AI Interview Questions

Essential Fundamentals to Know

To build a strong understanding of AI, you should be familiar with the following concepts:

• Artificial Intelligence (AI): A broad field in computer science aimed at creating systems that exhibit human-like intelligence.
• Machine Learning (ML): A subset of AI that uses statistical techniques to enable systems to learn and improve from experience without explicit programming.
• Deep Learning (DL): A specialized branch of ML that employs multi-layered neural networks to process and analyze complex data.
• Generative AI: A type of AI that creates new content—such as text, images, or music—based on patterns learned from existing data.
• Bias-Variance Trade-Off: The balance between underfitting (high bias) and overfitting (high variance) to achieve optimal model performance.
• Loss Function: A metric that measures how well a model’s predictions align with actual data. Lower loss indicates better performance.
• Handling Overfitting: Techniques include adding more data, simplifying the model, and applying methods like cross-validation.

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Common AI Interview Questions

1. What is the difference between machine learning and deep learning?
Machine learning encompasses a range of algorithms for tasks like classification and prediction. Deep learning, a subset of ML, uses layered neural networks to handle highly complex data. In short, all deep learning is machine learning, but not all machine learning involves deep learning.

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2. How does the bias-variance trade-off affect model performance?
The bias-variance trade-off is critical for accuracy. High bias leads to underfitting, where the model fails to capture important patterns. High variance causes overfitting, where the model memorizes noise in the training data. The goal is to strike a balance to minimize overall error.

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3. What is a loss function and why is it important in training models?
A loss function (or cost function) measures the difference between predicted and actual values. It guides optimization algorithms—such as gradient descent—during training to minimize errors. The choice of loss function impacts model performance significantly. Examples include Mean Squared Error for regression and Cross-Entropy Loss for classification.

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4. What is Generative AI and how is it applied across industries?
Generative AI creates new data resembling the training set, including text, images, videos, and music. Applications span multiple sectors:

• Media & Entertainment: Generating realistic game environments and music compositions.
• Marketing: Producing personalized content to enhance customer engagement.
• Healthcare & Simulation: Creating synthetic data for research and training purposes.