AI is the simulation of human intelligence in machines that are programmed to think and learn.
Q: What are the main types of AI?
The main types are Narrow AI, General AI, and Superintelligent AI.
Q: What is Machine Learning?
Machine Learning is a subset of AI that enables systems to learn from data and improve over time without being explicitly programmed.
Q: What is Deep Learning?
Deep Learning is a type of Machine Learning that uses neural networks with many layers to analyze complex data.
Q: What is a neural network?
A neural network is a series of algorithms that attempt to recognize underlying relationships in a set of data through a process that mimics the way the human brain operates.
2. AI Fields and Applications
Q: What is Natural Language Processing (NLP)?
NLP is a field of AI that focuses on the interaction between computers and humans using natural language.
Q: What is computer vision?
Computer vision is a field of AI that trains computers to interpret and understand the visual world.
Q: What is speech recognition?
Speech recognition is the process of converting spoken language into text.
Q: What is text-to-speech (TTS)?
TTS is the process of converting written text into spoken voice output.
Q: What is optical character recognition (OCR)?
OCR is the technology to convert different types of documents, such as scanned paper documents or images, into editable and searchable data.
3. Machine Learning Types
Q: What is supervised learning?
Supervised learning is a type of Machine Learning where the model is trained on labeled data.
Q: What is unsupervised learning?
Unsupervised learning is a type of Machine Learning where the model finds patterns in unlabeled data.
Q: What is reinforcement learning?
Reinforcement learning is a type of Machine Learning where an agent learns to make decisions by performing actions and receiving rewards or penalties.
4. Data and Features
Q: What is an algorithm in AI?
An algorithm is a set of rules or instructions given to an AI, computer, or other machine to help it learn on its own.
Q: What is training data?
Training data is the dataset used to teach an AI model how to make predictions or decisions.
Q: What is a dataset?
A dataset is a collection of data used for training or testing AI models.
Q: What is a label in machine learning?
A label is the output or target value that a model is trying to predict.
Q: What is a feature in AI?
A feature is an individual measurable property or characteristic of the data being analyzed.
Q: What is feature extraction?
Feature extraction is the process of transforming raw data into numerical features that can be processed while preserving the information in the original data.
Q: What is feature selection?
Feature selection is the process of selecting the most important variables for use in model construction.
Q: What is feature scaling?
Feature scaling is the process of normalizing or standardizing features so they have similar ranges or distributions.
Q: What is one-hot encoding?
One-hot encoding is a method to convert categorical variables into a binary matrix for use in machine learning models.
Q: What is data preprocessing in AI?
Data preprocessing is the process of cleaning and transforming raw data into a suitable format for a machine learning model.
Q: What is data augmentation?
Data augmentation is the process of creating new training samples by modifying existing data, often used in image processing.
5. Model Training and Evaluation
Q: What is a model in AI?
A model is a mathematical representation of a real-world process, trained to make predictions or decisions based on data.
Q: What is classification in AI?
Classification is the process of predicting the category or class of given data points.
Q: What is regression in AI?
Regression is a type of predictive modeling technique that estimates relationships among variables.
Q: What is clustering in AI?
Clustering is an unsupervised learning technique that groups similar data points together.
Q: What is a confusion matrix?
A confusion matrix is a table used to evaluate the performance of a classification algorithm by comparing predicted and actual values.
Q: What is accuracy in AI?
Accuracy is the ratio of correctly predicted observations to the total observations.
Q: What is precision in AI?
Precision is the ratio of correctly predicted positive observations to the total predicted positive observations.
Q: What is recall in AI?
Recall is the ratio of correctly predicted positive observations to all actual positive observations.
Q: What is F1 score?
The F1 score is the harmonic mean of precision and recall, providing a balance between the two metrics.
Q: What is cross-validation?
Cross-validation is a technique for assessing how a machine learning model will generalize to an independent dataset.
Q: What is a training set?
A training set is a subset of data used to train a machine learning model.
Q: What is a test set?
A test set is a subset of data used to evaluate the performance of a trained model.
Q: What is validation set?
A validation set is a subset of data used to tune model parameters and prevent overfitting.
Q: What is a loss function?
A loss function measures how well a model’s predictions match the actual values.
Q: What is gradient descent?
Gradient descent is an optimization algorithm used to minimize the loss function by iteratively adjusting model parameters.
Q: What is backpropagation?
Backpropagation is an algorithm for training neural networks by propagating the error backward and updating the weights.
Q: What is an epoch in machine learning?
An epoch is one complete pass through the entire training dataset during the training process.
Q: What is batch size?
Batch size is the number of training samples processed before the model’s internal parameters are updated.
Q: What is a hyperparameter?
A hyperparameter is a configuration value set before training a model, such as learning rate or number of layers.
Q: What is regularization in AI?
Regularization is a technique used to prevent overfitting by adding a penalty to the loss function.
Q: What is dropout in neural networks?
Dropout is a regularization technique where randomly selected neurons are ignored during training to prevent overfitting.
Q: What is a learning rate in machine learning?
The learning rate is a hyperparameter that controls how much the model’s weights are updated during training.
Q: What is a validation curve?
A validation curve is a plot that shows how a model’s performance varies with changes in a hyperparameter.
Q: What is early stopping?
Early stopping is a technique to stop training when a model’s performance on a validation set starts to degrade, preventing overfitting.
6. Algorithms and Techniques
Q: What is a decision tree?
A decision tree is a model that makes decisions by splitting data into branches based on feature values.
Q: What is an ensemble method?
Ensemble methods combine predictions from multiple models to improve accuracy and robustness.
Q: What is bagging in machine learning?
Bagging (Bootstrap Aggregating) is an ensemble technique that trains multiple models on random subsets of the data and averages their predictions.
Q: What is boosting in machine learning?
Boosting is an ensemble technique that trains models sequentially, each focusing on correcting the errors of the previous one.
Q: What is a random forest?
A random forest is an ensemble of decision trees, often used for classification and regression tasks.
Q: What is a support vector machine (SVM)?
SVM is a supervised learning algorithm used for classification and regression by finding the best boundary between classes.
Q: What is a k-nearest neighbors (KNN) algorithm?
KNN is a simple algorithm that classifies data points based on the majority class among their k closest neighbors.
Q: What is principal component analysis (PCA)?
PCA is a dimensionality reduction technique that transforms data into a set of uncorrelated variables called principal components.
Q: What is transfer learning?
Transfer learning is a technique where a model developed for one task is reused as the starting point for a model on a second task.
Q: What is a generative model?
A generative model is a type of model that can generate new data samples similar to the training data.
Q: What is a discriminative model?
A discriminative model is a type of model that distinguishes between different classes of data.
Q: What is zero-shot learning?
Zero-shot learning is the ability of a model to recognize objects or concepts it has never seen before.
Q: What is few-shot learning?
Few-shot learning is the ability of a model to learn from a very small amount of labeled data.
Q: What is continual learning?
Continual learning is the ability of a model to learn continuously from new data without forgetting previous knowledge.
7. Deep Learning Architectures
Q: What is a convolutional neural network (CNN)?
A CNN is a type of deep learning model especially effective for image and video recognition tasks.
Q: What is a recurrent neural network (RNN)?
An RNN is a neural network designed for sequential data, such as time series or natural language.
Q: What is a long short-term memory (LSTM) network?
LSTM is a type of RNN that can learn long-term dependencies in sequential data.
Q: What is a generative adversarial network (GAN)?
A GAN is a model with two neural networks (generator and discriminator) that compete to generate realistic data.
Q: What is a transformer model?
A transformer is a deep learning model architecture that uses self-attention mechanisms, widely used in NLP.
Q: What is attention mechanism in AI?
Attention allows models to focus on relevant parts of the input when making predictions.
Q: What is word embedding?
Word embedding is a technique to represent words as dense vectors capturing semantic meaning.
Q: What is BERT?
BERT is a transformer-based model for NLP tasks, pre-trained on large text corpora.
Q: What is GPT?
GPT (Generative Pre-trained Transformer) is a language model that generates human-like text.
8. Computer Vision Tasks
Q: What is image classification?
Image classification is the task of assigning a label to an image from a set of categories.
Q: What is object detection?
Object detection identifies and locates objects within an image.
Q: What is semantic segmentation?
Semantic segmentation classifies each pixel in an image into a category.
Q: What is anomaly detection?
Anomaly detection is the identification of rare items, events, or observations that differ significantly from the majority of the data.
9. Model Deployment and Production
Q: What is model evaluation?
Model evaluation is the process of assessing how well a trained model performs on unseen data.
Q: What is model deployment?
Model deployment is the process of integrating a trained model into a production environment to make real-world predictions.
Q: What is a pipeline in machine learning?
A pipeline is a sequence of data processing and modeling steps applied to data.
Q: What is a baseline model?
A baseline model is a simple model used as a reference point for evaluating more complex models.
Q: What is hyperparameter tuning?
Hyperparameter tuning is the process of finding the best configuration values for a machine learning model.
Q: What is AutoML?
AutoML is the process of automating the end-to-end process of applying machine learning to real-world problems.
10. Reinforcement Learning
Q: What is an agent in AI?
An agent is an entity that perceives its environment and takes actions to achieve a goal.
Q: What is an environment in reinforcement learning?
The environment is everything the agent interacts with and tries to learn from in reinforcement learning.
Q: What is a reward in reinforcement learning?
A reward is feedback from the environment that tells the agent how good its action was.
Q: What is a policy in reinforcement learning?
A policy is a strategy used by the agent to decide which action to take in a given state.
Q: What is Q-learning?
Q-learning is a reinforcement learning algorithm that learns the value of actions in states to maximize rewards.
Q: What is a Markov Decision Process (MDP)?
An MDP is a mathematical framework for modeling decision-making with states, actions, rewards, and transitions.
Q: What is reinforcement learning used for?
Reinforcement learning is used for training agents to make sequences of decisions, such as in robotics or game playing.
11. AI Ethics, Fairness, and Privacy
Q: What is AI bias?
AI bias occurs when an AI system produces prejudiced results due to biased data or algorithms.
Q: What is fairness in AI?
Fairness in AI means ensuring that AI systems make decisions impartially and do not discriminate against individuals or groups.
Q: What is data privacy in AI?
Data privacy in AI involves protecting personal or sensitive information used in AI systems.
Q: What is explainable AI (XAI)?
XAI refers to methods and techniques that make the results of AI models understandable to humans.
Q: What is model interpretability?
Model interpretability refers to how easily a human can understand the decisions or predictions made by a model.
12. AI Infrastructure and Trends
Q: What is federated learning?
Federated learning is a technique where multiple devices collaboratively train a model without sharing raw data.
Q: What is edge AI?
Edge AI refers to running AI algorithms locally on a hardware device rather than in a centralized data center.
Q: What is cloud AI?
Cloud AI refers to using cloud computing resources to train, deploy, and manage AI models.
13. AI History and Benchmarks
Q: What is the Turing Test?
The Turing Test is a test of a machine’s ability to exhibit intelligent behavior indistinguishable from that of a human.