In this series, "Ask INDIAai," you can submit questions and get answers. INDIAai's expert team will react to your inquiries.

Send your questions to askindiaai@mail.nasscom.in.

What are AI agents? Is chatbot an AI agent? - Krishnakanth, Chennai.

In artificial intelligence, intelligent agents are autonomous entities that act upon an environment utilising sensors and actuators to achieve their objectives. Moreover, intelligent agents may learn from their surroundings to fulfil these objectives. Self-driving automobiles and the virtual assistant Siri are examples of sophisticated AI agents.

AI assistants, such as Alexa and Siri, are intelligent agents because they use sensors to detect a user's request and autonomously obtain data from the internet without the user's intervention. For example, we can use them to gather information about its perceived environment, such as the time and the weather.

In messaging apps, chatbots, often known as chatterbots, are a form of artificial intelligence (AI). This tool adds convenience for customers; it consists of automated programmes that communicate with clients in a human-like manner at a minimal cost.

What is the AI playground? Who made it? - Farheen sultan, Noida.

The OpenAI Playground is a new platform that allows you to request that an AI bot write almost anything for you. You may converse with the Playground AI, ask it questions, and utilise it to produce short tales, among other things. To use the Playground AI, you must first create an account on the OpenAI website.

It collaborates with meldCX, the University of South Australia, and Intel. It challenges students to build, train, and navigate their Mars rover in a virtual outer space world driven by AI.

Is there an AI that can feel emotions? Are robots with emotions possible? - Regina Jeremiah, Pondicherry.

Researchers in AI and neuroscience concur that while existing forms of AI cannot experience their own emotions, they may mimic emotions like empathy. Additionally, artificial voice helps these services operate less robotically and express emotions more realistically.

This thinking style results from the unconscious assumption that a robot is capable of feeling emotions (that it is sentient) and that these emotions would cause the robot to attempt to wipe out the human species. The reality is that machines designed to have intelligence do not have emotions.

What does transfer learning mean? What is it in neural networks? - Hari Ram Vishwakarma, Bhopal.

Transfer of learning is the application of learned knowledge to novel settings. Examples of transferable knowledge: A student learns to solve polynomial equations in class and then applies those skills to similar homework problems. In class, the instructor outlines numerous psychological diseases.

Because it can train deep neural networks with relatively minimal data, it is currently quite popular in deep learning.

What does data augmentation mean? What is its objective? - Sheetal Malhotra, Varanasi.

Data augmentation is producing new data points from existing data to increase the amount of data artificially. It may involve minimal modifications to data or utilising machine learning models to generate additional data points in the latent space of the original data to augment the dataset.

It is a series of strategies to generate new data points from current data to enhance the amount of data artificially. It includes making modest adjustments to data or generating new data points using deep learning models.

What is a random forest method? Why is it called so? - Ilamaaran, Trichy.

Random forests, also known as random choice forests, are an ensemble learning method for classification, regression, and other problems that works by generating a large number of decision trees during training. For example, in classification problems, the random forest output is the class chosen by most trees.

We call it a Random Forest because we employ random selections of data and attributes to create a forest of decision trees (many trees). Random Forest is also a typical example of a bagging strategy because each model makes predictions using distinct subsets of data.