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{{#seo:
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|title=PRIMO.ai
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|keywords=Game, design, ChatGPT, artificial, intelligence, machine, learning, NLP, NLG, NLC, NLU, models, data, singularity, moonshot, Sentience, AGI, Emergence, Moonshot, Explainable, TensorFlow, Google, Nvidia, Microsoft, Azure, Amazon, AWS, Hugging Face, OpenAI, Tensorflow, OpenAI, Google, Nvidia, Microsoft, Azure, Amazon, AWS, Meta, LLM, metaverse, assistants, agents, digital twin, IoT, Transhumanism, Immersive Reality, Generative AI, Conversational AI, Perplexity, Bing, You, Gemini, Ernie, prompt Engineering LangChain, Video/Image, Vision, End-to-End Speech, Synthesize Speech, Speech Recognition, Stanford, MIT |description=Helpful resources for your journey with artificial intelligence; videos, articles, techniques, courses, profiles, and tools 
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On {{LOCALDAYNAME}} {{LOCALMONTHNAME}} {{LOCALDAY}}, {{LOCALYEAR}} PRIMO.ai has {{NUMBEROFPAGES}} pages  
 
On {{LOCALDAYNAME}} {{LOCALMONTHNAME}} {{LOCALDAY}}, {{LOCALYEAR}} PRIMO.ai has {{NUMBEROFPAGES}} pages  
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<b>Primo.ai</b> provides links to articles and videos on Artificial intelligence (AI) concepts and techniques such as [[Generative AI]], [[Natural Language Processing (NLP)]], [[Vision|Computer Vision]], [[Deep Learning]], [[Reinforcement Learning (RL)]], and [[Quantum|Quantum Technology]] -- providing [[Perspective|perspectives]] for individuals who are passionate about learning and developing new skills.   
  
 
= Getting Started =
 
= Getting Started =
== Overview ==
+
* [[How do I leverage Artificial Intelligence (AI)?]]
* [[How do I leverage AI?]]
+
* [[What is Artificial Intelligence (AI)?]]
* [[Courses]]
+
** [[History of Artificial Intelligence (AI)]]  
* [[Reading Material & Glossary]]
+
** [[Courses & Certifications]]
 
+
** [[Reading Material & Glossary]]
== Background ==
+
** [[Podcasts]]
* [[What is AI?]]
 
* [[History of AI]]
 
 
* [[Current State]]
 
* [[Current State]]
 +
* [[Life~Meaning#Can_Meaning_Exist_in_Artificial_Systems|Can ''Meaning'' Exist in Artificial Systems?]] ... Explore the condition that separates simulation from ''Meaning''
  
== AI Breakthroughs ==
+
=== AI Fun ===
* [[Capabilities]]
+
* Try [[ChatGPT]] | [[OpenAI]]
* [[Case Studies]]
+
* Create your own [[music]] with [https://www.udio.com/ Udio]
* [http://patft.uspto.gov/netacgi/nph-Parser?Sect1=PTO2&Sect2=HITOFF&u=%2Fnetahtml%2FPTO%2Fsearch-adv.htm&r=0&p=1&f=S&l=50&Query=%28%28abst%2F%28intelligence+and+%28artificial+or+machine%29%29%29+or+%28aclm%2F%28intelligence+and+%28artificial+or+machine%29%29%29%29+and++%28ISD%2F1%2F1%2F2014-%3E1%2F1%2F2050%29&d=PTXT AI Patents after 2013]
+
* Try [[Stability_AI#DreamStudio | DreamStudio]] | Stability AI ... text-to-image [[Diffusion|diffusion]] model capable of generating photo-realistic images
 
+
* [https://experiments.withgoogle.com/collection/ai Google AI Experiments]
== AI Fun ==
+
* [https://playground.tensorflow.org TensorFlow Playground] [[TensorFlow Playground|...learn more]]
* [http://experiments.withgoogle.com/collection/ai Google AI Experiments]
+
* [https://js.tensorflow.org/ TensorFlow.js Demos]
* [http://playground.tensorflow.org TensorFlow Playground]
+
* [[Google AIY Projects Program]]  - Do-it-yourself artificial intelligence
* [http://js.tensorflow.org/ TensorFlow.js Demos]
+
* [https://www.nvidia.com/en-us/research/ai-playground/ NVIDIA Playground]
* [http://aiyprojects.withgoogle.com/ Do-it-yourself artificial intelligence | AIY]
 
 
* [[Competitions]]
 
* [[Competitions]]
 +
* [https://colab.research.google.com/github/nickwalton/AIDungeon/blob/master/AIDungeon_2.ipynb AI Dungeon 2] AI generated text adventure  ... [[Gaming]] ... [[Game Design | Design]]
  
== How to... ==
+
<i>.. more [[Natural Language Processing (NLP)]] fun...</i>
*[[AI Solver]]
+
* [https://corenlp.run/ CoreNLP - see NLP parsing techniques by pasting your text | Stanford]
*[[Strategy & Tactics]]
+
* [https://nlp.stanford.edu:8080/sentiment/rntnDemo.html Sentiment Treebank Analysis Demo]
*[[Checklists]]
 
  
== Forward Thinking ==
+
=== How to... ===
* [[Moonshots]]
+
* [[Strategy & Tactics]] for developing AI investments
* [[Journey to Singularity]]
+
* [[AI Solver]] for determining possible algorithms for your needs
* [[Creatives]]
+
* [[Evaluation]]   ... Prompts for assessing AI projects
 +
* [[Checklists]] for ensuring consistency and completeness
  
= Datasets & Information Analysis =
+
=== Forward Thinking ===
* [[Datasets]]
+
* [[Moonshots]]   ... a project or goal that aims to achieve a major breakthrough in artificial intelligence that has the potential to transform society or address significant global challenges
* [[Batch Norm(alization) & Standardization]]
+
* [[Artificial General Intelligence (AGI) to Singularity]] ... a hypothetical future event in which artificial intelligence (AI) surpasses human intelligence in a way that fundamentally changes human society and civilization
* [[Data Preprocessing & Feature Exploration/Learning]]
+
* [https://www.uspto.gov/initiatives/artificial-intelligence Artificial Intelligence | United States Patent and Trademark Office] --> [https://patft.uspto.gov/netacgi/nph-Parser?Sect1=PTO2&Sect2=HITOFF&u=%2Fnetahtml%2FPTO%2Fsearch-adv.htm&r=0&p=1&f=S&l=50&Query=%28%28abst%2F%28intelligence+and+%28artificial+or+machine%29%29%29+or+%28aclm%2F%28intelligence+and+%28artificial+or+machine%29%29%29%29+and++%28ISD%2F1%2F1%2F2014-%3E1%2F1%2F2050%29&d=PTXT AI Patents after 2013]
* [[Hyperparameters]]
+
* [[Creatives]]   ... individuals who have significantly contributed to the development, advancement, or popularization of AI
* [[Data Augmentation]]
+
* [[Books, Radio & Movies - Exploring Possibilities]]
* [[Visualization]]
 
* [[Master Data Management  (MDM) / Feature Store / Data Lineage / Data Catalog]]
 
  
= Algorithms =
+
<hr>
* [[About Algorithms & Neural Network Models]]
 
* [http://www.youtube.com/user/IntegrateBiz/playlists Intersection of Artificial Intelligence and Architecture | Raj Ramesh]
 
  
== Discriminative ==
+
= Information Analysis =
 +
* [[Context]] ... the next AI frontier
 +
* [[Data Science]] ... [[Data Governance|Governance]] ... [[Data Preprocessing|Preprocessing]] ... [[Feature Exploration/Learning|Exploration]] ... [[Data Interoperability|Interoperability]] ... [[Algorithm Administration#Master Data Management (MDM)|Master Data Management (MDM)]] ... [[Bias and Variances]] ... [[Benchmarks]] ... [[Datasets]]
 +
* [[Data Quality]] ...[[AI Verification and Validation|validity]], [[Evaluation - Measures#Accuracy|accuracy]], [[Data Quality#Data Cleaning|cleaning]], [[Data Quality#Data Completeness|completeness]], [[Data Quality#Data Consistency|consistency]], [[Data Quality#Data Encoding|encoding]], [[Data Quality#Zero Padding|padding]], [[Data Quality#Data Augmentation, Data Labeling, and Auto-Tagging|augmentation, labeling, auto-tagging]], [[Data Quality#Batch Norm(alization) & Standardization| normalization, standardization]], and [[Data Quality#Imbalanced Data|imbalanced data]]
 +
* [[Natural Language Processing (NLP)#Managed Vocabularies |Managed Vocabularies]]
 +
* [[Excel]] ... [[LangChain#Documents|Documents]] ... [[Database|Database; Vector & Relational]] ... [[Graph]] ... [[LlamaIndex]]
 +
* [[Visualization]]
 +
* [[Analytics]] 
 +
* [[Algorithm Administration#Hyperparameter|Hyperparameter]]s
  
=== [[Supervised]] ===
+
= <span id="Algorithms"></span>[[Algorithms]] =
- labeled (desired solution) data is fed into the algorithm. The training data set has inputs as well as the desired output. During the training session, the model will adjust its variables to map inputs to the corresponding output.
+
* [https://huggingface.co/models Models | Hugging Face] ... click on Sort: Trending
 +
* [[Algorithms]]; the engines of AI
 +
* [[Model Zoos]]
 +
* [[Graphical Tools for Modeling AI Components]]
  
* ...predict values 
+
== [[Generative AI| Generative AI (Gen AI)]] ==
**[[Time Series Forecasting Methods - Statistical]]
+
The ability to generate new content or solutions, such as [[Writing/Publishing|writing]] or designing new products, using techniques such as [[Generative Adversarial Network (GAN)]] or neural [[Style Transfer|style transfer]].
**[[Time Series Forecasting - Deep Learning]]
 
**[[Linear Regression]]
 
**[[Bayesian Linear Regression]]
 
**[[Support Vector Regression (SVR)]]
 
**[[Ordinal Regression]]
 
**[[Poisson Regression]]
 
**[[Tree-based...]]
 
***[[Fast Forest Quantile Regression]]
 
***[[Decision Forest Regression]]
 
**[[Boosted Decision Tree Regression]]
 
**[[General Regression Neural Network (GRNN)]]
 
**[[One-class Support Vector Machine (SVM)]]
 
  
* Classification [[...predict categories]]
+
* [[Conversational AI]] ... [[ChatGPT]] | [[OpenAI]] ... [[Bing/Copilot]] | [[Microsoft]] ... [[Gemini]] | [[Google]] ... [[Claude]] | [[Anthropic]] ... [[Perplexity]] ... [[You]] ... [[phind]] ... [[Grok]] | [https://x.ai/ xAI] ... [[Groq]] ... [[Ernie]] | [[Baidu]] ... [[DeepSeek]]
**[[Perceptron (P)]] ...and Multi-layer Perceptron (MLP)
+
** [[Prompt Engineering (PE)]] ...[[Prompt Engineering (PE)#PromptBase|PromptBase]] ... [[Prompt Injection Attack]]
***[[Feed Forward Neural Network (FF or FFNN)]]
+
** [[Generative AI for Business Analysis]]
***[[Artificial Neural Network (ANN)]]
+
* [[Large Language Model (LLM)#Multimodal|Multimodal Language Model]]s
***[[Deep Neural Network (DNN)]]
+
* [[Video/Image]]
**[[Support Vector Machine (SVM)]]
+
* [[Synthesize Speech]]
**[[K-Nearest Neighbors (KNN)]]
+
* [[Game Development with Generative AI]] ... [[Gaming]] ... [[Game-Based Learning (GBL)]] ... [[Games - Security|Security]] ... [[Game Development with Generative AI|Generative AI]] ... [[Metaverse#Games - Metaverse|Games - Metaverse]] ... [[Games - Quantum Theme|Quantum]] ... [[Game Theory]] ... [[Game Design | Design]]
**[[Logistic Regression]]
 
**[[Naive Bayes]]
 
**[[Tree-based...]]
 
***[[Boosted Decision Tree]]
 
***[[Random Forest (or) Random Decision Forest]]
 
***[[Decision Jungle]]
 
  
* Other
+
== Predict values - [[Regression]] ==
**[[Hopfield Network (HN)]]
+
Analyze large amounts of data and make predictions or recommendations based on that data.
**[[Energy-based Model (EBN)]]
 
  
==== Convolutional; Image & Object Recognition ====
+
* [[Linear Regression]]
*[[(Deep) Convolutional Neural Network (DCNN/CNN)]]
+
* [[Ridge Regression]]
*[[(Deep) Residual Network (DRN) - ResNet]]
+
* [[Lasso Regression]]
**[[ResNet-50]]
+
* [[Elastic Net Regression]]
 +
* [[Bayes#Bayesian Linear Regression|Bayesian Linear Regression]]
 +
* [[Bayes#Bayesian Deep Learning (BDL)|Bayesian Deep Learning (BDL)]]
 +
* [[Logistic Regression (LR)]]
 +
* [[Support Vector Regression (SVR)]]
 +
* [[Ordinal Regression]]
 +
* [[Poisson Regression]]
 +
* Tree-based...
 +
** [[Fast Forest Quantile Regression]]
 +
** [[Decision Forest Regression]]
 +
* [[General Regression Neural Network (GRNN)]]
 +
* [[One-class Support Vector Machine (SVM)]]
 +
* [[Gradient Boosting Machine (GBM)]]
  
==== [[Graph Convolutional Network (GCN)]] ====
+
== [[Classification]] [[...predict categories]] ==
- includes social networks, sensor networks, the entire Internet, 3D Objects (point cloud)
+
* <span id="Supervised"></span>[[Supervised]]
 +
** Naive [[Bayes]]
 +
** [[K-Nearest Neighbors (KNN)]]
 +
** [[Perceptron (P)]] ...and Multi-layer Perceptron (MLP)
 +
** [[Feed Forward Neural Network (FF or FFNN)]]
 +
** [[Neural Network]]
 +
*** [[Deep Learning]] - [[Neural Network#Deep Neural Network (DNN)|Deep Neural Network (DNN)]]
 +
** Kernel Approximation - [[Kernel Trick]]
 +
*** [[Support Vector Machine (SVM)]]
 +
** [[Logistic Regression (LR)]]
 +
*** [[Softmax]] Regression; Multinominal Logistic Regression
 +
** Tree-based...
 +
*** [[(Boosted) Decision Tree]]
 +
*** [[Random Forest (or) Random Decision Forest]]
 +
*** [[Decision Jungle]]
 +
** [[Apriori, Frequent Pattern (FP) Growth, Association Rules/Analysis]]
 +
** [[Markov Model (Chain, Discrete Time, Continuous Time, Hidden)]]
 +
* <span id="Unsupervised"></span>[[Unsupervised]]
 +
** [[Radial Basis Function Network (RBFN)]]
 +
** <span id="Self-Supervised"></span>[[Self-Supervised]]
 +
*** [[Autoencoder (AE) / Encoder-Decoder]]
 +
*** [[(Stacked) Denoising Autoencoder (DAE)]]
 +
*** [[Sparse Autoencoder (SAE)]]
  
* [[Point Cloud Convolutional Neural Network (CNN)]]
+
== [[Recommendation]] ==
 +
* [[Alternating Least Squares (ALS)]]
 +
* [[Matrix Factorization]]
  
==== Deconvolutional ====
+
== [[Clustering]] - Continuous - [[Dimensional Reduction]] ==
*[[Deconvolutional Neural Network (DN) / Inverse Graphics Network (IGN)]]
+
* [[Singular Value Decomposition (SVD)]]
 +
* [[Principal Component Analysis (PCA)]]
 +
* [[K-Means]]
 +
* [[Fuzzy C-Means (FCM)]]
 +
* [[K-Modes]]
 +
* [[Association Rule Learning]]
 +
* [[Mean-Shift Clustering]]
 +
* [[Density-Based Spatial Clustering of Applications with Noise (DBSCAN)]]
 +
* [[Expectation–Maximization (EM) Clustering using Gaussian Mixture Models (GMM)]]
 +
* [[Restricted Boltzmann Machine (RBM)]]
 +
* [[Variational Autoencoder (VAE)]]
 +
* [[Biclustering]]
 +
* [https://en.wikipedia.org/wiki/Multidimensional_scaling Multidimensional Scaling (MDS)]
  
 +
=== [[Hierarchical]] ===
 +
* [[Hierarchical Cluster Analysis (HCA)]]
 +
* [[Hierarchical Clustering;  Agglomerative (HAC) & Divisive (HDC)]]
 +
* [[Hierarchical Temporal Memory (HTM)]] Time
 +
* [[Mixture Models; Gaussian]]
  
=== [[Unsupervised]] ===
+
=== Convolutional ===
- a probability distribution over a set of classes for each input sample. Unlabeled data is classified as (1) conditional probability of the target Y, or (2) conditional probability of the observable X given a target Y
+
* [[(Deep) Convolutional Neural Network (DCNN/CNN)]]
 +
* [[(Deep) Residual Network (DRN) - ResNet]]
 +
** [[ResNet-50]]
  
* Classification
+
=== Deconvolutional ===
**[[Radial Basis Function Network (RBFN)]]
+
*[[Deconvolutional Neural Network (DN) / Inverse Graphics Network (IGN)]]
  
* Categorical
+
== Graph ==
**[[Apriori, Frequent Pattern (FP) Growth, Association Rules/Analysis]]
+
- includes social networks, sensor networks, the entire Internet, 3D Objects ([[Point Cloud]])
**[[Markov Model (Chain, Discrete Time, Continuous Tme, Hidden)]]
+
* [[Graph Convolutional Network (GCN), Graph Neural Networks (Graph Nets), Geometric Deep Learning]]
 +
* [[Point Cloud]]
 +
* [https://techxplore.com/news/2019-04-hierarchical-rnn-based-scene-graphs-images.html A hierarchical RNN-based model to predict scene graphs for images]
 +
* [https://techxplore.com/news/2019-01-multi-granularity-framework-social-recognition.html A multi-granularity reasoning framework for social relation recognition]
 +
* [[Neural Structured Learning (NSL)]]
  
* [[Clustering]] - Continuous - Dimensional Reduction
+
== [[Time#Sequence/Time-based Algorithms|Sequence/Time-based Algorithms]] ==
**[[Restricted Boltzmann Machine (RBM)]]
+
* [[Mamba]]
**[[Variational Autoencoder (VAE)]]
 
**[[Singular Value Decomposition (SVD)]]
 
**[[Principal Component Analysis (PCA)]]
 
**[[K-Means]]
 
**[[Mean-Shift Clustering]]
 
**[[Density-Based Spatial Clustering of Applications with Noise (DBSCAN)]]
 
**[[Expectation–Maximization (EM) Clustering using Gaussian Mixture Models (GMM)]]
 
  
==== [[Hierarchical]] ====
+
== Competitive  ==
 
+
* [[Generative Adversarial Network (GAN)]]
*[[Hierarchical Cluster Analysis (HCA)]]
+
* [[Image-to-Image Translation]]
*[[Hierarchical Clustering;  Agglomerative (HAC) & Divisive (HDC)]]
 
*[[Hierarchical Temporal Memory (HTM)]] Time
 
 
 
==== Unsupervised: Non-Probabilistic; e.g. Deterministic  ====
 
- unlabeled data is fed into the algorithm with the algorithm seperating the feature space and return the class associated with the space where a sample originates from.
 
 
 
*[[Autoencoder (AE) / Encoder-Decoder]]
 
*[[(Stacked) Denoising Autoencoder (DAE)]]
 
*[[Sparse Autoencoder (SAE)]]
 
 
 
== [[Generative]] ==
 
 
 
* [[Generative Query Network (GQN)]]
 
 
* [[Conditional Adversarial Architecture (CAA)]]
 
* [[Conditional Adversarial Architecture (CAA)]]
 
=== Competitive  ===
 
* [[Generative Adversarial Network (GAN)]]
 
 
* [[Kohonen Network (KN)/Self Organizing Maps (SOM)]]
 
* [[Kohonen Network (KN)/Self Organizing Maps (SOM)]]
 +
* [[Quantum Generative Adversarial Learning (QuGAN - QGAN)]]
  
=== [[Semi-Supervised]] ===
+
== <span id="Semi-Supervised"></span>[[Semi-Supervised]] ==
 +
In many practical situations, the cost to label is quite high, since it requires skilled human experts to do that. So, in the absence of labels in the majority of the observations but present in few, semi-supervised algorithms are the best candidates for the model building. These methods exploit the idea that even though the group memberships of the unlabeled data are unknown, this data carries important information about the group parameters. Reference: [[Learning Techniques]]
 
* [[Semi-Supervised Learning with Generative Adversarial Network (SSL-GAN)]]
 
* [[Semi-Supervised Learning with Generative Adversarial Network (SSL-GAN)]]
 
* [[Context-Conditional Generative Adversarial Network (CC-GAN)]]
 
* [[Context-Conditional Generative Adversarial Network (CC-GAN)]]
  
== [[Reinforcement Learning (RL)]]  ==
+
== <span id="Natural Language"></span>Natural Language  ==
- an algorithm receives a delayed reward in the next time step to evaluate its previous action. Therefore based on those decisions, the algorithm will train itself based on the success/error of output. In combination with Neural Networks it is capable of solving more complex tasks.
+
 
 +
* [[Natural Language Processing (NLP)]] involves speech recognition, (speech) translation, understanding (semantic parsing) complete sentences, understanding synonyms of matching words, and sentiment analysis
 +
** [[Natural Language Generation (NLG)]]
 +
** [[Natural Language Classification (NLC)]] 
 +
** [[Large Language Model (LLM)]]   
 +
** [[Natural Language Tools & Services]]
 +
*** [[Embedding]]
 +
*** [[Fine-tuning]]
 +
*** [[Agents#AI-Powered Search|Search]] (where results are ranked by relevance to a query string)
 +
*** [[Clustering]] (where text strings are grouped by similarity)
 +
*** [[Recommendation]]s (where items with related text strings are recommended)
 +
*** [[Anomaly Detection]] (where outliers with little relatedness are identified)
 +
*** [[Classification]] (where text strings are classified by their most similar label)
 +
*** [[Dimensional Reduction]]
 +
*** [[...find outliers]] ... diversity measurement (where similarity distributions are analyzed)
  
 +
== <span id="Reinforcement Learning (RL)"></span>[[Reinforcement Learning (RL)]]  ==
 +
an algorithm receives a delayed reward in the next time step to evaluate its previous action. Therefore based on those decisions, the algorithm will train itself based on the success/error of output. In combination with Neural Networks it is capable of solving more complex tasks. [[Policy Gradient (PG)]] methods are a type of reinforcement learning techniques that rely upon optimizing parametrized policies with respect to the expected return (long-term cumulative reward) by [[Gradient Descent Optimization & Challenges |gradient descent]].
 +
 +
* [[Monte Carlo]] (MC) Method - Model Free Reinforcement Learning
 
* [[Markov Decision Process (MDP)]]
 
* [[Markov Decision Process (MDP)]]
* [[Deep Reinforcement Learning (DRL)]]
 
* [[Deep Q Learning (DQN)]]
 
* [[Neural Coreference]]
 
 
* [[State-Action-Reward-State-Action (SARSA)]]
 
* [[State-Action-Reward-State-Action (SARSA)]]
* [[Deep Deterministic Policy Gradient (DDPG)]]
+
* [[Q Learning]]
* [[Trust Region Policy Optimization (TRPO)]]
+
** [[Deep Q Network (DQN)]]
* [[Proximal Policy Optimization (PPO)]]
+
* [[Deep Reinforcement Learning (DRL)]] DeepRL
 +
* [[Distributed Deep Reinforcement Learning (DDRL)]]
 +
* [[Evolutionary Computation / Genetic Algorithms]]
 +
* [[Actor Critic]]
 +
** [[Asynchronous Advantage Actor Critic (A3C)]]
 +
** [[Advanced Actor Critic (A2C)]]
 +
** [[Lifelong Latent Actor-Critic (LILAC)]]
 
* [[Hierarchical Reinforcement Learning (HRL)]]
 
* [[Hierarchical Reinforcement Learning (HRL)]]
 +
* [[Reinforcement Learning (RL) from Human Feedback (RLHF)]]
  
== Sequence ==
+
== [[Neuro-Symbolic]] ==
 
+
the “connectionists” seek to construct artificial [[Neural Network]]s, inspired by biology, to learn about the world, while the “symbolists” seek to build intelligent machines by coding in logical rules and representations of the world. Neuro-Symbolic combines the fruits of group.
* [[Sequence to Sequence (Seq2Seq)]]
 
* [[Neural Turing Machine]]
 
* [[Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Recurrent Neural Network (RNN)]]
 
** [[Bidirectional Long Short-Term Memory (BI-LSTM)]]
 
** [[Bidirectional Long Short-Term Memory (BI-LSTM) with Attention Mechanism]]
 
** [[Average-Stochastic Gradient Descent (SGD) Weight-Dropped LSTM (AWD-LSTM)]]
 
* [[(Tree) Recursive Neural (Tensor) Network (RNTN)]]
 
 
 
=== Time ===
 
 
 
* [[Temporal Difference (TD) Learning]]
 
  
 +
* [[Neuro-Symbolic]] ... [[Symbolic Artificial Intelligence]]
 +
* [[Neuro-Symbolic Concept Learner (NS-CL)]]
  
 +
== Other ==
 +
* [[Hopfield Network (HN)]]
 +
* [[Energy-based Model (EBN)]] ... non-normalized probabilistic model
 +
* [[Generative Query Network (GQN)]]
  
 
= Techniques =
 
= Techniques =
== Foundation ==
+
* [[Math for Intelligence]] ... [[Finding Paul Revere]]
* [[Math for Intelligence]]
+
* [https://www.arxiv-sanity.com/ Arxiv Sanity Preserver] to accelerate research
* [http://www.arxiv-sanity.com/ Arxiv Sanity Preserver] to accelerate research
+
* [[Theory-free Science]]
  
== Methods ==
+
=== Methods & Concepts ===
 
* [[Backpropagation]]
 
* [[Backpropagation]]
* [[Gradient Boosting Algorithms]]
+
* [[Gradient Descent Optimization & Challenges|Stochastic Gradient Descent]]
 +
* [[Gradient Descent Optimization & Challenges#Learning Rate Decay|Learning Rate Decay]]
 +
* [[Pooling / Sub-sampling: Max, Mean|Max Pooling]]
 +
* [[Data Quality#Batch Norm(alization) & Standardization|Batch Normalization]]
 
* [[Overfitting Challenge]]
 
* [[Overfitting Challenge]]
 
** [[Regularization]]
 
** [[Regularization]]
 
** [[Dropout]]
 
** [[Dropout]]
* [[Softmax]]
+
* [[Manifold Hypothesis]] and [[Dimensional Reduction]]; identification - what influences an observed outcome
* [[Dimensional Reduction Algorithms]]
 
** [[Principal Component Analysis (PCA)]]
 
** [[T-Distributed Stochastic Neighbor Embedding (t-SNE)]]
 
** [[Pooling / Sub-sampling: Max, Mean]]
 
** [[Zero Padding]]
 
 
* [[Activation Functions]]
 
* [[Activation Functions]]
* [[Attention Mechanism/Model]]
+
* [[Memory]]
 +
** [[Memory Networks]]
 
* [[Multiclassifiers; Ensembles and Hybrids; Bagging, Boosting, and Stacking]]
 
* [[Multiclassifiers; Ensembles and Hybrids; Bagging, Boosting, and Stacking]]
* [[Object Detection; Faster R-CNN, YOLO, SSD]]
+
* [[Optimizer]]s
* [[Optimizers]]
 
 
** [[Optimization Methods]]
 
** [[Optimization Methods]]
 
** [[Objective vs. Cost vs. Loss vs. Error Function]]
 
** [[Objective vs. Cost vs. Loss vs. Error Function]]
 
** [[Gradient Descent Optimization & Challenges]]
 
** [[Gradient Descent Optimization & Challenges]]
 
** [[Parameter Initialization]]
 
** [[Parameter Initialization]]
** [http://www.kdnuggets.com/2018/04/right-metric-evaluating-machine-learning-models-1.html Choosing the Right Metric for Evaluating Machine Learning Models]
+
* [[Neural Network Pruning]]
** [[Approach to Bias and Variances]]
+
* [[Repositories & Other Algorithms]]
** [[Evaluation Measures - Classification Performance]]
+
* [https://dawn.cs.stanford.edu/benchmark/index.html DAWNBench] An End-to-End Deep Learning Benchmark and Competition
* [[Few Shot Learning]]
+
* [[Knowledge Graphs]]
* [[Multitask Learning]]
+
* [[Quantization]]
 +
* [[Causation vs. Correlation]]
 +
* [[Deep Features]]
 +
* [[Local Features]]
 +
* [[Loop#Unintended Feedback Loop|Unintended Feedback Loop]]
 +
* [[Backtesting]]
 +
* [[Digital Twin]]
 +
 
 +
==== [[Policy]] ====
 +
* [[Policy]]  ... [[Policy vs Plan]] ... [[Constitutional AI]] ... [[Trust Region Policy Optimization (TRPO)]] ... [[Policy Gradient (PG)]] ... [[Proximal Policy Optimization (PPO)]]
 +
 
 +
=== <span id="Learning Techniques"></span>[[Learning Techniques]] ===
 +
* [[In-Context Learning (ICL)]] ... [[Context]]
 +
* [[Out-of-Distribution (OOD) Generalization]]
 +
* [[PRIMO.ai#Supervised|Supervised Learning]] ... [[PRIMO.ai#Semi-Supervised|Semi-Supervised Learning]] ... [[PRIMO.ai#Self-Supervised|Self-Supervised Learning]] ... [[PRIMO.ai#Unsupervised|Unsupervised Learning]]
 +
* [[PRIMO.ai#Reinforcement Learning (RL)|Reinforcement Learning (RL)]]
 +
* [[Reinforcement Learning (RL) from Human Feedback (RLHF)]]
 +
* [[Deep Learning]]
 
* [[Transfer Learning]] a model trained on one task is re-purposed on a second related task
 
* [[Transfer Learning]] a model trained on one task is re-purposed on a second related task
 
** [[Text Transfer Learning]]  
 
** [[Text Transfer Learning]]  
 
** [[Image/Video Transfer Learning]]
 
** [[Image/Video Transfer Learning]]
* [[Recommendation]] System & Algorithms
+
* [[Few Shot Learning]] ... [[Few Shot Learning#One-Shot Learning|One-Shot Learning]] ... [[Few Shot Learning#Zero-Shot Learning|Zero-Shot Learning]]
* [[Repositories & Other Algorithms]]
+
* [[Ensemble Learning]]
 +
* [[Multi-Task Learning (MTL)]]
 +
* [[Apprenticeship Learning - Inverse Reinforcement Learning (IRL)]]
 +
* [[Imitation Learning (IL)]]
 +
* [[Lifelong Learning]] - Catastrophic Forgetting Challenge
 +
* [[Neural Structured Learning (NSL)]]
 +
* [[Meta-Learning]]
 +
* [[Online Learning]]
 +
* [[Human-in-the-Loop (HITL) Learning]] / Active Learning
 +
* [[Decentralized: Federated & Distributed]] Learning
 +
* [[Large Language Model (LLM)#Multimodal|Multimodal Machine Learning]]
 +
* [[Embodied AI| Action Learning ... Embodied AI]]
 +
* [[Simulated Environment Learning]]
 +
 
 +
=== Opportunities & Challenges ===
 +
* [[Generative AI]]
 +
* [[Inside Out - Curious Optimistic Reasoning]] 
 +
* Nature
 +
** [[Evolutionary Computation / Genetic Algorithms]]
 +
** [[Bio-inspired Computing]]
 +
* [[Connecting Brains]]
 +
** [[Molecular Artificial Intelligence (AI)]]
 +
** [[Neuroscience]]
 +
* [[Architectures]]
 +
** [[Deep Distributed Q Network Partial Observability]] 
 +
** [[Graph Convolutional Network (GCN), Graph Neural Networks (Graph Nets), Geometric Deep Learning]]
 +
** [[Capsule Networks (CapNets)]]
 +
** [[Messaging & Routing]]
 +
** [[Processing Units - CPU, GPU, APU, TPU, VPU, FPGA, QPU]]
 +
* [[Integrity Forensics]]
 +
* [[Metaverse]]
 +
* [[Omniverse]]
 +
* [[Cybersecurity]]
 +
* [[Robotics]]
 +
* [[Other Challenges]] in Artificial Intelligence
 +
* [[Quantum]]
 +
 
 +
 
 +
<hr>
 +
= <span id="Development & Implementation"></span>[[Development]] & Implementation =
 +
* [https://aitoptools.com/ Tool Assist | AI Top Tools] ... largest directory of AI Tools, Ranked with dynamic algorithms
 +
* [[Development]]
 +
** [[Project Management]]
 +
** [[Generative AI for Business Analysis]]
 +
** [[Diagrams for Business Analysis]]
 +
** [[Requirements Management]]
 +
** [[Risk, Compliance and Regulation]]
 +
** [[Evaluation]]
 +
*** [[Evaluation - Measures]]
 +
** [[Train, Validate, and Test]]
 +
* [[Building Your Environment]]
 +
* [[Algorithm Administration]]
 +
** [[Algorithm Administration#AIOps/MLOps|AIOps/MLOps]]
 +
* [[ChatGPT#Integration | ChatGPT Integration]]
 +
* [[Game Development with Generative AI]] ... [[Gaming]] ... [[Game-Based Learning (GBL)]] ... [[Games - Security|Security]] ... [[Game Development with Generative AI|Generative AI]] ... [[Metaverse#Games - Metaverse|Games - Metaverse]] ... [[Games - Quantum Theme|Quantum]] ... [[Game Theory]] ... [[Game Design | Design]]
 +
* [[Agents]] ... [[Robotic Process Automation (RPA)|Robotic Process Automation]] ... [[Assistants]] ... [[Personal Companions]] ... [[Personal Productivity|Productivity]] ... [[Email]] ... [[Negotiation]] ... [[LangChain]]
 +
* [[Service Capabilities]]
 +
* [[AI Marketplace & Toolkit/Model Interoperability]]
 +
 
 +
== No Coding ==
 +
* [[Algorithm Administration#Automated Learning|Automated Learning]]
 +
* [[Neural Architecture]] Search (NAS) Algorithm
 +
* [[Codeless Options, Code Generators, Drag n' Drop]]
 +
 
 +
== Coding ==
 +
* [[Development#AI Pair Programming Tools|AI Pair Programming Tools]]
 +
* [[Python]] ... [[Generative AI with Python|GenAI w/ Python]] ... [[JavaScript]] ... [[Generative AI with JavaScript|GenAI w/ JavaScript]] ... [[TensorFlow]] ... [[PyTorch]]
 +
* [[R Project]]
 +
* [[Other Coding options]]
  
= Implementation =
+
=== [[Libraries & Frameworks]] ===
== [[Libraries & Frameworks]] ==
 
 
* [[Libraries & Frameworks Overview]]
 
* [[Libraries & Frameworks Overview]]
  
=== [[TensorFlow]] ===
+
==== [[TensorFlow]] ====
* [[TensorFlow Overview & Tutorials]]
 
 
* [[TensorBoard]]
 
* [[TensorBoard]]
 +
* [[TensorFlow Playground]]
 +
* [https://js.tensorflow.org/ TensorFlow.js Demos]
 
* [[TensorFlow.js]]   
 
* [[TensorFlow.js]]   
* [http://playground.tensorflow.org TensorFlow Playground]
 
 
* [[TensorFlow Lite]]
 
* [[TensorFlow Lite]]
 
* [[TensorFlow Serving]]
 
* [[TensorFlow Serving]]
Line 227: Line 369:
 
** [[Swift]]
 
** [[Swift]]
  
== Tooling ==
+
=== Tooling ===
 
 
 
* [[Model Search]]
 
* [[Model Search]]
* [[Model Monitoring]]
+
* [[Algorithm Administration#Model Monitoring|Model Monitoring]]
* [[Notebooks; Jupyter and R Markdown]]
+
* [[Notebooks]]; [[Jupyter]] and R Markdown
 
 
== Coding ==
 
* [[Javascript]]
 
* [[Python]]
 
* [[Other Coding options]]
 
 
 
== [[Platforms: Machine Learning as a Service (MLaaS)]] ==
 
* [[Service Capabilities]]
 
 
 
=== [[Google Cloud Platform (GCP)]] ...AI with TensorFlow ===
 
* [http://www.kubeflow.org/ Kubeflow] ML workflows on Kubernetes
 
** [[Pipelines]]
 
* [[Colaboratory]] - Jupyter notebooks
 
* [http://codelabs.developers.google.com/ Google Developers Codelabs]
 
* [[Dopamine]] - reinforcement learning algorithms
 
* [http://experiments.withgoogle.com/collection/ai Google AI Experiments]
 
* [[ML Engine]]
 
* [[Prediction API]]
 
* [http://cloud.google.com/vision/ Cloud Vision API] - drag & drop picture on webpage
 
* [http://grow.google/ Grow with Google]
 
* [http://ai.google/education/ Learn from ML experts at Google]
 
 
 
=== [[Amazon AWS]] ===
 
* [[AWS with TensorFlow]]
 
* [[DeepLens - deep learning enabled video camera]]
 
** [[Getting Started & Project: Object Detection]]
 
** [[More DeepLens Projects]]
 
* [[AWS Internet of Things (IoT)]]
 
** [[AWS IoT Button]]
 
* [[AmazonML]]
 
* [[Deep Learning (DL) Amazon Machine Image (AMI) - DLAMI]]
 
* [http://www.floydhub.com/ FloydHub - training and deploying your DL models]
 
* [http://aws.amazon.com/about-aws/events/monthlywebinarseries/on-demand/ On-Demand AWS Tech Talks]
 
* [http://aws.amazon.com/training/ AWS Training and Certification]
 
 
 
=== [[Microsoft Azure]] ===
 
* [[Azure with TensorFlow]]
 
* [[Azure AI Process]]
 
* [[ML Studio]]
 
* [[Cognitive Services]]
 
* [[Bot Framework]]
 
* [http://aischool.microsoft.com/learning-paths AI School]
 
 
 
=== [[NVIDIA]] ===
 
* [[RAPIDS]]
 
* [[NVIDIA Deep Learning Institute]]
 
* [http://on-demand-gtc.gputechconf.com/gtcnew/on-demand-gtc.php?searchByKeyword=&searchItems=&sessionTopic=&sessionEvent=2&sessionYear=2018&sessionFormat=&submit=&select= GTC Sessions]
 
 
 
=== Kaggle ===
 
* [[Kaggle Overview]]
 
* [[Kaggle Kernels]]
 
* [[Kaggle Competitions]]
 
** [[Passenger Screening]]
 
* [http://www.kaggle.com/learn/overview Hands-On Data Science Education]
 
 
 
=== Intel ===
 
* [[Neural Compute Stick (NCS)]]
 
* [http://software.intel.com/en-us/ai-academy AI Academy]
 
 
 
=== Apple ===
 
* [[Turi]]
 
 
 
= Research & Development =
 
* [[Natural Language Processing (NLP)]]
 
* [[Generative]] Modeling
 
* [[Automated Machine Learning (AML) - AutoML]]
 
* [[Explainable Artificial Intelligence (EAI)]]
 
* [[AI Marketplace & Toolkit/Model Interoperability]]
 
* [[Self Learning Artificial Intelligence - AutoML & World Models]]
 
** [[Inside Out - Curious Optimistic Reasoning]] 
 
** [[Evolutionary Computation / Genetic Algorithms]]
 
** [[Apprenticeship Learning - Inverse Reinforcement Learning (IRL)]]
 
** [[Imitation Learning]]
 
** [[Simulated Environment Learning]]
 
** [[3D Simulation Environments]]
 
* [[Connecting Brains]]
 
** [[Molecular Artificial Intelligence (AI)]]
 
** [[Neuroscience]]
 
* [[Architectures]]
 
** [[Deep Distributed Q Network Partial Observability]] 
 
** [[Graph Nets - Graph Neural Networks]]
 
** [[Capsule Networks (CapNets)]]
 
** [[Messaging & Routing]]
 
** [[Pipelines]]
 
** [[Processing Units - CPU, GPU, APU, TPU, VPU, FPGA, QPU]]
 
* [[Cybersecurity]]
 
* [[Integrity Forensics]]
 
* [[Other Challenges]]
 
  
 +
=== [[Platforms: AI/Machine Learning as a Service (AIaaS/MLaaS)]] ===
 +
* [[Amazon]] AWS 
 +
* [[Apple]]
 +
* [[Google]] Cloud Platform (GCP)
 +
* [[Hugging Face]]
 +
* [[IBM]]
 +
* [[Intel]]
 +
* [[Kaggle]]
 +
* [[Microsoft]] [[Azure AI Process|Azure Machine Learning]]
 +
* [https://modal.com/ Modal]
 +
* [[NVIDIA]]
 +
* [[OpenAI]]
 +
* [[Palantir]]
 +
* [[xAI]]
  
 +
=== ... and other leading organizations ===
 +
* [[Meta]]
 +
* [[Sakana]]
 +
* [https://allenai.org/ Allen Institute for Artificial Intelligence, or AI2]
 +
* [[Government Services]]
 +
** [[National Institute of Standards and Technology (NIST)]]
 +
** [[U.S. Department of Homeland Security (DHS)]]
 +
** [[Defense]]
 +
* [https://ai.stanford.edu/ Stanford University], [https://www.csail.mit.edu/ MIT], [https://www2.eecs.berkeley.edu/Research/Areas/AI/ UC Berkeley], [https://ai.cs.cmu.edu/ Carnegie Mellon University], [https://aiml.cs.princeton.edu/ Princeton University], [https://www.cs.ox.ac.uk/research/ai_ml/ University of Oxford], [https://www.cs.utexas.edu/concentrations/mlai University of Texas Austin], [https://samueli.ucla.edu/big-data-artificial-intelligence-and-machine-learning/ UCLA], [https://www.cs.duke.edu/research/artificialintelligence Duke University], [https://www.epfl.ch/research/ EPFL], [https://digital.hbs.edu/topics/artificial-intelligence-machine-learning/ Harvard University], [https://www.cs.cornell.edu/research/ai Cornell University], [https://inf.ethz.ch/ ETH], [https://www.cs.tsinghua.edu.cn/publish/csen/4917/index.html Tsinghua University], [https://www.comp.nus.edu.sg/about/depts/cs/research/ai/ National University of Singapore], [https://priml.upenn.edu/ University of Pennsylvania], [https://www.technion.ac.il/en/technion-research-units-2/ Technion], [https://www.cs.washington.edu/research/ai University of Washington], [https://ai.ucsd.edu/ UC San Diego], [https://www.cs.umd.edu/researcharea/ai-and-robotics University of Maryland], [https://www.cil.pku.edu.cn/ Peking University], [https://ic.gatech.edu/content/artificial-intelligence-machine-learning Georgia Institute of Technology], [https://machinelearning.illinois.edu/ University of Illinois at Urbana-Champaign], [https://research.cs.wisc.edu/areas/ai/ University of Wisconsin Madison], [https://www.engineering.utoronto.ca/research-innovation/industry-partnerships-with-u-of-t-engineering/data-analytics-artificial-intelligence/ University of Toronto], [https://www.umontreal.ca/en/artificialintelligence/ Université de Montréal] - [https://mila.quebec/en/mila/ Mila], [https://www.kaist.ac.kr/en/html/research/04.html KAIST], [https://engineering.tamu.edu/cse/research/areas/artificial-intelligence.html Texas A&M University], [https://www.riken.jp/en/research/labs/aip/ RIKEN], [https://www.cl.cam.ac.uk/research/ai/ University of Cambridge], [https://www.cs.columbia.edu/areas/ai/ Columbia University], [https://www.cics.umass.edu/research/area/artificial-intelligence UMass Amherst], [https://www.inria.fr/en National Institute for Research in Digital Science and Technology (INRIA)], [https://engineering.nyu.edu/research-innovation/centers-and-institutes/ai-now New York University],  [https://www.ucl.ac.uk/ai-centre/ University College London], [https://www.cs.usc.edu/academic-programs/masters/artificial-intelligence/ University of Southern California], [https://cpsc.yale.edu/research/artificial-intelligence Yale University], [https://yandexdataschool.com/ Yandex], [https://en.sjtu.edu.cn/ Shanghai Jiao Tong University], [https://www.cs.umn.edu/research/research_areas/robotics-and-artificial-intelligence University of Minnesota], [https://voices.uchicago.edu/machinelearning/ University of Chicago], [https://www.mcgill.ca/desautels/category/tags/artificial-intellligence-ai McGill University], [https://cse.snu.ac.kr/en Seoul National University], [https://uni-tuebingen.de/fakultaeten/mathematisch-naturwissenschaftliche-fakultaet/fachbereiche/informatik/studium/studiengaenge/machine-learning/ University of Tuebingen], [https://www.ualberta.ca/computing-science/research/research-areas/artificial-intelligence.html University of Alberta], [https://engineering.rice.edu/research-faculty/research-focus-areas/artificial-intelligence-machine-learning Rice University], [https://ep.jhu.edu/programs-and-courses/programs/artificial-intelligence Johns Hopkins University]
  
  
  
 
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Latest revision as of 08:47, 11 February 2026

On Sunday March 29, 2026 PRIMO.ai has 825 pages

Primo.ai provides links to articles and videos on Artificial intelligence (AI) concepts and techniques such as Generative AI, Natural Language Processing (NLP), Computer Vision, Deep Learning, Reinforcement Learning (RL), and Quantum Technology -- providing perspectives for individuals who are passionate about learning and developing new skills.

Getting Started

AI Fun

.. more Natural Language Processing (NLP) fun...

How to...

Forward Thinking


Information Analysis

Algorithms

Generative AI (Gen AI)

The ability to generate new content or solutions, such as writing or designing new products, using techniques such as Generative Adversarial Network (GAN) or neural style transfer.

Predict values - Regression

Analyze large amounts of data and make predictions or recommendations based on that data.

Classification ...predict categories

Recommendation

Clustering - Continuous - Dimensional Reduction

Hierarchical

Convolutional

Deconvolutional

Graph

- includes social networks, sensor networks, the entire Internet, 3D Objects (Point Cloud)

Sequence/Time-based Algorithms

Competitive

Semi-Supervised

In many practical situations, the cost to label is quite high, since it requires skilled human experts to do that. So, in the absence of labels in the majority of the observations but present in few, semi-supervised algorithms are the best candidates for the model building. These methods exploit the idea that even though the group memberships of the unlabeled data are unknown, this data carries important information about the group parameters. Reference: Learning Techniques

Natural Language

Reinforcement Learning (RL)

an algorithm receives a delayed reward in the next time step to evaluate its previous action. Therefore based on those decisions, the algorithm will train itself based on the success/error of output. In combination with Neural Networks it is capable of solving more complex tasks. Policy Gradient (PG) methods are a type of reinforcement learning techniques that rely upon optimizing parametrized policies with respect to the expected return (long-term cumulative reward) by gradient descent.

Neuro-Symbolic

the “connectionists” seek to construct artificial Neural Networks, inspired by biology, to learn about the world, while the “symbolists” seek to build intelligent machines by coding in logical rules and representations of the world. Neuro-Symbolic combines the fruits of group.

Other

Techniques

Methods & Concepts

Policy

Learning Techniques

Opportunities & Challenges



Development & Implementation

No Coding

Coding

Libraries & Frameworks

TensorFlow

Tooling

Platforms: AI/Machine Learning as a Service (AIaaS/MLaaS)

... and other leading organizations


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