- Activation Function
- Confusion Matrix
- Convolutional Neural Networks
- Forward Propagation
- Generative Adversarial Network
- Gradient Descent
- Linear Regression
- Logistic Regression
- Machine Learning Algorithms
- Multilayer Perceptron
- Naive Bayes
- Neural Networking and Deep Learning
- RuleFit
- Stack Ensemble
- Word2Vec
- XGBoost
- Attention Mechanism
- BERT
- Binary Classification
- Classify Token ([CLS])
- Conversational Response Generation
- GLUE (General Language Understanding Evaluation)
- GPT (Generative Pre-Trained Transformers)
- Language Modeling
- Layer Normalization
- Mask Token ([MASK])
- Probability Distribution
- Probing Classifiers
- SQuAD (Stanford Question Answering Dataset)
- Self-attention
- Separate token ([SEP])
- Sequence-to-sequence Language Generation
- Sequential Text Spans
- Text Classification
- Text Generation
- Transformer Architecture
- WordPiece
- AUC-ROC
- Analytical Review
- Autoencoders
- Bias-Variance Tradeoff
- Decision Optimization
- Explanatory Variables
- Exponential Smoothing
- Level of Granularity
- Long Short-Term Memory
- Loss Function
- Model Management
- Precision and Recall
- Predictive Learning
- ROC Curve
- Recommendation system
- Stochastic Gradient Descent
- Target Leakage
- Target Variable
- Underwriting
A
C
D
G
L
M
N
P
R
S
T
X
- Activation Function
- Confusion Matrix
- Convolutional Neural Networks
- Forward Propagation
- Generative Adversarial Network
- Gradient Descent
- Linear Regression
- Logistic Regression
- Machine Learning Algorithms
- Multilayer Perceptron
- Naive Bayes
- Neural Networking and Deep Learning
- RuleFit
- Stack Ensemble
- Word2Vec
- XGBoost
- Attention Mechanism
- BERT
- Binary Classification
- Classify Token ([CLS])
- Conversational Response Generation
- GLUE (General Language Understanding Evaluation)
- GPT (Generative Pre-Trained Transformers)
- Language Modeling
- Layer Normalization
- Mask Token ([MASK])
- Probability Distribution
- Probing Classifiers
- SQuAD (Stanford Question Answering Dataset)
- Self-attention
- Separate token ([SEP])
- Sequence-to-sequence Language Generation
- Sequential Text Spans
- Text Classification
- Text Generation
- Transformer Architecture
- WordPiece
- AUC-ROC
- Analytical Review
- Autoencoders
- Bias-Variance Tradeoff
- Decision Optimization
- Explanatory Variables
- Exponential Smoothing
- Level of Granularity
- Long Short-Term Memory
- Loss Function
- Model Management
- Precision and Recall
- Predictive Learning
- ROC Curve
- Recommendation system
- Stochastic Gradient Descent
- Target Leakage
- Target Variable
- Underwriting
What is an AI model?
An artificial intelligence (AI) model is a program that analyzes datasets to find patterns and make predictions. AI modeling is the development and implementation of the AI model. AI modeling replicates human intelligence and is most effective when it receives multiple data points. Organizational implementation of an AI model can accurately solve complex issues while keeping operational cost low. The initial steps to AI modeling include:
Modeling
After gathering quality data, the user creates an AI model that replicates human intelligence and decision making.
Training
The user provides the AI model quality datasets. The data has three processing phases: training, validation, and testing. Throughout the three phases, the AI model interprets the data to draw conclusions.
Inference
Before this step, the AI model needs to be extensively trained. Once trained, the user provides a live dataset and launches the model for practical usage.
AI models vs. machine learning models
AI models are designed to replicate human intelligence using algorithms, whereas machine learning (ML) is designed to teach machines to operate and optimize themselves. With ML, the machine will learn from previous decisions to improve its efficiency over time. While all ML models are AI models, not every AI model is an ML model.
Learn more about ML models
Popular AI models
Each AI model works differently while serving a specific purpose. There is an overlap between ML and AI due to all ML models being AI models. Users may combine models to achieve a target function. These are ten popular models among AI and ML systems:
Deep Neural Networks (DNN)
DNN is a subset of ML. DNN imitates the human brain with multiple layers for input variables to pass through.
Linear Regression
Linear regression, a common ML type, searches for a correlation between input and output variables.
Logistic Regression
Logistic regression is a subset of ML which estimates the outcome and predicts one of two values for dependent variables.
Learning Vector Quantization (LVQ)
LVQ groups similar input values into data points, then into prototypes.
K-nearest Neighbors (KNN)
KNN is an ML algorithm that groups input values to be graphed near one another.
Linear Discriminant Analysis (LDA)
LDA is a subsection of logistic regression. It is most frequently used when more than two values need to be defined in the output.
Decision Trees
Decision trees are a supervised ML algorithm considered to be one of the most efficient AI models. Decision trees solve regression and classification problems using previous datasets.
Random Forest
A type of ML that uses multiple decision trees to produce accurate decisions.
Support Vector Machines
A common ML model that accurately categorizes information with limited data..
Naive Bayes
An ML that assumes the input data values are unrelated.
Future of AI modeling
AI modeling provides organizations a means of efficient decision making. For an organization to maximize benefits from AI modeling, the model requires extensive AI training which will produce complete automation. Effective AI modeling has already assisted organizations across several fields.
Industries that have seen the largest growth with AI modeling are:
Telecommunications
AI Model Resources
H2O Driverless AI: Model Deployment