artificial intelligence Archives

AI and ML for a better future

Have you ever considered about what motivates artificial intelligence programmes like Tony Stark’s JARVIS or the common man’s Alexa, Google Assistant, or Siri? These programmes can answer your calls and help you make decisions, but have you ever wondered what motivates them? In what ways does their brain operate? The application of Artificial Intelligence (AI) and Machine Learning is the straightforward response to each and every one of your questions (ML). The mechanical brains are controlled by artificial intelligence, which attempts to simulate human intellect so that they can perform like a human brain. With more and more research being done on AI and ML, there is the potential for AI and ML to assist in training computers to make decisions on their own, which will eventually make our lives easier by reducing the amount of work we have to perform.

This article, which was meticulously researched and penned with the intention of depicting the future reach of AI and ML in India, was created with the goal of assisting students in understanding how this subject will be advantageous to them if they decide to pursue it.

The potential applications of artificial intelligence in India are still in the process of being adopted, but the technology is gradually being put to use to find intelligent solutions to modern problems in almost all of the country’s most important industries, including agriculture, healthcare, education and infrastructure, transportation, cyber security, banking, manufacturing, business, hospitality, and entertainment. Readers who are interested in pursuing a course in artificial intelligence can find helpful information in this article. Candidates will gain some insight into the potential of artificial intelligence in India if they read this article and consider its contents.

Scope of AI in India

Both artificial intelligence and machine learning have a promising future in India and an immense potential to alter every area of the economy for the benefit of society. Artificial intelligence and machine learning have a bright future in India. AI is an umbrella term that incorporates a variety of helpful technologies, such as self-improving algorithms, machine learning, pattern recognition, and large amounts of data. Soon, there will not be a single business or market segment in India that is immune to the effects of this powerful instrument. This is one of the reasons why there is a growing demand for online courses in artificial intelligence in India.

Scope of AI and ML in Education Sector

By utilizing a variety of AI applications such as text translation systems, real-time message to speech, automating mundane and also repeated tasks such as taking presence, automating grading, and also customizing the learning journey based on ability, comprehension, and also experience, artificial intelligence can help our instructors be more effective. Within the purview of Artificial Intelligence education and learning is the consideration of the prospect of utilizing AI-powered rating machines that are able to evaluate solutions in an objective manner. This is being carried out in college and university settings in a step-by-step fashion. Real-time text-to-speech synthesis and text translation are two further AI-based applications in the educational sector.

The Role of AI and ML in the Development of Chatbots

The combination of chatbots in the digital framework or availability via the IVRS system education domain can be transformative in a country as diverse as India. They can be educated on the subject matter, and a large percentage of the students’ doubts can be answered quickly. This reduces the current workload of educators, allowing them to focus on more creative tasks.

The Integration of AI and ML into the Automated Grading System

On a more global scale, methods of machine learning such as Natural Language Processing could be used for automated grading of assessments on systems such as E-PATHSHALA, SWAYAM (Study Webs of Active Learning for Young Aspiring Minds), and DIKSHA. This would apply not only to inquiries that are subjective but also to those that are objective. This is because of the National Education Policy 2019, which places an emphasis on computer and internet literacy.

The Role of AI and ML in the Healthcare Industry

The healthcare industry in India is one of the most rapidly developing and competitive markets in the world. There is a dearth of doctors and services, including competent nurses and technicians, as well as infrastructure. This is one of the primary issues, but there are many others as well, including affordability and accessibility. As a result of the majority of high-quality medical facilities in India being situated in close proximity to tier 1 and tier 2 cities, access to healthcare in India is not uniformly distributed across the country physically. Aside from that, as Artificial Intelligence develops, there will be an increase in efficiency, which will lead to a reduction in the overall cost of medical treatment.

Because AI is able to process vast volumes of data in a short amount of time, it can be of assistance in the creation of medical equipment, as well as in design and innovation. Having a system that is enabled with AI helps to eliminate medical errors and increases overall productivity. Artificial intelligence has the potential to both circumvent access barriers and provide a solution to the accessibility problem by applying early detection followed by suitable diagnostic conclusions.

AI and ML in the Agriculture Sector

In India, agriculture is a major source of income for many people. Traditional farming methods pose a slight challenge for Indian farmers. Thermal imaging cameras can be used to continuously monitor agricultural land to ensure that plants receive adequate water. When it comes to selecting the right crop and the optimum method of sowing, this tool can help you get the most out of your land and save money.

As a result, improved insect control preparation can benefit from the application of artificial intelligence to predict behaviour and investigate parasites. Artificial intelligence-assisted anticipating modelling can be effective for delivering more detailed demand-supply details and for predicting the needs of farmers in terms of agricultural produce.

Automated Vehicles Using AI and ML

In the transportation industry, artificial intelligence offers a lot of potential. Artificial intelligence (AI) has the potential to be useful in a few specific contexts. Since its invention in 1922, autopilot has been used to keep ships, planes, and spacecraft on course. Self-driving cars are another field of research. Self-driving automobiles are being researched by companies around the world, including India. The use of artificial intelligence and machine learning has been prevalent in the design of these automobiles from the beginning. Self-driving cars, according to experts, will have various advantages, such as reducing pollution and eliminating human error from driving.

Artificial Intelligence and Machine Learning for a Smart Home

We are surrounded by artificial intelligence. Most of the time, we don’t even realize we’re interacting with devices powered by artificial intelligence. As an example, we routinely use OK GOOGLE, ALEXA, or CORTANA to execute a variety of chores by simply speaking to them. Artificial Intelligence and Machine Learning are used by these intelligent assistants for voice recognition. Learning from the user’s commands improves their productivity. You may ask a question, play a song, and buy anything online all with the help of this clever assistant.

Applied Artificial Intelligence and Machine Learning in Cybersecurity

Cybersecurity is another area where AI is being applied. Many companies have to deal with a lot of data. A security system is required, for example, in the banking industry or government entities that maintain vast databases containing the personal information of citizens. An good example of this topic is Cognitive Artificial Intelligence (CAI). Additionally, it helps analysts make better judgments by detecting, analysing, and reporting on hazards.

Machine Learning formulae and Deep Learning networks are used to improve and strengthen the AI over time. As a framework and central point of control for safety and security responses, IBM Resilient is an open and agnostic platform.

In the Manufacturing Industry, AI and ML

The industrial sector is a popular target market for AI-based firms from India. In order to assist the manufacturing industry flourish, these companies are developing AI-based solutions. Various types of robots are controlled by artificial intelligence in the workplace. The ability to examine data and forecast the future is a unique AI technology.

Using this AI capabilities, companies may estimate future supply and demand based on data from prior years’ sales or market surveys, allowing them to make faster decisions and better use of existing products. Artificial intelligence (AI) will be widely used in manufacturing in the future years.

Is Operations Research useful in Data Science?

“Operations research (OR) is defined as the scientific process of transforming data into insights to making better decisions.”

–The Institute for Operations Research and the Management Sciences (INFORMS)

Introduction:

In the twenty-first century, especially in the last decade, the most trending domain of study is may be Data Science and Data Analytics. In this domain of study, people work with data from different fields and they use different tools and techniques from the domain of Mathematics, Statistics, and Computer Science to study and analyze the data. Then make some conclusion from the data and use them to predict the future of the phenomenon under study. Before the rise of data science as a domain of study, Operations Research analyst and Statisticians are used to do the similar kind of job. Due to these facts, the overlap between the domain of Data Science and the domain of OR is misunderstood. Also, there is a common perception that OR is not useful in for Data Science or Data Analytics. Actually, the marketing of OR products and services which are applied to solve the real world problems leads to this kind of misconception, as most of the time the end-users do not have an understanding or background of OR and data science. Another possible reason may be that the availability of machine learning models which are available as packages of several platforms like Python and do not really contain specific any OR models. In practical, OR tools and techniques are applicable to data science. In fact, a lot of ideas which are used in Artificial Intelligence (AI) and data science problem solving, have cross-pollinated from OR due to the large overlap in the techniques and methods used. In this blog, I try to explore these relations of OR with Data Science and Data Analytics.

Operations Research and Data Science:

Before going to the discussion on the role and relation between Data Science and OR, let us try to understand another very important term called Analytics. According to INFORMS, Analytics is the application of scientific & mathematical methods to the study & analysis of problems involving complex systems. There are three distinct types of analytics:

i) Descriptive Analytics gives insight into past events, using historical data;

ii) Predictive Analytics provides insight on what will happen in the future; and

iii) Prescriptive Analytics helps with decision making by providing actionable advice [https://www.informs.org/Explore/Operations-Research-Analytics]. In an INFORMS podcast, depending on organizational backgrounds, Glenn Wegryn divides Analytics into two distinct camps: Data Centric Analytics where data is used to find interesting insights and information to predict or anticipate what might happen; and Decision Centric or Problem Centric Analytics which is used to understand the problem, then determine the specific methodologies and information needed to solve the specific problem. This data centric analytics are done by using Data Science whereas problem centric analytics are done by Operations Research. The above mention figure clearly give an idea about this. From the figure, it is very clear that there is a common point of interest from both the domain. Hence OR plays a very important role in Data Science domain.

Operations Research and Machine Learning:

Machine learning is the area of data science where most of the OR tools and techniques are used. Linear programming and Optimization techniques are fundamental part of the overall machine learning lifecycle. Some of the examples of OR are:

Enabling smart human resource management by forecasting human resource requirements and optimizing daily schedule for resource persons (linear programming model)
Increasing TV program viewership by optimal scheduling of programs’ promotion (linear programming model)
Enabling supply chain transformation by providing AI/machine learning-based recommendations for optimized product utilization
AI-enabled forecasting for retail and eCommerce applications to optimize funnel and customer traffic
Data-driven optimization models for automated inventory management where we need to do warehouse management, inspection and quality control

Operations Research and Artificial Intelligence:

Another important area of data science is Artificial Intelligence where we can observe the use of OR algorithm. AI is used to build an automated system. Now, any real-life system have many decision variables and parameters, so if we want to build an automated system then we have to deal with a lot of decision variables. That’s why operations research algorithm must be a core engine in the system.

An Artificial Intelligence development lifecycle consists of the following steps: (Link)

Descriptive and Predictive steps:

In the first step, we need to define the problem to be solved
In the next step, we need to understand the current state of the problem and accordingly we have to define the work scope
Next we need to develop a Machine Learning model, where the machine learning solution is developed and tested.

Prescriptive steps:

Machine learning outputs or the predictions obtained using machine learning are given as OR inputs. Here, the OR techniques are used to make recommendations based on the outputs from the ML model. This is a critical step for the entire life cycle.
Finally, the solution output is delivered to the client.

Covid-19 impact:

During the COVID-19 pandemic, more than ever, data science has become a powerful weapon in combating an infectious disease epidemic and arguably any future infectious disease epidemic. Computer scientists, data scientists, physicists and mathematicians have joined public health professionals and virologists to confront the largest pandemic in the century by capitalizing on the large-scale ‘big data’ generated and harnessed for combating the COVID-19 pandemic. (Zhang, Qingpeng, et al. “Data science approaches to confronting the COVID-19 pandemic: a narrative review.” Philosophical Transactions of the Royal Society A 380, No. 2214 (2022)). Covid-19 has a big impact on supply chain strategies also. People from data science community are analyzing “lessons learned” from the pandemic to better prepare and more efficiently and effectively respond to the next disaster, interested people can visit the following for a discussion on it (Link1).

Conclusion:

From the above discussion, it is very much clear that Data Science and Operations Research have some overlapping objectives with clear line of difference between these two domains of study. Also, we observe that there are several OR techniques and algorithms which have important role to play in different topics of data science. In my opinion, operations research together with data science and analytics is going to play a very important role to build the future of us.

Digitalization in Marketing Process-A New Skill in Marketing Specialization

Marketing is the process that satisfies human and social needs. It is nothing but a value-creation process. If we look into the marketing process, the job of most marketers is to design and develop the value in such a way that attracts customers and makes them happy buying. But this scenario is changing very rapidly due to the pandemic and the huge development of digital technology. Now market and marketing processes are more digitalized than the conventional marketing system. The job of a salesman is changing to the digital selling process. Marketers need not make a flow-up plan, it is automatically set up by the technology and responses are coming within a specific time. So, the process becomes more hybrid through embarked digitalization in the system. Therefore, it is imperative that using of digital technology in marketing and its associated function is a substitute for marketing success. Looking into this, concept marketers are focusing more on digital expert professionals than the salesman. Due to the huge demand for this, manpower is scarce. This is one part of the other way the process of marketing is also changing. Like the development of promotion strategy, communicate with the customers and find out the most effective methods for approaching customers. Though all these are experience stages, on the other side to get succeed in the fast-paced environment they always look into the audience’s requirements, it is difficult to stay ahead of the audience because market nature is monopolistic.

The recent trend in the Marketing Process:

Mass marketing converted into customized marketing and especially influencer marketing which is more common through digital technology like artificial intelligence, and machine learning, Marketers identify the preference of buying of customers and they try to influence them by offering more customized products. Therefore, targeting an individual is much easier than the conventional process. Development of user-generated content is another tool to identify prospects, it is a technique that allows the customer to design their product, and using digital space marketers publish those products on the web and counts the most effective design out of the available design and makes the product based on these design. Companies also do marketing through publicize the video content and using web analytics they publish it through various social media like various web pages, Facebook, Twitter, and LinkedIn, and personalizing the email. This video makes confidence the buyer about product information, brand, service, and other components associated with the product. It applies to business to business and business to consumer and both the process learn and evaluate the impact of promotion using video content in social media which makes their marketing promotion faster. The current market depends on millennials and Gen Z, they are more inclined toward digital process and most of them prefer digital buying process, not only digital buying but also other parts of marketing, they prefer digitalization. Therefore, to enhance their buying power, there is a need for mobile-optimized digital services which may be an important part for business owners and houses who are looking to attract fast-paced tech-savvy generations.

Ephemeral content is a new arena of digital marketing. Here company publishes its information through social media and they always stay on social media through standard posts, videos, and live events. Customer is not able to show the message if they do not save it or achieve it. Therefore, it makes curiosity customers give more concentration on the information. It is an effective platform for marketing campaigns.

Application of Digital Technology in Marketing

Artificial Intelligence in marketing is mainly developed with the help of three main marketing disciplines research, strategy, and action, and three levels of AI intelligence, that is, mechanical, thinking, and feeling AI. While mechanical AI entails automation of repetitive and routine tasks mainly covering market research, strategy, and standardization, thinking AI relates to processing data for new insights and decision-making, and feeling AI refers to interactions with humans or analyzing human feelings and emotions. Another, important techniques are big data analytics, using these techniques marketers predict the outcome of the customers and it also techniques which help marketers to identify the preference, maintain inventory management, and manage distribution and logistics system. Machine learning is another digitalized technique that helps marketers to do proper market segmentation, it processes customer data and analyzes it for discovering recurring patterns across various features. It helps to do proper clustering of different various demographic segments and helps to measure the preference difference between various demographic segments. Using blockchain analysis marketers maintain the logistics system of the firms and maintain a smooth and faster delivery system. One other important area used by blockchain technology like user verification, Blockchain can be employed through advertisement networks and reduce the interface of agents and middlemen and help those users who want the information by clicking through the ad system and combat fraud. It helps advertisers to identify the source of fraud and advertisers can make more user interface design.

Therefore, digital technology must be not only an effective tool for modern marketing but in the future, it should be the only way for business growth and survival. Most large-scale firms have started their marketing practices and maintain all the marketing processes from taking the order to supplying feedback through digital space. In India, most middle-order firms were trying to adopt the blended process, with a few parts of technology-based and another part traditional because of the nature of Indian consumers. It is a challenging task for a small firm because its market and investment level is low. Therefore, the marketing professionals need to develop some skills that small firms can be benefitted without much more investment. Last but not the list, it can be commented that the traditional skill of marketing will not work for long. Digitalized skills need to learn by the marketing professionals at the time of career selection. Few specialized skills need to be imparted to get a better market understanding.

Applications of Machine Learning in Geography: Present and future trends

Machine learning is one word at present time machine learning can be defined as a system where computers can perform tasks that require human intervention and intelligence. Machine learning is part of artificial intelligence which is growing rapidly in the present era. Artificial intelligence (AI) is the broader umbrella of computer-driven applications where Human support is hardly required. Deep learning is a subset of machine learning. AI is also associated with the development and design of algorithms. Artificial intelligence and machine learning have reduced the role of humans in many activities Like in industry public affairs research and academia. Geography as a discipline has also benefited from the applications of Artificial Intelligence and machine learning. Geographical information system Remote Sensing and GIScience as a whole get the benefit of machine learning which is termed GeoAI. Different organizations like the American Association of Geographers (AAG) in Environment System Research Institute (ESRI) and Microsoft are collaborating to develop a GeoAI-based platform for the analysis and visualization of different data.

The advantage of machine learning algorithms lies in the fact that the algorithm can use the data pattern to train itself and come up with better predictive or classification solutions. Machine learning is classified into different groups supervised learning, unsupervised learning, Semi-supervised learning, and reinforced learning.

In recent years automation in geography and location-based study is taking place at a quick pace. Mechanisms to monitor different spheres (terrestrial, atmospheric, and aquatic) are growing at high speed. This leads to a large quantum of data availability of different types and formats. Machine learning is helping to analyze this data using different advanced algorithms like Support vector machine, Random Forest, Artificial neural network (ANN), or even one step ahead deep learning methods like Convolution Neural network (CNN) and dynamic learning methods like Q- Learning.

In the area of human geography, machine learning has been used to explore the distribution of products and goods, population genetics, detecting, characterizing, and evaluating human epidemiology, tourism projections, detecting military patterns, international politics, and agricultural production systems. Within physical geography, ML is used to advance research on environmental diversity, biogeography, and abundance, water resource management, climate modeling, climate change, etc.

In the Geographical Information System (GIS), users can use it to comprehend the spatial elements of their work and connect it to data such as population data. The GIS data can be utilized for a variety of reasons, including transportation, drought analysis, agricultural, disease outbreak analysis, land occupation, and so on. At the same time, GIS enables the storing of a large volume of data in a secure manner, as well as access to it at any time and on a rapid basis. So, the purpose of this chapter is to examine previous works and research in this area to better understand the current status and capabilities; also, it will be an attempt to prepare for future improvements in the field of GIS.

There are also many challenges faced by machine learning in the part of lack of data and as well as the quality of data. Quality issues in machine learning lead to improper predictions and classification. As the technology is still evolving real-world complexities are sometimes mishandled by the algorithms. Ethical issues are very much prominent in countering human geographical contents. They’re also voids left for spatially explicit machine learning and other spatiotemporal models. This vacuum of Spatio-temporal models hinders the growth of machine learning applications in geography.

In the future, machine learning will be more robust with advanced GeoAI algorithms which can encounter issues like handling large and complex data. More spatial explicit models and algorithms will be available to provide solutions to complex and multifaceted real-world problems. The emergence of automatic machine learning (AutoML) is showing the path for such a solution. In solving geographical problems of the future integration is required between the conceptualisation and implementation of models. As artificial intelligence has started touching down nearly all spheres of life, machine learning is going to play a key role in unlocking the different mysteries of nature near future.