Future of AI in Edtech: Powered by GPU's

November 17, 2020

Today, Artificial Intelligence has proved to be immensely helpful in almost every sector. Scientists across the world have claimed that AI will amplify human intelligence and effectiveness. We are already utilizing AI for applications such as pattern recognition, speech recognition, complex decision-making, analytics, machine learning, and more. Another very powerful sector that AI can help is Edtech. AI has the potential to revolutionize the Edtech industry.

Recently, E2E Networks limited conducted a webinar chaired by Mr. Jiwitesh Kumar, Chief Data Scientist & CTO at iNeuron. He is an experienced artificial intelligence engineer, with a demonstrated history of working in the automotive industry. He is skilled in machine learning, neural network, deep learning, NLP database, Python, Java, and various front-end tech. In the webinar, he busted some common misconceptions, and discussed how AI could help the Education industry. He also guided college students how to start their career in the AI industry, and explained how even college freshers can start their AI journey.

Even though Artificial Intelligence is a much-renowned field, most people don’t exactly understand the meaning of Artificial intelligence. AI is not just robots, chatbots, machines talking to humans, it is much more than that.

Mr. Jiwitesh also mentioned that “Knowing algorithms, networks, and doing some projects does not make you an AI Engineer. Though it is good for learning and exploration purposes; however, unless you understand the basics and solve a real-life problem, it has no meaning”.

Next, Mr. Jiwitesh gave an example of China, where they have designed a headband for students. Using the device installed in the headband, the teachers can track whether students are paying attention in school. They can measure the concentration level of each student and generate a report. By this example, Mr. Jiwitesh tried to explain how AI can help to improve and revolutionize the education sector.

What is Machine Learning?

Technically, machine learning is the practice of using algorithms to parse data, learn from it, and then determine or predict something in the world.

But when explaining to a layman, these technical terms are complex. Mr. Jiwitesh explained the concept in very simple terms. In schools, we learn in classes, and our teachers provide us the problems with their solutions and in this way we learn. In exams, we are shown only problems, and we have to write their solutions. In exams, we can solve the problem because the problem is the same that we have learned previously. Machine learning is similar; first, we have to feed a lot of data to train it, after that, when we feed a problem to the algorithm, we get the desired output.

People are often confused between machine learning, artificial intelligence, and data science.

When we talk about AI, we accommodate each and everything from machine learning, NLP, hardware devices, embedded systems. To build a complete AI solution, we need all those components.

How can college freshers start their AI journey?

College freshers can start with simple projects such as chatbots, and learn the fundamental concepts. Starting with fundamentals will give students the confidence to build further. There are numerous resources available on platforms like YouTube, Udemy, Coursera to learn and start working. Initially, students need not work on complex systems like the concentration tracking headband, but they can work on simple systems that can be added to existing devices like normal phone cameras and detect movements.

Importance of Data in AI

When working with artificial intelligence, we need a great amount of Data. We usually work with say, 10,000 records, 20,000 records, and even more. The question is why such a huge amount of data is required?

Mr. Jiwitesh explained this with the help of an example, “Let’s consider that you are working with a linear model. As you must have studied in high school math, for a linear model, we need 2 parameters- x and y to fit a straight line. However, in the case of a quadratic model, we need a minimum of 3 data points. Similarly, the more complex our models become, we need more amounts of data to determine the relationships and achieve accurate results”.

The required data for training the machine learning models can even reach millions of data points, as in the Inception V3 by Google, where it needs a little less than 24 million parameters and 1.2 million data points.

How can AI improve Education?

Artificial intelligence can greatly improve the overall learning efficiency of students. For example, AI can track the learning curve of students, and guide the parents and teachers on what parameters they can work with their child to improve his results. AI can also help in personalized mentoring, and suggest the best pathways to study.

Along with Augmented Reality and Virtual Reality technologies, students perform experiments and do practicals in a virtual environment, and this is especially very useful in scenarios such as the COVID pandemic, or cases when ideal environments aren’t available.

Mr. Jiwitesh ends his session by saying, “ Though AI can appear to be intimidating at first, do not get confused or frightened. Just start, even if you don’t understand initially, keep working and I can assure you that you will understand how it works”.

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Reference Links

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https://www.cloudways.com/blog/customer-acquisition-strategy-for-startups/

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Reference Links

https://tongtianta.site/paper/68922

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Reference Links

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