Robotics and AI to answer healthcare challenges

June 21, 2022

AI and robotics are two of the most futuristic technologies that the world is utilizing now. Implementing these two technologies can lead to breakthroughs in a variety of industries, including healthcare.

But in contrast to other industries, healthcare is not easily manageable. Traditional medicine, medical personnel, diseases, patients, behaviors, and systematic concerns are just a few of the many challenges that the industry faces today. It is not an easy effort to bring innovations and emerging technologies to handle these issues. 

Artificial Intelligence (AI) and robotics may be able to fill up the gaps. You can see the staggering statistics, such as an aging population putting pressure on healthcare systems. As a result, there aren't enough individuals working in adult social care to offer the amount of care that patients require. From genetic testing to robotic surgery to cancer research and data collection, AI and robotics are advancing the healthcare business. For example, in dermatology AI is already being applied in practice, with medical practitioners using an experimental mobile version to identify skin cancer.

We've highlighted more examples of how robotics and AI are revolutionizing the healthcare and medical sector in this blog.

Robotics in Healthcare:

Robotic technology is applied in several scenarios that have a direct influence on patient care. They may be used to sterilize patient rooms and operating rooms, reducing infection risks for both patients and medical personnel. They gather samples, transport them, analyze them, and store them in laboratories. 

Robots in the medical field provide high-quality patient care, efficient clinical procedures, and a secure environment for patients and healthcare workers. Down below are a few instances of how robotics is helping to improve the healthcare business.

  1. Superior Patient Care: Medical robots help with minimally invasive treatments, personalized and frequent monitoring for chronic disease patients, intelligent therapies, and social interaction for the elderly. Furthermore, because robots reduce workloads, nurses and other caregivers may provide more empathy and personal interaction with patients, which can improve long-term health.

  1. Robotic Surgical Assistance: Surgical-assistance robots have gotten more precise as motion control technologies have progressed. With AI and computer vision capabilities, these robots enable surgeons to attain unprecedented levels of speed and accuracy while executing challenging surgeries. Some surgical robots may even be capable of performing tasks on their own, allowing physicians to supervise procedures from a console. Robotics is very important in surgeon education. Surgical robotics training is provided through simulation systems that combine artificial intelligence and virtual reality. Surgeons can practice procedures and build skills in a virtual environment using robotics controls.

  1. Automated Mobile Robots: AMRs are frequently used by healthcare organizations because of their capacity to help with key needs including disinfection, telepresence, and transportation of medication and medical supplies, all while allowing personnel to spend more time with patients. AMRs can self-navigate to patients in examination or hospital rooms when equipped with light detection and ranging (LiDAR) devices, visual computing, or mapping capabilities, allowing doctors to interact from distance. If an AMR is managed by a remote specialist or other workers, it can accompany doctors on their rounds in the hospital, allowing a specialist to contribute via an on-screen consultation about patient diagnosis and care.

  1. Service Robots: By addressing mundane logistical chores, service robots ease the daily stress on healthcare professionals. Many of these robots are self-contained and can generate reports after completing a task. Set up patient rooms, manage supplies and file purchase orders, replenish medical supply cabinets, and move bed linens to and from washing facilities are all handled by these robots. Having service robots execute some regular activities frees up healthcare professionals to focus on acute patient needs, which can improve job satisfaction. 

AI in Healthcare:

Traditional analytics and clinical decision-making methodologies have a variety of disadvantages. As learning algorithms interact with training data, they can become more exact and accurate, giving people unparalleled insights into diagnostics, care processes, treatment variability, and patient outcomes. AI in healthcare is taking over such challenges. 

  1. Mind and Machine unification: By no means we are using computers to communicate a novel concept, but developing direct connections between technology and the human mind without the use of keyboards, or monitors is a cutting-edge field of research with substantial implications for some patients. Some patients' abilities to speak, move, and interact meaningfully with others and their settings might be taken away by neurological illnesses and nervous system injuries. Artificial intelligence-assisted brain-computer interfaces (BCIs) may be able to restore such essential experiences to those who worry they will be lost forever.

  1. Expanding healthcare services for the underprivileged:  In underdeveloped countries around the world, shortages of qualified healthcare providers, such as ultrasound technologists and radiologists, can severely limit access to life-saving care. The half-dozen hospitals that line Boston's renowned Longwood Avenue employ more radiologists than all of West Africa, indeed a worrying situation. Artificial intelligence may be able to assist minimize the effects of the significant shortage of skilled clinical personnel by taking over some of the diagnostic tasks that are normally performed by humans. AI imaging techniques, for example, can scan chest x-rays for symptoms of tuberculosis, frequently with accuracy comparable to people. This capability could be made available to providers in low-resource locations via an app, decreasing the need for a trained person.

  1. Wearable devices for personal health monitoring: Almost every customer now has access to devices with sensors that can collect useful health information. A growing amount of health-related data is created, from cellphones with step trackers to wearables that can detect a heartbeat around the clock. Collecting and analyzing this data, as well as complementing it with information from patients via apps and other home monitoring devices, can provide a unique perspective on individual and population health. Artificial intelligence will be critical in extracting useful information from this huge and varied data source.

  1. Using AI for clinical decision-making: As the healthcare business moves away from fee-for-service, it is moving away from reactive treatment as well. Every physician wants to be ahead of chronic diseases, expensive acute events, and unexpected deterioration, and reimbursement mechanisms are finally allowing them to build the processes that will allow proactive predictive interventions. Predictive analytics and clinical decision support technologies that alert doctors to problems long before they would otherwise realize the need to intervene will be powered by artificial intelligence, which will offer much of the underpinning for that evolution. For illnesses like seizures or sepsis, AI can provide earlier warnings, which sometimes require intense analysis of large datasets.

AI and Robotics in Healthcare: What the Future Holds

Many academicians agree that AI and robotics have a bright future. AI or robots are not likely to take over the healthcare industry very soon but will ensure 100 percent accuracy in the total process and human participation and oversight would still be required. Patients are also known to form closer bonds with their doctors, nurses, and other medical personnel. This particular relationship offers patients the impression that they are not alone. Machines or robots will never be able to reproduce this sensation. As a result, people will always be there alongside AI and robotics to treat patients and provide a pleasant and comforting experience.

Furthermore, AI and robotics are expected to thrive in the coming years. Artificial Intelligence and robots provide healthcare benefits that are unrivaled by what we might do manually.

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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. 

Reference Links:,product%20on%20the%20right%20platform.

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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.

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