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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To resolve this, typically, companies invest, but if that is not channelized properly, it will be futile.

So, the best way out of this dilemma is to have a clear customer acquisition strategy in place.

How can you create the ideal customer acquisition strategy for your business?

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You need to define your goals so that you can meet the revenue expectations you have for the current fiscal year. You need to find a value for the metrics –

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Combining these strategies with your long-term business plan will bring results. However, there will be challenges on the way, where you need to adapt as per the requirements to make the most of it. At the same time, introducing new technologies like AI and ML can also solve such issues easily. To learn more about the use of AI and ML and how they are transforming businesses, keep referring to the blog section of E2E Networks.

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Image-based 3D Object Reconstruction State-of-the-Art and trends in the Deep Learning Era

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The Main Objective of the 3D Object Reconstruction

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Training with the help of one or multiple RGB images, where the segmentation of the 3D ground truth needs to be done. It could be one image, multiple images or even a video stream.

The testing will also be done on the same parameters, which will also help to create a uniform, cluttered background, or both.

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  • Training used

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Volumetric representations and surface representations can do the reconstruction. Powerful computer systems need to be used for reconstruction.

Given below are some of the places where 3D Object Reconstruction Deep Learning Systems are used:

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  • They can be used in plastic surgery where the organs, face, limbs or any other portion of the body has been damaged and needs to be rebuilt.
  • It can be used in airport security, where concealed shapes can be used for guessing whether a person is armed or is carrying explosives or not.
  • It can also help in completing DNA sequences.

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A Comprehensive Guide To Deep Q-Learning For Data Science Enthusiasts

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So, read on to know more.

What is Deep Q-Learning?

Deep Q-Learning utilizes the principles of Q-learning, but instead of using the Q-table, it uses the neural network. The algorithm of deep Q-Learning uses the states as input and the optimal Q-value of every action possible as the output. The agent gathers and stores all the previous experiences in the memory of the trained tuple in the following order:

State> Next state> Action> Reward

The neural network training stability increases using a random batch of previous data by using the experience replay. Experience replay also means the previous experiences stocking, and the target network uses it for training and calculation of the Q-network and the predicted Q-Value. This neural network uses openAI Gym, which is provided by taxi-v3 environments.

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Now, the understanding of reinforcement learning is incomplete without knowing about Markov Decision Process (MDP). MDP is involved with each state that has been presented in the results of the environment, derived from the state previously there. The information which composes both states is gathered and transferred to the decision process. The task of the chosen agent is to maximize the awards. The MDP optimizes the actions and helps construct the optimal policy.

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In case the Q-table size is huge, then the generation of the model is a time-consuming process. This situation requires Deep Q-learning.

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