performance. Wi Fi 6 infrastructure is ready to go however Wi Fi 6 capable devices such as computers and mobile phones manufacturers need to adopt new standards.

2. Artificial Intelligence (AI) and Machine Learning (ML)

Complex network and business problems can be addressed in real time using AI and ML capabilities. Application of user cases ranging from small cities, manufacturing, security, and networking. ML can make predictions based on network data and AI can take intelligent actions based on those prophecies. Advanced analytics into automation systems will bring in self-operating networks.

3. Augmented reality and virtual reality

Augmented reality (AR) and virtual reality (VR) technologies empower the applications and customer experiences.  AR is mainly used on smartphone and tablets to present interior design, allowing shopkeepers to have virtual presentation of furniture.

4. Cloud computing 

Cloud allows faster transition to remote work and help to organize remote workplace more efficiently and this contributed to business continuity during any crisis. Maintaining consistent network and security policies across multiple clouds using multi cloud policy management.

5. DevOps 

DevOps is tied up to software development and IT. DevOps improve relationship between network service designers and engineers to make operational changes to the services.

6. Digital transformation 

It enables adoption of digital technology to transform services or businesses, by replacing on digital or manual processes with digital processes. Process of digitization transforms into digital forms that are processed, stored, and transmitted via digital devices and networks.

7. Intent-based networking (IBN)

This approach bridges the gap between business and IT. Business intent is captured and continuously aligned to end to end network related to application service levels, security policies, compliance, operational and business processes. Virtual segmentation of IoT devices from remaining network will be one of the major tasks for Networking teams. The creation of secure zones called Microsegments which will allow IoT devices to operate on same corporate network and reducing the risks to other parts of the network.

8. Internet of Things (IoT)

IoT is all about connecting the unconnected. Majority of objects are unconnected however with IoT devices are acquiring capabilities to communicate and connect with other devices and people changing the way we work.

9. Data Security

usability and integrity of network is crucial to security. Effective network security manages network access effectively and stops a variety of threats entering or spreading within the network

10. SD-WAN 

SD-WAN is a software-based approach to manage Wide Area networks (WANs). This technology lowers operational costs and improvement in resource usage in multisite deployments. Network administrator using this SD-WAN technology can use bandwidth more efficiently and help to ensure enhanced performance for business-critical applications without compromising data security and privacy.

SD concept in SD-WAN technologies separate the control plane from the data plane and centralize the control plane from which multiple devices are controlled. Control plane acts like a shared service which is accessible to all administrators within an organization or in a multi tenancy environment.

SD-WAN supports on premises data centres, Software-as-a-service (SaaS) and public cloud infrastructure-as-a-service (laaS) applications to optimize their performance.  SD-WAN allows proper security for each user and devices irrespective of their physical location.

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What is DevOps?

What is Wi-Fi 6 Technology?

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

One Architecture. One Network. Any Cloud.

Aviatrix solves multi cloud environment problem by providing a point for connectivity between the major cloud providers including AWS, Azure, and Google Cloud. In addition, Aviatrix provides a centralized control to manage, monitor and troubleshoot and encrypted IPSEC tunnel connections between clouds. Aviatrix Controller auto discovers AWS VPCs, Azure VNETs, GCP VPCs in multiple cloud accounts and their associated IP information. Additionally, it uses policy and software defined routing to dynamically connect VNETs and VPCs, with its auto discover feature it doesn’t required administrator with in depth knowledge. Multi Cloud also supports high availability (HA) connections for redundancy and fault tolerance. Private cloud and on premise sites can also be connected using the Site to Cloud VPN solution.

3 Layers of Multi Cloud Network Architecture 

Aviatrix MCNA (Multi Cloud Network Architecture) is made of 3 primary layers (Components) which are –

• Cloud Core
• Cloud Access
• Cloud Operations

Related – Aviatrix (Multi Cloud Networking) Interview  Q&A 

Cloud Core is made up of 2 sub components i.e. Network and Application Workload. Core layer is where the majority of Routing decisions take place, in addition to service layer and most importantly, the applications workloads and storage. Just like the MPLS Core of a WAN provider Network, we have Cloud Core in MCNA framework.

Cloud Access is the pathway to enter and exit from Cloud. On-premise Data centers, Partners, remote customer locations and VPN users – All use Core Access to reach the Cloud. The technologies under this scope includes SDWAN, MPLS, Direct Connect, Express Route, 5G and IOT and others. In simple terms, Cloud Access relates to in and out for customer traffic towards the Cloud environment where actual workloads reside.

Cloud Operations resides on top of Cloud Core and Cloud Access layer. The architecture is conducive to troubleshooting, operational activities including logging, orchestration, alerting and flow analysis.

Now that we know the 3 layers of Multi Cloud Network Architecture, its imperative to know that MCNA is tailor made for enterprises with

• single region in single cloud
• multiple regions in single cloud and
• multiple clouds being leveraged

Having said that, MCNA architecture of Aviatrix is setup for single region, Multiple regions and multiple clouds also. MCNA creates an abstraction layer which is responsible for common control, data and orchestration plane, which is Aviatrix.

Aviatrix Operations Overview

• Manageability
• Automation
• Visibility
• Monitoring
• Logging
• Troubleshooting
• High Availability
• Compliance
• Software and Technical Support
• Flexible Consumption Mode

Features and Capabilities of Aviatrix Solution:

Centralized Controller

Aviatrix controller is the main processing unit of the cloud network platform. The platform provides the centralized intelligence and knowledge of the controller to dynamically program both native cloud network and Aviatrix’s own advanced services.

Network Service Gateways

Aviatrix gateways delivers advanced cloud networking and security services. Gateways are primarily deployed to deliver transit network and security services such as intelligent dynamic routing, active-active network HA, end-to-end and high-performance encryption and collect operational data.

High-Availability Networking

Aviatrix is designed with active-active HA and redundant pathing. Pair of Aviatrix Gateways deployed in different availability zones and establish a full mesh multi path connection that enhance both throughput performance and network availability. High-Performance Encryption with standard IPsec encryption is limited to 1.25 Gbps. Aviatrix’s high performance encryption distributes traffic across multiple cores and aggregates IPSec tunnels to achieve wire speed encryption up to 75 Gbps.

Secure Cloud Ingress and Egress

Aviatrix gateways offer both ingress and egress filtering. Centrally managed multi cloud security for any cloud application communicate with Internet based resources and service.

Multi-Cloud Network Service

Insertion Aviatrix Transit provides a secure point of access for network and security services such as next-generation firewalls, IDS/IPS and SD-WAN cloud edge connections. Aviatrix gateway provides load balancing to connected services and ensures redundant and failover HA.

Operational Visibility

Enterprise network operations must have in depth visibility into network activity. Public cloud networks are transparent, even basic analytics must be obtained from multiple sources.

Dynamic Network Mapping

Aviatrix collects the central intelligence and knowledge of the controller to dynamically generate and maintain an accurate multi cloud network topology map that includes all network resources and network configurations the controller is managing.

FlowIQ–Intelligence Network Traffic Flow Analytics

Aviatrix collects network traffic flow of data from Aviatrix controller including source port, destination port and application filtering and combined with additional data such as latency and tagging to deliver multi cloud flow inspection analyses.

Centralized Console

Controller automates the deployment of network configuration of Aviatrix gateways in your VPCs and VNETs making connectivity across public cloud services very simply and efficiently.

High Availability Connections

Gateways and tunnels can be deployed as HA configurations to enhance redundancy and fault tolerance.

Compatibility with Existing Infrastructure

Cloud to Cloud and Site to Cloud VPN connections support the on premise infrastructure that terminates VPN connections from the cloud. Engineers can also easily produce configuration templates for on premise routers and firewalls.

Simplified Troubleshooting

Aviatrix offers the troubleshooting tools which provide network performance report link status and alerts to simplify troubleshooting. In addition, events across all clouds can be logged and forwarded to tools such as Splunk and Data log for further analyses.

Multicloud Gateways Enabled via Cloud Provider Partnerships

Automate networking across multiple cloud providers – AWS, Azure and Google REST APIs to make multi cloud networking simple and dynamic.

Conclusion

Aviatrix is a cloud networking company helping customers to connect with the different clouds. Aviatrix offers end to end secure, automated routing, monitoring, and management and automates the handling of VPC networks. Aviatrix curriculum covers solutions for AWS, Azure and Google Cloud Platform, enables connectivity between data center public cloud and different clouds through VPN.

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Hybrid Cloud vs Multi Cloud

DATA CENTER VS CLOUD

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Cloud computing has opened a breath for enterprise and telecom related opportunities to hosting applications and services. While market talk is that somewhere in near future IT Cloud and Telco Cloud will merge to form a consolidated Cloud to serve the customers, however present landscape considers both the Cloud types as different. Telco Cloud commonly refers to a Private Cloud deployment within a Telco/ISP environment that hosts Virtual Network Functions (VNFs) of the Telco/ISP Network utilizing NFV techniques. On the other hand, IT Cloud is related to an enterprise workload and is a private cloud deployment. IT Cloud provides cloud based services to render enterprise requirements.

Related – Telco Cloud Architecture

Telco Cloud commonly refers to a Private Cloud environment to host Virtual Network Functions (VNFs) of the Telco Network by utilizing NFV techniques. On the other hand, VNF functions are not extensively employed in enterprise Cloud.

Further, Telco Cloud is very stringent on secured traffic flow and latency requirements. Delay needs to be very low upto scale of milliseconds. One the other hand, IT Cloud has low latency requirements , however not as strict as Telco Cloud. Moreover, Internet based access is also favored approach for IT Clouds, while Telco Clouds prefer using dedicated Service provider/Telcom Links to deliver Cloud services. In terms of over-subscription ratios, Telecom Clouds need 1:1 ratio for CPU allocation, while IT cloud can bear stealing of CPUs across applications.

With reference to standards, Open standard is the strategic approach for Service provider/Telco Cloud environment, while IT and Enterprise Clouds may employ vendor proprietary technologies.

Related – Public Cloud vs Private Cloud

Comparison Table: Telco Cloud vs IT Cloud

The above stated facts have been encapsulated in below table:

Parameter	Telco cloud	IT Cloud
Terminology	Telco Cloud commonly refers to a Private Cloud deployment within a Telco/ISP environment that hosts Virtual Network Functions (VNFs) of the Telco/ISP Network utilizing NFV techniques.	Related to an enterprise workload and is a private cloud deployment. IT Cloud provides cloud based services to render enterprise requirements.
Application Stack	Telecommunication applications	End user web based IT application
Related terms	BSS, OSS, VNF,NFV, SDN	Multi-tenancy, virtualization, IT workload
Delay / Latency	Very low latency requirements.	Low latency requirements
Throughput	Very High throughput .Port speed are required to be 100G or above	High throughput requirements and port speeds may start from 10G and beyond.
Oversubscription and CPU Allocations	Ratios are typically 1:1	Ratios may vary from 8:1 upto 16:1
Reliability	Very High due to distrusted Data Centres	High
Setup	Distributed Data Centres across locations	Consolidated Data Centers
Strategy	Open Standards	May use vendor proprietary technologies.

Download the difference table here.

Table of Content:

1. Definition of TelcoCloud
2. Definition of Network Function Virtualization (NFV)
3. NFV Architecture
4. Benefits of NFV
5. Application of NFV
6. Conclusion

Definition of Telco Cloud

Telco cloud represents the Data Center resources which are required to deploy and manage a mobile phone network. Telco clouds are based in private Data Center facilities which are used to manage the telecommunication requirements of 3G/4G and LTE networks. With the current roll-out of 5G equipment across the mobile service provider, vendors have adopted strategies related to network function virtualization (NFV) and software-defined data center (SDDC) management which have become indispensable part of a telco setup.

Related – Telco Cloud vs IT Cloud

Definition of Network Function Virtualization (NFV)

Network functions virtualization (NFV) is a way to virtualize network services, such as routers, firewalls, and load balancers, that have traditionally been run on proprietary hardware or dedicated hardware. NFV’s functions like routing, load balancing and firewalls are packaged as virtual machines (VMs). NFV doesn’t depend on dedicated hardware for each network function. NFV improves scalability and agility by allowing service providers to deliver new network services and applications on demand, without requiring additional hardware resource.

Related – SDN vs NFV

NFV Architecture – A Telco Cloud Architecture

The NFV architecture was proposed by the European Telecommunications Standards Institute (ETSI) which has helped to define standards for NFV implementation. Each component of the architecture is based on these standards with approach to promote better stability and interoperability. NFV architecture consists of:

1. Virtualization Network Function (VNF) Layer
2. NFV Infrastructure (NFVI) Layer
3. Operation Support Subsystem (OSS) Layer
4. Management, Automation and Network Orchestration (MANO) Layer

Virtualization Network Function (VNF) Layer

Virtualized network functions (VNFs) are software applications that deliver network functions such as file sharing, directory services, and IP configuration. Virtual Network Function (VNF) is the key component of NFV Architecture. It virtualizes network function e.g. when a router is virtualized, it is known as a Router VNF and when a base station is virtualized it is known as a base station VNF. Similarly, it can be DHCP server VNF and Firewall VNF. When one sub-function of a network element is virtualized, it is known as a VNF. VNF is deployed on Virtual Machines (VMs). A VNF can be deployed on multiple VMs where each VM host performs a single function of VNF. However, the whole VNF can also be deployed on a single VM as well.

Element Management System (EMS) has capability for the functional management of VNF. It includes –

• Fault management
• Configuration management
• Accounting management
• Performance and Security Management.

Depending on infrastructure, there can be provision of one EMS per VNF or else one EMS that can manage multiple VNFs. EMS can be deployed as Virtual Network Function (VNF).

NFV Infrastructure (NFVI) Layer

Network functions virtualization infrastructure (NFVi) consists of the infrastructure components (compute, storage and networking) on a platform to support software, such as a hypervisor (like KVM) or a container management platform, needed to run network apps. NFV Infrastructure refers to the hardware and software components which build up the environment where VNFs are deployed, managed and executed. NFV Infrastructure includes following:

• Hardware Resources
• Virtualization Layer
• Virtual Resources

Operation Support Subsystem (OSS)/Business Support System (BSS) Layer

OSS deals with network management, fault management, configuration management and service management.

Management, Automation and Network Orchestration (MANO) Layer

Management, Automation and Network Orchestration (MANO) is responsible for managing NFV infrastructure and provisioning new VNFs. Management and Orchestration Layer is also abbreviated as MANO. Three components of this layer:

• Virtualized Infrastructure Manager
• VNF Manager
• Orchestrator

MANO interacts with both NFVI and VNF layer. It manages all the resources in the infrastructure layer, and also creates and deletes resources and manages their allocation of the VNFs.

Benefits of using NFV

• Reduced physical space required for network hardware.
• Reduced network power consumption.
• Reduced network maintenance costs.
• Easier network upgrades.
• Longer life cycle for network hardware.
• Reduced maintenance and hardware costs.
• Reduced physical hardware requirement.
• Increased flexibility to run VNFs across different servers or move them around as needed when demand changes.
• If the function is no longer needed, the VM can be decommissioned.

Application of NFV

• Mobile Edge Computing, this technology was born from the ongoing rollouts of 5G networks. The MEC uses individual components in its architecture which are similar to the NFV.
• Software Defined Wide Area Network (SD-WAN).
• Virtual Customer Premise Equipment (vCPE).
• Pre-NFV/SDN-based virtualized legacy infrastructure equipment.
• NFV telco Data Centers using SDN controllers.
• Evolved Packet Core (EPC).

Conclusion

Telco Cloud refers to a Private Cloud within a Telco/ISP environment. VNF functions are an imperative part of Telco Cloud and not extensively employed in enterprise Cloud or IT Cloud.

Over many years, Perimeter security has been considered the key to presenting a robust and secured network ecosystem . This was considered a suitable methodology when the network attacks were not so advanced and North-South traffic was the major traffic type. However, with advancement in technology, new attack types are knocking the doors. Infact, substantial growth in East-West traffic communication across workloads has diverted the attention towards rollout of what we call microsegmentation. In this article we will brief through Network Segmentation and Micro-segmentation and what are their differences.

To start with,  concept of Micro-segmentation logically divides the Data Centre into distinct security segments upto individual workload level. The granularity level at which micro-segmentation works is upto VMs and individual hosts unlike Network Segmentation. Network segmentation creates sub-networks  (using VLANs, Subnets and Security Zones) within the overall network to prevent attackers from moving inside the perimeter and attack the  production workload.

Related – Microsegmentation vs Zero Trust

Microsegmentation works on the notion that by controlling this traffic, you can minimize the risk of security threats and create a zero-trust security model. It requires centralized management and can be applied to individual machines providing a more secure environment without the additional overhead of host-specific configuration. If the centralized management is not used , it would become cumbersome (rather nightmare) to attend the security policies across various VMs or hosts. When it comes to network segmentation, it is not mandatory to perform central management via orchestrators. Infact, Policies in former are pretty granular while in latter, policies are network/ Zone or segment level. Further, policies and permissions for microsementation are based on resource identity

Below are some of key benefits we can achieve via microsegmentation –

• Enforce granular tier-level segmentation within the same application group. Hence greater security
• Enforce policy upto Layer 7
• Critical applications will keep safe, even in the case of a breach at perimeter

On the other hand, Network segmentation provides below benefits –

• Enforce security at perimeter to protect against entry of attacks.
• Simpler to implement than Micro-segmentation

Another thing to note here is that microsegmentation requires highly Skilled resources who understand application level visibility to employ Microsegmentation. While medium level of proficiency is required to deploy network based segmentation solution.

Comparison Table: Micro segmentation vs Network Segmentation

Below table enumerates difference between Microsegmentation and Network Segmentation –

Parameter	Micro Segmentation	Network Segmentation
Terminology	Micro-segmentation is used to logically divide the data centre into distinct security segments upto individual workload level.	Network segmentation creates sub-networks within the overall network to prevent attackers from moving inside the perimeter and attack the  production workload.
Related terminologies	firewalls, virtual LANs (VLANs), and access control lists (ACLs)	VMs, containers, Cloud, Data Centers
SDN based control	Essential	Optional
Management and control	Centralized. This reduces overhead of managing security for individual hosts	Not mandatory to perform central management via orchestrators
Policies	Granular policies	Network/segment level policies
Policy enforcement on	Subnets and VLANs.	VMs and hosts
Network Virtualization	Required	Not required
Scope	More Granular since controls lateral movement across hosts	More on perimeter level and across Zones and subnets
Host to Host communication control	Microsegmentation can be useful tool in this case	Network Segmentation will not be able to control/detect security threat
Traffic path control	Eat-West traffic (lateral traffic movement)	North-South
Benefits	•Enforce granular tier-level segmentation within the same application group. Hence greater security •Enforce policy upto Layer 7 •Critical applications will keep safe, even in the case of a breach	•Enforce security at perimeter to protect against entry of attacks. •Simpler to implement than Micro-segmentation
Disadvantages	High Skill required including application level visibility to employ Microsegmentation.	Less proficient skill requirement for deploying network based segmentation solution

Download the Comparison table.

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