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5 Chatbots You Should Know About

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5 Chatbots You Should Know About

Chatbots or “conversational agents” are software applications that mimic or imitate written or spoken human speech for the purposes of facilitating a conversation or interaction with a human being.

These applications have become one of the most ubiquitous software applications out there with the advancement of machine learning technology and NLP.

“Today’s chatbots are smarter, more responsive, and more useful – and we’re likely to see even more of them in the coming years… chatbots are used most commonly in the customer service space, assuming roles traditionally performed by living, breathing human beings such as Tier-1 support operatives and customer satisfaction reps.”

Conversational agents are becoming a common occurrence partly due to the fact that barriers to entry in creating chatbots such as sophisticated programming knowledge have become redundant.

How Chatbots work

The crux of chatbot technology is natural language processing or NLP, the same technology “that forms the basis of the voice recognition systems used by virtual assistants such as Google Now, Apple’s Siri, and Microsoft’s Cortana.” “Chatbots process the text presented to them by the user…infer what they mean and/or want, and determine a series of appropriate responses based on this information.”

Here are 5 companies using chatbots for various roles like marketing, communicating with marginalized groups and patients suffering from sleeplessness and memory loss.

Endurance

Russian technology company Endurance developed a companion chatbot to help dementia patients cope with decreased verbal ability. Many patients with Alzheimer’s disease use the chatbot to converse with. In turn, the chatbot identifies deviations in conversational patterns of the patient that might indicate a problem with memory and recollection.

Casper

Casper’s Insomnobot 3000 is a conversational agent that aims to help insomniacs by posing as a companion to talk to while the rest of the world sleeps. However, at this point, “Insomnobot 3000 is a little rudimentary.”

UNICEF

International child advocacy nonprofit UNICEF is using chatbots to help people living in developing countries speak out about the most urgent needs in their communities. The bot, named U-Report, focuses on large-scale data gathering via polls. UNICEF then uses feedback as the basis for potential policy recommendations.

MedWhat

This chatbot aims at making medical diagnoses faster, easier, and more transparent for both patients and physicians. MedWhat is powered by a highly sophisticated machine learning system that offers increasingly accurate responses to user questions based on behaviors that it “learns” by interacting with human beings. Also, it acts as a repository of a vast source of medical journals and medical advice.

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Roof Ai

Roof Ai is a chatbot that helps real-estate marketers to “automate interacting with potential leads and lead assignment via social media”. The bot identifies potential leads via social media and responds immediately, irrespective of the time of the day. “Based on user input, Roof Ai prompts potential leads to provide a little more information, before automatically assigning the lead to a sales agent.”

To learn more about machine learning powered technology, follow DexLab Analytics. DexLab Analytics is a premiere institute for Machine Learning training in Gurgaon.

 


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A Guide to Free Ebooks on Statistics and Machine Learning

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A Guide to Free Ebooks on Statistics and Machine Learning

Machine Learning is an acquired knowledge science. It has to be taught and studied. For this, it is imperative to have the best books on the subject at hand. However, most books on the subject are expensive and not easily accessible. This is only fair given the amount of hard word that goes into writing these books.

In a situation this critical, it is best to rely on the good old Internet for assistance. There are some good Samaritans who have chosen to make their works freely available to all. Here is a great guide to free ebooks available online so you can brush up on your concepts and be industry ready at the earliest.

Think Stats – Probability and Statistics for Programmers by Allen B Downey

For the free ebook click here http://www.greenteapress.com/thinkstats.

This is an introduction to statistics and probability for those who have a basic grounding in Python programming. “It’s based on a Python library for probability distributions (PMFs and CDFs). To make things easier for the reader, most of the exercises have short programs,” says a report.

Bayesian Reasoning and Machine Learning by David Barber

For the free ebook click here http://web4.cs.ucl.ac.uk/staff/D.Barber/textbook/091117.pdf.

When it comes to Bayesian statistics, this book is a classic. “This takes a Bayesian statistics approach to machine learning.”This is a book worth checking out for anyone getting into the machine learning field and trying to make a career out of the subject.

An Introduction to Statistical Learning by Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani

For the free ebook click here http://faculty.marshall.usc.edu/gareth-james.

This popular entry is an introduction to data science through machine learning. “This book gives clear guidance on how to implement statistical and machine learning methods for newcomers to this field. It’s filled with practical real-world examples of where and how algorithms work. For those with an inclination towards R programming, this book even has practical examples in R.”

Understanding Machine Learning by ShaiShalev-Shwartz and Shai Ben-David

For the free ebook click here https://www.cse.huji.ac.il/~shais/UnderstandingMachineLearning/index.html.

“This book gives a structured introduction to machine learning. It looks at the fundamental theories of machine learning and the mathematical derivations that transform these concepts into practical algorithms. Following that, it covers a list of ML algorithms, including…stochastic gradient descent, neural networks, and structured output learning.”

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A Programmer’s Guide to Data Mining by Ron Zacharski

For the free ebook click here http://guidetodatamining.com.

This book has chapters covering recommendation systems. “It takes a…visually entertaining look at social filtering and item-based filtering methods and how to use machine learning to implement them. Other concepts like Naive Bayes and Clustering are also covered. There is a chapter on Unstructured Text and how to deal with it, in case you are thinking about getting into Natural Language Processing. Examples in Python are also available in case you want to practice.”

For more on Machine Learning do peruse the DexLab Analytics website. DexLab Analytics is a premiere institute offering Machine Learning courses in Delhi.

 


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How Pharma Companies Are Using Machine Learning and AI to Fight Against COVID-19

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How Pharma Companies Are Using Machine Learning and AI to Fight Against COVID-19

In the midst of a crisis as big as COVID-19 which has as per the current update taken more than 80,000 lives around the world and caused nations to go under a lockdown, machine learning and artificial intelligence has played a vital role in detecting COVID-19 positive cases and helping fast track the tests and analysis of drugs that can be a solution to the problem.

Countries around the world are building algorithms to better understand the molecular structure of the diseaseso that the so-called protein around the SARS-CoV-2, the virus that causes COVID-19, can be blocked.

Use of AI and machine learning around the world

The legendary story of a Canada-based AI named Bluedot is not something one can forget. Buledot is a low cost AI based tool that predicted the outbreak of the disease in December 2019 and also predicted the top 20 destinations where passengers from Wuhan, the origin of pandemic, would arrive.

Deargen a South-Korean company built an AI tool named MT-DTI to test and analyze the molecular bond of the SARS-CoV-2 protein and recommend medication on the basis of the same. Even though their recommendations are yet to be reviewed and tested properly, their find might prove to be a super solution to reduce the number of death counts and covid positive cases.

Insilico Medicine, a Hong-Kong based company instead of building a platform to recommend the currently available medication to break the protein of covid-19 virus and stop it from replicating, built an AI based tool to formulate and test the potency of new and chemically advanced medication to break the chain.

Benevolent AI, a British startup based on its machine learning tool discovered six important covid-19 virus fighting compounds that can prove to be helpful in curing and limiting the spread of the disease. Out of the six compounds discovered, ‘baricitinib’, a compound used to cure rheumatoid arthritis according to the sources, proves to be the safest compound that can be tested on the covid positive volunteers.

Use of AI and machine learning in India

The spread of the covid-19 pandemic in India began by the end of January 2020 and till date more than 5000 cases have been reported. The Government of India declared a lockdown by mid-March 2020 and in the middle of all this when misinformation and rumors were rising day by day, to tackle the problem the Government of India launched its covid-19 tracking app called Aarogya Setu that means ‘a bridge of health’.

This AI-based app uses bluetooth and the mobile location of the user and informs them if there is any person covid positive within 6 feet of their location or not. The app checks the user’s data in their own database and in case the data matches a corona positive patient then it only notifies their presence keeping their identity anonymous.

India has also launched a whatsapp chatbot and any one using the whatsapp platform can access the chatbot by simply typing “MyGov Corona Helpdesk” and get the relevant and authentic information relating to the symptoms and how they can get help from direct sources.

Apart from the above initiatives to tackle the pandemic, medical practitioners, data analysts and data scientists from around the world are trying to find ways and means and analyzing the database to capture the trends and are working day and night to find best possible cure to fight the disease.

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To know more, do read DexLab Analytics previous blogs on how AI is helping fight the coronavirus pandemic here. DexLab Analytics is a premiere artificial intelligence training institute in Gurgaon that gives Machine Learning training in Gurgaon.

 


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AI’s Fight Against Coronavirus Continues

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AI’s Fight Against Coronavirus Continues

The world has come to a grinding halt with the spread of the deadly novel coronavirus (COVID-19), one of the most contagious diseases to affect us as a people of late.

In little over three months since the virus was first detected in China’s Wuhan late last year, it has spread to more than 90 countries, infected almost a million people, and taken about48,000 lives as of Thursday, April 2, 2020.

Scientists, governments and health organizations are doing the best they can to contain and fight the disease and they are trying to take all the help they can. Including the help of technology – and Artificial Intelligence – in tracking, diagnosing cases, disinfecting areas and speeding up the hunt for a cure.

Here is a short essay on how artificial intelligence is assisting in the fight against COVID-19.“Data science and machine learning might be two of the most effective weapons we have in the fight against the coronavirus outbreak,” says a report.

Tracking the virus outbreak with machine learning

In December last year, a Canada-based artificial intelligence platform that tracks the spread of infectious diseases around the world, detected a cluster of “unusual pneumonia” cases reported in China’s Wuhan. You can read about the detection of the disease here.

The Toronto-based start-up uses Natural Language Processing or NLP and machine learning algorithms to scour tons of information on infectious diseases from sources like statements from health organizations, commercial flights and live stock health reports. It alerted its clients about the outbreak of the disease almost a week before international health organizations declared the outbreak of COVID-19 in China.

Computer Vision being used to detect coronavirus infection

As of today, authorities the world over are checking temperatures of their citizens at airports and railway stations or other crowded places through thermal guns and manually screening them for signs of fever, cough and breathing difficulties.

However, computer vision algorithms can accelerate this crucial screening process by equipping cameras with computer vision technology.

In China, a tech giant has invented a thermal scanner that uses computer vision and infrared sensors to check people’s temperatures in public places. The system invented can check up to 200 people per minute.

Another tech giant has invented a system that uses AI to detect coronavirus in chest CT scans (in 20 seconds as opposed to the 15 minutes spent by human health workers) with a 96 per cent accuracy rate. The system is reportedly being adopted by 100 hospitals in China.

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AI is speeding up drug research

Developing new drugs and vaccines, in the race to win the fight against coronavirus, is proving long and tedious. Some reports suggest it can take anywhere near 12 years at a cost of billions of dollars.

However, that said, AI will help speed up the process of drug discovery to some extent. “DeepMind, the AI research lab acquired by Google in 2014, recently declared that it has used deep learning to find new information about the structure of proteins associated with COVID-19”, a process that could have taken many more months.

“Understanding protein structures can provide important clues to the coronavirus vaccine formula. DeepMind is one of several organizations engaged in the race to unlock the coronavirus vaccine. It has leveraged the result of decades of machine learning progress as well as research on protein folding.”

To know more about how AI is helping in the fight against the novel coronavirus, do read DexLab Analytics’ previous blog on the topic. DexLab Analytics is a premiere Machine Learning institute in Gurgaon.

 


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5 Problems Machine Learning Can Solve

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5 Problems Machine Learning Can Solve

Machine Learning, a subset of Artificial Intelligence, has taken the world by storm. A method of data analysis, it is a system that is equipped with expertise to learn from data, identify patterns and take decisions with minimal human intervention.

From clearing our email inboxes of spam to tagging our friends’ faces in social media photograph uploads, Machine Learning is crucial to all aspects of our lives. Here are some problems that Machine Learning can easily take care of.

Manual Data Entry

The problem of inaccuracy and duplication of data that business houses wish to avoid when automating their processes can be tackled with the help of Machine Learning (ML). A report says, “ML programs use the discovered data to improve the process as more calculations are made. Thus machines can learn to perform time-intensive documentation and data entry tasks.”

Moreover, ML knowledge workers can, nowadays spend more time solving problems of higher-value while ML takes care of repetitive work. “Arria, an AI based firm has developed a natural language processing technology which scans texts and determines the relationship between concepts to write reports.”

Detecting Spam

Spam detection, one of the earliest tasks for ML systems, has upgraded.“Four years ago, email service providers used pre-existing rule-based techniques to remove spam. But now the spam filters create new rules themselves using ML.”This is because of ‘neural networks’ installed in spam filters, “Google now boasts of 0.1 percent of spam rate.”

Neural Networks fitted in spam filters can teach themselves to learn to recognize junk mail and phishing messages “by analyzing rules across an enormous collection of computers. In addition to spam detection, social media websites are using ML as a way to identify and filter abuse.”

Product Recommendation

Unsupervised learning enables companies to put in place a product based recommendation system. By studying purchase history of a customer and a correspondingly large inventory of products, ML models can identify certain products in which a customer is likely to be interested.

“The algorithm identifies hidden pattern among items and focuses on grouping similar products into clusters. A model of this decision process would allow a program to make recommendations to a customer and motivate product purchases.”

Medical Diagnosis

Machine Learning in the medical field is touted to improve patients’ health with minimum costs. “Use cases of ML are making near perfect diagnoses, recommend best medicines, predict readmissions and identify high-risk patients. These predictions are based on the dataset of anonymized patient records and symptoms exhibited by a patient.”

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Computer Vision

Computer vision “produces numerical or symbolic information from images and high-dimensional data. It involves machine learning, data mining, database knowledge discovery and pattern recognition.” Potential business applications of image recognition technology can be found in healthcare and automobiles. A tech giant has produced a computer vision powered earpiece that can narrate its interpretation of the outside world to a visually impaired person.

Machine Learning has many applications in industries the world over. For more on this, or a related subject, do peruse the DexLab Analytics website. DexLab Analytics is the best Machine Learning course in Delhi.


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AutoML (Machine Learning) in 2020

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AutoML (Machine Learning) in 2020

AutoML, with its ability to perform data pre-processing, ETL tasks, and transformation, is likely to become the most sought after development in computing sciences for more reasons than one.

Data scientists with competent skills who can work on big data, advanced analytics, and predictive models are few and hard to find. However, AutoML programs have made life easier for businesses and organisations by coming to the rescue of lesser skilled professionals.

Bridging the skill gap, AutoML is helping lesser skilled professionals build models using the best diagnostic and predictive analytics tools.

“AutoML packages like auto-sk learn can automatically do the model selection, scoring, and hyperparameter optimisation. Services like Amazon Forecast and Google’s Cloud AutoML also help in determining the algorithm to fit best with the data,” says a report.

With time, the amount of data generated by computer systems will have grown exponentially, and “the world of analytics, AI, machine learning and data science will see a wave of data and training. And, with the increasing amount of data, here’s why AutoML might be the most used technology in 2020.”

Hastening The ML Process

It takes human beings a longer time to build ML models than it takes automatic systems to, and accuracy is not always at par on the part of human beings. It would take less time for AutoML to construct a model and businesses are slowly preferring to use automated machine learning to amplify their predictive power for the need for insights from big data is only growing.

“An ML process typically consists of data pre-processing, feature selection, feature extraction, feature engineering, algorithm selection, and hyperparameter tuning. These take up more time to implement and require considerable expertise; AutoML, on the other hand, removes the trouble of going through some of these tedious processes.”

Addressing The Skills Gap

AutoMLis helping bridge the skills gap, especially in non-tech companies or companies with less data science expertise. “With the launch of Cloud AutoML, based on Neural Architecture Search (NAS) and transfer learning, Google believes that it has the potential to make the existing AI/ML experts more productive along with helping the less skilled engineered to build a powerful AI system.”

AutoML, also, hasmade machine learning a democraticsystem. It has helped “to carry out processes like hyperparameter tuning, selection of algorithms, and finding the appropriate model — as these tasks are tedious and at the same time complex.”

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Bettering Scalability

Machine Learning requires massive amounts of data to work on and training a model takes a long time, especially if the model is big. “AutoML, on the other hand, makes it easy to handle data, train model, evaluate, experiment, and even deploy the model for different use cases as it takes on the task to find the best algorithm for the task to be done.”

To enrol in a course on AutoML, do peruse the DexLab Analytics website today. DexLab Analytcis is a premiere Machine Learning training institute in Delhi and NCR.


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Why Machine Learning Matters

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Why Machine Learning Matters

Machine Learning, a subset of artificial intelligence, is a process of data analysis that automates analytical model building. It is a branch of artificial intelligence based on the idea that computing systems can learn from data, identify patterns in them and make intelligent decisions with minimal human intervention.

Importance of Machine Learning

The growth in volumes of data sets, and cheaper and more powerful computational processing and affordable data storage has triggered resurgence in interest in machine learning.

“All of these things mean it’s possible to quickly and automatically produce models that can analyze bigger, more complex data and deliver faster, more accurate results – even on a very large scale. And by building precise models, an organization has a better chance of identifying profitable opportunities – or avoiding unknown risks,” a report says.

Uses of Machine Learning

Machine Learning has been adopted by several key industries working with large amounts of data. Machine Learning helps businesses grow by gleaning actionable insights from these data sets.

Financial services

Machine Learning has revolutionised the banking sector giving financial institutions and banks the opportunity to “identify important insights in data, and prevent fraud.” The business insights can help companies identify investment opportunities or help investors know when to trade. “Data mining can also identify clients with high-risk profiles, or use cyber-surveillance to pinpoint warning signs of fraud.”

Government

Governmentsown an unimaginable amount of data and they can use this to their advantage. With the help of machine learning, they can mine data sets for insights. “Analyzing sensor data, for example, identifies ways to increase efficiency and save money. Machine learning can also help detect fraud and minimize identity theft.”

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Health care

Machine Learning has helped the healthcare industry evolve thanks to wearable devices and sensors that can use data to assess a patient’s health in real time and improve diagnosis and treatment. 

Retail

Machine Learning helps study and analyse customers’ purchase history and recommends what items a customer is likely to prefer buying. It predicts buying patterns and tastes and choices. It helps retailers offer a personalised experience to shoppers, implement a marketing campaign, optimize prices and plan merchandise supply.

Oil and gas

“Finding new energy sources. Analyzing minerals in the ground. Predicting refinery sensor failure. Streamlining oil distribution to make it more efficient and cost-effective. The number of machine learning use cases for this industry is vast – and still expanding.”

Transportation

“Analyzing data to identify patterns and trends is key to the transportation industry, which relies on making routes more efficient and predicting potential problems to increase profitability. The data analysis and modeling aspects of machine learning are important tools to delivery companies, public transportation and other transportation organizations.”

For more on Machine Learning algorithms and artificial intelligence, do checkout the DexLab Analytics blog section. DexLab Analytics is a premiere institute of Machine Learning training in Delhi which trains professionals and students in all aspects of the technological science through both online classes and classes conducted in the National Capital Region.


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Machine Learning Algorithms in Self-Driving Cars

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Machine Learning Algorithms in Self-Driving Cars

Machine Learning algorithms have revolutionized sectors like automation in ways one could have hardly imagined a few years ago. For instance, take the self-driving car. According to a report, with“the integration of sensor data processing in a centralized electronic control unit (ECU) in a car, it is imperative to increase the use of machine learning to perform new tasks. Potential applications include driving scenario classification or driver condition evaluation via data fusion from different internal and external sensors – such as cameras, radars, LIDAR or the Internet of Things.”

An expert explains how machine learning algorithms are used in autonomous cars. Supervised and unsupervised algorithms are used to perceive information through the car’s infotainment system. For instance, the system can relay information about the driver’s health status and direct the vehicle to a nearby hospital if something is found to be wrong. “This machine learning-based application can also incorporate the driver’s gesture and speech recognition, and language translation.”

The algorithms can be classified into two major categories on the basis of their learning ability- supervised algorithm and an unsupervised algorithm.

Supervised algorithms “learn using a training data­set, and keep on learning until they reach the desired level of confidence (minimization of probability error).” They can be sub-classified into classification, regression and dimension reduction or anomaly detection.

Unsupervised algorithms “try to make sense of the available data. That means an algorithm develops a relationship within the available data set to identify patterns, or divides the data set into subgroups based on the level of similarity between them.” Unsupervised algorithms can be largely sub­-classified into clustering and association rule learning.

The third set of machine learning algorithms falls somewhere between supervised and unsupervised learning. Reinforcement learning has sparse and time-­delayed labels – the future rewards. “Based only on those rewards, the agent has to learn to behave in the environment.”

One of the main tasks of any machine learning algorithm in the self­-driving car is continuous rendering of the surrounding environment and the prediction of possible changes to those surroundings. These tasks are mainly divided into four sub-­tasks:

  • Object detection
  • Object Identification or recognition
  • Object classification
  • Object localization and prediction of movement

Machine learning algorithms can be loosely divided into four categories: regression algorithms, pattern recognition, cluster algorithms and decision matrix algorithms. One category of machine learning algorithms can be used to execute two or more different sub­tasks. For example, regression algorithms can be used for object detection as well as for object localization or prediction of movement.

Regression Algorithms

This type of algorithm is used to predict events. “Regression analysis estimates the relationship between two or more variables, compare the effects of variables measured on different scales and are mostly driven by three metrics, namely:

  • The number of independent variables
  • The type of dependent variables
  • The shape of the regression line.”

Pattern Recognition Algorithms (Classification)

“In ADAS, the images obtained through sensors possess all types of environmental data; filtering of the images is required to recognize instances of an object category by ruling out the irrelevant data points. Pattern recognition algorithms are good at ruling out these unusual data points. Recognition of patterns in a data set is an important step before classifying the objects. These types of algorithms can also be defined as data reduction algorithms.”

Clustering

Sometimes the images gathered by the system are unclear and it is difficult to detect and locate objects in them. It is also possible that the classification algorithms may miss the object and fail to classify and report it to the system because the images are low-resolution, with very few data points or discontinuous data. “This type of algorithm is good at discovering structure from data points. Like regression, it describes the class of problem and the class of methods.” The most commonly used type of algorithm is K-­means, Multi-­class Neural Network.”

Decision Matrix Algorithms

“This type of algorithm is good at systematically identifying, analyzing, and rating the performance of relationships between sets of values and information. These algorithms are mainly used for decision-making. Whether a car needs to take a left turn or it needs to brake depends on the level of confidence the algorithms have on the classification, recognition and prediction of the next movement of objects.”

Check out the course structure at DexLab Analytics, a premiere artificial intelligence institute and machine learning institute in Delhi for more on the subject.


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The AI Revolution in The Education Sector

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The AI Revolution in The Education Sector

Artificial Intelligence (AI) is revolutionizing innumerable aspects of our lives, education being one of them. AI has transformed the way we learn, the relationship between the student and the teacher and the very manner in which our curriculum is perceived. This article, the third part of a series on the applications of artificial intelligence, delineates how AI has come to transform the education sector, as we know it.

The biggest contribution of AI to the education sector has been towards enhancing and streamlining the system of teaching students with varying needs across the spectrum, from elementary schools to adult learning centers. Students can be mentally developed in the left side of the brain with more analytical skills or they can be mentally developed in the right side of the brain with more creative and literary skills. Likewise, there may be students with different interests and passions. A strictly uniform curriculum does not suit all students of the same class because people differ in their learning ability and interests.

AI-Enabled Hyper-Personalization

AI is thus being used to customise curricula according to specific needs of each student of a single class. This is being done through the power of machine learning via a method called hyper-personalization. The AI powered system studies and examines the profile of a student and prescribes suitable curricula for her/him. According to a report, it is expected that by the year 2024 onwards, almost 50 percent of learning management tools will be powered by AI capabilities. These AI-enabled e-Learning tools will touch over $6 Billion in market size by 2024.

Smart Learning Tools

Machine Learning and AI are also defining the way hyperper sonalized and on-demand digital content is created to digitise the learning environment. Now students do not have to rote-learn chapter after chapter from textbooks. They are absorbing learning material in the form of condensed bits of information in the form of smaller study guides, chapter summaries, flashcards, as well as short smart notes designed for better reading and comprehension. Learning is therefore becoming gradually paperless. AI systems also have an online interactive interface that helps in putting in place a system of feedback from students to professors regarding areas they are facing trouble understanding.

Digital Conversations

AI systems are also being used to develop the system of tutoring with personalized conversational education assistants. These autonomous conversational agents are capable of answering questions, providing assistance with learning or assignments, and strengthening concepts by throwing up additional information and learning material to reinforce the curriculum. “These intelligent assistants are also enhancing adaptive learning features so that each of the students can learn at their own pace or time frames”. 

Adoption of Voice Assistants 

In addition, educators are relying heavily on using voice assistants in the classroom environment. Voice assistants such as Amazon Alexa, Google Home, Apple Siri, and Microsoft Cortana have transformed the way students interact with their study material. In the higher education environment, universities and colleges are distributing voice assistants to students in place of traditionally printed handbooks or hard-to-navigate websites.

Assisting Educators

AI powered systems are not only helping students with course work, they are also empowering teachers with teaching material and new innovative ways to educationally express themselves. It is easier to explain a theory with the help of picture cues and graphical representation than mere definitions. The Internet has become a treasure trove of teaching material for teachers to borrow from. Also, teachers are burdened with responsibilities “such as essay evaluation, grading of exams…ordering and managing classroom materials, booking and managing field trips, responding to parents, assisting with conversation and second-language related issues…Educators often spend up to 50% of their time on non-teaching tasks.”AI powered systems can help streamline these tasks and handle repetitive and routine work, digitise interaction with parents and guardians and leave educators with more time to teach students.

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When it comes to higher learning, in India at least, more and more artificial intelligence and machine learning institutes are opening up. DexLab Analytics is a premiere artificial intelligence course in Delhi that trains professionals in both AI and machine learning.


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