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Got Questions? We’ve Got Answers!

Explore our FAQs to uncover insights about AI, our services, and how we can help you innovate, optimize, and grow with intelligent solutions.

Discover answers to the most common questions about AI services, from implementation to benefits, and learn how AI can transform your business. Whether you’re curious about AI integration, scalability, or real-world applications, we’ve got you covered with clear and insightful answers.

General Questions
How does AI Convert text to video , automatically?
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AI uses Natural Language Processing (NLP) to understand text, selects relevant visuals and audio, and then video generation models synthesize them into a cohesive video.
How do you approach solving a new problem with AI?
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Solving a new problem with AI involves under standing the problem domain, collecting and preprocessing data, choosing the appropriate model and algorithm
How do you keep up with the rapidly evolving field of AI?
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I stay updated by reading research papers and industry news. I also follow AI experts, attend conferences, and engage with AI communities.
What is Artificial Intelligence?
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AI is the development of machines that mimic human intelligence. These systems can perform tasks such as learning, reasoning, and problem-solving. AI aims to create machines that can operate autonomously.
What are the types of Artificial Intelligence?
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There are three main types: Narrow AI, which is task-specific; General AI, a theoretical AI capable of any task; and Super AI, which would surpass human intelligence but remains hypothetical.
Explain Artificial Intelligence and give its applications.
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AI refers to machines simulating human intelligence to perform tasks autonomously, such as speech recognition, autonomous vehicles, personalized recommendations, and medical diagnostics.
How many layers are in a Neural Network?
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A neural network has three main types of layers: input, hidden, and output. Hidden layers vary in number depending on the model’s complexity. Each layer processes the data differently.
What are common data structures used in deep learning?
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Common data structures include tensors, matrices, and graphs. Tensors are widely used because they store multi-dimensional data, which is essential for training deep learning models.
Explain forward propagation and backpropagation.
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Forward propagation passes input through the network to get an output. Backpropagation adjusts the weights by calculating the loss gradient, improving model accuracy through iterations.

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