AI and Data Privacy: All You Need to Know About It in 2022

January 27, 2021

Privacy is something that everyone tries to ensure wherever and whenever possible. From an individual point of view, privacy is a big concern as we do not want anyone to snoop on the things we are doing and want to exercise our basic human rights. Establishing this in the real world is not that difficult, but the scenario in the virtual world is considerably different. No matter what app or website we use, we always leave behind a large volume of digital footprints with the app/website (cookies or log-in details), which they can use to give us a personalized experience and create our digital profile in their GPU cloud servers

With the advent of the information age, we get limited control over how our data is stored, processed, or shared among different services. Companies and government entities use this data to train their powerful AI and machine learning algorithms present in the NVIDIA GPU cloud servers, whose purpose is to learn as much as possible about the user/consumer/customer. These types of AI-driven practices have led to some serious privacy issues in the consumer sector. Many have cited that this humongous amount of data can be misused very easily. The rise of continuously growing, sophisticated artificial intelligence systems has paved the way for these privacy concerns. 

There is no doubt that AI is capable of both disrupting and improving our lives, as it innately has certain loopholes and pitfalls. In this article, we will talk about various applications regarding data privacy policies using artificial intelligence. Before going into that, let’s take a look at how AI compromises privacy issues.

AI Compromising Privacy Issues

AI has proven to be a very convenient tool for data-gathering. Its speed and efficiency are unattainable by normal human beings, and human analysts cannot match the computational power of AI. This enormous computation power comes from thousands of GPU (Graphics Processing Unit) present in the secure VPS server (effectively a supercomputer). These NVIDIA GPUs work collectively as a single node for deep learning. Ultimately, this powerful AI system is used as a data-gathering unit. We can enhance its computational efficiency by adding hardware. 

AI can utilize large data sets for analysis and is usually the best way to process big data within a reasonable time. Before the advent of AI, this was not possible because it was practically impossible to properly interpret large volumes of unstructured data from innumerable sources. But artificial intelligence and machine learning technologies have given us the power to parse such unstructured data and extract useful information. This is the underlying reason for the widespread privacy issues. 

Data Exploitation

In 2021, it is expected there will be almost 3.8 billion smartphone users worldwide. Our smartphones, laptops, tablets, and PCs generate almost 2.5 quintillion bytes of data each day. These bytes contain information about our device properties, model name, serial numbers, the manufacturing company, location (GPS), voice and facial data, screen-on time, year of issue, and more. It also contains data produced by the different software or apps we use on our devices. All of this data makes us vulnerable to data exploitation. We are becoming more reliant on digital technology every day, which only increases the potential for exploitation. For instance, the data of about 500 million Marriott International guests were exposed in a data breach in 2018. That information comprised some combination of the guests’ names, physical addresses, email addresses of users, passport number, account number information, telephone number, date of birth, gender, arrival, and departure time information. 

The total number of data breaches in the US from 2005 to the 1st half of 2020

Source: Statista

Data Tracking and Identification

Many online services, like apps and websites, use our location to fetch the most verified results available within a certain range of our location. For example, if we search for any restaurants in Google, it will ask for location permission. Once we grant the permission, it will show us verified restaurants near our current location. If it brings up results of restaurants from another city or country, it will not make any sense. Other apps use this location data to show us personalized ads. It can be useful for those businesses, but it can adversely affect us as the app can monitor our location through our smartphone’s GPS. 

AI uses this data to make a bigger pool of user profiles and target individuals with personalized ads and recommendations. Once our data becomes a part of the larger data set, AI and machine learning algorithms can de-anonymize this data based on various aspects and preferences. It ultimately smudges the line between personal and non-personal data. Governing bodies and legislations have to take these issues into account.

Profiling Based on Predictions

AI and machine learning use sophisticated algorithms to extract useful and sensitive information from seemingly meaningless data. For instance, a person’s sentiment analysis is possible with image recognition algorithms by analyzing the types of pictures one uploads on social media. It can help identify the interests of the particular person and the types of posts they like the most. Not only that, this information, along with their typing patterns, exposes vital stats about an individual’s identity, political views, ethnic identity, and health conditions. Many companies use social media and the internet to extract more information about their potential and existing employees. It can have adverse effects, as people can be (and usually are) held accountable for the content they post online.

The prowess of AI is not bound only to data-gathering capabilities. It also shines with analyzing that data and making crucial predictions about the persons or entities (sources of data). This is known as ‘profiling’. Its purpose is to rank people in a particular manner in a list and assign some scores to them based on various factors. In the evaluation of such profiles, the user does not get any consent. As a simple example, we can take a situation where there are two customers for an e-commerce website. One of them buys lots of electronic products from the online retailer, while the other does not shop so often. The first type of customer is probably ranked higher than the second inside an undisclosed AI model and is considered an ‘asset’. The person’s profile is a target for various deals, discounts, and offers. Such is not the case for the second customer who does not buy very often. 

There is a saying that goes: the more the available data, the better the machine learning algorithm will perform. It essentially means businesses are investing a lot to gather as much user data as possible and this affects the privacy of users. Needless to say, these businesses are capable of providing us with personalized experiences based on our data only. If it is not based on our data, then user experience would have taken a hit. Thus, it can be a win-win situation for both end-users and providers (mostly). Technological advancements have skyrocketed in the fields of AI, namely, improved and powerful NVIDIA server GPUs.   

It is needless to say that organizational and personal data privacy has to always be kept under strict security and vigilance. This data security is present both in software form and hardware form (latest NVidia graphics card).

How AI Protects Data Breaches

Source: NVIDIA 

According to Gartner Security and Risk Survey (2019), 40% of private companies and technical agencies will use AI-based service providers by the end of 2023, compared to 5% in 2019. Avoiding data threats is one of the best applications of artificial intelligence. Companies face various malpractices regarding data protection. Before knowing the significance of AI in data security, firstly, you must understand the various kinds of data breach and hacks companies come across. Here are some of them:

  • Social engineering: Here, the cyber attackers and hackers force users to give their personal information and security codes and provoke them to download malicious software or open malicious websites.
  • Phishing: This kind of malpractice is done to send messages and emails to users with the aim of eliciting personal data. Sometimes, the downloaded file itself causes data breaches, e.g., Trojan horses. 
  • SQL injection: In this malpractice, hackers inject a SQL injection into a GPU server or cloud server and get access to run ransom codes. In this way, they help themselves by breaching privacy codes and secret resources. 
  • Insider threats and data breaches: Sometimes, attackers target the inside information of companies and agencies and try to exploit them. This malpractice of breaching sensitive data causes loss of customers’ privacy.

Apart from these issues, companies face other malware threats such as account hijacking, DDoS, misconfiguration, vulnerability, etc. 

Source: CyberDB

All the above-mentioned malpractices can be eliminated with the assistance of artificial intelligence. So AI plays a key role in developing privacy solutions. 

AI-Driven Privacy Solutions

Security services that are AI-based are very efficient in managing data protection issues. The activities get performed in two distinct ways:

  • Automated security system 
  • Security operation centers and teams

Now, AI-powered security tools are categorized along the following lines:

  • The security tool which takes the help of rules and statistical data and finds relevant information regarding security events is called ‘Security Information and Event Management’ (SIEM). SIEM helps security operation centres to take action and deal with ransomware and malware activities. 
  • The artificial intelligence-based tool used in tracking and analyzing valuable information from computers and websites is called User and Entity Behaviour Analytics (UEBA). UEBA helps companies detect insider attacks and suspicious activities. It understands the patterns of legitimacy and, by this, tracks security threats. 
  • The more efficient and faster security tool gets performed in four steps –
  • Security
  • Orchestration
  • Automation
  • Response

It detects cybersecurity threats and data breaches more quickly and efficiently. This solution determines threats that jeopardize crucial databases and takes action against them. Therefore, companies stress the importance of these security tools in collecting data and providing security alerts. 

E2E Networks Services

As far as a low-cost cloud provider is concerned, E2E Networks ranks right at the top. E2E Networks provides best value for money and easy-to-use GPU cloud server, NVIDIA server, and affordable Cloud servers to customers. E2E Networks enables AI-based applications to protect customers’ privacy. They give utmost care to ensure customers’ data security. Hence, people trust E2E’s cloud services. E2E Networks is gradually becoming increasingly popular among clients. People choose E2E Networks for the following features:

  • Budget-friendly 
  • Cost-efficient
  • Scalability 
  • Reasonable services 
  • Trustworthy guidance 
  • Advanced privacy policy 
  • User-friendly facilities 

E2E Networks has not only become a world-class cloud in India, but it also provides arguably one of the best GPU cloud servers that perform various crucial applications. The GPU cloud can be used for the following: 

  • Artificial Intelligence (AI) 
  • Computational vision and finance 
  • Big Data 
  • Data science and algorithms 
  • Machine Learning 

E2E Networks would help you to use NVIDIA GPU services at a very reasonable price.

Wrapping Up 

In this article, we have discussed all you need to know regarding AI and Data Privacy in 2021. Starting with how artificial intelligence integrates the issue of privacy, we have described data exploitation, data tracking, and identification. We demonstrated how profiling is done based on predictions, how AI helps to protect data breaches, and the solutions put up against cyber attacks with the help of AI-enabled security tools. Lastly, we highlighted the rise of E2E Networks in terms of providing the best GPU services. Hopefully, this blog provides a clear picture of the main things you need to know regarding AI and data privacy. 

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June 29, 2022

Project Management for AI-ML-DL Projects

Managing a project properly is one of the factors behind its completion and subsequent success. The same can be said for any artificial intelligence (AI)/machine learning (ML)/deep learning (DL) project. Moreover, efficient management in this segment holds even more prominence as it requires continuous testing before delivering the final product.

An efficient project manager will ensure that there is ample time from the concept to the final product so that a client’s requirements are met without any delays and issues.

How is Project Management Done For AI, ML or DL Projects?

As already established, efficient project management is of great importance in AI/ML/DL projects. So, if you are planning to move into this field as a professional, here are some tips –

  • Identifying the problem-

The first step toward managing an AI project is the identification of the problem. What are we trying to solve or what outcome do we desire? AI is a means to receive the outcome that we desire. Multiple solutions are chosen on which AI solutions are built.

  • Testing whether the solution matches the problem-

After the problem has been identified, then testing the solution is done. We try to find out whether we have chosen the right solution for the problem. At this stage, we can ideally understand how to begin with an artificial intelligence or machine learning or deep learning project. We also need to understand whether customers will pay for this solution to the problem.

AI and ML engineers test this problem-solution fit through various techniques such as the traditional lean approach or the product design sprint. These techniques help us by analysing the solution within the deadline easily.

  • Preparing the data and managing it-

If you have a stable customer base for your AI, ML or DL solutions, then begin the project by collecting data and managing it. We begin by segregating the available data into unstructured and structured forms. It is easy to do the division of data in small and medium companies. It is because the amount of data is less. However, other players who own big businesses have large amounts of data to work on. Data engineers use all the tools and techniques to organise and clean up the data.

  • Choosing the algorithm for the problem-

To keep the blog simple, we will try not to mention the technical side of AI algorithms in the content here. There are different types of algorithms which depend on the type of machine learning technique we employ. If it is the supervised learning model, then the classification helps us in labelling the project and the regression helps us predict the quantity. A data engineer can choose from any of the popular algorithms like the Naïve Bayes classification or the random forest algorithm. If the unsupervised learning model is used, then clustering algorithms are used.

  • Training the algorithm-

For training algorithms, one needs to use various AI techniques, which are done through software developed by programmers. While most of the job is done in Python, nowadays, JavaScript, Java, C++ and Julia are also used. So, a developmental team is set up at this stage. These developers make a minimum threshold that is able to generate the necessary statistics to train the algorithm.  

  • Deployment of the project-

After the project is completed, then we come to its deployment. It can either be deployed on a local server or the Cloud. So, data engineers see if the local GPU or the Cloud GPU are in order. And, then they deploy the code along with the required dashboard to view the analytics.

Final Words-

To sum it up, this is a generic overview of how a project management system should work for AI/ML/DL projects. However, a point to keep in mind here is that this is not a universal process. The particulars will alter according to a specific project. 

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June 29, 2022

Top 7 AI & ML start-ups in Telecom Industry in India

With the multiple technological advancements witnessed by India as a country in the last few years, deep learning, machine learning and artificial intelligence have come across as futuristic technologies that will lead to the improved management of data hungry workloads.


The availability of artificial intelligence and machine learning in almost all industries today, including the telecom industry in India, has helped change the way of operational management for many existing businesses and startups that are the exclusive service providers in India.


In addition to that, the awareness and popularity of cloud GPU servers or other GPU cloud computing mediums have encouraged AI and ML startups in the telecom industry in India to take up their efficiency a notch higher by combining these technologies with cloud computing GPU. Let us look into the 7 AI and ML startups in the telecom industry in India 2022 below.


Top AI and ML Startups in Telecom Industry 

With 5G being the top priority for the majority of companies in the telecom industry in India, the importance of providing network affordability for everyone around the country has become the sole mission. Technologies like artificial intelligence and machine learning are the key digital transformation techniques that can change the way networks rotates in the country. The top startups include the following:


Founded in 2021, Wiom is a telecom startup using various technologies like deep learning and artificial intelligence to create a blockchain-based working model for internet delivery. It is an affordable scalable model that might incorporate GPU cloud servers in the future when data flow increases. 


As one of the companies that are strongly driven by data and unique state-of-the-art solutions for revenue generation and cost optimization, TechVantage is a startup in the telecom industry that betters the user experiences for leading telecom heroes with improved media generation and reach, using GPU cloud online


As one of the strongest performers is the customer analytics solutions, Manthan is a supporting startup in India in the telecom industry. It is an almost business assistant that can help with leveraging deep analytics for improved efficiency. For denser database management, NVIDIA A100 80 GB is one of their top choices. 


Just as NVIDIA is known as a top GPU cloud provider, NetraDyne can be named as a telecom startup, even if not directly. It aims to use artificial intelligence and machine learning to increase road safety which is also a key concern for the telecom providers, for their field team. It assists with fleet management. 

KeyPoint Tech

This AI- and ML-driven startup is all set to combine various technologies to provide improved technology solutions for all devices and platforms. At present, they do not use any available cloud GPU servers but expect to experiment with GPU cloud computing in the future when data inflow increases.



Actively known to resolve customer communication, it is also considered to be a startup in the telecom industry as it facilitates better communication among customers for increased engagement and satisfaction. 


An AI startup in Chennai, Facilio is a facility operation and maintenance solution that aims to improve the machine efficiency needed for network tower management, buildings, machines, etc.


In conclusion, the telecom industry in India is actively looking to improve the services provided to customers to ensure maximum customer satisfaction. From top-class networking solutions to better management of increasing databases using GPU cloud or other GPU online services to manage data hungry workloads efficiently, AI and MI-enabled solutions have taken the telecom industry by storm. Moreover, with the introduction of artificial intelligence and machine learning in this industry, the scope of innovation and improvement is higher than ever before.




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June 29, 2022

Top 7 AI Startups in Education Industry

The evolution of the global education system is an interesting thing to watch. The way this whole sector has transformed in the past decade can make a great case study on how modern technology like artificial intelligence (AI) makes a tangible difference in human life. 

In this evolution, edtech startups have played a pivotal role. And, in this write-up, you will get a chance to learn about some of them. So, read on to explore more.

Top AI Startups in the Education Industry-

Following is a list of education startups that are making a difference in the way this sector is transforming –

  1. Miko

Miko started its operations in 2015 in Mumbai, Maharashtra. Miko has made a companion for children. This companion is a bot which is powered by AI technology. The bot is able to perform an array of functions like talking, responding, educating, providing entertainment, and also understanding a child’s requirements. Additionally, the bot can answer what the child asks. It can also carry out a guided discussion for clarifying any topic to the child. Miko bots are integrated with a companion app which allows parents to control them through their Android and iOS devices. 

  1. iNurture

iNurture was founded in 2005 in Bengaluru, Karnataka. It provides universities assistance with job-oriented UG and PG courses. It offers courses in IT, innovation, marketing leadership, business analytics, financial services, design and new media, and design. One of its popular products is KRACKiN. It is an AI-powered platform which engages students and provides employment with career guidance. 

  1. Verzeo

Verzeo started its operations in 2018 in Bengaluru, Karnataka. It is a platform based on AI and ML. It provides academic programmes involving multi-disciplinary learning that can later culminate in getting an internship. These programmes are in subjects like artificial intelligence, machine learning, digital marketing and robotics.

  1. EnglishEdge 

EnglishEdge was founded in Noida in 2012. EnglishEdge provides courses driven by AI for getting skilled in English. There are several programmes to polish your English skills through courses provided online like professional edge, conversation edge, grammar edge and professional edge. There is also a portable lab for schools using smart classes for teaching the language. 

  1. CollPoll

CollPoll was founded in 2013 in Bengaluru, Karnataka. The platform is mobile- and web-based. CollPoll helps in managing educational institutions. It helps in the management of admission, curriculum, timetable, placement, fees and other features. College or university administrators, faculty and students can share opinions, ideas and information on a central server from their Android and iOS phones.

  1. Thinkster

Thinkster was founded in 2010 in Bengaluru, Karnataka. Thinkster is a program for learning mathematics and it is based on AI. The program is specifically focused on teaching mathematics to K-12 students. Students get a personalised experience as classes are conducted in a one-on-one session with the tutors of mathematics. Teachers can give scores for daily worksheets along with personalised comments for the improvement of students. The platform uses AI to analyse students’ performance. You can access the app through Android and iOS devices.

  1. ByteLearn 

ByteLearn was founded in Noida in 2020. ByteLean is an assistant driven by artificial intelligence which helps mathematics teachers and other coaches to tutor students on its platform. It provides students attention in one-on-one sessions. ByteLearn also helps students with personalised practice sessions.

Key Highlights

  • High demand for AI-powered personalised education, adaptive learning and task automation is steering the market.
  • Several AI segments such as speech and image recognition, machine learning algorithms and natural language processing can radically enhance the learning system with automatic performance assessment, 24x7 tutoring and support and personalised lessons.
  • As per the market reports of P&S Intelligence, the worldwide AI in the education industry has a valuation of $1.1 billion as of 2019.
  • In 2030, it is projected to attain $25.7 billion, indicating a 32.9% CAGR from 2020 to 2030.

Bottom Line

Rising reliability on smart devices, huge spending on AI technologies and edtech and highly developed learning infrastructure are the primary contributors to the growth education sector has witnessed recently. Notably, artificial intelligence in the education sector will expand drastically. However, certain unmapped areas require innovations.

With experienced well-coordinated teams and engaging ideas, AI education startups can achieve great success.

Reference Links:

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