Difference between revisions of "PRIMO.ai"
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+ | |title=PRIMO.ai | ||
+ | |titlemode=append | ||
+ | |keywords=artificial, intelligence, machine, learning, models, algorithms, cybersecurity, data, singularity, moonshot, Tensorflow, Google, Nvidia, Microsoft, Azure, Amazon, AWS | ||
+ | |description=Helpful resources for your journey with artificial intelligence; videos, articles, techniques, courses, profiles, and tools | ||
+ | |||
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* [[Capabilities]] | * [[Capabilities]] | ||
* [[Case Studies]] | * [[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] | + | * [http://www.uspto.gov/initiatives/artificial-intelligence Artificial Intelligence | United States Patent and Trademark Office] --> [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 === | ||
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* [[Competitions]] | * [[Competitions]] | ||
− | <i>Try GPT-2...</i> | + | <i>Try [[Generative Pre-trained Transformer | GPT-2]]...</i> |
− | |||
* [http://colab.research.google.com/github/nickwalton/AIDungeon/blob/master/AIDungeon_2.ipynb AI Dungeon 2] AI generated text adventure | * [http://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> | + | <i>.. more [[Natural Language Processing (NLP)]] fun...</i> |
* [http://corenlp.run/ CoreNLP - see NLP parsing techniques by pasting your text | Stanford] | * [http://corenlp.run/ CoreNLP - see NLP parsing techniques by pasting your text | Stanford] | ||
* [http://nlp.stanford.edu:8080/sentiment/rntnDemo.html Sentiment Treebank Analysis Demo] | * [http://nlp.stanford.edu:8080/sentiment/rntnDemo.html Sentiment Treebank Analysis Demo] | ||
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* [[Elastic Net Regression]] | * [[Elastic Net Regression]] | ||
* [[Bayesian Linear Regression]] | * [[Bayesian Linear Regression]] | ||
+ | * [[Bayesian Deep Learning (BDL)]] | ||
* [[Logistic Regression (LR)]] | * [[Logistic Regression (LR)]] | ||
* [[Support Vector Regression (SVR)]] | * [[Support Vector Regression (SVR)]] | ||
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== Sequence / Time == | == Sequence / Time == | ||
+ | * [[Transformer]] | ||
+ | ** [[Generative Pre-trained Transformer (GPT)]] | ||
+ | ** [[Attention]] Mechanism/[[Transformer]] Model | ||
+ | ** [[Transformer-XL]] | ||
* [[Sequence to Sequence (Seq2Seq)]] | * [[Sequence to Sequence (Seq2Seq)]] | ||
* [[End-to-End Speech]] | * [[End-to-End Speech]] | ||
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== 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)]] | ||
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* [[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]] | ||
− | * [[ | + | ** [[Deep Q Network (DQN)]] |
* [[Deep Reinforcement Learning (DRL)]] DeepRL | * [[Deep Reinforcement Learning (DRL)]] DeepRL | ||
* [[Distributed Deep Reinforcement Learning (DDRL)]] | * [[Distributed Deep Reinforcement Learning (DDRL)]] | ||
− | |||
* [[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)]] | ||
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* [[Dimensional Reduction]]; identification - what influences an observed outcome | * [[Dimensional Reduction]]; identification - what influences an observed outcome | ||
* [[Activation Functions]] | * [[Activation Functions]] | ||
− | + | * [[Memory Networks]] | |
− | |||
− | |||
− | |||
* [[Multiclassifiers; Ensembles and Hybrids; Bagging, Boosting, and Stacking]] | * [[Multiclassifiers; Ensembles and Hybrids; Bagging, Boosting, and Stacking]] | ||
* [[Optimizer]]s | * [[Optimizer]]s | ||
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* [[Quantization]] | * [[Quantization]] | ||
* [[Causation vs. Correlation]] | * [[Causation vs. Correlation]] | ||
− | * [[Object Detection; Faster R-CNN, YOLO, SSD | + | * [[Image Retrieval / Object Detection]]; Faster Region-based Convolutional Neural Networks (R-CNN), You only Look Once (YOLO), Single Shot Detector(SSD) |
* [[Deep Features]] | * [[Deep Features]] | ||
* [[Local Features]] | * [[Local Features]] | ||
+ | * [[Human in the Loop]] | ||
=== [[Learning Techniques]] === | === [[Learning Techniques]] === | ||
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** [[Capsule Networks (CapNets)]] | ** [[Capsule Networks (CapNets)]] | ||
** [[Messaging & Routing]] | ** [[Messaging & Routing]] | ||
− | |||
** [[Federated]] Learning | ** [[Federated]] Learning | ||
** [[Distributed]] Learning | ** [[Distributed]] Learning | ||
** [[Processing Units - CPU, GPU, APU, TPU, VPU, FPGA, QPU]] | ** [[Processing Units - CPU, GPU, APU, TPU, VPU, FPGA, QPU]] | ||
* [[Integrity Forensics]] | * [[Integrity Forensics]] | ||
+ | * [[Metaverse]] | ||
* [[Other Challenges]] in Artificial Intelligence | * [[Other Challenges]] in Artificial Intelligence | ||
= Development & Implementation = | = Development & Implementation = | ||
* [[Building Your Environment]] | * [[Building Your Environment]] | ||
− | * [[ | + | * [[AIOps / MLOps]] |
* [[Service Capabilities]] | * [[Service Capabilities]] | ||
* [[AI Marketplace & Toolkit/Model Interoperability]] | * [[AI Marketplace & Toolkit/Model Interoperability]] | ||
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* [http://allenai.org/ Allen Institute for Artificial Intelligence, or AI2] | * [http://allenai.org/ Allen Institute for Artificial Intelligence, or AI2] | ||
* [http://openai.com/ OpenAI] | * [http://openai.com/ OpenAI] | ||
+ | * [http://www.nist.gov/topics/artificial-intelligence NIST] | ||
+ | * [http://ai.stanford.edu/ Stanford University], [http://www.csail.mit.edu/ MIT], [http://www2.eecs.berkeley.edu/Research/Areas/AI/ UC Berkeley], [http://ai.cs.cmu.edu/ Carnegie Mellon University], [http://aiml.cs.princeton.edu/ Princeton University], [http://www.cs.ox.ac.uk/research/ai_ml/ University of Oxford], [http://www.cs.utexas.edu/concentrations/mlai University of Texas Austin], [http://samueli.ucla.edu/big-data-artificial-intelligence-and-machine-learning/ UCLA], [http://www.cs.duke.edu/research/artificialintelligence Duke University], [http://www.epfl.ch/research/ EPFL], [http://digital.hbs.edu/topics/artificial-intelligence-machine-learning/ Harvard University], [http://www.cs.cornell.edu/research/ai Cornell University], [http://inf.ethz.ch/ ETH], [http://www.cs.tsinghua.edu.cn/publish/csen/4917/index.html Tsinghua University], [http://www.comp.nus.edu.sg/about/depts/cs/research/ai/ National University of Singapore], [http://priml.upenn.edu/ University of Pennsylvania], [http://www.technion.ac.il/en/technion-research-units-2/ Technion], [http://www.cs.washington.edu/research/ai University of Washington], [http://ai.ucsd.edu/ UC San Diego], [http://www.cs.umd.edu/researcharea/ai-and-robotics University of Maryland], [http://www.cil.pku.edu.cn/ Peking University], [http://ic.gatech.edu/content/artificial-intelligence-machine-learning Georgia Institute of Technology], [http://machinelearning.illinois.edu/ University of Illinois at Urbana-Champaign], [http://research.cs.wisc.edu/areas/ai/ University of Wisconsin Madison], [http://www.engineering.utoronto.ca/research-innovation/industry-partnerships-with-u-of-t-engineering/data-analytics-artificial-intelligence/ University of Toronto], [http://www.umontreal.ca/en/artificialintelligence/ Université de Montréal] - [http://mila.quebec/en/mila/ Mila], [http://www.kaist.ac.kr/en/html/research/04.html KAIST], [http://engineering.tamu.edu/cse/research/areas/artificial-intelligence.html Texas A&M University], [http://www.riken.jp/en/research/labs/aip/ RIKEN], [http://www.cl.cam.ac.uk/research/ai/ University of Cambridge], [http://www.cs.columbia.edu/areas/ai/ Columbia University], [http://www.cics.umass.edu/research/area/artificial-intelligence UMass Amherst], [http://www.inria.fr/en National Institute for Research in Digital Science and Technology (INRIA)], [http://engineering.nyu.edu/research-innovation/centers-and-institutes/ai-now New York University], [http://www.ucl.ac.uk/ai-centre/ University College London], [http://www.cs.usc.edu/academic-programs/masters/artificial-intelligence/ University of Southern California], [http://cpsc.yale.edu/research/artificial-intelligence Yale University], [http://yandexdataschool.com/ Yandex], [http://en.sjtu.edu.cn/ Shanghai Jiao Tong University], [http://www.cs.umn.edu/research/research_areas/robotics-and-artificial-intelligence University of Minnesota], [http://voices.uchicago.edu/machinelearning/ University of Chicago], [http://www.mcgill.ca/desautels/category/tags/artificial-intellligence-ai McGill University], [http://cse.snu.ac.kr/en Seoul National University], [http://uni-tuebingen.de/fakultaeten/mathematisch-naturwissenschaftliche-fakultaet/fachbereiche/informatik/studium/studiengaenge/machine-learning/ University of Tuebingen], [http://www.ualberta.ca/computing-science/research/research-areas/artificial-intelligence.html University of Alberta], [http://engineering.rice.edu/research-faculty/research-focus-areas/artificial-intelligence-machine-learning Rice University], [http://ep.jhu.edu/programs-and-courses/programs/artificial-intelligence Johns Hopkins University] | ||
Revision as of 10:31, 9 August 2020
On Wednesday April 17, 2024 PRIMO.ai has 736 pages
Contents
- 1 Getting Started
- 2 Information Analysis
- 3 Algorithms
- 3.1 Predict values - Regression
- 3.2 Classification ...predict categories
- 3.3 Recommendation
- 3.4 Clustering - Continuous - Dimensional Reduction
- 3.5 Convolutional
- 3.6 Graph
- 3.7 Sequence / Time
- 3.8 Competitive
- 3.9 Semi-Supervised
- 3.10 Natural Language
- 3.11 Reinforcement Learning (RL)
- 3.12 Neuro-Symbolic
- 3.13 Other
- 4 Techniques
- 5 Development & Implementation
Getting Started
Overview
Background
AI Breakthroughs
- Capabilities
- Case Studies
- Artificial Intelligence | United States Patent and Trademark Office --> AI Patents after 2013
AI Fun
- Google AI Experiments
- TensorFlow Playground
- TensorFlow.js Demos
- Google AIY Projects Program - Do-it-yourself artificial intelligence
- NVIDIA Playground
Try GPT-2...
- 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...
- AI Solver for determining possible algorithms for your needs
- Strategy & Tactics for developing applications
- Checklists for ensuring consistency and completeness
Forward Thinking
Information Analysis
- Framing Context
- Datasets & Benchmarks
- Imbalanced Data
- Data Preprocessing
- Data Augmentation, Data Labeling, and Auto-Tagging
- Feature Exploration/Learning
- Batch Norm(alization) & Standardization
- Hyperparameters
- Zero Padding
- Train, Validate, and Test
- Model Assessment:
- Visualization
- Master Data Management (MDM) / Feature Store / Data Lineage / Data Catalog
- Data Interoperability
Algorithms
Predict values - Regression
- 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...
- 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)
- Artificial Neural Network (ANN)
- Deep Learning - Deep Neural Network (DNN)
- Kernel Approximation - Kernel Trick
- Logistic Regression (LR)
- Softmax Regression; Multinominal Logistic Regression
- Tree-based...
- Apriori, Frequent Pattern (FP) Growth, Association Rules/Analysis
- Markov Model (Chain, Discrete Time, Continuous Time, Hidden)
- Unsupervised
Recommendation
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
Deconvolutional
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)
Sequence / Time
- Transformer
- Generative Pre-trained Transformer (GPT)
- Attention Mechanism/Transformer Model
- Transformer-XL
- Sequence to Sequence (Seq2Seq)
- End-to-End Speech
- Neural Turing Machine
- Recurrent Neural Network (RNN)
- (Tree) Recursive Neural (Tensor) Network (RNTN)
Time
- Temporal Difference (TD) Learning
- Predict values
Spatialtemporal
Spatial-Temporal Dynamic Network (STDN)
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
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 Reinforcement Learning (DRL) DeepRL
- Distributed Deep Reinforcement Learning (DDRL)
- Evolutionary Computation / Genetic Algorithms
- Actor Critic
- Hierarchical Reinforcement Learning (HRL)
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
- Math for Intelligence
- Arxiv Sanity Preserver to accelerate research
Methods & Concepts
- Backpropagation
- Overfitting Challenge
- Dimensional Reduction; identification - what influences an observed outcome
- Activation Functions
- Memory Networks
- Multiclassifiers; Ensembles and Hybrids; Bagging, Boosting, and Stacking
- Optimizers
- Neural Network Pruning
- Repositories & Other Algorithms
- DAWNBench An End-to-End Deep Learning Benchmark and Competition
- Knowledge Graphs
- Quantization
- Causation vs. Correlation
- Image Retrieval / Object Detection; Faster Region-based Convolutional Neural Networks (R-CNN), You only Look Once (YOLO), Single Shot Detector(SSD)
- Deep Features
- Local Features
- Human in the Loop
Learning Techniques
- Text Transfer Learning
- Image/Video Transfer Learning
- Few Shot Learning
- Transfer Learning a model trained on one task is re-purposed on a second related task
- Ensemble Learning
- Multi-Task Learning (MTL)
- Apprenticeship Learning - Inverse Reinforcement Learning (IRL)
- Imitation Learning
- Simulated Environment Learning
- Lifelong Learning - Catastrophic Forgetting Challenge
- Neural Structured Learning (NSL)
- Meta-Learning
Opportunities & Challenges
- Generative Modeling
- Inside Out - Curious Optimistic Reasoning
- Nature
- Connecting Brains
- Architectures
- Integrity Forensics
- Metaverse
- Other Challenges in Artificial Intelligence
Development & Implementation
- Building Your Environment
- AIOps / MLOps
- Service Capabilities
- AI Marketplace & Toolkit/Model Interoperability
No Coding
- Automated Machine Learning (AML) - AutoML
- Neural Architecture Search (NAS) Algorithm
- Other codeless options, Code Generators, Drag n' Drop
Coding
Libraries & Frameworks
TensorFlow
- TensorBoard
- TensorFlow Playground
- TensorFlow.js Demos
- TensorFlow.js
- TensorFlow Lite
- TensorFlow Serving
- Related...
Tooling
- Model Search
- Model Monitoring
- Notebooks; Jupyter and R Markdown
Platforms: Machine Learning as a Service (MLaaS)
... and other leading organizations
- Allen Institute for Artificial Intelligence, or AI2
- OpenAI
- NIST
- 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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