Future of Practical AI

February 16, 2021

We are in the new phase of the 21st century, a phase where the champions will be the organizations that have the data and the strength to use data in business processes. Only 1% of the available data has been utilized practically.

As there is a lot of information available on windows servers and windows cloud computing that is yet to be commercialized. The companies that know to utilize information will stand apart and succeed by getting the most value out of them with the help of artificial intelligence.

On a large scale, machines are replacing human work, as machines are far more efficient at certain functions and eliminate possible human errors. Infusing AI in machines is like having a human mind with a robotic body, i.e. it will have the whip-smart thinking power and untiring puissant work mechanics.

This tech amalgamation is transforming the world at a fast pace, as tech giants such as Google, Salesforce, Tesla, and many others are adopting AI into their products and services. AI has a lot to offer, the ability to grasp data continually while learning, scanning, and adjusting, it can provide services more flexibly and cost-effectively.


Right from being in imaginations only to getting featured in science fiction movies and tales, automation and self-thinking computers have made people fascinated and awestruck.

Today, almost every big-scale tech-businesses are spending huge resources on AI. Famous personalities namely Bill Gates, Elon Musk, Jeff Bezos, Sundar Pichai, or everyone is pondering over the possibilities and impact of this future innovation. But, the question arises, what is Intelligence and how machines can have it?

Intelligence is the measure of an agent’s ability to acquire and apply knowledge and skills in a wide range of environments. Artificial intelligence (AI) is the simulation of human intelligence in machines that are programmed to think like humans and mimic their actions.

All the tasks which were once considered to be time-consuming and required fewer skills like data typing, text editors, maps, etc. are already automated to date. Now, the more focus is on the tasks which require more complex algorithms and data like face and voice recognition, self-driven cars etc.

The Internet has become less of an entertainment and more of a utility, reaching nearly 5 billion people, according to a study by Domo. Amenities such as GPU cloud server, google search, and Wordpress Cloud are in rising demand. GPU server price is hefty, but NVIDIA GPU cloud is economical.

E2E Networks’, one of the noteworthy GPU Cloud vendors has been offering world-class GPU infrastructure powered by emerging NVIDIA GPU technologies, NVIDIA T4 and NVIDIA Tesla V100. It accelerates workload and can be availed at inexpensive prices. NVIDIA Tesla v100 price starts at 55,000 per month.

Artificial Intelligence works on algorithms, establishing the connection between data. Barry Smyth, Computer science Professor at University College Dublin, says: "Data is to AI what food is to humans." So, in a more digital world, the exponential growth of data is constantly feeding AI improvements.
Since the modern WordPress hosting price is getting cheaper and the WordPress cloud is easily accessible, there is a hike in the availability of data. Jim Short, a lead scientist at the San Diego Supercomputer Center, estimates a data growth rate of 40 percent per year.

1.Artificial Intelligence V/s Machine Learning

Machine Learning, a term coined by Artur Samuel in 1959, meant “the ability to learn without being explicitly programmed.” It is a subset of artificial intelligence, which refers to the concept that computer programs can automatically learn from and adapt to new data.
It requires millions of pictures or documents, draw a pattern connection and carry out future tasks.
It is easy for humans to program computers to do a task step by step, for simple tasks, but we need the computers to learn on their own by comparing and analyzing data, that’s where machine learning comes into play for complex advanced tasks.
Today, Machine learning has 2 major tasks, first to classify data based on certain criteria and second, to predict future outcomes. Deep learning, a machine-learning technique includes speech recognition systems and is one the fastest-evolving AI technique.
Machine Learning requires a lot of time to collect, read and analyze data, that’s why we use GPU machines and GPU servers.

E2E Networks has been offering EOS or E2E Object storage, an SSD based object storage for handling machine learning and deep learning workloads.

2.Rise of AI Machines and its Impacts

AI is growing exponentially for a few years and it’s impacting our lives every day, without our knowledge. Right from search results on Google, Instagram and YouTube, predicting weather and guiding maps, etc. we are all surrounded by AI.

When talking about commercial importance, Accenture says that the AI technology's impact on business will boost labor productivity by up to 40%. It includes all the repetitive and less engaging tasks. Now, almost every service sector is becoming autonomous

AI services are far more efficient and effective compared to manual ones, thus, saving time, cost, and effort. It can reduce operating costs and save a huge amount of money. One estimate from McKinsey predicts big data could save medicine and pharma up to $100B annually. Human workload and burden will decrease cumulatively, improving living standards.

There are both sides to a coin, AI comes with a few cons too. The most important issue is that the bottom 90 percent, especially the bottom 50 percent of the world in terms of income or education, will get badly hurt with job displacement and unemployment. This will dramatically increase unemployment ratios, impacting the GDP of the country.

Another major concern is related to the safety and privacy of the data since this technology requires loads of data, and it’s a common threat that some companies may use the personal information of people.

3.1 AI for WordPress
AI can offer a smarter user experience to the users helping with WordPress search, grammar checks, improving conversions, and boosting eCommerce sales, with WordPress hosting. This can be accomplished with WordPress plugins using AI.
One such related offering is the CDN service offered by E2E Networks assuring ‘CMS made simple’. It is a global network helping in distributing content & web pages to users with minimum latency resulting in enhanced customer experience.

3.AI Holds the Power to Change World Economy
The use of artificial intelligence in enterprises has tripled during the past two years, requiring IT leaders to re-evaluate their core infrastructures and optimize for AI productivity. The adoption of AI will necessarily and unavoidably change the nature of work.
E2E Networks’ offering NVIDIA A100 is a Universal AI Infrastructure System used for enabling Enterprises to integrate training, inference, and analytics. It is the world’s first AI system fabricated on NVIDIA A100. It offers the benefits such as higher throughput, maximizing GPU machine utility, and improved end performance of the model.
AI is making mass-customization possible by leveraging Machine learning-enabled hyper-personalization to provide better customer service and better products and solutions.
Different industries have been harnessing the potential of AI. It is for Stock Market prediction and also for research purposes in the medical field.
Artificial intelligence can efficiently increase 16 percent or around $13 trillion by 2030 to current global economic output-- an annual average contribution to productivity growth of about 1.2 percent between now and 2030, according to a September 2018 report by the McKinsey Global Institute on the impact of AI on the world economy.

4.Cloud Artificial Intelligence And Machine Learning

AI and ML have been emerging technologies and have shown their impact on Cloud applications alike, let’s see how:

5.1 Machine Learning with Cloud

Cloud Machine Learning Engine can run machine learning training jobs and predictions when being hosted. E2E Networks’ offering high memory cloud service works on training and predictions independently by leveraging the GPU and TPU infrastructure.
The resultant of this is a fully-trained machine learning model that is hosted in other environments such as on-premise, own cloud infrastructure as well as public cloud. It can deploy a model trained in external environments. Machine Learning Engine deployed on cloud automates resource provisioning and monitoring along with their versions.

5.2 Artificial Intelligence with Cloud

Mckinsey estimates that across 19 business areas and more than 400 potential use cases, AI could create $3.5 trillion and $5.8 trillion per year in value. Businesses and organizations generate a humongous amount of data. A study by Deloitte University Press predicted that the digital universe will contain over 44 Zettabytes of data by 2020.
AI tools help to manage, monitor and maintain data in public and private cloud systems. It creates a backup of data, identifies the condition of hardware, and makes it safe from virus and malware attacks.
The public cloud industry makes over $200 billion and is predicted to be worth over $1,250 billion by 2025. AI application helps in moderating and handling server crashes.
It is cost-effective since it doesn't require thousands of storing hardware. But, it requires enormous power for complex algorithms processing and handling tons of GBs data.
This data can also be used in certain machine learning technologies, which can use this data, and can learn from them. This will leverage innovation and development.
Digital assistants such as Alexa, Cortana, Google Assistant and Siri amalgamate advanced Artificial Intelligence algorithms with cloud-computed storage for providing seamless user interface and service.
Internet of Things (IoT) is the next-generation technology, which relies on the principle of 'smart machines'. We can store the data generated by all the devices and can use AI algorithms to command and use them.
Within a few years, all the small scale and large scale businesses will be seen to use AI with cloud storage.

5.3 E2E Cloud Offerings for Intensive Operations such as AI and ML

E2E networks has four significant cloud offerings:
i. CPU Intensive Cloud
ii. Windows SQL Cloud
iii. Plesk Windows Cloud as cloud VPS windows
iv. cPanel Linux Cloud as cPanel cloud Server
All the above E2E products have been designed for high-performance computing, simplifying machines, analytics and are capable of deploying a secure VPS server.


All the modes of transport including trucking, aviation, marine, etc. will be self-driven, which will be much efficient, safer, and time-saving. In 2017, the global market for transportation-related AI technologies reached $1.2 to $1.4 billion, according to estimates from global research firms. It could grow to $3.1 to $3.5 billion by 2023

Andrew Ng, the co-founder of Google Brain and Coursera, infers that, “AI will manufacture, control quality , reduce design time, and materials waste, improving overall production reuse and performing predictive maintenance.” It can help in quality checks, generate designs, regulate machines, etc.

According to Accenture analysis, when combined, key clinical health AI applications can potentially create $150 billion in annual savings for the US healthcare economy by 2026. Growth in the AI health market is expected to reach $6.6 billion by 2021. AI can be used for early detection, diagnosing, and treatment of diseases. AI devices and machines are developed for imaging, measurement, and arrangement of health information.

AI can be employed in the education field to gauge a student’s performance, and give him personalized content, i.e. the right resources and study materials. It can help the visually challenged students by voice recognition and text-to-speech translation. It can help assess assignments and papers. GPU cloud and dual GPU create high- resolution graphic interfaces to make learning intuitive and interactive. Nvidia server GPU and Nvidia v100 are used since Nvidia GPU cloud pricing is lower and it is better than other GPU servers.

Customer Services
AI allows a better personalized user experience, with chatting using bots, analyzing and optimizing product recommendations. It can predict the product delivery expectancies and customer reviews. It can be used in place of human agents, saving costs and labor. Vala Afshar, Salesforce’s Chief Digital Evangelist, predicts that “The line-of-business that is most likely to embrace AI first will be the customer service – typically the most process-oriented and technology savvy organization within most companies.” GPUs are used to make this process easier.

Military and Defense: AI can be used to make strategies, operational and tactical plans. It can be used to detect enemies in places where the conditions are harsh in terms of weather. It can improve missile targets with high precision. AI cameras can be used to detect unusual activity and enemies. But since military data is highly confidential, the best windows GPU servers and virtual private server with windows OS and NVIDIA GPU shall be used.

6.The Sky Is No Longer The Limit (The Future Of AI)

The future of AI is indeed beyond our imaginations, and it’s going to impact every industry and every human. Only 23% of businesses have incorporated AI into processes and product/service offerings today, according to Forbes. AI will not only be limited to search recommendations and face recognition, but it’ll soon be a part of our overall human experience.

AI holds the potential to change humanity. AI will be used to give us powers of language translation and augmented creativity. Machine translation technology will help humans understand and communicate in many languages. Certain shop sites, WordPress servers and WordPress web hosting also use AI and are expected to grow in future.

More accurate Face recognition features are developed. It’ll improve the security domain incrementally and can be used to identify any missing person.
Humanoid Robots' development is on the surge. It’s one of the most in-demand AI technologies since it can be used both domestically and in industries.

Artificial intelligence has acted as the main driver of emerging technologies like big data, robotics, and IoT, and it will continue to act as a technological innovator for the foreseeable future.
By 2025, the AI market will take a leap to a $190 billion industry, according to research firm Markets and Markets.
AI is the new electricity. It has the potential to transform every industry and to create huge economic value.
Russian President Vladimir Putin rightly said that “Whoever becomes the leader in this sphere [AI] will become the ruler of the world.”
Ray Kurzweil, Google’s director of engineering, forecasts that by 2029 machines will reach a human level of intelligence.
If governments, universities, and corporations work together to encourage education and innovation, all nations and all people have an opportunity to be part of this new AI economy.
“AI offers us unprecedented opportunities to engage consumers in new and different ways. Today we have massive datasets at our fingertips as well as the extraordinary processing power to extract patterns, connections, and meaning from that data to get to the intent of consumers.”- says Devin Wenig, President, and CEO, eBay.

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October 18, 2022

A Complete Guide To Customer Acquisition For Startups

Any business is enlivened by its customers. Therefore, a strategy to constantly bring in new clients is an ongoing requirement. In this regard, having a proper customer acquisition strategy can be of great importance.

So, if you are just starting your business, or planning to expand it, read on to learn more about this concept.

The problem with customer acquisition

As an organization, when working in a diverse and competitive market like India, you need to have a well-defined customer acquisition strategy to attain success. However, this is where most startups struggle. Now, you may have a great product or service, but if you are not in the right place targeting the right demographic, you are not likely to get the results you want.

To resolve this, typically, companies invest, but if that is not channelized properly, it will be futile.

So, the best way out of this dilemma is to have a clear customer acquisition strategy in place.

How can you create the ideal customer acquisition strategy for your business?

  • Define what your goals are

You need to define your goals so that you can meet the revenue expectations you have for the current fiscal year. You need to find a value for the metrics –

  • MRR – Monthly recurring revenue, which tells you all the income that can be generated from all your income channels.
  • CLV – Customer lifetime value tells you how much a customer is willing to spend on your business during your mutual relationship duration.  
  • CAC – Customer acquisition costs, which tells how much your organization needs to spend to acquire customers constantly.
  • Churn rate – It tells you the rate at which customers stop doing business.

All these metrics tell you how well you will be able to grow your business and revenue.

  • Identify your ideal customers

You need to understand who your current customers are and who your target customers are. Once you are aware of your customer base, you can focus your energies in that direction and get the maximum sale of your products or services. You can also understand what your customers require through various analytics and markers and address them to leverage your products/services towards them.

  • Choose your channels for customer acquisition

How will you acquire customers who will eventually tell at what scale and at what rate you need to expand your business? You could market and sell your products on social media channels like Instagram, Facebook and YouTube, or invest in paid marketing like Google Ads. You need to develop a unique strategy for each of these channels. 

  • Communicate with your customers

If you know exactly what your customers have in mind, then you will be able to develop your customer strategy with a clear perspective in mind. You can do it through surveys or customer opinion forms, email contact forms, blog posts and social media posts. After that, you just need to measure the analytics, clearly understand the insights, and improve your strategy accordingly.

Combining these strategies with your long-term business plan will bring results. However, there will be challenges on the way, where you need to adapt as per the requirements to make the most of it. At the same time, introducing new technologies like AI and ML can also solve such issues easily. To learn more about the use of AI and ML and how they are transforming businesses, keep referring to the blog section of E2E Networks.

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This is a decorative image for: Constructing 3D objects through Deep Learning
October 18, 2022

Image-based 3D Object Reconstruction State-of-the-Art and trends in the Deep Learning Era

3D reconstruction is one of the most complex issues of deep learning systems. There have been multiple types of research in this field, and almost everything has been tried on it — computer vision, computer graphics and machine learning, but to no avail. However, that has resulted in CNN or convolutional neural networks foraying into this field, which has yielded some success.

The Main Objective of the 3D Object Reconstruction

Developing this deep learning technology aims to infer the shape of 3D objects from 2D images. So, to conduct the experiment, you need the following:

  • Highly calibrated cameras that take a photograph of the image from various angles.
  • Large training datasets can predict the geometry of the object whose 3D image reconstruction needs to be done. These datasets can be collected from a database of images, or they can be collected and sampled from a video.

By using the apparatus and datasets, you will be able to proceed with the 3D reconstruction from 2D datasets.

State-of-the-art Technology Used by the Datasets for the Reconstruction of 3D Objects

The technology used for this purpose needs to stick to the following parameters:

  • Input

Training with the help of one or multiple RGB images, where the segmentation of the 3D ground truth needs to be done. It could be one image, multiple images or even a video stream.

The testing will also be done on the same parameters, which will also help to create a uniform, cluttered background, or both.

  • Output

The volumetric output will be done in both high and low resolution, and the surface output will be generated through parameterisation, template deformation and point cloud. Moreover, the direct and intermediate outputs will be calculated this way.

  • Network architecture used

The architecture used in training is 3D-VAE-GAN, which has an encoder and a decoder, with TL-Net and conditional GAN. At the same time, the testing architecture is 3D-VAE, which has an encoder and a decoder.

  • Training used

The degree of supervision used in 2D vs 3D supervision, weak supervision along with loss functions have to be included in this system. The training procedure is adversarial training with joint 2D and 3D embeddings. Also, the network architecture is extremely important for the speed and processing quality of the output images.

  • Practical applications and use cases

Volumetric representations and surface representations can do the reconstruction. Powerful computer systems need to be used for reconstruction.

Given below are some of the places where 3D Object Reconstruction Deep Learning Systems are used:

  • 3D reconstruction technology can be used in the Police Department for drawing the faces of criminals whose images have been procured from a crime site where their faces are not completely revealed.
  • It can be used for re-modelling ruins at ancient architectural sites. The rubble or the debris stubs of structures can be used to recreate the entire building structure and get an idea of how it looked in the past.
  • They can be used in plastic surgery where the organs, face, limbs or any other portion of the body has been damaged and needs to be rebuilt.
  • It can be used in airport security, where concealed shapes can be used for guessing whether a person is armed or is carrying explosives or not.
  • It can also help in completing DNA sequences.

So, if you are planning to implement this technology, then you can rent the required infrastructure from E2E Networks and avoid investing in it. And if you plan to learn more about such topics, then keep a tab on the blog section of the website

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October 18, 2022

A Comprehensive Guide To Deep Q-Learning For Data Science Enthusiasts

For all data science enthusiasts who would love to dig deep, we have composed a write-up about Q-Learning specifically for you all. Deep Q-Learning and Reinforcement learning (RL) are extremely popular these days. These two data science methodologies use Python libraries like TensorFlow 2 and openAI’s Gym environment.

So, read on to know more.

What is Deep Q-Learning?

Deep Q-Learning utilizes the principles of Q-learning, but instead of using the Q-table, it uses the neural network. The algorithm of deep Q-Learning uses the states as input and the optimal Q-value of every action possible as the output. The agent gathers and stores all the previous experiences in the memory of the trained tuple in the following order:

State> Next state> Action> Reward

The neural network training stability increases using a random batch of previous data by using the experience replay. Experience replay also means the previous experiences stocking, and the target network uses it for training and calculation of the Q-network and the predicted Q-Value. This neural network uses openAI Gym, which is provided by taxi-v3 environments.

Now, any understanding of Deep Q-Learning   is incomplete without talking about Reinforcement Learning.

What is Reinforcement Learning?

Reinforcement is a subsection of ML. This part of ML is related to the action in which an environmental agent participates in a reward-based system and uses Reinforcement Learning to maximize the rewards. Reinforcement Learning is a different technique from unsupervised learning or supervised learning because it does not require a supervised input/output pair. The number of corrections is also less, so it is a highly efficient technique.

Now, the understanding of reinforcement learning is incomplete without knowing about Markov Decision Process (MDP). MDP is involved with each state that has been presented in the results of the environment, derived from the state previously there. The information which composes both states is gathered and transferred to the decision process. The task of the chosen agent is to maximize the awards. The MDP optimizes the actions and helps construct the optimal policy.

For developing the MDP, you need to follow the Q-Learning Algorithm, which is an extremely important part of data science and machine learning.

What is Q-Learning Algorithm?

The process of Q-Learning is important for understanding the data from scratch. It involves defining the parameters, choosing the actions from the current state and also choosing the actions from the previous state and then developing a Q-table for maximizing the results or output rewards.

The 4 steps that are involved in Q-Learning:

  1. Initializing parameters – The RL (reinforcement learning) model learns the set of actions that the agent requires in the state, environment and time.
  2. Identifying current state – The model stores the prior records for optimal action definition for maximizing the results. For acting in the present state, the state needs to be identified and perform an action combination for it.
  3. Choosing the optimal action set and gaining the relevant experience – A Q-table is generated from the data with a set of specific states and actions, and the weight of this data is calculated for updating the Q-Table to the following step.
  4. Updating Q-table rewards and next state determination – After the relevant experience is gained and agents start getting environmental records. The reward amplitude helps to present the subsequent step.  

In case the Q-table size is huge, then the generation of the model is a time-consuming process. This situation requires Deep Q-learning.

Hopefully, this write-up has provided an outline of Deep Q-Learning and its related concepts. If you wish to learn more about such topics, then keep a tab on the blog section of the E2E Networks website.

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This is a decorative image for: GAUDI: A Neural Architect for Immersive 3D Scene Generation
October 13, 2022

GAUDI: A Neural Architect for Immersive 3D Scene Generation

The evolution of artificial intelligence in the past decade has been staggering, and now the focus is shifting towards AI and ML systems to understand and generate 3D spaces. As a result, there has been extensive research on manipulating 3D generative models. In this regard, Apple’s AI and ML scientists have developed GAUDI, a method specifically for this job.

An introduction to GAUDI

The GAUDI 3D immersive technique founders named it after the famous architect Antoni Gaudi. This AI model takes the help of a camera pose decoder, which enables it to guess the possible camera angles of a scene. Hence, the decoder then makes it possible to predict the 3D canvas from almost every angle.

What does GAUDI do?

GAUDI can perform multiple functions –

  • The extensions of these generative models have a tremendous effect on ML and computer vision. Pragmatically, such models are highly useful. They are applied in model-based reinforcement learning and planning world models, SLAM is s, or 3D content creation.
  • Generative modelling for 3D objects has been used for generating scenes using graf, pigan, and gsn, which incorporate a GAN (Generative Adversarial Network). The generator codes radiance fields exclusively. Using the 3D space in the scene along with the camera pose generates the 3D image from that point. This point has a density scalar and RGB value for that specific point in 3D space. This can be done from a 2D camera view. It does this by imposing 3D datasets on those 2D shots. It isolates various objects and scenes and combines them to render a new scene altogether.
  • GAUDI also removes GANs pathologies like mode collapse and improved GAN.
  • GAUDI also uses this to train data on a canonical coordinate system. You can compare it by looking at the trajectory of the scenes.

How is GAUDI applied to the content?

The steps of application for GAUDI have been given below:

  • Each trajectory is created, which consists of a sequence of posed images (These images are from a 3D scene) encoded into a latent representation. This representation which has a radiance field or what we refer to as the 3D scene and the camera path is created in a disentangled way. The results are interpreted as free parameters. The problem is optimized by and formulation of a reconstruction objective.
  • This simple training process is then scaled to trajectories, thousands of them creating a large number of views. The model samples the radiance fields totally from the previous distribution that the model has learned.
  • The scenes are thus synthesized by interpolation within the hidden space.
  • The scaling of 3D scenes generates many scenes that contain thousands of images. During training, there is no issue related to canonical orientation or mode collapse.
  • A novel de-noising optimization technique is used to find hidden representations that collaborate in modelling the camera poses and the radiance field to create multiple datasets with state-of-the-art performance in generating 3D scenes by building a setup that uses images and text.

To conclude, GAUDI has more capabilities and can also be used for sampling various images and video datasets. Furthermore, this will make a foray into AR (augmented reality) and VR (virtual reality). With GAUDI in hand, the sky is only the limit in the field of media creation. So, if you enjoy reading about the latest development in the field of AI and ML, then keep a tab on the blog section of the E2E Networks website.

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