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.

1.Introduction

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.

5.Industries





Transportation
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





Manufacturing
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.

Healthcare
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.






Education
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.






7.Conclusion
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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This is a decorative image for Project Management for AI-ML-DL Projects
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. 

Reference Links:

https://www.datacamp.com/blog/how-to-manage-ai-projects-effectively

https://appinventiv.com/blog/ai-project-management/#:~:text=There%20are%20six%20steps%20that,product%20on%20the%20right%20platform.

https://www.datascience-pm.com/manage-ai-projects/

https://community.pmi.org/blog-post/70065/how-can-i-manage-complex-ai-projects-#_=_

This is a decorative image for Top 7 AI & ML start-ups in Telecom Industry in India
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:

Wiom

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. 

TechVantage

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

Manthan

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. 

NetraDyne

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.

 

Helpshift

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. 

Facilio

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.

 

 

References

https://www.inventiva.co.in/trends/telecom-startup-funding-inr-30-crore/

https://www.mygreatlearning.com/blog/top-ai-startups-in-india/

This is a decorative image for Top 7 AI Startups in Education Industry
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:

https://belitsoft.com/custom-elearning-development/ai-in-education/ai-in-edtech

https://www.emergenresearch.com/blog/top-10-leading-companies-in-the-artificial-intelligence-in-education-sector-market

https://xenoss.io/blog/ai-edtech-startups

https://riiid.com/en/about

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