AI Interview Questions

AI refers to the simulation of human intelligence in machines programmed to think, reason, learn, and make decisions. It includes areas like machine learning, natural language processing (NLP), robotics, and vision.

• Narrow AI (Weak AI): Performs a specific task (e.g., Google Assistant).
• General AI (Strong AI): Human-level cognition (not yet achieved).
• Superintelligent AI: Hypothetical AI surpassing human intelligence.

Proposed by Alan Turing to assess a machine’s ability to exhibit intelligent behavior indistinguishable from that of a human.

• Symbolic AI: Uses rules and logic (e.g., expert systems).
• Connectionist AI: Based on neural networks and learning from data (e.g., deep learning).

NLP enables machines to understand, interpret, and generate human language. Applications: chatbots, translation, sentiment analysis, summarization.

LLMs like GPT, LLaMA, and Claude are AI models trained on massive text data to understand and generate human-like text. Used in chatbots, code generation, and writing assistants.

The practice of designing effective prompts to guide LLMs like GPT to give accurate, desired responses.

• Stemming: Trims words to their root (e.g., "running" → "run").
• Lemmatization: Considers the word’s meaning and reduces it to its base form ("better" → "good").

AI (especially deep learning with CNNs) helps machines recognize objects, faces, gestures, and scenes from images or videos.

SLAM (Simultaneous Localization and Mapping) helps robots map their environment while tracking their own location in real time.

Robotics involves building machines; AI gives them “brains” to make decisions. Not all robots are AI-powered, and not all AI is used in robots.

An AI program that mimics human decision-making using a knowledge base and inference engine (e.g., MYCIN for medical diagnosis).

A form of logic that handles reasoning with uncertain or imprecise information, useful in thermostats, automatic brakes, etc.

Ontologies define structured relationships between concepts in a domain, often used in knowledge graphs and semantic web AI.

• Bias and fairness
• Privacy
• AI misuse (e.g., deepfakes)
• Transparency and explainability
• Job displacement

AI systems that provide human-understandable justifications for their outputs — critical in high-stakes areas like healthcare or law.

Ensuring AI systems’ goals and actions align with human values and intent, especially important in autonomous or general AI.

Large pre-trained models like GPT, PaLM, and DALL·E that serve as general-purpose models and can be fine-tuned for various tasks.

• Self-driving cars
• AI in finance (fraud detection)
• AI in healthcare (diagnostics, drug discovery)
• Smart assistants (Siri, Alexa)
• Recommendation systems (Netflix, Amazon)

Governments and organizations (like EU’s AI Act, OECD, and U.S. Executive Orders) are pushing for responsible AI with standards around safety, bias, and transparency.