Artificial Intelligence
Basic AI Interview Questions and Answers
1. What is Artificial Intelligence?
AI is the simulation of human intelligence in machines using algorithms like machine learning (ML) and deep learning (DL) to solve complex problems.
2. What are the types of Artificial Intelligence?
- Narrow AI: Specialized tasks (e.g., virtual assistants).
- General AI: Human-like capabilities.
- Super AI: Beyond human intelligence (theoretical).
3. What is the difference between AI and Machine Learning?
AI is a broader concept where machines mimic intelligence, while ML is a subset of AI focused on learning from data.
4. What is Deep Learning in AI?
Deep Learning is a branch of AI that uses neural networks with multiple layers to learn complex data patterns.
5. What is Natural Language Processing (NLP)?
NLP is an AI field enabling machines to understand and generate human language, used in tools like chatbots and speech recognition.
6. What are the main applications of AI in Data Science?
Applications include predictive analytics, computer vision, speech recognition, recommendation systems, and fraud detection.
7. What is a Neural Network?
A neural network is a set of algorithms modeled after the human brain, consisting of input, hidden, and output layers.
8. What is Reinforcement Learning?
Reinforcement Learning trains models by rewarding desired actions, commonly used in robotics and game AI.
9. What are AI biases, and how do you handle them?
AI biases arise from biased training data or algorithms. They’re addressed by ensuring diverse datasets and testing for fairness.
10. What is Computer Vision in AI?
Computer Vision enables machines to interpret visual data like images and videos, used in applications like object detection and facial recognition.
11. What is the Turing Test?
The Turing Test evaluates a machine's ability to exhibit behavior indistinguishable from a human.
12. What are the challenges in AI implementation?
Challenges include data quality, interpretability, scalability, and addressing ethical concerns.
13. What is Explainable AI (XAI)?
XAI focuses on making AI decisions understandable to humans, ensuring transparency and trust.
14. What is GAN (Generative Adversarial Network)?
GANs are AI models with two networks (generator and discriminator) that create realistic data, often used in image synthesis.
15. What is Transfer Learning in AI?
Transfer Learning applies knowledge from a pre-trained model to a new task, reducing the need for large datasets.
16. How do you ensure AI models are ethical?
Use diverse datasets, follow regulations, test for bias, and implement frameworks like AI ethics principles.
17. What is the difference between Supervised and Unsupervised Learning?
- Supervised Learning: Models learn from labeled data.
- Unsupervised Learning: Models identify patterns in unlabeled data.
18. What is Overfitting in AI?
Overfitting occurs when an AI model performs well on training data but fails to generalize to unseen data.
19. What is the role of AI in Recommendation Systems?
AI uses techniques like collaborative filtering and content-based filtering to provide personalized recommendations.
20. What is AI-based Predictive Analytics?
Predictive Analytics uses AI to analyze historical data and predict future outcomes, commonly used in sales forecasting.
21. What are common AI frameworks and tools?
Popular tools include TensorFlow, PyTorch, Keras, Scikit-learn, and OpenCV for building AI models.
22. What is the role of Big Data in AI?
Big Data provides the vast amounts of information required to train complex AI models, enabling scalability and precision.
23. What is Hyperparameter Tuning in AI?
Hyperparameter Tuning optimizes AI model performance by fine-tuning settings like learning rate and batch size.
24. What is AI Model Evaluation?
Model evaluation assesses performance using metrics like accuracy, precision, recall, and F1 score.
25. How is AI used in Autonomous Vehicles?
AI powers autonomous vehicles by using sensor data and computer vision for navigation and obstacle detection.
