PRIMO.ai

On Saturday December 21, 2024 PRIMO.ai has 802 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

• How do I leverage Artificial Intelligence (AI)?
  • Capabilities
  • Case Studies
• What is Artificial Intelligence (AI)?
  • History of Artificial Intelligence (AI)
  • Courses & Certifications
  • Reading Material & Glossary
  • Podcasts
• Current State

AI Fun

• Try ChatGPT | OpenAI
• Try Stable Diffusion | Stability AI ... text-to-image diffusion model capable of generating photo-realistic images
• Google AI Experiments
• TensorFlow Playground ...learn more
• TensorFlow.js Demos
• Google AIY Projects Program - Do-it-yourself artificial intelligence
• NVIDIA Playground
• Competitions
• AI Dungeon 2 AI generated text adventure

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

• CoreNLP - see NLP parsing techniques by pasting your text | Stanford
• Sentiment Treebank Analysis Demo

How to...

• Strategy & Tactics for developing AI investments
• AI Solver for determining possible algorithms for your needs
• AI Governance to reduce unnecessary risks and assure success
• Evaluation ... Prompts for assessing AI projects
• Checklists for ensuring consistency and completeness

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
• Journey to Singularity ... a hypothetical future event in which artificial intelligence (AI) surpasses human intelligence in a way that fundamentally changes human society and civilization
• Artificial Intelligence | United States Patent and Trademark Office --> AI Patents after 2013
• Creatives ... individuals who have significantly contributed to the development, advancement, or popularization of AI
• Books, Radio & Movies - Exploring Possibilities

Generative 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.

• Conversational AI ... OpenAI's ChatGPT ... Perplexity ... Microsoft's Bing ... You ...Google's Bard ... Baidu's Ernie
  • Prompt Engineering (PE) ...PromptBase ... Prompt Injection Attack
  • Generative AI for Business Analysis
• Multimodal Language Models ... Generative Pre-trained Transformer (GPT-4) ... GPT-5
• Video/Image
• Synthesize Speech
• Game Development with Generative AI

Information Analysis

• Context ... the next AI frontier
• Data Science ... Governance ... Preprocessing ... Exploration ... Interoperability ... Master Data Management (MDM) ... Bias and Variances ... Benchmarks ... Datasets
• Data Quality ...validity, accuracy, cleaning, completeness, consistency, encoding, padding, augmentation, labeling, auto-tagging, normalization, standardization, and imbalanced data
• Managed Vocabularies
• Excel - Data Analysis
• Visualization
• Analytics
• Hyperparameters

Algorithms

• 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.

• Linear Regression
• Ridge Regression
• Lasso Regression
• Elastic Net Regression
• Bayesian Linear Regression
• 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)

Classification ...predict categories

• 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 - 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)
• Unsupervised
  • Radial Basis Function Network (RBFN)
  • Self-Supervised
    • Autoencoder (AE) / Encoder-Decoder
    • (Stacked) Denoising Autoencoder (DAE)
    • Sparse Autoencoder (SAE)

Recommendation

• Alternating Least Squares (ALS)
• Matrix Factorization

Clustering - Continuous - Dimensional Reduction

• 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
• 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
• A hierarchical RNN-based model to predict scene graphs for images
• A multi-granularity reasoning framework for social relation recognition
• Neural Structured Learning (NSL)

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)

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)

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)
  • Natural Language Tools & Services
  • Embedding:
    • Search (where results are ranked by relevance to a query string)
    • Clustering (where text strings are grouped by similarity)
    • Recommendations (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)

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.

• 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)

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.

• Neuro-Symbolic ... Symbolic Artificial Intelligence
• Neuro-Symbolic Concept Learner (NS-CL)

Other

• Hopfield Network (HN)
• Energy-based Model (EBN)
• Generative Query Network (GQN)

Techniques

• Math for Intelligence ... Finding Paul Revere
• Arxiv Sanity Preserver to accelerate research
• Theory-free Science

Methods & Concepts

• Backpropagation
• Stochastic Gradient Descent
• Learning Rate Decay
• Max Pooling
• Batch Normalization
• Overfitting Challenge
  • Regularization
  • Dropout
• Manifold Hypothesis and Dimensional Reduction; identification - what influences an observed outcome
• Activation Functions
• Memory Networks
• Multiclassifiers; Ensembles and Hybrids; Bagging, Boosting, and Stacking
• Optimizers
  • Optimization Methods
  • Objective vs. Cost vs. Loss vs. Error Function
  • Gradient Descent Optimization & Challenges
  • Parameter Initialization
• Neural Network Pruning
• Repositories & Other Algorithms
• DAWNBench An End-to-End Deep Learning Benchmark and Competition
• Knowledge Graphs
• Quantization
• Causation vs. Correlation
• Deep Features
• Local Features
• 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)

Learning Techniques

• In-Context Learning (ICL) ... Context
• Out-of-Distribution (OOD) Generalization
• Supervised Learning
• Unsupervised Learning
• Reinforcement Learning (RL)
• Reinforcement Learning (RL) from Human Feedback (RLHF)
• Semi-Supervised Learning
• Self-Supervised Learning
• 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 ... One-Shot Learning ... Zero-Shot Learning
• Ensemble Learning
• Multi-Task Learning (MTL)
• Apprenticeship Learning - Inverse Reinforcement Learning (IRL)
• Imitation Learning
• 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
• Multimodal Machine Learning
• 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
• Cybersecurity
• Robotics
• Other Challenges in Artificial Intelligence
• Quantum

Development & Implementation

• 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
  • AIOps/MLOps
• ChatGPT Integration
• Game Development with Generative AI
• Assistants ... Agents ... Negotiation ... LangChain
• Service Capabilities
• AI Marketplace & Toolkit/Model Interoperability

No Coding

• Automated Learning
• Neural Architecture Search (NAS) Algorithm
• Codeless Options, Code Generators, Drag n' Drop

Coding

• AI Pair Programming Tools
• Python ... Generative AI with Python ... Javascript ... Generative AI with Javascript
• R Project
• Other Coding options

Libraries & Frameworks

• Libraries & Frameworks Overview

TensorFlow

• TensorBoard
• TensorFlow Playground
• TensorFlow.js Demos
• TensorFlow.js
• TensorFlow Lite
• TensorFlow Serving
• Related...
  • Keras.js
  • TFLearn
  • Swift

Tooling

• Model Search
• 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 Machine Learning
• Modal
• NVIDIA
• OpenAI
• Palantir

... and other leading organizations

• Meta
• Allen Institute for Artificial Intelligence, or AI2
• Government Services
  • National Institute of Standards and Technology (NIST)
  • U.S. Department of Homeland Security (DHS)
  • Defense
• Stanford University, MIT, UC Berkeley, Carnegie Mellon University, Princeton University, University of Oxford, University of Texas Austin, UCLA, Duke University, EPFL, Harvard University, Cornell University, ETH, Tsinghua University, National University of Singapore, University of Pennsylvania, Technion, University of Washington, UC San Diego, University of Maryland, Peking University, Georgia Institute of Technology, University of Illinois at Urbana-Champaign, University of Wisconsin Madison, University of Toronto, Université de Montréal - Mila, KAIST, Texas A&M University, RIKEN, University of Cambridge, Columbia University, UMass Amherst, National Institute for Research in Digital Science and Technology (INRIA), New York University, University College London, University of Southern California, Yale University, Yandex, Shanghai Jiao Tong University, University of Minnesota, University of Chicago, McGill University, Seoul National University, University of Tuebingen, University of Alberta, Rice University, Johns Hopkins University

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