Difference between revisions of "PRIMO.ai"

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*Courses*
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__NOTOC__
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{{#seo:
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|title=PRIMO.ai
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|titlemode=append
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|keywords=Game, design, ChatGPT, Claude, Gemini, Grok, DeepSeek, artificial, intelligence, machine, learning, NLP, NLG, NLC, NLU, models, data, singularity, moonshot, Sentience, AGI, Emergence, Moonshot, Explainable, Transformer, Attention, RAG, retrieval augmented generation, agents, agentic, reasoning, multimodal, foundation models, TensorFlow, PyTorch, Google, Nvidia, Microsoft, Azure, Amazon, AWS, Hugging Face, OpenAI, Anthropic, Meta, LLM, metaverse, assistants, digital twin, IoT, Transhumanism, Immersive Reality, Generative AI, Conversational AI, Perplexity, Bing, You, Ernie, prompt engineering, LangChain, LlamaIndex, Video/Image, Vision, End-to-End Speech, Synthesize Speech, Speech Recognition, AI governance, AI safety, ethics, privacy, Stanford, MIT
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|description=Helpful resources for your journey with artificial intelligence; videos, articles, techniques, courses, profiles, and tools
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}}
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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 =
 +
* [[How do I leverage Artificial Intelligence (AI)?]]
 +
* [[What is Artificial Intelligence (AI)?]]
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** [[History of Artificial Intelligence (AI)]]
 +
** [[Reading Material & Glossary]]
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** [[Podcasts]]
 +
* [[Current State]]
 +
* [[Capabilities]] ... what AI can do today, mapped by input and output ... [[Case Studies]] ... how organizations have actually applied it
 +
* Enjoy the short story [[Three-Second Pause]] ... [[Life~Meaning#Can_Meaning_Exist_in_Artificial_Systems|Can ''Meaning'' Exist in Artificial Systems?]]
  
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*TensorFlow*
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=== Interactive Playgrounds ===
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an open-source software library for dataflow programming across a range of tasks. It is a symbolic math library
 
  
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**[[TensorFlow.js]]**
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* [https://aistudio.google.com/ Google AI Studio]: Direct prototyping sandbox for multimodal reasoning over massive context windows (audio, video, text, and code).
 +
* [https://notebook.google/ Google Gemini Notebook]: generate customizable AI podcast discussions, short visual explainer videos, presentation slide decks with talking points, written reports, interactive study aids, and organized data visualizations.
 +
* [https://madewithclaude.com/ Claude Artifacts] ... examples for sharing, get inspired to create or remix amazing artifacts with Claude AI [[Anthropic]]
 +
* [https://academy.openai.com/public/clubs/work-users-ynjqu/resources/canvas OpenAI Canvas]: Dynamic interactive runtimes allowing users to run, render, and iterate on generated HTML5, React, Python, and SVG components in real-time.  [[OpenAI]]
 +
* [https://huggingface.co/spaces Spaces]: Community-driven hosting of state-of-the-art Gradio and Streamlit interfaces, supporting zero-GPU client-side execution via WebGPU (Transformers.js) or cloud-hosted tensor accelerators. [[Hugging Face]]
  
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== Other Resources ==
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=== Forward Thinking ===
 +
* [[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
 +
* [[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
 +
* [https://www.uspto.gov/initiatives/artificial-intelligence Artificial Intelligence | United States Patent and Trademark Office] --> [https://ppubs.uspto.gov/pubwebapp/static/pages/landing.html Patent Public Search] ... search AI patents by keyword or classification; replaced the retired PatFT/AppFT databases in 2022
 +
* [[Creatives]]  ... individuals who have significantly contributed to the development, advancement, or popularization of AI
 +
* [[Books, Radio & Movies - Exploring Possibilities]]
  
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See also... [http://businesscentricmethodology.com/ Business-Centric Methodology]
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<hr>
  
−
[[Getting Started]]
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= 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]] ... [[Retrieval-Augmented Generation (RAG)]]
 +
* [[Visualization]]
 +
* [[Analytics]] 
 +
* [[Algorithm Administration#Hyperparameter|Hyperparameter]]s
 +
* How to ...
 +
** [[Strategy & Tactics]] for developing AI investments
 +
** [[AI Solver]] for determining possible algorithms for your needs
 +
** [[Evaluation]]  ... Prompts for assessing AI projects
 +
** [[Checklists]] for ensuring consistency and completeness
 +
 
 +
= <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]]
 +
 
 +
== <span id="Architecture"></span>Architecture of Modern Models ==
 +
Nearly every system described further down this page -- chat assistants, image generators, coding tools, [[Agents/Assistants|Agents]] -- rests on the same architectural lineage. It is worth reading in order.
 +
 
 +
* [[Attention]] ... the mechanism that lets a model weigh which parts of its input matter for each element it produces
 +
* [[Transformer]] ... the architecture built entirely on attention, dispensing with recurrence and convolution
 +
* [[Generative Pre-trained Transformer (GPT)]] ... decoder-only transformers pre-trained on broad corpora, then adapted to specific tasks
 +
* [[Large Language Model (LLM)]] ... [[Large Language Model (LLM)#Multimodal|Multimodal]] ... models that accept and produce more than text
 +
* [[Retrieval-Augmented Generation (RAG)]] ... grounding generation in retrieved documents rather than relying on what the weights happen to encode
 +
* [[Mamba]] ... [[Time#Sequence/Time-based Algorithms|sequence models]] that scale differently than attention does
 +
* [[State Space Model (SSM)]] capture the system's internal behavior over time
 +
* [[Foundation Models (FM)]] trained on broad data that can be adapted to a wide range of tasks
 +
 
 +
== [[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]] ... [[Gemini]] | [[Google]] ... [[Claude]] | [[Anthropic]] ... [[Bing/Copilot]] | [[Microsoft]] ... [[Apple| Siri | Apple]] ... [[Meta]] ... [[Perplexity]] ... [[You]] ... [[phind]] ... [[Grok]] | [https://x.ai/ xAI] ... [[Groq]] ... [[Ernie]] | [[Baidu]] ... [[DeepSeek]] ...  [[Alibaba]]
 +
** [[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
 +
* [[Video/Image]]
 +
* [[Synthesize Speech]]
 +
* [[Retrieval-Augmented Generation (RAG)]] ... [[Embedding]] ... [[Database|Vector Database]] ... [[LangChain]] ... [[LlamaIndex]]
 +
* [[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]]
 +
 
 +
== Predict values - [[Regression]] ==
 +
Analyze large amounts of data and make predictions or recommendations based on that data.
 +
 
 +
* [[Linear Regression]]
 +
* [[Ridge Regression]]
 +
* [[Lasso Regression]]
 +
* [[Elastic Net Regression]]
 +
* [[Bayes#Bayesian Linear Regression|Bayesian Linear Regression]]
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* [[Bayes#Bayesian Deep Learning (BDL)|Bayesian Deep Learning (BDL)]]
 +
* [[Logistic Regression (LR)]]
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* [[Support Vector Regression (SVR)]]
 +
* [[Ordinal Regression]]
 +
* [[Poisson Regression]]
 +
* Tree-based...
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** [[Fast Forest Quantile Regression]]
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** [[Decision Forest Regression]]
 +
* [[General Regression Neural Network (GRNN)]]
 +
* [[One-class Support Vector Machine (SVM)]]
 +
* [[Gradient Boosting Machine (GBM)]]
 +
 
 +
== [[Classification]] [[...predict categories]] ==
 +
* <span id="Supervised"></span>[[Supervised]]
 +
** Naive [[Bayes]]
 +
** [[K-Nearest Neighbors (KNN)]]
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** [[Perceptron (P)]] ...and Multi-layer Perceptron (MLP)
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** [[Feed Forward Neural Network (FF or FFNN)]]
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** [[Neural Network]]
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*** [[Deep Learning]] - [[Neural Network#Deep Neural Network (DNN)|Deep Neural Network (DNN)]]
 +
** Kernel Approximation - [[Kernel Trick]]
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*** [[Support Vector Machine (SVM)]]
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** [[Logistic Regression (LR)]]
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*** [[Softmax]] Regression; Multinominal Logistic Regression
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** Tree-based...
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*** [[(Boosted) Decision Tree]]
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*** [[Random Forest (or) Random Decision Forest]]
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*** [[Decision Jungle]]
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** [[Apriori, Frequent Pattern (FP) Growth, Association Rules/Analysis]]
 +
** [[Markov Model (Chain, Discrete Time, Continuous Time, Hidden)]]
 +
* <span id="Unsupervised"></span>[[Unsupervised]]
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** [[Radial Basis Function Network (RBFN)]]
 +
** <span id="Self-Supervised"></span>[[Self-Supervised]]
 +
*** [[Autoencoder (AE) / Encoder-Decoder]]
 +
*** [[(Stacked) Denoising Autoencoder (DAE)]]
 +
*** [[Sparse Autoencoder (SAE)]] ... also the workhorse of feature-level interpretability research
 +
 
 +
== [[Recommendation]] ==
 +
* [[Alternating Least Squares (ALS)]]
 +
* [[Matrix Factorization]]
 +
 
 +
== [[Clustering]] - Continuous - [[Dimensional Reduction]] ==
 +
* [[Singular Value Decomposition (SVD)]]
 +
* [[Principal Component Analysis (PCA)]]
 +
* [[K-Means]]
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* [[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]]
 +
 
 +
=== Convolutional ===
 +
* [[(Deep) Convolutional Neural Network (DCNN/CNN)]]
 +
* [[(Deep) Residual Network (DRN) - ResNet]]
 +
** [[ResNet-50]]
 +
 
 +
=== Deconvolutional ===
 +
*[[Deconvolutional Neural Network (DN) / Inverse Graphics Network (IGN)]]
 +
 
 +
== Graph ==
 +
- includes social networks, sensor networks, the entire Internet, 3D Objects ([[Point Cloud]])
 +
* [[Graph Convolutional Network (GCN), Graph Neural Networks (Graph Nets), Geometric Deep Learning]]
 +
* [[Point Cloud]]
 +
* [[Knowledge Graphs]] ... increasingly paired with [[Retrieval-Augmented Generation (RAG)]] to give retrieval explicit structure
 +
* [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)]]
 +
 
 +
== [[Time#Sequence/Time-based Algorithms|Sequence/Time-based Algorithms]] ==
 +
* [[Mamba]]
 +
* [[Transformer]] ... see also [[Attention]]
 +
 
 +
== Competitive  ==
 +
* [[Generative Adversarial Network (GAN)]]
 +
* [[Image-to-Image Translation]]
 +
* [[Conditional Adversarial Architecture (CAA)]]
 +
* [[Kohonen Network (KN)/Self Organizing Maps (SOM)]]
 +
* [[Quantum Generative Adversarial Learning (QuGAN - QGAN)]]
 +
 
 +
== <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)]]
 +
* [[Context-Conditional Generative Adversarial Network (CC-GAN)]]
 +
 
 +
== <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 Generation (NLG)]]
 +
** [[Natural Language Classification (NLC)]] 
 +
** [[Large Language Model (LLM)]] 
 +
** [[Attention]] ... [[Transformer]] ... [[Generative Pre-trained Transformer (GPT)]]
 +
** [[Natural Language Tools & Services]]
 +
*** [[Embedding]]
 +
*** [[Fine-tuning]]
 +
*** [[Retrieval-Augmented Generation (RAG)]] (where an external knowledge base is consulted before generating)
 +
*** [[Agents/Assistants]](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)]]
 +
* [[State-Action-Reward-State-Action (SARSA)]]
 +
* [[Q Learning]]
 +
** [[Deep Q Network (DQN)]]
 +
* [[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)]]
 +
* [[Reinforcement Learning (RL) from Human Feedback (RLHF)]] ... the technique that turned raw language models into usable assistants; see also [[Constitutional AI]]
 +
 
 +
== [[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.
 +
 
 +
* [[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 =
 +
* [[Math for Intelligence]] ... [[Finding Paul Revere]]
 +
* [https://www.arxiv-sanity.com/ Arxiv Sanity Preserver] to accelerate research
 +
* [[Theory-free Science]]
 +
 
 +
=== Methods & Concepts ===
 +
* [[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]]
 +
** [[Regularization]]
 +
** [[Dropout]]
 +
* [[Manifold Hypothesis]] and [[Dimensional Reduction]]; identification - what influences an observed outcome
 +
* [[Activation Functions]]
 +
* [[Memory]]
 +
** [[Memory Networks]]
 +
* [[Multiclassifiers; Ensembles and Hybrids; Bagging, Boosting, and Stacking]]
 +
* [[Optimizer]]s
 +
** [[Optimization Methods]]
 +
** [[Objective vs. Cost vs. Loss vs. Error Function]]
 +
** [[Gradient Descent Optimization & Challenges]]
 +
** [[Parameter Initialization]]
 +
* [[Neural Network Pruning]]
 +
* [[Quantization]] ... shrinking model precision so larger models fit on smaller hardware
 +
* [[Repositories & Other Algorithms]]
 +
* [[Benchmarks]] ... [[Evaluation]] ... [[Evaluation - Measures]]
 +
** [https://mlcommons.org/benchmarks/ MLPerf | MLCommons] ... the industry-standard suite for training and inference performance, covering datacenter, edge, client, and tiny deployments
 +
** [https://dawn.cs.stanford.edu/benchmark/index.html DAWNBench] ... an end-to-end deep learning benchmark and competition; archived, submissions closed in 2020 and the effort was consolidated into MLPerf, but the historical results remain a useful record of how fast training costs fell
 +
* [[Knowledge Graphs]]
 +
* [[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
 +
** [[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]]
 +
* [[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]]
 +
 
 +
=== <span id="Trust, Safety & Governance"></span>Trust, Safety & Governance ===
 +
Capability and trustworthiness are separate problems, and the second one does not solve itself. These pages cover the constraints that determine whether a working system is one you can actually deploy.
 +
 
 +
* [[Explainable / Interpretable AI]]
 +
* [[Risk, Compliance and Regulation]]  ... [[Ethics]]  ... [[Privacy]]  ... [[Law]]  ... [[AI Governance]]  ... [[AI Verification and Validation]]
 +
* [[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]], [[Data Quality#Imbalanced Data|imbalanced data]]
 +
* [[Constitutional AI]] ... [[Reinforcement Learning (RL) from Human Feedback (RLHF)]] ... [[Policy]]
 +
* [[Cybersecurity]] ... [[Prompt Injection Attack]] ... [[Integrity Forensics]]
 +
* [[Government Services]] ... [[National Institute of Standards and Technology (NIST)]] ... [[U.S. Department of Homeland Security (DHS)]] ... [[Defense]]
 +
* [[Human-in-the-Loop (HITL) Learning]] ... keeping a person in the decision path where the stakes justify the cost
 +
 
 +
=== 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]] ... compute availability is now a first-order constraint on what gets built
 +
* [[Integrity Forensics]]
 +
* [[Metaverse]]
 +
* [[Omniverse]]
 +
* [[Cybersecurity]]
 +
* [[Robotics]] ... [[Embodied AI]]
 +
* [[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]]
 +
** [[Algorithm Administration#Model Monitoring|Model Monitoring]]
 +
* [[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/Assistants]] ... [[Robotic Process Automation (RPA)|Robotic Process Automation]] ... [[Personal Companions]] ... [[Personal Productivity|Productivity]] ... [[Email]] ... [[Negotiation]] ... [[LangChain]]
 +
* [[Retrieval-Augmented Generation (RAG)]] ... [[Embedding]] ... [[Fine-tuning]] ... [[Database|Vector Database]] ... the usual alternative to retraining when a system needs current or proprietary knowledge
 +
* [[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]]
 +
 
 +
=== [[Libraries & Frameworks]] ===
 +
* [[Libraries & Frameworks Overview]]
 +
 
 +
==== [[TensorFlow]] ====
 +
* [[TensorBoard]]
 +
* [[TensorFlow Playground]]
 +
* [https://www.tensorflow.org/js TensorFlow.js on tensorflow.org] ... tutorials, models, and browser demos
 +
* [[TensorFlow.js]] 
 +
* [[TensorFlow Lite]]
 +
* [[TensorFlow Serving]]
 +
* Related...
 +
** [[Keras.js]]
 +
** [[TFLearn]]
 +
** [[Swift]]
 +
 
 +
=== Tooling ===
 +
* [[Model Search]]
 +
* [[Algorithm Administration#Model Monitoring|Model Monitoring]]
 +
* [[Notebooks]]; [[Jupyter]] and R Markdown
 +
 
 +
=== [[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 ===
 +
* [[Anthropic]]
 +
* [[Meta]]
 +
* [[Sakana]]
 +
* [[DeepSeek]]
 +
* [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 17:29, 16 September 2026

On Wednesday September 16, 2026 PRIMO.ai has 819 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

Interactive Playgrounds

  • Google AI Studio: Direct prototyping sandbox for multimodal reasoning over massive context windows (audio, video, text, and code).
  • Google Gemini Notebook: generate customizable AI podcast discussions, short visual explainer videos, presentation slide decks with talking points, written reports, interactive study aids, and organized data visualizations.
  • Claude Artifacts ... examples for sharing, get inspired to create or remix amazing artifacts with Claude AI Anthropic
  • OpenAI Canvas: Dynamic interactive runtimes allowing users to run, render, and iterate on generated HTML5, React, Python, and SVG components in real-time. OpenAI
  • Spaces: Community-driven hosting of state-of-the-art Gradio and Streamlit interfaces, supporting zero-GPU client-side execution via WebGPU (Transformers.js) or cloud-hosted tensor accelerators. Hugging Face

Forward Thinking

Information Analysis

Algorithms

Architecture of Modern Models

Nearly every system described further down this page -- chat assistants, image generators, coding tools, Agents -- rests on the same architectural lineage. It is worth reading in order.

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

Trust, Safety & Governance

Capability and trustworthiness are separate problems, and the second one does not solve itself. These pages cover the constraints that determine whether a working system is one you can actually deploy.

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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