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__NOTOC__
 
{{#seo:
 
{{#seo:
 
|title=PRIMO.ai
 
|title=PRIMO.ai
 
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|titlemode=append
|keywords=artificial, intelligence, machine, learning, models, algorithms, cybersecurity, data, singularity, moonshot, Tensorflow, Google, Nvidia, Microsoft, Azure, Amazon, AWS  
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|keywords=ChatGPT, artificial, intelligence, machine, learning, GPT-4, GPT-5, 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
|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]]. It is a resource 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 & Certifications]]
+
** [[History of Artificial Intelligence (AI)]]
* [[Reading Material & Glossary]]
+
** [[Courses & Certifications]]
 
+
** [[Reading Material & Glossary]]
=== Background ===
+
** [[Podcasts]]
* [[What is AI?]]
 
 
* [[Current State]]
 
* [[Current State]]
* [[History of AI]]
 
 
=== AI Breakthroughs ===
 
* [[Capabilities]]
 
* [[Case Studies]]
 
* [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]
 
  
 
=== AI Fun ===
 
=== AI Fun ===
* [http://experiments.withgoogle.com/collection/ai Google AI Experiments]
+
* Try [[ChatGPT]] | [[OpenAI]]
* [http://playground.tensorflow.org TensorFlow Playground]
+
* Try [[Stability_AI#DreamStudio | DreamStudio]] | Stability AI ... text-to-image [[Diffusion|diffusion]] model capable of generating photo-realistic images
* [http://js.tensorflow.org/ TensorFlow.js Demos]
+
* [https://experiments.withgoogle.com/collection/ai Google AI Experiments]
 +
* [https://playground.tensorflow.org TensorFlow Playground] [[TensorFlow Playground|...learn more]]
 +
* [https://js.tensorflow.org/ TensorFlow.js Demos]
 
* [[Google AIY Projects Program]]  - Do-it-yourself artificial intelligence
 
* [[Google AIY Projects Program]]  - Do-it-yourself artificial intelligence
* [http://www.nvidia.com/en-us/research/ai-playground/ NVIDIA Playground]
+
* [https://www.nvidia.com/en-us/research/ai-playground/ NVIDIA Playground]
* [http://talktotransformer.com/ Try GPT-2...Talk to Transformer] - completes your text. | [http://adamdking.com/ Adam D King], [http://huggingface.co/ Hugging Face] and [http://openai.com/ OpenAI]
 
 
* [[Competitions]]
 
* [[Competitions]]
 +
* [https://colab.research.google.com/github/nickwalton/AIDungeon/blob/master/AIDungeon_2.ipynb AI Dungeon 2] AI generated text adventure
 +
 +
<i>.. more [[Natural Language Processing (NLP)]] fun...</i>
 +
* [https://corenlp.run/ CoreNLP - see NLP parsing techniques by pasting your text | Stanford]
 +
* [https://nlp.stanford.edu:8080/sentiment/rntnDemo.html Sentiment Treebank Analysis Demo]
  
 
=== How to... ===
 
=== How to... ===
*[[AI Solver]] for determining possible algorithms for your needs
+
* [[Strategy & Tactics]] for developing AI investments
*[[Strategy & Tactics]] for developing applications
+
* [[AI Solver]] for determining possible algorithms for your needs
*[[Checklists]] for ensuring consistency and completeness
+
* [[Evaluation]]   ... Prompts for assessing AI projects
 +
* [[Checklists]] for ensuring consistency and completeness
  
 
=== Forward Thinking ===
 
=== Forward Thinking ===
* [[Moonshots]]
+
* [[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
* [[Journey to Singularity]]
+
* [[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
* [[Creatives]]
+
* [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]
 +
* [[Creatives]]   ... individuals who have significantly contributed to the development, advancement, or popularization of AI
 +
* [[Books, Radio & Movies - Exploring Possibilities]]
 +
 
 +
 
 +
<hr>
 +
 
 +
= [[Generative AI| Generative AI (Gen AI)]] =
 +
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]].
 +
 
 +
* [[Conversational AI]] ... [[ChatGPT]] | [[OpenAI]] ... [[Bing/Copilot]] | [[Microsoft]] ... [[Gemini]] | [[Google]] ... [[Claude]] | [[Anthropic]] ... [[Perplexity]] ... [[You]] ... [[phind]] ... [[Ernie]] | [[Baidu]]
 +
** [[Prompt Engineering (PE)]] ...[[Prompt Engineering (PE)#PromptBase|PromptBase]] ... [[Prompt Injection Attack]]
 +
** [[Generative AI for Business Analysis]]
 +
* [[Large Language Model (LLM)#Multimodal|Multimodal Language Model]]s ... Generative Pre-trained Transformer ([[GPT-4]]) ... [[GPT-5]]
 +
* [[Video/Image]]
 +
* [[Synthesize Speech]]
 +
* [[Game Development with Generative AI]]
 +
 
 +
 
 +
<hr>
  
 
= Information Analysis =
 
= Information Analysis =
* [[Framing Context]]
+
* [[Context]] ... the next AI frontier
* [[Datasets]]
+
* [[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]]  
* [[Imbalanced Data]]
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* [[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]]
* [[Data Preprocessing]]
+
* [[Natural Language Processing (NLP)#Managed Vocabularies |Managed Vocabularies]]
* [[Data Augmentation]], Data Labeling, and Auto-Tagging
+
* [[Excel]] ... [[LangChain#Documents|Documents]] ... [[Database|Database; Vector & Relational]] ... [[Graph]] ... [[LlamaIndex]]
* [[Feature Exploration/Learning]]
 
* [[Batch Norm(alization) & Standardization]]
 
* [[Hyperparameter]]s
 
* [[Zero Padding]]
 
* [[Train, Validate, and Test]]
 
* Model Assessment:
 
** [http://www.kdnuggets.com/2018/04/right-metric-evaluating-machine-learning-models-1.html Choosing the Right Metric for Evaluating Machine Learning Models]
 
** [[Approach to Bias and Variances]]
 
** [[Evaluation Measures - Classification Performance]]
 
**  [[Explainable Artificial Intelligence (XAI)]]
 
 
* [[Visualization]]
 
* [[Visualization]]
* [[Master Data Management  (MDM) / Feature Store / Data Lineage / Data Catalog]]
+
* [[Analytics]]  
* [[Data Interoperability]]
+
* [[Algorithm Administration#Hyperparameter|Hyperparameter]]s
  
= [[Algorithms]] =
+
= <span id="Algorithms"></span>[[Algorithms]] =
 +
* [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 - [[Regression]] ==
 +
Analyze large amounts of data and make predictions or recommendations based on that data.
  
== Predict values - [[Regression]] ==
 
 
* [[Linear Regression]]
 
* [[Linear Regression]]
 
* [[Ridge Regression]]
 
* [[Ridge Regression]]
 
* [[Lasso Regression]]
 
* [[Lasso Regression]]
 
* [[Elastic Net Regression]]
 
* [[Elastic Net Regression]]
* [[Bayesian Linear Regression]]
+
* [[Bayes#Bayesian Linear Regression|Bayesian Linear Regression]]
 +
* [[Bayes#Bayesian Deep Learning (BDL)|Bayesian Deep Learning (BDL)]]
 
* [[Logistic Regression (LR)]]
 
* [[Logistic Regression (LR)]]
 
* [[Support Vector Regression (SVR)]]
 
* [[Support Vector Regression (SVR)]]
 
* [[Ordinal Regression]]
 
* [[Ordinal Regression]]
 
* [[Poisson Regression]]
 
* [[Poisson Regression]]
* [[Tree-based...]]
+
* Tree-based...
 
** [[Fast Forest Quantile Regression]]
 
** [[Fast Forest Quantile Regression]]
 
** [[Decision Forest Regression]]
 
** [[Decision Forest Regression]]
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* [[Gradient Boosting Machine (GBM)]]
 
* [[Gradient Boosting Machine (GBM)]]
  
== Classification [[...predict categories]] ==
+
== [[Classification]] [[...predict categories]] ==
* [[Supervised]]
+
* <span id="Supervised"></span>[[Supervised]]
** [[Naive Bayes]]
+
** Naive [[Bayes]]
 
** [[K-Nearest Neighbors (KNN)]]
 
** [[K-Nearest Neighbors (KNN)]]
 
** [[Perceptron (P)]] ...and Multi-layer Perceptron (MLP)
 
** [[Perceptron (P)]] ...and Multi-layer Perceptron (MLP)
 
** [[Feed Forward Neural Network (FF or FFNN)]]
 
** [[Feed Forward Neural Network (FF or FFNN)]]
** [[Artificial Neural Network (ANN)]]
+
** [[Neural Network]]
** [[Deep Learning]] - [[Deep Neural Network (DNN)]]
+
*** [[Deep Learning]] - [[Neural Network#Deep Neural Network (DNN)|Deep Neural Network (DNN)]]
 
** Kernel Approximation - [[Kernel Trick]]
 
** Kernel Approximation - [[Kernel Trick]]
 
*** [[Support Vector Machine (SVM)]]
 
*** [[Support Vector Machine (SVM)]]
 
** [[Logistic Regression (LR)]]
 
** [[Logistic Regression (LR)]]
 
*** [[Softmax]] Regression; Multinominal Logistic Regression
 
*** [[Softmax]] Regression; Multinominal Logistic Regression
** [[Tree-based...]]
+
** Tree-based...
 
*** [[(Boosted) Decision Tree]]
 
*** [[(Boosted) Decision Tree]]
 
*** [[Random Forest (or) Random Decision Forest]]
 
*** [[Random Forest (or) Random Decision Forest]]
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** [[Apriori, Frequent Pattern (FP) Growth, Association Rules/Analysis]]
 
** [[Apriori, Frequent Pattern (FP) Growth, Association Rules/Analysis]]
 
** [[Markov Model (Chain, Discrete Time, Continuous Time, Hidden)]]
 
** [[Markov Model (Chain, Discrete Time, Continuous Time, Hidden)]]
* [[Unsupervised]]
+
* <span id="Unsupervised"></span>[[Unsupervised]]
 
** [[Radial Basis Function Network (RBFN)]]
 
** [[Radial Basis Function Network (RBFN)]]
** [[Autoencoder (AE) / Encoder-Decoder]]
+
** <span id="Self-Supervised"></span>[[Self-Supervised]]
** [[(Stacked) Denoising Autoencoder (DAE)]]
+
*** [[Autoencoder (AE) / Encoder-Decoder]]
** [[Sparse Autoencoder (SAE)]]
+
*** [[(Stacked) Denoising Autoencoder (DAE)]]
 +
*** [[Sparse Autoencoder (SAE)]]
  
 
== [[Recommendation]] ==
 
== [[Recommendation]] ==
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* [[Matrix Factorization]]
 
* [[Matrix Factorization]]
  
== [[Clustering]] - Continuous - Dimensional Reduction ==
+
== [[Clustering]] - Continuous - [[Dimensional Reduction]] ==
 
* [[Singular Value Decomposition (SVD)]]
 
* [[Singular Value Decomposition (SVD)]]
 
* [[Principal Component Analysis (PCA)]]
 
* [[Principal Component Analysis (PCA)]]
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* [[Variational Autoencoder (VAE)]]
 
* [[Variational Autoencoder (VAE)]]
 
* [[Biclustering]]
 
* [[Biclustering]]
* [http://en.wikipedia.org/wiki/Multidimensional_scaling Multidimensional Scaling (MDS)]
+
* [https://en.wikipedia.org/wiki/Multidimensional_scaling Multidimensional Scaling (MDS)]
  
 
=== [[Hierarchical]] ===
 
=== [[Hierarchical]] ===
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* [[Mixture Models; Gaussian]]
 
* [[Mixture Models; Gaussian]]
  
== Convolutional ==
+
=== Convolutional ===
 
* [[(Deep) Convolutional Neural Network (DCNN/CNN)]]
 
* [[(Deep) Convolutional Neural Network (DCNN/CNN)]]
 
* [[(Deep) Residual Network (DRN) - ResNet]]
 
* [[(Deep) Residual Network (DRN) - ResNet]]
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* [[Graph Convolutional Network (GCN), Graph Neural Networks (Graph Nets), Geometric Deep Learning]]  
 
* [[Graph Convolutional Network (GCN), Graph Neural Networks (Graph Nets), Geometric Deep Learning]]  
 
* [[Point Cloud]]  
 
* [[Point Cloud]]  
* [http://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/2019-04-hierarchical-rnn-based-scene-graphs-images.html A hierarchical RNN-based model to predict scene graphs for images]
* [http://techxplore.com/news/2019-01-multi-granularity-framework-social-recognition.html A multi-granularity reasoning framework for social relation recognition]
+
* [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)]]
 
* [[Neural Structured Learning (NSL)]]
  
== Sequence / Time ==
+
== [[Time#Sequence/Time-based Algorithms|Sequence/Time-based Algorithms]] ==
* [[Sequence to Sequence (Seq2Seq)]]
+
* [[Mamba]]
* [[End-to-End Speech]]
 
* [[Neural Turing Machine]]
 
* [[Recurrent Neural Network (RNN)]]
 
** [[Long Short-Term Memory (LSTM)]]
 
** [[Gated Recurrent Unit (GRU)]]
 
** [[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]]
 
* Predict values
 
** [[Time Series Forecasting Methods - Statistical]]
 
** [[Time Series Forecasting - Deep Learning]]
 
 
 
=== Spatialtemporal ===
 
[[Spatial-Temporal Dynamic Network (STDN)]]
 
  
 
== Competitive  ==
 
== Competitive  ==
 
* [[Generative Adversarial Network (GAN)]]
 
* [[Generative Adversarial Network (GAN)]]
 +
* [[Image-to-Image Translation]]
 
* [[Conditional Adversarial Architecture (CAA)]]
 
* [[Conditional Adversarial Architecture (CAA)]]
 
* [[Kohonen Network (KN)/Self Organizing Maps (SOM)]]
 
* [[Kohonen Network (KN)/Self Organizing Maps (SOM)]]
 
* [[Quantum Generative Adversarial Learning (QuGAN - QGAN)]]
 
* [[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.
+
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)]]
  
== Natural Language ==
+
== <span id="Natural Language"></span>Natural Language  ==
  
* [[Natural Language Processing (NLP)]] involves speech recognition, (speech) translation, understanding (semantic parsing) complete sentences, understanding synonyms of matching words, and sentiment analysis  
+
* [[Natural Language Processing (NLP)]] involves speech recognition, (speech) translation, understanding (semantic parsing) complete sentences, understanding synonyms of matching words, and sentiment analysis
** Current State of the Art:
+
** [[Natural Language Generation (NLG)]]  
*** [[(Deep) Convolutional Neural Network (DCNN/CNN)]]
+
** [[Natural Language Classification (NLC)]]
*** [[Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Recurrent Neural Network (RNN)]]
+
** [[Large Language Model (LLM)]]
*** [[Attention Mechanism/Model - Transformer Model]]
+
** [[Natural Language Tools & Services]]
*** [[Bidirectional Encoder Representations from Transformers (BERT)]]
+
*** [[Embedding]]
*** [[XLNet]] extends [[Transformer-XL]]
+
*** [[Fine-tuning]]
** Methods:
+
*** [[Agents#AI-Powered Search|Search]] (where results are ranked by relevance to a query string)
*** [[Natural Language Processing (NLP)#Text Preprocessing |Text Preprocessing]]
+
*** [[Clustering]] (where text strings are grouped by similarity)
**** [[Natural Language Processing (NLP)#Text Regular Expressions (Regex) |Regular Expressions (Regex)]]
+
*** [[Recommendation]]s (where items with related text strings are recommended)
**** [[Natural Language Processing (NLP)#Soundex |Soundex]]
+
*** [[Anomaly Detection]] (where outliers with little relatedness are identified)
**** [[Natural Language Processing (NLP)#Tokenization / Sentence Splitting |Tokenization / Sentence Splitting]]  
+
*** [[Classification]] (where text strings are classified by their most similar label)
***** [[Natural Language Processing (NLP)#Word Embeddings |Word Embeddings]]
+
*** [[Dimensional Reduction]]
**** [[Natural Language Processing (NLP)#Normalization |Normalization]]
+
*** [[...find outliers]] ... diversity measurement (where similarity distributions are analyzed)
***** [[Natural Language Processing (NLP)#Stemming (Morphological Similarity) |Stemming (Morphological Similarity)]]
 
***** [[Natural Language Processing (NLP)#Lemmatization |Lemmatization]]
 
**** [[Natural Language Processing (NLP)#Similarity |Similarity]]
 
***** [[Natural Language Processing (NLP)#Word Similarity |Word Similarity]]
 
***** [[Natural Language Processing (NLP)#Text Clustering |Text Clustering]]
 
***** [[Natural Language Processing (NLP)#Sentence/Document Similarity |Sentence/Document Similarity]]
 
***** [[Natural Language Processing (NLP)#Text Classification |Text Classification]]
 
***** [[Natural Language Processing (NLP)#Topic Modeling |Topic Modeling]]
 
**** [[Natural Language Processing (NLP)#Whole Word Masking |Whole Word Masking]]
 
**** [[Natural Language Processing (NLP)#Identity Scrubbing |Identity Scrubbing]]
 
**** [[Natural Language Processing (NLP)#Stop Words |Stop Words]]
 
*** [[Natural Language Processing (NLP)#Relating Text |Relating Text]]
 
**** [[Natural Language Processing (NLP)#Part-of-Speech (POS) Tagging |Part-of-Speech (POS) Tagging]]
 
**** [[Natural Language Processing (NLP)#Chunking |Chunking]] - chunks or patterns, e.g. telephone number
 
**** [[Natural Language Processing (NLP)#Chinking |Chinking]] - unwanted chunk removal
 
**** [[Natural Language Processing (NLP)#Named Entity Recognition (NER) |Named Entity Recognition (NER)]]
 
**** [[Natural Language Processing (NLP)#Relation Extraction |Relation Extraction]]
 
**** [[Natural Language Processing (NLP)#Neural Coreference |Neural Coreference]]
 
** [[Natural Language Processing (NLP)#Natural Language Understanding (NLU) |Natural Language Understanding (NLU)]] or Natural Language Interpretation (NLI)
 
*** [[Natural Language Processing (NLP)#Managed Vocabularies |Managed Vocabularies]]
 
**** [[Natural Language Processing (NLP)#Corpora |Corpora]]
 
**** [[Natural Language Processing (NLP)#Ontologies |Ontologies]] and [[Natural Language Processing (NLP)#Taxonomies |Taxonomies]]
 
*** [[Natural Language Processing (NLP)#Natural Language Inference (NLI) and Recognizing Textual Entailment (RTE)|Natural Language Inference (NLI) and Recognizing Textual Entailment (RTE)]]
 
**** [[Natural Language Processing (NLP)#Semantic Role Labeling (SRL) |Semantic Role Labeling (SRL)]]
 
*** [[Natural Language Processing (NLP)#Deep Learning Algorithms |Deep Learning Algorithms]]
 
*** [[Natural Language Processing (NLP)#Capabilities |Capabilities]]
 
**** [[Natural Language Processing (NLP)#Summarization |Summarization]]
 
**** [[Natural Language Processing (NLP)#Sentiment Analysis |Sentiment Analysis]]
 
**** [[Natural Language Processing (NLP)#Wikifier |Wikifier]]
 
*** [[Natural Language Processing (NLP)#Workbench / Pipeline |Workbench / Pipeline]]
 
* [[Natural Language Generation (NLG)]] involves writing/generating complete grammatically correct sentences and paragraphs
 
  
== [[Reinforcement Learning (RL)]]  ==
+
== <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]].
 
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
 
* [[Monte Carlo]] (MC) Method - Model Free Reinforcement Learning
 
* [[Markov Decision Process (MDP)]]
 
* [[Markov Decision Process (MDP)]]
 +
* [[State-Action-Reward-State-Action (SARSA)]]
 
* [[Q Learning]]
 
* [[Q Learning]]
* [[State-Action-Reward-State-Action (SARSA)]]
+
** [[Deep Q Network (DQN)]]
 
* [[Deep Reinforcement Learning (DRL)]] DeepRL
 
* [[Deep Reinforcement Learning (DRL)]] DeepRL
 
* [[Distributed Deep Reinforcement Learning (DDRL)]]
 
* [[Distributed Deep Reinforcement Learning (DDRL)]]
* [[Deep Q Network (DQN)]]
 
 
* [[Evolutionary Computation / Genetic Algorithms]]
 
* [[Evolutionary Computation / Genetic Algorithms]]
 
* [[Actor Critic]]
 
* [[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)]]
  
 
== [[Neuro-Symbolic]] ==
 
== [[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.
+
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.
  
 +
* [[Neuro-Symbolic]] ... [[Symbolic Artificial Intelligence]]
 
* [[Neuro-Symbolic Concept Learner (NS-CL)]]
 
* [[Neuro-Symbolic Concept Learner (NS-CL)]]
  
 
== Other ==
 
== Other ==
 
* [[Hopfield Network (HN)]]
 
* [[Hopfield Network (HN)]]
* [[Energy-based Model (EBN)]]
+
* [[Energy-based Model (EBN)]] ... non-normalized probabilistic model
 
* [[Generative Query Network (GQN)]]
 
* [[Generative Query Network (GQN)]]
  
 
= Techniques =
 
= Techniques =
* [[Math for Intelligence]]
+
* [[Math for Intelligence]] ... [[Finding Paul Revere]]
** [[Statistics for Intelligence]]
+
* [https://www.arxiv-sanity.com/ Arxiv Sanity Preserver] to accelerate research
** [[Finding Paul Revere]]
+
* [[Theory-free Science]]
* [http://www.arxiv-sanity.com/ Arxiv Sanity Preserver] to accelerate research
 
  
 
=== Methods & Concepts ===
 
=== Methods & Concepts ===
 
* [[Backpropagation]]
 
* [[Backpropagation]]
 +
* [[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]]
* [[Dimensional Reduction]]; identification - what influences an observed outcome
+
* [[Manifold Hypothesis]] and [[Dimensional Reduction]]; identification - what influences an observed outcome
 
* [[Activation Functions]]
 
* [[Activation Functions]]
* Memory
+
* [[Memory]]
 
** [[Memory Networks]]
 
** [[Memory Networks]]
** [[Attention]] Mechanism/[[Transformer]] Model
 
** [[Transformer-XL]]
 
 
* [[Multiclassifiers; Ensembles and Hybrids; Bagging, Boosting, and Stacking]]
 
* [[Multiclassifiers; Ensembles and Hybrids; Bagging, Boosting, and Stacking]]
 
* [[Optimizer]]s
 
* [[Optimizer]]s
Line 277: Line 262:
 
* [[Neural Network Pruning]]
 
* [[Neural Network Pruning]]
 
* [[Repositories & Other Algorithms]]
 
* [[Repositories & Other Algorithms]]
* [http://dawn.cs.stanford.edu/benchmark/index.html DAWNBench] An End-to-End Deep Learning Benchmark and Competition
+
* [https://dawn.cs.stanford.edu/benchmark/index.html DAWNBench] An End-to-End Deep Learning Benchmark and Competition
 
* [[Knowledge Graphs]]
 
* [[Knowledge Graphs]]
 
* [[Quantization]]
 
* [[Quantization]]
 
* [[Causation vs. Correlation]]
 
* [[Causation vs. Correlation]]
* [[Object Detection; Faster R-CNN, YOLO, SSD]]
 
 
* [[Deep Features]]  
 
* [[Deep Features]]  
 
* [[Local 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)]]
  
=== [[Advanced Learning]] ===
+
=== <span id="Learning Techniques"></span>[[Learning Techniques]] ===
* [[Text Transfer Learning]]  
+
* [[In-Context Learning (ICL)]] ... [[Context]]
* [[Image/Video Transfer Learning]]
+
* [[Out-of-Distribution (OOD) Generalization]]
* [[Few Shot Learning]]
+
* [[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]]
 +
** [[Image/Video Transfer Learning]]
 +
* [[Few Shot Learning]] ... [[Few Shot Learning#One-Shot Learning|One-Shot Learning]] ... [[Few Shot Learning#Zero-Shot Learning|Zero-Shot Learning]]
 
* [[Ensemble Learning]]
 
* [[Ensemble Learning]]
 
* [[Multi-Task Learning (MTL)]]
 
* [[Multi-Task Learning (MTL)]]
 
* [[Apprenticeship Learning - Inverse Reinforcement Learning (IRL)]]
 
* [[Apprenticeship Learning - Inverse Reinforcement Learning (IRL)]]
* [[Imitation Learning]]
+
* [[Imitation Learning (IL)]]
* [[Simulated Environment Learning]]
 
 
* [[Lifelong Learning]] - Catastrophic Forgetting Challenge
 
* [[Lifelong Learning]] - Catastrophic Forgetting Challenge
 
* [[Neural Structured Learning (NSL)]]
 
* [[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 ===
 
=== Opportunities & Challenges ===
* [[Generative]] Modeling
+
* [[Generative AI]]
 
* [[Inside Out - Curious Optimistic Reasoning]]   
 
* [[Inside Out - Curious Optimistic Reasoning]]   
 
* Nature
 
* Nature
Line 312: Line 314:
 
** [[Capsule Networks (CapNets)]]  
 
** [[Capsule Networks (CapNets)]]  
 
** [[Messaging & Routing]]  
 
** [[Messaging & Routing]]  
** [[Pipeline]]s
 
** [[Federated]]
 
 
** [[Processing Units - CPU, GPU, APU, TPU, VPU, FPGA, QPU]]
 
** [[Processing Units - CPU, GPU, APU, TPU, VPU, FPGA, QPU]]
 
* [[Integrity Forensics]]
 
* [[Integrity Forensics]]
 +
* [[Metaverse]]
 +
* [[Omniverse]]
 +
* [[Cybersecurity]]
 +
* [[Robotics]]
 
* [[Other Challenges]] in Artificial Intelligence
 
* [[Other Challenges]] in Artificial Intelligence
 +
* [[Quantum]]
 +
  
= Development & Implementation =
+
<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]]
 
* [[Building Your Environment]]
* [[Pipeline]]s
+
* [[Algorithm Administration]]
 +
** [[Algorithm Administration#AIOps/MLOps|AIOps/MLOps]]
 +
* [[ChatGPT#Integration | ChatGPT Integration]]
 +
* [[Game Development with Generative AI]]
 +
* [[Agents]] ... [[Robotic Process Automation (RPA)|Robotic Process Automation]] ... [[Assistants]] ... [[Personal Companions]] ... [[Personal Productivity|Productivity]] ... [[Email]] ... [[Negotiation]] ... [[LangChain]]
 
* [[Service Capabilities]]
 
* [[Service Capabilities]]
* [[AI Marketplace & Toolkit/Model Interoperability]]  
+
* [[AI Marketplace & Toolkit/Model Interoperability]]
  
 
== No Coding ==
 
== No Coding ==
* [[Automated Machine Learning (AML) - AutoML]]
+
* [[Algorithm Administration#Automated Learning|Automated Learning]]
 
* [[Neural Architecture]] Search (NAS) Algorithm
 
* [[Neural Architecture]] Search (NAS) Algorithm
* [[Other codeless options, Code Generators, Drag n' Drop]]
+
* [[Codeless Options, Code Generators, Drag n' Drop]]
  
 
== Coding ==
 
== Coding ==
* [[Javascript]]
+
* [[Development#AI Pair Programming Tools|AI Pair Programming Tools]]
* [[Python]]
+
* [[Python]] ... [[Generative AI with Python|GenAI w/ Python]] ... [[JavaScript]] ... [[Generative AI with JavaScript|GenAI w/ JavaScript]] ... [[TensorFlow]] ... [[PyTorch]]
 
* [[R Project]]
 
* [[R Project]]
 
* [[Other Coding options]]
 
* [[Other Coding options]]
Line 340: Line 361:
 
==== [[TensorFlow]] ====
 
==== [[TensorFlow]] ====
 
* [[TensorBoard]]
 
* [[TensorBoard]]
* [http://playground.tensorflow.org TensorFlow Playground]
+
* [[TensorFlow Playground]]
* [http://js.tensorflow.org/ TensorFlow.js Demos]
+
* [https://js.tensorflow.org/ TensorFlow.js Demos]
 
* [[TensorFlow.js]]   
 
* [[TensorFlow.js]]   
 
* [[TensorFlow Lite]]
 
* [[TensorFlow Lite]]
Line 352: Line 373:
 
=== Tooling ===
 
=== Tooling ===
 
* [[Model Search]]
 
* [[Model Search]]
* [[Model Monitoring]]
+
* [[Algorithm Administration#Model Monitoring|Model Monitoring]]
 
* [[Notebooks]]; [[Jupyter]] and R Markdown
 
* [[Notebooks]]; [[Jupyter]] and R Markdown
  
=== [[Platforms: Machine Learning as a Service (MLaaS)]] ===
+
=== [[Platforms: AI/Machine Learning as a Service (AIaaS/MLaaS)]] ===
* [[Google]] Cloud Platform (GCP)
+
* [[Amazon]] AWS 
* [[Amazon]] AWS
+
* [[Apple]]
* [[Microsoft]] Azure  
+
* [[Google]] Cloud Platform (GCP)  
 +
* [[Hugging Face]]
 +
* [[IBM]]
 +
* [[Intel]]
 +
* [[Kaggle]]
 +
* [[Microsoft]] [[Azure AI Process|Azure Machine Learning]]
 +
* [https://modal.com/ Modal]
 
* [[NVIDIA]]  
 
* [[NVIDIA]]  
* [[Kaggle]]
+
* [[OpenAI]]
* [[Intel]]
+
* [[Palantir]]
* [[Apple]]
+
* [[xAI]]
 +
 
 
=== ... and other leading organizations ===
 
=== ... and other leading organizations ===
* [http://allenai.org/ Allen Institute for Artificial Intelligence, or AI2]
+
* [[Meta]]
* [http://openai.com/ OpenAI]
+
* [[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]
  
  
 +
<hr>
 +
Sponsored by... [https://www.etsy.com/shop/LittleHouseOnTheBay Little House On The Bay]
 +
<hr>
  
  
  
 
If you get a 502 or 503 error please try the webpage again, as your message is visiting the island which the server is located, perhaps deciding to relax in the Sun before returning. Thank you.
 
If you get a 502 or 503 error please try the webpage again, as your message is visiting the island which the server is located, perhaps deciding to relax in the Sun before returning. Thank you.

Latest revision as of 08:35, 23 March 2024

On Friday March 29, 2024 PRIMO.ai has 733 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. It is a resource 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



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.



Information Analysis

Algorithms

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



Sponsored by... Little House On The Bay



If you get a 502 or 503 error please try the webpage again, as your message is visiting the island which the server is located, perhaps deciding to relax in the Sun before returning. Thank you.