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Tag: Machine Learning career

How digitization through artificial intelligence and machine learning technologies has gained momentum post COVID-19?

Posted on by Imarticus Learning

In just a few months, the COVID-19 pandemic has managed to do what normal times would have taken years to achieve – a paradigm shift in the way companies in every industry and sector do business. Artificial intelligence and machine learning have been at the forefront during these challenging times. 

As the world gradually finds its way back to usual ways of life, it is interesting to see how the global crisis has paved the way for behavioral shifts, learning, and innovation. 

AI and ML in the Post-Covid-19 World

With the acceleration of digitization through Artificial Intelligence (AI) and Machine Learning (ML), digital sales have seen a boost, and businesses have focused their tech investments on cloud-based products and services. From online grocery stores and EdTech sites to online pharmacies and OTT players, the post-COVID-19 world looks very different through the AI and ML lens.

So, here are some examples to show how AI and ML technologies have gained momentum post-COVID-19:

  • AI and ML have been impacting the healthcare industry since long before the pandemic hit. AI algorithms have and continue to help in quickly sifting through large datasets to help identify similar diseases and their possible cures to accelerate the COVID-19 research work. 
  • AI and automation technology have also eased the healthcare sector’s administrative load by automating various processes. For example, data processing algorithms to extract data from internal systems and automatically generate medical reports and necessary audit trails have gained momentum post-pandemic. 
  • Also, advancements in ML will continue to help create new revenue streams. For example, scientists, drug researchers, and pharma companies are increasingly turning to AI and ML data processing algorithms to facilitate vaccine and drug discovery and their possible impacts on people. 
  • Lockdowns and social distancing norms have boosted online markets and the digital economy. However, even when the pandemic is gradually ebbing, customers are expected to continue using doorstep services as they did during the peak crisis. Hence, technologies like Augmented Reality (AR) and Virtual Reality (VR) have increased among eCommerce platforms to deliver a better customer experience. 
  • Talking about customer experience, the online retail industry has ramped up its use of AI chatbots and smart assistants to attend to the ever-increasing numbers of digital customers. Hence, the use of AI has helped streamline digital services, online ordering, and delivery systems. 
  • The pandemic has given rise to a digital workforce. To this end, the use of AI to quickly process applications, scan for eligibility and qualifications and perform other mandatory hiring checks has become the norm and is only expected to increase in the near future. 
  • The financial sector has also seen a dramatic rise in the use of AI and automation to serve its customers better and quicker during challenging times. For instance, banks leverage AI to help customers safely upload documents, categorize them and expedite processes without any delay. 
  • Lastly, greater digitization has also increased the risks of cybersecurity threats during the pandemic. While conventional cybersecurity risk management systems have failed to keep up with evolving cyber threats, AI offers innovative defenses. The pandemic has only nudged organizations to adopt holistic approaches to cybersecurity through AI and ML and create an integrated security system. 

How to Find the Best Artificial Intelligence Course?

If you want to learn AI and get a certification in AI and ML, opting for an online course can be the best call. But before you sign up for the course, ensure that it offers hands-on experience with real-world projects and has a curriculum with extensive coverage of concepts related to machine learning, NLP, deep learning, data science, and computer vision. 

Here’s how you can improve customer service for mid-market and enterprise businesses with artificial intelligence and machine learning

Posted on by Imarticus Learning

Customers are the reason for businesses to drive! Whether it’s a small, medium, or an established business, it holds equal importance for all. A customer would like to take services from a company that provides easy access to the platform, understand their needs, is quickly responsive, and resolve the queries optimally.

Technology has a way of making life easier. This is especially true for businesses, which can save time and money by utilizing machine learning and artificial intelligence to analyze customer data to provide better service.

In this blog, we’ll understand a few important ways businesses use AI and Machine Learning for improved customer service. So, let’s get started:

How AI and Machine Learning Contributes to Enhance Customer Service

AI and Machine learning is used in the following areas to enrich customer experience.

  • Customer service interaction
  • Enhancing returns
  • Troubleshooting problems
  • Uses of Website
  • Messaging
  • Customized offerings

Following are the ways used to enhance customers’ experience using AI and machine learning.

1. Chatbot – For Faster and Efficient Assistance: 

The chatbot is one of the most used AI applications by business enterprises. It understands human communication in an accurate context and provides relevant answers to the questions. With the chatbot, you can avail the following benefits:

  • Help businesses to gain insights about user’s requirement
  • Reduces customer’s wait time and get them where they want to be quicker
  • Available for 24 by 7 and provide basic customer support

2. Eliminate Language Barrier to Improve Customer Engagement 

With AI, an enterprise can overcome barriers to doing business with an audience that speaks different languages. Language analysis tool enables office associates to extract main information from the customer feedback and, based on that, adapt their communication.

Language analysis is an important asset to improve the call center experience. With it, the executive can detect if the customer they’re talking to is happy or unhappy and adjust their tone accordingly.

3. Machine Learning Algorithm – Better Understanding of Customer’s Need 

Machine learning offers businesses to get to know more about their customers. Relying on a machine learning solution helps businesses organize daily support requests, answer common inquiries, completely understand a customer’s requirement, and provide a faster solution.

4. Predictive Analytics

Analytics refers to the effort to analyze the data, and it’s crucial for marketing a product. Predictive analytics, with the help of AI tool, analyses past data and predict future outcomes.

 Take Your Career to Next Level with Imarticus Learning 

Imarticus Learning offers the certification in Artificial Intelligence course that the industry’s best leaders have designed to provide a quality learning experience.

The artificial intelligence course will take 9 months to give you a holistic learning experience. Choosing this course will unlock the lucrative creative opportunities in the coveted field of AI.

Our Artificial Intelligence and Machine Learning Course USPs:

  • Master the skills of Machine Learning and Artificial Intelligence through the most relevant curriculum designed by E&ICT Academy, IIT Guwahati, and leaders from the industry.
  • An opportunity to get educated about what new-age AI & ML engineers do by solving real-time problems in their job. Engage in a world-class education program while mastering practical application.

With rampant use of artificial intelligence and machine learning, how are financial institutions dealiing with problems related to data bias and transparency?

Posted on by Imarticus Learning

The public and private sectors are increasingly turning to machine learning (ML) algorithms and artificial intelligence (AI) systems to automate every decision-making process, and financial institutions are no exception.

In addition to widespread use in the capital markets, artificial intelligence and machine learning are used in financial services to make insurance decisions, monitor user behavior, recruitments, fraud detection, credit referencing, and underwriting loans.

However, while AI and ML have brought innumerable benefits to financial institutions, they also have their share of woes in the form of data biases and transparency issues. The question is, how are financial institutions dealing with these problems?

Bias and Transparency in the AI Context

AI systems are powered by algorithms that “train” by reviewing massive datasets to ultimately identify patterns and make decisions based on the observations. Hence, these systems are no better than the fed data, resulting in unconscious data biases.

On the contrary, transparency in the context of AI refers to the ability to explain AI-based decisions. Given the increasingly complex findings and algorithms, ensuring transparency to different stakeholders is vital in the financial sector, both from compliance and business value perspectives.

Biases can occur in many ways. For example, bias due to incomplete data occurs when the AI system has been trained on data that is not representative of the population.

Likewise, the dataset could be biased towards previous decision-making processes, the programmer may introduce their own bias into codes, or business policies pertaining to AI decisions could be biased themselves. The bias of any form eventually leads to unfairness and inequities in financial services.

Dealing With AI Bias and Transparency

Although the use of AI and ML give rise to data bias and transparency issues, they have become indispensable for the functioning of financial services. So, the only course of action left to financial institutions is to adopt ways to get around the problems. Some of them are listed below:

  • Financial institutions and firms can have appropriate controls and monitoring tools to ensure that new data entering the pool is reliable and of high quality. 
  • In addition, some organizations have developed tools to determine if a potential AI solution is biased. 
  • When building AI systems, it is wise to gather a team with domain expertise, model development skills, data engineering capabilities, and commercial expertise. 
  • Organizations can undertake impact assessments of the AI solutions to ensure they are transparent and explainable, as well as determine how the AI-based decision-making process will impact customers. 
  • When engaging with AI technologies, financial services can apply safeguards to ensure that business outcomes are achieved, and customers’ interests are protected. 
  • Another way to minimize data biases is to be open on the user data, match and align data with the target segment, and set up review cycles with legal and statistical experts. 
  • Tracking mechanisms that allow one to track the decision-making mechanism of algorithms can be put in place to eliminate bias and ensure transparency as much as possible. 
  • Lastly, it is pertinent for institutions to document their approach to handling bias and review it after every stage of development and use of the algorithm.

What to Look for in an Artificial Intelligence Course?

If you want to learn AI and ML, there are several online courses you can choose from. An AI and ML certification course that makes you future-ready will have a robust curriculum covering critical concepts related to data science, machine learning, NLP, deep learning, and computer vision.

In addition, the program should offer in-depth experiential learning through hands-on involvement with real-world projects.

Why artificial intelligence and machine learning is the new blue print for the data science industry

Posted on by Imarticus Learning

Data science is a broad discipline concerning data systems and processes with the aim to maintain data sets and derive insights from them. On the other hand, Artificial Intelligence (AI) pertains to mathematical algorithms that can replicate human thought processes to understand complex relationships, plan for the future and make actionable decisions.

Machine Learning (ML), on the contrary, helps to implement AI by “training” computers to solve various tasks. Data science incorporates several areas of artificial intelligence and machine learning while primarily focusing on gaining insights from data.

But, how are these three fields related, and what is the impact of AI and ML in shaping the data science industry? Let’s find out.

Data Science, AI, and ML: Where Lies the Difference? 

Data science finds widespread use in several businesses to improve production processes, innovation of product design, and enable strategic planning. It involves techniques of mathematics, statistics, computer science, and even ML to extract knowledge from data and provide insights and decision paths. 

On the contrary, AI enables computers to observe their environment and make decisions based on what they observe. Some of the most widespread uses of AI include processing clinical data, creating chatbots and smart assistants, and financial planning. Add the machine learning component, and AI can enable computers to solve new problems such as classification and predictions.

The fields of data science, AI and ML overlap significantly and yet have subtle differences. In a nutshell, data science gives insights, AI produces actions, and ML facilitates predictions.

The Combined Effect of AI and ML on Data Science

Data science and data analytics have long been revolutionizing the business landscape. Companies that have mastered their use of data science and analytics aim to delve deeper into data to increase efficiency, boost their bottom lines and gain a competitive edge.

Thus, they are looking to incorporate AI and ML into their data infrastructure to achieve business goals. For instance, call centers have long been using conversation analytics software, platforms that leverage AI and ML to gain better data insights. 

Following are a few more examples to show how AI and ML combined with data science make a remarkable difference to organizations:

  • Conversational AI systems such as chatbots and smart assistants engage in highly interactive conversations with customers and users and capture actionable user insights in the process. 
  • Predictive analytics applications enable the analysis of dynamic datasets to make financial predictions, forecast business trends, customer behavior, etc. 
  • Hyper-personalization systems enable customized offerings to customers, such as product recommendations, targeted advertising, personalized medical care, and financial planning. 
  • Also, organizations can consistently respond to evolving threats, thanks to anomaly detection systems that leverage the potential of adaptive fraud detection and cybersecurity processes.

The business value of data science alone cannot be understated. However, integrating it with the tools and techniques of AI and ML has way more potential to produce actionable insights from the ever-expanding data pool. In conclusion, AI and ML have been impacting the data science industry for a long time and will continue to do so in the foreseeable future with even more ground-breaking innovations.

How to Learn AI?

Looking for an artificial intelligence course? There are several AI and ML courses available online with extensive coverage of data science, ML, NLP, deep learning, and computer vision. But before you settle on a course, ensure that the curriculum offers practical learning through real-world projects, has scope for ample industry exposure, and provides a globally recognized certification after course completion.

The future of artificial intelligence and machine learning in the Biosciences

Posted on by Imarticus Learning

Do you know why artificial intelligence courses are so popular? For the last 70 to 80 years, we have been trying to simulate our intelligence in many artificial entities, which has given rise to the growing field of artificial intelligence (AI). Although AI has surpassed humans in many respects, it still does not live up to its name. AI, as we define it, does not yet exist, nor is there a consensus among experts as to whether it can be achieved.

However, while AI is captivating with its incredible applications and rapid growth (autonomous cars, nanorobots, etc), AI has infiltrated almost all disciplines and has had a particular impact on biosciences. AI offers sufficient computational power and capacity to address the complexity of biological research through simulations (known as “artificial life”). It presents itself as an ideal testing ground, a bounded but unbounded environment where physical laws are adaptable, all parameters are traceable, measurable, storable and retrievable.

AI in Biology

This translates into the possibility of overcoming some of the most important challenges of research in biology. For example, the ethical limits of animal experimentation with drugs for cancer and other diseases, or the methodological difficulties in studying complex systems such as human language, multicellularity or collective intelligence. AI also benefits from this interaction. After all, the key to being able to reproduce a natural system in an artificial environment depends on the knowledge one has of the system in question.

Deep Learning

Deep Learning is one of the many approaches to AI and is inspired by the structure and functioning of the brain through the interconnection of neurons, mimicking the biological structure of the brain through algorithms called Artificial Neural Networks that specialise in detecting specific features, through different layers of neurons, to achieve unsupervised learning. The concept is given by the multiple layers it can comprise.

A neural network needs approximately 50,000 times more energy to function than the human brain. For this reason, computers with traditional architectures are not suited to support the parallel processing that the brain carries out so efficiently. Therefore, research is being carried out into brain-mimicking computing techniques called Neuromorphic Computing.

Artificial Immune Systems

There is an initiative that aims to understand how different parts of the brain work in order to diagnose and treat brain diseases and to develop neuromorphic computers that can learn in the same way as the brain does. These advances need to incorporate multidisciplinary knowledge from neuroscience research, psychology, and ICTs. But it is not only the human brain that is a source of inspiration. Artificial Immune Systems comprise computational methods based on the processes and mechanisms of the human immune system and are used for learning and protecting information systems from malware.

AI and IOT

Finally, we could compare the relationship between Artificial Intelligence and the Internet of Things as the relationship between the brain and the human body. Our bodies collect sensory information (sight, hearing, touch, etc) and send it to the brain, to make sense of this information in order to make the decisions and/or actions, sending signals back to our body if necessary, for example, to pick up an object.

Conclusion

In conclusion, the symbiotic relationship between AI and bioscience has provided the ultimate testing ground for solving some mysteries of biology, as well as the theoretical framework needed to achieve real artificial intelligence. Any of us can learn AI or do a machine learning certification, but only the best prepared will be part of this amazing field of study, so study with Imarticus and go as far as you want.

The Popular Use Cases of Artificial Intelligence in BFSI

Posted on by Imarticus Learning

AI has revolutionized every industry and has changed the way businesses function. Everyone is finding ways to adopt AI into their work. Similarly, the banking, financial services, and insurance sector is also looking to integrate AI into their business and one major reason for doing that is the security of customers.

Customers expect banks to deliver flawless experiences and improve methods of interaction. In the last few years, banks have faced a rise in security threats which is why the banks had to come up with new ways of tightening the security because they were starting to lose clients.

A global study indicates that 85% of respondents have already implemented AI within their organizations and expect to use AI in the new use cases that come and 77% of respondents are anticipating the use of AI processes in their business in the next 2 years.

How is AI strengthening the competitiveness of banks?

Artificial Intelligence and machine learning is the future of banking as it has the power to combat fraudulent activities and improve customer service. Here are some examples of what changes AI has brought about in the banking industry –

  1. Mobile banking – Mobile apps are becoming more advanced and personalized. Banks can generate more revenue with mobile banking services than they generate from customers visiting the branches. This has also saved a lot of time for consumers and improved the quality of services provided.
  2. AI chatbots – In banking, chatbots are used to create an interactive experience for the customers. Bots can communicate with the customers and solve their queries while staying within reason and following the rules. Chatbots can also work 24 hours a day without breaks, which increases the productivity of the banks.
  3. Enhanced security – With the unlimited amount of personal data that is digitized and the number of digital frauds that are happening, it is customary for the banks to keep the client’s money and confidential information safe, and AI helps with that. They have come up with cybersecurity measures like fingerprints, iris, and voice recognition. These measures are almost impossible to forge, therefore everything is safe.
  4. Conformity – It is important to regulate data constantly. There are different processes like “Know Your Customer (KYC)” or “Anti Money Laundering (AML)” that can gather data and transactions digitally, which makes the work faster and easier. If done manually, this process can take up a lot of time. AI algorithms can integrate data very accurately and efficiently.
  5. Financial evaluation – Banks usually earn their profits through the interest they receive on loans. To keep a check that the loans are given back on time with proper interest, they have machine learning algorithms that analyze millions of data and assess whether the client is a risk or not, and then come up with a decision.

Conclusion

People are looking for exceptional services at the click of a button, they don’t want to wait for days for a transaction or money transfer. AI is not only the future of banking, it is also the present. It is advised that all financial institutions start investing in AI right now so that they can optimize their services. With the help of AI certification that is offered by AI and machine learning courses, people can make this revolutionary change in their businesses.

How can a machine learning and artificial intelligence course help you become a social media analyst?

Posted on by Imarticus Learning

Social media has become an integral part of our lives. It is how we keep up to date with the world, and it is also a way for businesses to promote their products/services. With all of this in mind, many people are looking for ways to get into the social media industry.

One of the popular routes is through a job in social media analysis. Social media analysts are becoming more and more important as time goes on. This position requires you to monitor and analyze data on your company’s various social channels.

Thus, machine learning and artificial intelligence courses are becoming more popular among people looking for a potent solution.

How AI and ML are used in social media?

Social media is a very lucrative and competitive industry. Those who can best analyze data, find useful patterns and insights into the business end up earning the most money. This has led to many big players such as Twitter, Facebook, and LinkedIn investing heavily in AI systems that help them better understand their users’ behaviors without even gathering any specific user information!

Social media marketing agencies also use these analytics tools for understanding consumer behavior around products or services offered on social channels like Instagram & Snapchat. The same technologies are used by internet giants like Amazon and Google to offer seemingly personalized search results with just one keyword input from anyone trying out something new online – be it buying a product or browsing through material freely available on the web!

This ongoing trend of personalization based upon customer behavior and interests has made AI a huge part of our lives today.

How do ML and AI courses help you become a social media analyst?

Many companies are now looking for social media analysts to help them understand consumer insights and market expansion opportunities. If you want to become a successful analyst, it is important that you learn how machine learning and artificial intelligence can aid your efforts as marketers in various ways.

Here’s how ML and AI help you become a social media analyst:

Track consumer behavior patterns. ML and AI help you understand the behavioral pattern of your customers by tracking their social media activity. This information enables you to make a business decision or product development strategy that will help gain customer attention in the future!

Increase ROI with AI-assisted marketing campaigns: ML and AI will help you identify the best marketing campaign to increase your brand exposure. You can use AI-driven tools such as chatbots, ads bots, etc., for effective customer engagement using social media platforms like Facebook or Twitter!

Use Sentiment Analysis: You can easily understand consumer sentiment by tracking what they say about a product on different platforms with ML assistance. This information is crucial in understanding their needs so that you can provide them with better quality products/services!

These were just some of the many ways how ML & AI courses can help you become a successful Social Media Analyst!

Elevate your social media analyst’s profile with Imarticus Learning

Imarticus Learning offers Machine Learning and Artificial Intelligence courses. The comprehensive curriculum of these courses will help you build a strong foundation in machine learning, data analysis, deep learning, and artificial intelligence to take on complex problems for social media strategies.

What’s unique about this AI ML certification course?

  • Cutting-edge curriculum and certification by E&ICT Academy, IIT Guwahati
  • Opportunity to participate in campus immersion module
  • Learn what new-age AI & ML Engineers do in a real-world scenario
  • Build an impressive AI & ML project portfolio for future employers

This comprehensive program can take your career a step ahead towards rewarding opportunities in this domain.

How Long-Term Modelling of Our Future Energy System Can Be Mapped With Artificial Intelligence and Machine Learning?

Posted on by Imarticus Learning

Today, technology and sustainability are the main axes of development. To secure the planet and continue the growth of industry, we are engaged in a global energy transition. Most countries have become aware that measures must be taken to address a problem that, if not curbed, will have catastrophic consequences for the environment and, of course, for human beings themselves.

ai and ml courses by E&ICT Academy, IIT GuwahatiHowever, such a transformation requires the support of technology and, because of the enormous amount of data, artificial intelligence and machine learning courses are the basis to ensure the advancement of the energy sector.

At Imarticus you can join the postgraduate program in data analytics & machine learning (AIML). 

Technology as a tool

Changing the energy paradigm of the last century will be an arduous and complicated task. That is why new technologies have a lot to say as tools to facilitate evolution. The Internet of Things, machine learning, artificial intelligence, and Big Data will be key to making the processes of change as effective as possible. Massive data analysis must become a fundamental pillar for transforming how energy is generated, transmitted, and distributed.

Artificial Intelligence allows us to handle enormous quantities and analyze them logically and reasonably. About energy, in particular, we have data on meteorology, health, or the behavior of the people involved in the system: who generates electricity, who transports and distributes it, and who consumes it. Data that, when properly analyzed, can provide a tailor-made understanding of the sector.

The development and implementation of intelligent systems must not only facilitate the massive introduction of alternative energy sources but will also have the task of achieving rationalized storage of this energy, as well as providing greater flexibility for the demand, i.e. the people who use it.

Three levels of analytics can be applied: descriptive, to know what information is available and where to apply intelligence, predictive analytics, to anticipate production or demand, and prescriptive analytics. With the data, we work on predicting production, including renewable energies and demand, with the implementation of smart meters.

In addition, technical and non-technical incidents, such as energy fraud, are detected. All of this is aimed at optimizing the energy model, with the resulting economic and environmental benefits. We will see a huge take-off in the number of professionals who will choose to pursue a machine learning career.

Tools for the consumer

In this scenario, smart meters and internet-enabled sensors will be commonplace, which will improve our energy use while at the same time making it possible to bring costs in line with what each individual actually consumes. Thus, machine learning will automate processes, while artificial intelligence will make it possible for devices to work automatically and learn from consumers’ habits. This will also be possible on a large scale, so that the operation of future solar or wind power plants, to give just two examples, will be more effective in a shorter space of time.

In this respect, we should note that although everyone is involved in the energy transition and awareness must start in every household, the technology will be geared towards people having little to do in terms of reducing consumption and costs.

Artificial intelligence-based models and predictions facilitate and will continue to be a major advantage in mapping energy systems. What is most surprising is that this is just one of the many applications of these technologies. If you want to contribute to the change, you can sign up for AI and ML courses by E&ICT Academy, IIT Guwahati.

How Providers Can Use AI to Improve the Payment Integrity Process

Posted on by Imarticus Learning

Nowadays AI is utilized successfully and has proven to be an efficient, cost-effective, and reliable solution to cut down inappropriate payment claims worth a million dollars every year. The anomalies and patterns can be detected in less than a minute which helps to decrease fraud, system abuse, and future wastes.

From the provider’s point of view, they can be educated well to ensure evidence-based and high-quality alternatives. Learn more to know how the AIML program by Imarticus uses AI to improve the payment integrity process.

AI and Payment Integrity

A huge data volume from the providers, facilities, labs, etc. is integrated with AI-based computer power systems. This recognizes patterns in the data in a very effective and automatic way and helps to identify false claims. However, the billing behavior of the providers is difficult to detect as they are usually dealing directly with third-party enterprises for handling billing and coding issues.

This outsourcing may result in missing clarity and inconsistent processes which can ultimately lead to upcoding errors and fraudulent claims.

Thanks to the AI certification course, the identification of errors and fraud is a quick procedure with high precision and accuracy and the errors can be avoided drastically.

artificial intelligence and machine learning coursesInteroperability, APIs, and NLP Efficiency

The real innovation lies in the fact that the medical records of the patients can be directly obtained from the providers of EHRs with firm signed contracts.

This kind of interoperability helps in making the tasks work automatically like pre-authorization of the requests as per the need. This saves the manual working hours and makes the entire system run fluidly.

AI-based natural language processing (NLP) can further accelerate the time-saving process by around 40 percent when used on unfiltered data in the review stages. This helps in the augmentation of the staff efficiency and reduction of the costly human resources like nurses.

Integrating technologies like AI, NLP, robotic processing, and machine learning courses can give the payers the advantage of controlling the expenditure. Furthermore, it gives a helping hand to the providers to better manage the revenue systems to have a more unified and fluid cash flow within the system.

Prepayment cost avoidance model

One of the emerging trends of the industry is a significant shift to a prepayment from a post-payment cost avoidance model. It results in cost reduction related to reprocessing, reworking, and claim recoveries. But, the payers have to be super cautious when adopting this method as it is not yet well demonstrated and proven. Payment integrity based on AI is positioned very uniquely and this prepayment cost reduction model is close to becoming a reality in the industry soon.

Educating the providers

To overcome overutilization and fraud claims another approach that can be employed is their pre-detection by the providers themselves even before the claim submission. During the overpayment or appeal recovery process, the providers can be educated about the non-compliance, errors, overpayment issues, or the reasons for service rejection. This can increase the cooperation from the providers and helps decrease the number of appeals made.

On the same lines, AI-based technologies can analyze the data sets and send responses to the doctors, and list all the factors causing the denial of the claim and also about the unnecessary medical care as mentioned in the health plans.

Conclusion

Finally, analytics and solutions based on AI can ensure to cut down inappropriate claims significantly by identifying the wrong claims and acting upon them. Learn AI and improve the healthcare systems by making proper and efficient use of AI-based algorithms and methods.

Artificial Intelligence skilling has to start from a young age! How? Explore…

Posted on by Imarticus Learning

The chasm between machines and living things is shrinking. Artificial intelligence (AI) is deeply rooted in all aspects of technology, from robots to social networks. India has the potential to skyrocket in the domain of Artificial Intelligence and surpass USA and China, largely owing to:

  • It’s deep-rooted IT &ITeS infrastructure
  • Innovation ( India ranked among the top 50 countries in the Global Innovations Index 2020)
  • Accessibility to large datasets

These have pioneered more than a handful of start-ups and private investments in this sector. For AI to flourish further, there needs to be a nationwide upskilling of the younger generation in Artificial Intelligence Training. The GenZ needs to be acquainted with the theoretical and practical aspects of AI application to increase its scope of innovation and entrepreneurship.

Artificial Intelligence CareerIn the future, the interaction between humans and AI will define in a lot of ways the structure and functioning of a modern-tech society.

Thus it becomes imperative to lay down the basis of friendship for the years to come by exposing the young ones to AI.

While a lot of minds will wander to an Artificial Intelligence Career it is also important that others are no less familiar with the upsides and downsides of such a powerful technology.

Here is how we can ensure the frontiers of the same:

  • Introduce young people to the concepts of AI and machine learning through education curriculum. In India, the Central Board of Secondary Education (CBSE) announced the integration of AI in partnership with IBM for the academic year 2020-21
  • Encourage learning through hands-on projects so that student can make better, informed and critical use of these technologies
  • Enrolling young minds on various Edu-tech platforms specializing in the field of Machine Learning and AI which help them gauge interest and real-life applications of such technologies using intuitive software

Some of these websites include- Scratch, App Inventor, Cognimates etc

  • Experiments with Google is an easy-access, affordable, and user-friendly tool to explore artificial intelligence training at a young age with exciting experiments on AI, VR, AR, Chrome, Voice, Android etc to apply creativity and technological dexterity at the same place. One of these fun-filled learnings includes MixLab that uses voice commands to create music
  • Engage in the practice of cultural inquiry – like what is the goal of You tube’s recommendations or how do my Amazon purchases reflect on my Instagram feed
  • Lastly, before introducing your children to the world of AI and machine learnings, self-education of the same is very crucial

Apart from exploring the possibilities of AI, these junior minds also need to know the limitations of AI to have a balanced approached. That is to say, AI is not the ultimate machine as it is created by humans and will improve along the way by errors made and rectified by humans.

Artificial Intelligence CareerIn recent studies, a scientist is experimenting to teach AI to learn like a kid. They want to inoculate the eager learning attitude and swift skills of young people into the algorithms of machines.

And, AI does not create everything. It is the innovation and vision of responsible human beings that will introduce, implement, and maintain the technological structure in human society.