Artificial Intelligence (AI) Interview Questions and Answers | AI Interview Preparation | Edureka

Lake Hoff introduces a session on common AI interview questions, categorizing them into basic, intermediate, and scenario-based levels. The necessity of AI is emphasized due to the exponential data generation from the technological revolution, requiring structured analysis for business growth, leveraging AI, machine learning, deep learning, and data science.

Lake Hoff clarifies the confusion among AI, machine learning, and deep learning, tracing their evolution and defining their distinct roles. AI simulates human intelligence, machine learning enables machines to make decisions with data without explicit programming, and deep learning uses neural networks to solve complex problems. Different AI types, including reactive, limited memory, theory of mind, and self-aware AI, are discussed, highlighting their theoretical and practical aspects.

The discussion transitions into various domains within AI such as machine learning, neural networks, robotics, and expert systems, illustrating their unique approaches to problem-solving. Additionally, Lake tackles common misconceptions about the relationship between AI and machine learning, explaining the hierarchy of learning types: supervised, unsupervised, and reinforcement learning.

Lake provides an in-depth look at deep learning and its core component, neural networks. He explains the structure of neural networks, including input, hidden, and output layers, and how they mimic the human brain to solve advanced problems. Key concepts of perceptrons, artificial neurons, and commonly used neural networks like feed-forward and convolutional neural networks are detailed.

This section covers recurrent neural networks, autoencoders, and Bayesian networks, explaining their functionality and practical applications, especially in signal and image processing. The focus then shifts to assessing machine intelligence through tests like the Turing Test, and discussing the theoretical aspects of more advanced AI, including types of intelligence and their potential for realization.

Lake delves into the intermediate level AI topics, starting with reinforcement learning (RL). He uses analogies and examples, such as being stranded on an island, to explain how RL works through interaction with the environment, learning from actions and rewards. The section includes explanations of key concepts in RL like the policy, reward maximization, and the exploration-exploitation trade-off.

Continuing with intermediate AI topics, Lake explains Markov decision processes and reward maximization strategies in reinforcement learning, including techniques like alpha-beta pruning to optimize minimax strategies in games. The discussion includes practical applications of these strategies in game theory and real-world scenarios.

Lake outlines how AI is utilized in real-world scenarios, such as Facebook's face verification and targeted marketing. He explains the technologies behind these applications, including the algorithms and machine learning models involved, and discusses their implications for privacy and user interaction.

The final segment offers an in-depth look at scenario-based interview questions, which test an applicant's understanding of complex AI concepts and their ability to apply these in practical situations. Examples include optimizing game strategies using AI, and leveraging machine learning for market analysis and fraud detection.

The video concludes with Lake encouraging viewers to engage with the content by asking questions and exploring further learning opportunities in AI. He emphasizes the importance of continuous learning and staying updated with the latest developments in AI technology.