You can submit questions and obtain clarifications in this series "Ask INDIAai". INDIAai's expert team will respond to your inquiries.

You can send your inquiries to: askindiaai@mail.nasscom.in.

What kind of AI can be used in schools and classroom teaching? - J Prabavathi, Varanasi.

Machine learning-based artificial intelligence systems have many potential applications in the classroom, including but not limited to monitoring student activities and developing predictive models.

In addition, the automatic grading of essays and papers is much faster than human grading. As a result, educators will have more time to cultivate students' capacity for analytical and evaluative thought.

Furthermore, teachers can use these findings to allocate classroom time better. Learning AI systems may gradually adapt to each student's unique strengths and weaknesses, making them more effective educators. You can address individual student needs rather than a generic curriculum.

What is the relationship between Psychology and AI? Does AI require Psychology? - R Kannan, kozhikode.

Understanding the processes that give rise to intelligent behaviour is a central theme shared by AI and psychology. In the case of psychology, the focus is on humans, and mental processes are discussed. In the case of AI, the focus is on machines, and information processing is discussed.

Psychologists are assisting in developing and deploying AI software and technologies, ranging from therapeutic chatbots to facial recognition systems. In addition, they are accumulating an extensive body of literature on human-computer interaction, digital medicines, and the ethics of automation.

What is explainable AI? Does it exist? - Dinesh Roy, Meerut.

Explainable artificial intelligence (XAI) has become indispensable for comprehending how an AI model makes decisions and identifying error sources. This post discusses the significance of explainable AI, the accompanying problems, and the vital role of data. For example, diagnosing patients with pneumonia could be one of the scenarios in which AI forecasts could justify their conclusions. In healthcare, using medical imaging data to diagnose cancer is a second application where explainable AI can be incredibly valuable.

Furthermore, Explainability is essential for multiple reasons: - It assists analysts in swiftly and efficiently comprehending system outputs. Analysts can make efficient, informed decisions if they understand how the system operates. - Aids in overcoming false positives.

What is the advantage of AI in social media? - Joseph Noel, Goa.

Social monitoring or social listening solution powered by artificial intelligence can provide insights from your brand's social media profiles and audience. It often entails utilising the power of AI to analyse social data at scale, comprehend what is being said in them, and then derive conclusions based on this data. Using AI to post content to your social media profiles automatically is one of the most prevalent methods. It enables you to publish content at the appropriate moments for maximum engagement. By giving automated responses to messages, AI can also assist in boosting user engagement.

Moreover, AI-enabled facial recognition technology enables social media users to explore various features, including:

• The application of facial overlay filters (which replicate your facial movements in real-time), 
• the editing of images, and 
• the creation of photo- or video-based content with ease.

How do deep learning algorithms work? - Divya Priyadarshini, Kurnool.

Deep learning is a machine learning technique that trains computers to learn by example, as is natural for people. For example, deep learning is a crucial component of driverless cars, allowing them to identify stop signs and distinguish between pedestrians and lampposts.

It learns by discovering complex structures in the data it encounters. By constructing computational models with many processing layers, networks can generate multiple abstraction levels to describe the data.

Types of Algorithms used in Deep Learning

• Convolutional Neural Networks (CNNs)
• Long Short Term Memory Networks (LSTMs)
• Recurrent Neural Networks (RNNs)
• Generative Adversarial Networks (GANs)
• Radial Basis Function Networks (RBFNs)
• Multilayer Perceptrons (MLPs)
• Self Organizing Maps (SOMs)
• Deep Belief Networks (DBNs)