Flashcards for AI Basics
What is Data Science?
Extracting insights from raw data using stats, programming, and machine learning.
What is Artificial Intelligence (AI)?
Creating systems that perform tasks requiring human intelligence — learning, reasoning, adapting.
What’s the difference between Data and Information?
Data = Raw facts.
Information = Organized and meaningful data.
What are the 3 Types of AI?
Narrow AI, General AI, Super AI.
Example of Narrow AI?
Siri, Alexa, Netflix recommendations.
Two main types of Data?
Qualitative (Categorical)
Quantitative (Numerical)
Examples of Structured Data?
Sales records, employee databases.
Examples of Unstructured Data?
Tweets, videos, chat logs.
Key steps in Data Analytics Process?
Identify → Collect → Clean → Analyze → Visualize.
What % of ML project time is spent on data prep?
70%.
Key components of AI?
Machine Learning
NLP
Computer Vision
Robotics
What tool would you use for numerical computing in Python?
NumPy.
What is TensorFlow mainly used for?
Deep learning and neural networks.
What does NLP stand for?
Natural Language Processing.
What is Generative AI?
AI that creates new content (e.g., ChatGPT, DALL·E).
Big challenge in AI development?
Data privacy, bias, ethical concerns.
Future trends for AI?
AI everywhere in life, General AI progress, AI helping climate action.
In healthcare, what is AI used for?
Disease diagnosis, predictive patient care, robotic surgeries.
In transportation, how is AI used?
Autonomous cars, traffic optimization, route planning.
What is the goal of MLOps?
Managing the lifecycle of machine learning models (like DevOps for ML).
