A-Z of Machine Learning and Computer Vision Terms

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A
A
AI Agent
AI Agent
AI Assistants
AI Assistants
AI Assisted Labeling
AI Assisted Labeling
Active Learning
Active Learning
Algorithm
Algorithm
Anchor Boxes
Anchor Boxes
Anomaly Detection
Anomaly Detection
Artificial Intelligence (AI)
Artificial Intelligence (AI)
Attribute
Attribute
B
B
Backpropagation
Backpropagation
Bagging
Bagging
Batch
Batch
Batch Normalization
Batch Normalization
Bayesian Network
Bayesian Network
Bias
Bias
Big Data
Big Data
Binary Classification
Binary Classification
Blur
Blur
Boosting
Boosting
Bounding Box
Bounding Box
C
C
COCO
COCO
Calibration
Calibration
Calibration Curve
Calibration Curve
Canonical Correlation Analysis (CCA)
Canonical Correlation Analysis (CCA)
Case-Based Reasoning
Case-Based Reasoning
Chain of Thought (CoT)
Chain of Thought (CoT)
ChatGPT
ChatGPT
Chi-Squared Automatic Interaction Detection (CHAID)
Chi-Squared Automatic Interaction Detection (CHAID)
Class Boundary
Class Boundary
Class Boundary (Statistics & Machine Learning)
Class Boundary (Statistics & Machine Learning)
Class Imbalance
Class Imbalance
Clustering
Clustering
Collaborative Filtering
Collaborative Filtering
Computer Vision
Computer Vision
Computer Vision Model
Computer Vision Model
Concept Drift
Concept Drift
Conditional Random Field (CRF)
Conditional Random Field (CRF)
Confusion Matrix
Confusion Matrix
Constrained Clustering
Constrained Clustering
Contrastive Learning
Contrastive Learning
Convolutional Neural Network (CNN)
Convolutional Neural Network (CNN)
Convolutional Neural Networks (CNNs)
Convolutional Neural Networks (CNNs)
Cross-Validation
Cross-Validation
D
D
DICOM
DICOM
Data Approximation
Data Approximation
Data Augmentation
Data Augmentation
Data Drift
Data Drift
Data Error
Data Error
Data Mining
Data Mining
Data Operations
Data Operations
Data Pre-processing
Data Pre-processing
Data Quality
Data Quality
Dataset
Dataset
Decision Boundary
Decision Boundary
Decision List
Decision List
Decision Stump
Decision Stump
Decision Tree
Decision Tree
Deep Learning
Deep Learning
Deep Neural Networks
Deep Neural Networks
Dimensionality Reduction
Dimensionality Reduction
Dropout
Dropout
Dynamic and Event-Based Classifications
Dynamic and Event-Based Classifications
E
E
Edge Cases
Edge Cases
Edge Computing
Edge Computing
Edge Detection
Edge Detection
Elastic Net
Elastic Net
Embedding Spaces
Embedding Spaces
Ensemble Learning
Ensemble Learning
Epoch
Epoch
Expectation-Maximization Algorithm (EM)
Expectation-Maximization Algorithm (EM)
Extreme Learning Machine
Extreme Learning Machine
F
F
F1 Score
F1 Score
FP-Growth Algorithm
FP-Growth Algorithm
Factor Analysis
Factor Analysis
False Positive Rate
False Positive Rate
Feature
Feature
Feature Engineering
Feature Engineering
Feature Extraction
Feature Extraction
Feature Hashing
Feature Hashing
Feature Learning
Feature Learning
Feature Scaling
Feature Scaling
Feature Selection
Feature Selection
Feature Vector
Feature Vector
Few-shot Learning
Few-shot Learning
Fisher’s Linear Discriminant
Fisher’s Linear Discriminant
Foundation Models
Foundation Models
Frame Rate
Frame Rate
Frames Per Second (FPS)
Frames Per Second (FPS)
Fully Connected Layer
Fully Connected Layer
Fuzzy Logic
Fuzzy Logic
G
G
Generative Adversarial Network (GAN)
Generative Adversarial Network (GAN)
Generative Adversarial Networks
Generative Adversarial Networks
Generative Pre-Trained Transformer
Generative Pre-Trained Transformer
R

Responsible AI

Responsible AI refers to the broad set of practices, principles, and governance frameworks aimed at ensuring AI systems are developed and deployed in an ethical, transparent, and accountable manner​.It encompasses making sure that AI technologies are fair (avoiding unjust bias or discrimination against any group), inclusive (considering diverse users and impacts), and do not cause harm deliberately or inadvertently​.Key pillars of Responsible AI include: Accountability – organizations and developers should be accountable for the outcomes of their AI systems, implementing oversight and audit mechanisms; Transparency – providing explainability or at least insight into how AI decisions are made, and being open about when AI is in use; Fairness – taking steps to detect and mitigate bias in data or algorithms so that decisions do not systematically disadvantage certain groups; Privacy – ensuring AI systems comply with data privacy laws and respect individuals’ data rights (minimizing personal data usage, using techniques like anonymization or federated learning); and Safety/Security – designing AI to be robust against adversarial attacks and to fail safely. For example, a Responsible AI approach in a lending model would involve checking that the model’s approvals do not discriminate by race or gender (fairness), explaining to rejected applicants the main factors for denial (transparency), and having human review for edge cases (accountability). The term also extends to societal and legal dimensions, encouraging engagement with stakeholders and alignment with human values. Many companies and institutions have Responsible AI guidelines to guide their AI development. In essence, Responsible AI is about trustworthy AI: making sure AI systems are worthy of trust and benefit society, while minimizing risks and negative consequences​.

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