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All Projects → gopala-kr → ai-applications

gopala-kr / ai-applications

Licence: MIT License
roadmap to applied ai

Labels

applied-aiindustrial-ai

Applications of AI : Industrial

One Hundred Year Study on Artificial Intelligence (AI100) | “AI and Life in 2030” | AITopics |

AI index | 2017-index report | AI index public data |

Contents

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AI approaches

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| Workshop | logic-ai |

  • Symbolic AI(formal logic - (1950-1980) - "If an otherwise healthy adult has a fever, then they may have influenza"
  • Bayesian inference(Statistical AI) - "If the current patient has a fever, adjust the probability they have influenza in such-and-such way"
  • Machine learning and data mining - "After examining the records of known past patients whose temperature, symptoms, age, and other factors mostly match the current patient, X% of those patients turned out to have influenza"
  • Artificial Neural Network - inspired by how the brain's machinery works(neurons).
  • Evolutionary AI - uses mechanisms inspired by biological evolution, such as reproduction, mutation, recombination, and natural selection.

Note: But not only above five(extremely hyped and popular in routine business AI applications), researchers trying different approaches to achieve general AI

There is no established unifying theory or paradigm that guides AI research. Researchers disagree about many issues. A few of the most long standing questions that have remained unanswered are these: should artificial intelligence simulate natural intelligence by studying psychology or neurobiology? Or is human biology as irrelevant to AI research as bird biology is to aeronautical engineering? Can intelligent behavior be described using simple, elegant principles (such as logic or optimization)? Or does it necessarily require solving a large number of completely unrelated problems?

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What is the difference between data mining, statistics, machine learning and AI? Would it be accurate to say that they are 4 fields attempting to solve very similar problems but with different approaches? What exactly do they have in common and where do they differ? If there is some kind of hierarchy between them, what would it be?

In short

  • Statistics quantifies numbers
  • Data Mining explains patterns
  • Machine Learning predicts with models
  • Artificial Intelligence behaves and reasons

Statistics is just about the numbers, and quantifying the data. There are many tools for finding relevant properties of the data but this is pretty close to pure mathematics.

Data Mining is about using Statistics as well as other programming methods to find patterns hidden in the data so that you can explain some phenomenon. Data Mining builds intuition about what is really happening in some data and is still little more towards math than programming, but uses both.

Machine Learning uses Data Mining techniques and other learning algorithms to build models of what is happening behind some data so that it can predict future outcomes. Math is the basis for many of the algorithms, but this is more towards programming.

Artificial Intelligence uses models built by Machine Learning and other ways to reason about the world and give rise to intelligent behavior whether this is playing a game or driving a robot/car. Artificial Intelligence has some goal to achieve by predicting how actions will affect the model of the world and chooses the actions that will best achieve that goal. Very programming based.

more detailed explaination

The Multidisciplinary

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Why AI is on Rise

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AI Goals, Techniques and Tools

Goals

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  • Reasoning, problem solving
  • Knowledge representation (commonsense knowledge)
  • Planning
  • Learning
  • Natural language processing
  • Perception/Vision
  • Motion and manipulation
  • Social intelligence
  • General intelligence

Methods, categories, algorithms and applications

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Algorithm Categories

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  • Decision tree learning
  • Association rule learning
  • Artificial neural networks
  • Inductive logic programming
  • Support vector machines
  • Clustering
  • Bayesian networks
  • Reinforcement learning
  • Representation learning
  • Similarity and metric learning
  • Sparse dictionary learning
  • Genetic algorithms

Algorithms and Applications by Data Type

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  • Sound/Audio
    • Voice detection/recognition
    • Voice search
    • Speaker identification
    • Sentiment analysis
    • Flaw detection (engine noise)
    • Fraud detection (latent audio artifacts)
    • Speech-to-Text
  • Time Series/sequence
    • Log analysis/Risk detection
    • Enterprise resource planning
    • Predictive analysis using sensor data
    • Business and economic analysis
    • Recommendation engine
    • Examples and algorithms
      • Web log
        • RNN
      • Time series in general (has time stamp)
        • RNN
      • Sensors and measures over time
        • RNN
      • Arbitrarily long sequence that may take full input data
        • RNN
        • Markova model with large hidden state space
      • Fixed length sequence
        • CNN
        • Multilayer perceptron
  • Text
    • Sentiment analysis
    • Augmented search, theme detection
    • Threat detection
    • Fraud detection
    • Named-entity recognition
  • Image
    • Facial recognition and expression recognition
    • People identification
    • Image search
    • Machine vision
    • Photo clustering
    • Image recognition/classification
      • Is it a certain class or multiple classes (e.g., cat, car, ...)
    • Object recognition and detection
      • Detection is the location of the recognized object in the image (i.e., localization)
        • Output is bounding box (b_x, b_y, b_h, B_w), is object recognized in image, and class label(s)
        • Loss function calculation depends on whether the object is detected in the image
        • Sliding window detection (window size and stride)
          • Pass window as input to CNN
    • Landmark detection
      • X,Y point pairs representing individual landmarks in the image
      • Useful for emotion detection, filters, pose detection, ...
    • Algorithms
      • CNN
  • Video
    • Motion detection
    • Real-time threat detection
    • Gesture recognition
  • Unlabeled and/or unstructured data
    • Clustering
    • Anamoly detection (detecting anamolies)
    • Search (detecting similarities)
      • Compare docs, images, sounds, etc., and return similar items
  • Labeled data
    • Predictive analytics
      • Regression and classification
        • Hardware failure
        • Health degredation, failure, and disease
        • Customer churn
        • Employee churn
  • Columnar/tabular
    • Classic multilayer perceptrons + feature engineering

Algorithms and Applications by Task

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  • Prediction
    • Regression/classification
      • RNN
  • Recommendation
  • Generative
    • Novel output
      • RNN
  • Reconstruction
    • Example: MINST
  • Recognition and computer vision
    • Changing images in time (video)
      • LSTM (temporal aspect) with convolutions layers (capture structure/features)
  • NLP, NLG, NLU
    • Machine translation
      • CNN
    • Sentiment analysis
      • CNN
    • Sentence classification
      • CNN
  • Personal assistant
    • Voice to text then NLP to understand what the user wants to accomplish, then generating text, voice, action
  • Anamoly detection
  • Reinforcement learning
  • Reality capture and reality computing

Regression, Univariate, Multivariate

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  • Simple and multiple linear regression
  • Tree-based methods (e.g., decision tree or forest)
  • Generalized linear models (GLM)
    • Poisson regression, aka log-linear model
  • Generalized additive model (GAM)
  • Regression with shrinkage (e.g., regularization)
  • Stepwise regression
  • Ordinary least squares
  • Artificial Neural networks (ANN) and deep learning
  • Ordinal regression
  • Polynomial regression
  • Nearest neighbor methods (e.g., k-NN or k-Nearest Neighbors)
  • Gradient tree boosting
  • Logistic regression
  • Nonlinear regression

Example Applications

Classification Unary, Binary and Multiclass

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  • Linear
    • Linear discriminant analysis (LDA), aka Fisher's linear discriminant
    • Logistic regression and multinomial logistic regression
    • Bayesian classifiers (as opposed to frequentist)
      • Naive Bayes
    • Perceptron methods
  • Decision trees and random forests
  • Naive bayes
  • Hidden markov model
  • Support vector machines (SVM)
    • Least squares support vector machines
  • Artificial Neural networks (ANN) and deep learning
  • Kernel estimation
    • Nearest neighbor methods (e.g., k-NN or k-Nearest Neighbors)
  • One vs Rest and One vs One (binary transformation)
  • Gradient tree boosting

Example Applications

  • Many diseases or issues, including stroke, cancer, ...
  • Spam for email
  • Smart email categorization (Gmail)
    • Primary, social, and promotion inboxes, as well as labeling emails as important
  • Credit decisions

Regularization and Overfitting Prevention

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  • Least absolute shrinkage and selection operator (LASSO)
  • Ridge regression
  • Akaike information criterion (AIC)
  • Bayesian information criterion (BIC)

Clustering

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  • Hierarchical clustering, aka connectivity-basedclustering and Hierarchical Cluster Analysis (HCA)
    • Single-linkage clustering
    • Complete linkage clustering
    • Unweighted Pair Group Method with Arithmetic Mean (UPGMA), aka average linkage clustering
  • Centroid-based clustering
    • k-means
    • k-medoids
    • k-medians
    • K-means++
    • Fuzzy c-means
  • Distribution-based clustering
    • Gaussian mixture models via expectation-maximization algorithm
  • Density-based clustering
    • Density-based spatial clustering of applications with noise (DBSCAN)
    • Ordering points to identify the clustering structure (OPTICS)
    • Mean-shift
  • Canapoy
  • Association rule learning
    • Apriori
    • Eclat
  • Topic modeling (text data)
  • Fractal
  • Guassian mixture models

Example Applications

Ensemble Methods Supervised, Unsupervised

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  • Bootstrap aggregating (bagging)
    • Random Forests and ExtraTrees
  • Boosting
    • AdaBoost
    • Gradient boosting
    • Boost by majority
    • BrownBoost
    • xgboost
    • MadaBoost
    • LogitBoost
    • LPBoost
    • TotalBoost
  • Pasting
  • Bayesian model averaging (BMA)
  • Weak learner theory
  • Stacking (stacked generalization) and Blending
  • Bayes optimal classifier
  • Bayesian parameter averaging (BPA)
  • Bayesian model combination (BMC)
  • Bucket of models

Recommender Systems and Recommendations

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  • Collaborative filtering
  • Content-based filtering
  • Graph-based methods

Example Applications

  • Netflix
    • Increase engagement, retention, and revenues
    • Examples
      • "Because you watched ..."
      • "Top picks for ..."
      • Recommendations by category
        • Trending Now
        • Neflix originals
        • TV Documentaries
  • Amazon
    • Increase average order size and therefore sales (studies show between 5.9 to 30%)
    • Examples
      • "Customers who bought this item also bought"
      • "Customers who viewed this item also viewed"
      • "What other items do customers buy after viewing this item?"
      • "Recommendations for you in ..." (e.g., "Recommended for You in Kindle Books")
      • "New for you"
      • "Inspired by your shopping trends"
      • "Inspired by your Wish List"
  • Robo-advisors and portfolio rebalancing
  • Spotify
  • Personalized news feeds, including Facebook

Neural Networks and Deep Learning

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  • Feed forward neural networks (FF or FFNN) and perceptrons (P)
  • Radial basis function (RBF)
  • Hopfield network (HN)
  • Markov chains (MC or discrete time Markov Chain, DTMC)
  • Boltzmann machines (BM)
  • Restricted Boltzmann machines (RBM)
  • Autoencoders (AE)
  • Sparse autoencoders (SAE)
  • Variational autoencoders (VAE)
  • Denoising autoencoders (DAE)
  • Deep belief networks (DBN)
  • Convolutional neural networks (CNN or deep convolutional neural networks, DCNN)
  • Deconvolutional networks (DN)
  • Deep convolutional inverse graphics networks (DCIGN)
  • Generative adversarial networks (GAN)Recurrent neural networks (RNN)Long / short term memory (LSTM)
    • CycleGAN
    • DiscoGAN
    • StarGAN
  • Gated recurrent units (GRU)
  • Neural Turing machines (NTM)
  • Bidirectional recurrent neural networks, bidirectional long / short term memory networks and bidirectional gated recurrent units (BiRNN/BRNN, BiLSTM and BiGRU respectively)
  • Deep residual networks (DRN)
  • Echo state networks (ESN)
  • Extreme learning machines (ELM)
  • Liquid state machines (LSM)
  • Support vector machines (SVM)
  • Kohonen networks (KN, also self organising (feature) map, SOM, SOFM)

Example Applications

  • Feed forward neural network and Multilayer perceptron
    • Regression and classifications
  • Restricted Boltzmann machine
    • Dimensionality reduction
    • Feature extraction/learning
    • Classification
    • Recommender systems
    • Topic modeling
    • Pretraining for weight initialization
  • Autoencoders
    • Dimensionality reduction
    • Anomaly detection
    • Generative modeling
  • Convolutional neural network
    • Image recognition
    • Video recognition
    • Automatic speech recognition (ASR)
    • Recommender systems
    • Natural language processing
  • Recurrent neural network
    • Language modeling
    • Machine translation
    • Handwriting recognition
    • Speech recognition
    • Multilingual Language Processing
    • Natural language processing
  • Self-organizing map
    • Dimensionality reduction
  • Generative models
  • Combinations
    • Image captioning (LSTM + CNN)

Anomaly Detection Supervised, Unsupervised, Semisupervised

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Algorithms

  • Density-based techniques - K-nearest neighbor, Local outlier factor
  • Subspace and correlation-based outlier detection for high-dimensional data
  • One class support vector machines
  • Replicator neural networks
  • Cluster analysis-based outlier detection
  • Deviations from association rules and frequent itemsets
  • Fuzzy logic based outlier detection
  • Ensemble techniques, using feature bagging, score normalization and different sources of diversity
  • PCA (Principle component analysis)

Example Applications

  • Per Wikipedia
    • Intrusion detection
    • Fraud detection
    • Fault detection
    • System health monitoring
    • Event detection in sensor networks
  • Manufacturing
  • Data security
  • Personal security (security screenings at airports, stadiums, concerts, and other venues)
  • Law enforcement
  • Application performance
  • Credit card fraud detection

Reinforcement Learning

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rl

  • Q-learning
  • Markov decision process (MDP)
  • Finite MDPs
  • Monte Carlo methods
  • Criterion of optimality
  • Brute force
  • Value function estimation
  • Direct policy search
  • Temporal difference methods
  • Generalized policy iteration
  • Stochastic optimization
  • Gradient ascent
  • Simulation-based optimization
  • Learning Automata[edit]
  • Example
    • Multi-armed bandit problem

Example Applications

Model selection, validation, and resampling methods

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  • Cross-validation
  • Hyperparameter optimization
  • Bootstrap
  • Mallow’s Cp
  • Akaike information criterion (AIC)
  • Bayesian information criterion (BIC)
  • Minimum description length (MDL)

Model tuning: bias variance tradeoff and model complexity

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  • Validation curve
  • Learning curve
  • Residual sum of squares
  • Goodness-of-fit metrics
  • Grid search

Feature extraction, feature selection, and feature engineering

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  • Wrapper methods
  • Sensitivity analysis
  • PCA
  • Random forests
    • Mean decrease impurity
    • Mean decrease accuracy
  • Text-based
    • Stemming
    • Tokenizing
    • Synonym substitutions
  • Least absolute shrinkage and selection operator (LASSO)
  • Subset selection

Dimensionality Reduction

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  • Principle component analysis (PCA)
  • Kernel PCA
  • Locally-Linear Embedding (LLE)
  • t-distributed Stochastic Neighbor Embedding (t-SNE)
  • Factor analysis
  • K-means clustering
  • Canopy clustering
  • Feature hashing
  • Wrapper methods
  • Sensitivity analysis
  • Self organizing maps
  • Text data
    • Term frequency (TF)
    • Inverse document frequency (IDF)
  • Latent Dirichlet Allocation (LDA)

Information Retrieval

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  • Discounted cumulative gain (DCG)
  • Discounted cumulative gain (nDCG)
  • Term frequency–inverse document frequency (TF-IDF)

Logical Reasoning

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  • Expert systems
  • Logical reasoning

Optimization and Search

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  • Stochastic search
  • Stochastic optimization (SO) methods
  • Genetic algorithms
  • Simulated annealing
  • Gradient search
  • Linear programming
  • Integrer programming
  • Non-linear programming
  • Active learning
  • Ensemble learning
  • Minimum
  • Maximum
  • Optimal value or optimal combination
  • Metaheuristic methods
  • Randomized search methods
  • Tree search
  • Monte Carlo tree search (MCTS)
  • Evolutionary computation

Mathematical Finance and Risk Management

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Example Applications

Ranking

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Example Applications

Time-series

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  • Time series
  • Rolling means
  • Autocorrelation
  • Frequency vs time domains and transfers (e.g., spectral analysis)
  • Trend and residual component decomposition
  • ARIMA modeling for forecasting and detecting trends

Example Applications

Survival

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Example Applications

Forecasting

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  • Last period demand
  • Simple and weighted N-Period moving averages
  • Simple exponential smoothing
  • Poisson process model based forecasting and multiplicative seasonal indexes
  • Average approach
  • Naïve approach
  • Drift method
  • Seasonal naïve approach
  • Time series methods
    • Moving average
    • Weighted moving average
    • Kalman filtering
    • Exponential smoothing
    • Autoregressive moving average (ARMA)
    • Autoregressive integrated moving average (ARIMA)
    • Extrapolation
    • Linear prediction
    • Trend estimation
    • Growth curve (statistics)
  • Causal / econometric forecasting methods
    • Regression analysis
      • Parametric (linear or non-linear)
      • Non-parametric techniques
    • Autoregressive moving average with exogenous inputs (ARMAX)
  • Judgmental methods
    • Composite forecasts
    • Cooke's method
    • Delphi method
    • Forecast by analogy
    • Scenario building
    • Statistical surveys
    • Technology forecasting
  • Artificial intelligence methods
    • Artificial neural networks
    • Group method of data handling
    • Support vector machines
  • Other
    • Simulation
    • Prediction market
    • Probabilistic forecasting and Ensemble forecasting
  • Considerations
    • Seasonality and cyclic behaviour

Example Applications

Simulation

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  • Discrete event simulation
  • Markov models
  • Agent-based simulations
  • Monte carlo simulations
  • Systems dynamics
  • Activity-based simulation
  • ODES and PDES
  • Fuzzy logic

Example Applications

Segmentation

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  • Behavioral
  • Demographic
  • Geographic

Example Applications

Experimentation and Experimental Design

  • Design of Experiments (DOE)
  • A/B testing

Example Applications

Embedded

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  • Deep learning

Example Applications

  • Robotic cognition

Hypothesis Testing

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  • T-test - Compare two groups
  • ANOVA - Compare multiple groups

Hybrid Solutions and Applications

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Example Applications

  • Google search
  • Autonymous vehicles (Business insider)
    • Reduce accidents and related injuries and death
    • Improved traffic (via ridesharing and smart traffic lights) and fuel efficiency
    • Reduced carbon emissions
    • Faster commutes and travel time
    • Get your time back in the vehicle to do what you want
    • Efficient ride-sharing
    • Companies
  • Home monitoring, control, and security
  • Voice-controled robotics
  • Photo-realistic pictures generation from text or sketches
  • Music generation
  • Movie and script generation
  • Automatically generated software code
    • Companies
  • Authentication without passwords (using mobile phone that knows it's you)
  • Customer support
  • Optimized directions and routes
  • Plagiarism Checkers
  • Robo-readers and graders
  • Virtual reality
  • Gaming
  • Zillow’s “zestimate” feature, which estimates the price of homes
  • Medical/Health
  • Sales
  • Crime
    • Who, Type, and location
    • Based on previous crime and social media activity
    • Companies
  • Suicide risk
  • Agriculture - predicting crop yields
  • Uber's ETA

Other Algorithms

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  • Massive-scale graph
  • Geospatial temporal predictive analytics
  • Hyperfast analytics
  • Embedded deep learning
  • Cognitive machine learning and IoT
  • Natural language processing, generation, and understanding
  • Structured database generation
  • Game theory
  • Control theory
  • Operations research
  • Information theory
  • Simulation-based optimization
  • Multi-agent systems
  • Swarm intelligence
  • Genetic algorithms

Literature surveys

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CV Methods

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  • Image
  • Speech
  • Video
  • Text and optical character
  • Pattern
  • Audio
  • Facial
  • Handwriting

Example Applications

Related papers

NLP Methods

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  • Text processing
  • Lexical Analysis
  • Text Mining
    • Information retrieval
    • Text categorization
    • Text clustering
    • Concept/entity extraction
    • Production of granular taxonomies
    • Sentiment analysis
    • Document summarization
    • Entity relation modeling
    • Named entity recognition
    • Recognition of Pattern Identified Entities
    • Coreference
    • Syntactic parsing
    • Part-of-speech tagging
    • Quantitative text analysis
Natural Language Processing (NLP), Natural Language Generation (NLG), and Natural Language Understanding (NLU)
  • Syntax
    • Lemmatization
    • Morphological segmentation
    • Part-of-speech tagging
    • Parsing
    • Sentence breaking (also known as sentence boundary disambiguation)
    • Stemming
    • Word segmentation
    • Terminology extraction
  • Semantics
    • Lexical semantics
    • Machine translation
    • Named entity recognition (NER)
    • Natural language generation
    • Natural language understanding
    • Optical character recognition (OCR)
    • Question answering
    • Recognizing Textual entailment
    • Relationship extraction
    • Sentiment analysis
    • Topic segmentation and recognition
    • Word sense disambiguation
  • Discourse
    • Automatic summarization
    • Coreference resolution
    • Discourse analysis
  • Speech
    • Speech recognition
    • Speech segmentation
    • Text-to-speech

Example Applications

Speech Methods

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Robotics Methods

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AI Tools

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Have a quick look at these threads

Hardware Options Infrastructure for AI

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mlhardwareoptions

AI, Cloud, API's and Platforms

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iHA1CLBhh4eJL4oC article-image

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Vendor

Bots

APIs

ML Frameworks

Fully Managed ML

AWS

·   Lex

 

·  Comprehend

·  Polly

·  Rekognition

·  Translate

·  Transcribe

 

·   Machine Learning

·   Deep Learning AMIs

·   Apache MXNet on AWS

·   TensorFlow on AWS

SageMaker

Microsoft Azure

·  Bot Service

·  Bing Web Search API

·  Text Analytics API

·  Face API

·  Computer Vision API

 

·  Machine Learning

·  Machine Learning Studio

Google Cloud

· Dialogflow

· Natural Language

· Speech API

· Translation API

· Vision API

· Video Intelligence

 

· Machine Learning Engine

· AutoML

IBM Cloud

·Conversation

·Watson Virtual Agent

 

·Watson Discovery

·Discovery News

·Natural Language Understanding

·Watson Knowledge Studio

·Visual Recognition

·Speech to Text

·Text to Speech

·Language Translator

·Natural Language Classifier

·Personality Insights

·Tone Analyzer

 

·Watson Machine Learning

N/A

Media and blogs

DataMining MachineLearning DeepLearning Applications

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Quickly scan and skim below articles

Media and blogs

Computer Vision Applications

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-> Video surveillance
-> Machine/vehicular object detection/identification/avoidance
-> Medical image analysis
-> Augmented reality (AR)/virtual reality (VR) development
-> Localization and mapping
-> Converting paperwork into digital data
-> Human emotion analysis
-> Ad insertions into images and video
-> Face recognition
-> Real estate development optimization

NLP Applications

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Speech Recog Applications

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AI in Robotics Applications

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Bigdata and Data Science

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types-of-data-growing

Desciplines

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hot%202017-10-17%20at%209.30.13%20PM

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venn

Datascience puzzle and AI lock in Loop

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data-science-puzzle-2017

eiendomsbransjen

Data pipeline

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so, you have data --> build data pipelines

334-Think-Big-Analytics-RGB-KYLO-Diagram-v12

The Heirarchy

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Big Data Abstraction

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Big-Data-Abstraction fig1

Bigdata Tools

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ai-arch-09-p27

Datascience Desciplines

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Architectural Goals, Principles, and Considerations

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  • Latency (near real time)
  • Reliability and fault tolerance
  • Availability
  • Scalability/Volume handling
  • Performance/speed
    • Goals and implementation - Oracle
      • Analyze and transform data in real-time
      • Optimize data structures for intended use
      • Use parallel processing
      • Increase hardware and memory
      • Database configuration and operations
      • Dedicate hardware sandboxes
      • Analyze data at rest, in-place
  • Throughput
  • Extensibility
  • Security
  • Cost/financial
  • Data quality
  • Skills availability
  • Backup and recovery
  • Locations and placement
  • Privacy and sensitive data
  • Disaster recovery
  • Schema on read vs schema on write
    • Bringing the analytical capabilities to the data, VS
    • Bringing the data to the analytical capabilities through staging, extracting, transforming and loading
  • Maturity Considerations - Oracle
    • Reference architecture
    • Development patterns
    • Operational processes
    • Governance structures and polices

Data types and sources

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  • Structured
    • Transactions
    • Master and reference
  • Unstructured
    • Text
    • Image
    • Video
    • Audio
    • Social
  • Semi-structured
    • Machine generated
  • Data storage (databases)
  • Sensors
  • Events
  • Parquet
  • RFID tags
  • Instore WiFi logs
  • Machine Logs
    • Application
    • Events
    • Server
    • CDRs
    • Clickstream
  • Text, including documents, emails, scanned documents, records, ...
  • Social networks
  • Public web
  • Geo-location/geospatial
  • Feeds
  • Machine generated
  • Clickstream
  • Software
  • Media
    • Images
    • Video
    • Audio
  • Business applications
    • OLTP - Online transaction processing
    • ERP - Enterprise resource planning
    • CRM - Customer relationship management
    • SCM - Supply chain management
    • HR
    • Product/Project management
  • Online chat
  • Merchant listings
  • DMP - Data management platform (advertising/marketing)
  • CDR - Call detail records
  • Surveys, questionnaires, binary questions, and sentiment
  • Billing data
  • Product catalog
  • Network data
  • Subscriber data
  • Staffing
  • Inventory
  • POS and transactional
  • eCommerce transactions
  • Biometrics
  • Mobile devices
  • Weather data
  • Traffic pattern data
  • Mobile devices
  • Surveillance

Big Data Architecture Patterns

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  • Polyglot
  • Lambda
  • Kappa
  • IOT-A
    • Message Queue/Stream Processing (MQ/SP) block
      • Buffer data
        • Processing speed
        • Throughput handling of downstream components
        • Micro-batching can increase ingestion rate into downstream components
      • Process and filter data
        • Cleaning and removal
        • Stream processing
          • Continuous queries
          • Aggregates
          • Counts
          • Real-time machine learning/AI
      • Output
        • Real-time
        • Ingest data into downstream blocks (DB and/or DFS)
      • Example technologies
    • Database (DB) block
      • Provides granular, structured, low-latency access to the data
      • Typically NoSQL
        • MongoDB
        • Cassandra
        • HBase
      • Output
        • Interactive ad-hoc querying
          • Data store API (e.g., HBase, MongoDB, ...)
          • Standard SQL interface
      • Example technologies
    • Distributed File System (DFS) block
      • Batch jobs over entire dataset
        • Aggregations
        • Reporting
        • Integration across data sources
          • E.g., with unstructured data
      • Long term storage (archiving)
      • Example technologies

Enterprise Big Data Architectural Components

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  • Governance
    • Govern data quality
  • Operations, Infrastructure, and DevOps
  • Monitoring
  • Security and privacy
    • Authentication
    • Authorization
    • Accounting
    • Data protection
    • Compliance
  • Data Aquisition, Ingestion, and Integration
    • Messaging and message queues
    • ETL/ELT
    • Change data capture
    • FTP
    • API/ODBC
    • Replication
    • Bulk movement
    • Virtualization
    • Analytics types and options on ingestion - Oracle
      • Sensor-based real-time events
      • Near real-time transaction events
      • Real-time analytics
      • Near real time analytics
      • No immediate analytics
  • Data Processing
    • Batch and stream processing/computing (velocity)
      • Massive scaling and processing of multiple concurrent input streams
    • Parallel computing platform
      • Clusters or grids
      • Massively parallel processing (MPP)
      • High performance computing (HPC)
    • Options - Oracle
      • Leave it at the point of capture
      • Add minor transformations
      • ETL data to analytical platform
      • Export data to desktops
    • Fast data - Oracle
      • Streams
      • Events
      • Actions
  • Data Access
    • Querying
    • Real-time analytics
    • BI analytics
    • MapReduce analytics
  • Data Modeling and Structure
    • Star schema
    • Snowflake schema
  • Data Analysis, data mining, discovery, simulation, and optimization
    • Advanced analytics and modeling
    • Text and natural language analytics
    • Video and voice analytics
    • Geospatial analytics
    • Data visualization
    • Data mining
    • Where to do analysis - Oracle
      • At ingest – real time evaluation
      • In a raw data reservoir
      • In a discovery lab
      • In a data warehouse/mart
      • In BI reporting tools
      • In the public cloud
      • On premises
    • Data sets
    • Data science
    • Data discovery
    • In-place analytics
    • Faceted analytics
    • SQL analytics
  • Data Storage and Management
    • Data lake
    • Data warehouse (volume), aka enterprise information store
      • Centralized, integrated data store
      • Powers BI analytics, reporting, and drives actionable insights
      • Responsible for integrating data
      • Structured, prepared, and stored data optimized for
        • Analytical applications and decision support
        • Querying and reporting
        • Data mining
      • In-database analytics
      • Operational analytics
      • MPP engine
      • 'Deep analytical appliance' - IBM
    • Operational data store (ODS)
    • Database Systems and DBMS
      • Relational (RDBMS)
      • NoSQL
        • Real-time analytics and insights
      • NewSQL
      • Hybrid
    • Data marts
      • Data warehouse extracted data subsets oriented to specific business lines, departments or analytical applications
      • Can be a 'live' data mart
    • File systems (Non-distributed)
    • Distributed file systems (e.g., HDFS) and Hadoop (volume and variety)
      • Real-time and MapReduce analytics and insights
      • Deep analysis of petabytes of structured and unstructured data
    • In-memory
    • Data factory
    • Data Reservoir
    • Dedicated and ad-hoc
      • Discovery labs
      • Sandboxes
  • Data lifecycle management
    • Rule-based Data and Policy Tracking
    • Data compression
    • Data archiving
  • Deployment Choice
    • On-premise, aka traditional IT
    • In-cloud
      • Public cloud
      • Private cloud
    • Appliance
    • Managed services
  • Presentation, Analytics, and Applications (visibility)
    • Browser/web
    • Mobile
    • Desktop
    • Dashboards
    • Reports
    • Notifications and messaging
    • Scorecards
    • Charts and graphics
    • Visualization and discovery
    • Search
    • Alerting
    • EPM and BI applications
    • Recommendations

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11

missing


missing

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AWS

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Google Cloud Platform

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By Technology

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Oracle Architecture and Patterns Examples

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  • Designed to be a flexible and a “just-in-time” architecture development approach
  • Key Steps
    • Establish Business Context and Scope
    • Establish an Architecture Vision
    • Assess the Current State
    • Establish Future State and Economic Model
    • Develop a Strategic Roadmap
    • Establish Governance over the Architecture

IBM Architecture and Patterns Examples

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Key Functionalities

  • Data ingestion
    • Optimize the process of loading data in the data store to support time-sensitive analytic goals.
  • Search and survey
    • Secure federated navigation and discovery across all enterprise content.
  • Data transformation
    • Convert data values from source system and format to destination system and format.
  • Analytics
    • Discover and communicate meaningful patterns in data.
  • Actionable decisions
    • Make repeatable, real-time decisions about organizational policies and business rules.
  • Discover and explore
    • Discover, navigate, and visualize vast amounts of structured and unstructured information across many enterprise systems and data repositories.
  • Reporting, dashboards, and visualizations
    • Provide reports, analysis, dashboards, and scorecards to help support the way that people think and work.
  • Provisioning
    • Deploy and orchestrate on-premises and off-premises components of a big data ecosystem.
  • Monitoring and service management
    • Conduct end-to-end monitoring of services in the data center and the underlying infrastructure.
  • Security and trust
    • Detect, prevent, and otherwise address system breaches in the big data ecosystem.
  • Collaborate and share

Solution Patterns - IBM

  • Landing Zone Warehouse
  • Virtual Tables
  • Discovery Tables
  • Streams Dynamic Warehouse
  • Streams Detail with Update
  • Direct Augmentation
  • Warehouse Augmentation
  • Streams Augmentation
  • Dynamic Search Cube

Component Patterns - IBM

  • Source Data
  • Source Event
  • Landing Area Zone ETL
    • Extract
    • Normalize
    • Clean
  • Landing Area Zone Search and Survey
    • Find
    • Filter
    • Extract
  • Landing Area Zone Stream Filter
  • Landing Area Zone Stream Augmentation
  • Landing Area Zone Warehouse Augmentation
  • Landing Area Zone Index
  • Exploration Mart
  • Analytics Mart
  • Report Mart
  • Virtual Report Mart
  • Virtual Search Mart
  • Predictive Analytics

Big Data Exploration Example Architecture IBM

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  • Applications layer
    • Consists of
      • Visualization
      • Discovery
      • Analysis
      • Reporting
      • Statistics
      • Text and entity analytics
    • Access
      • SQL
      • MDX
      • Search
      • REST
  • Discovery and assembly layer
    • Consists of
      • Virtual search mart
        • Faceted search
      • Analytics mart
        • Report mart
        • Discovery table
        • Search and survey
      • Report mart
        • ETL
        • Analytics
        • Streams
    • Access
      • NoSQL
      • SQL
      • Search
      • REST
  • Landing layer
    • Consists of
      • Shared warehouse and ETL
        • Extract
        • Provision
    • Access
      • Search
      • REST
      • SQL
      • Files
  • Source layer
    • Sensors and telemetry
    • Internet
    • Social media
    • Public data
    • Enterprise data

Big data and analytics architecture on cloud IBM

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  • Analytics-as-a-service
    • Consumes both data at rest and in motion
    • Applies analytical algorithms
    • Provides
      • Dashboards
      • Reports
      • Visualizations
      • Insights
      • Predictive modeling
    • Abstracts away all complexity of data collection, storage, and cleansing
  • Data-as-a-service
    • Data-at-rest-service
    • Data-in-motion-service
  • NoSQL tools (Hive, Pig, BigSQL, ...)
  • EMR clusters (Hadoop, Cassandra, MongoDB, ...) and Traditional DW
  • Big data file system (HDFS, CFS, GPFS, S3, ...)
  • Infrastructure & Appliances (Baremetal or IaaS) and object storage

Bigdata, AI, Datascience Reads

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Advanced Analytics

Artificial Intelligence

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Machine Learning

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Data Science

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Data Scientists

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Statistics, Probability, and Mathematics

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Internet of Things (IoT)

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Jobs, Skills, and Salary Trends

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Tools and Language Popularity, Comparisons, Trends, ...

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Freelancing and Consulting

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Online Learning

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Big Data and Data Engineering

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Databases, Schemas, and Data Modeling

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Product

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Methodologies

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Computer Science and Programming

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Business

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Gartner Magic Quadrants

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Architectures

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References

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|| Big Data | Public Datasets | Hadoop | Data Engineering | Streaming | Apache Spark | Databases | MySQL | SQLAlchemy | InfluxDB | Neo4j | MongoDB | RethinkDB | TinkerPop | PostgreSQL | CouchDB | HBase | datascience | data-science-viz | data-science-ipython-notebooks | PythonDataScienceHandbook | go-ds| data-science-blogs | data-science | DataSciencePython | courses| data-science-from-scratch | spark-notebook | LearnDataScience | data-science-at-the-command-line | Data-Science-45min-Intros | DataScienceResources | cookiecutter-data-science | DataScienceR | awesome-time-series-database | bigdata-ecosystem

|| cheatsheets-ai | Learn Data Science open resources | List of Data Science/Big Data Resources | ISLR-python | Evaluation of Deep Learning Frameworks | Data Science Resources | Data science blogs | Machine learning algorithms | Machine Learning for Software Engineers | Microsoft Team Data Science Process Repository | Open Source Data Science Masters | The Field Guide to Data Science (Booz, Allen, Hamilton) | Amazon Web Services — a practical guide | Minimal and clean examples of machine learning algorithms | Machine Learning From Scratch ||

|| Awesome R | Awesome Data Science | Awesome Deep Learning | Awesome Python | Awesome Scala | Awesome Machine Learning | Awesome IoT | Awesome AWS | Awesome Cheatsheet | Awesome linux Software | Awesome Math ||

|| A Whirlwind Tour of Python | Scikit-learn Tutorial | theano-tutorial | IPython Theano Tutorials | Machine Learning & Deep Learning Tutorials | Python Data Science Tutorials | TensorFlow-World | GitHub-powered Jupyter nbviewer | A gallery of interesting Jupyter Notebooks | DeepSchool.io - Deep Learning tutorials in jupyter notebooks | Jupyter kernels | Data science IPython notebooks | machine_learning | Statistical Data Analysis in Python | ipython-notebooks | Spark Notebook | Statsmodels examples | Introduction to Artificial Neural Networks and Deep Learning: A Practical Guide with Applications in Python | Python Machine Learning book resources | Python Machine Learning book FAQ | Learning-Predictive-Analytics-with-R | Data Science from Scratch book resources | IPython Cookbook materials | Python Data Science Handbook Supplemental Materials | Hadoop Application Architectures | Advanced Analytics with Spark | Think Stats | Think Bayes | Think Python | Harvard's CS109 Data Science | General Assembly's Data Science course materials | Data Science Specialization resources | Data Science Specialization notes ||

Online Visualization

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What would you like to show

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1528892035

Cheatsheets

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Python

| Pandas Cheat Sheet - Python for Data Science | NumPy Cheat Sheet - Python for Data Science | Bokeh Cheat Sheet - DataCamp | PySpark Cheat Sheet: Spark in Python | Python For Data Science Cheat Sheet - Pandas Basic | Python 3 Cheat Sheet | Python For Data Science Cheat Sheet - Python Basics |

R

| RStudio Cheat Sheet Collection | R Reference Card for Data Mining | Data Science Resources : Cheat Sheets |

Data Science and Machine Learning

| Machine learning algorithm cheat sheet | 11 Steps for Data Exploration in R | Data Science Cheat Sheet | Machine Learning Periodic Table | Guide to Data Science Cheat Sheets | 50+ Data Science and Machine Learning Cheat Sheets, Updated | Comparing Supervised Learning Algorithms - Google Doc Spreadsheet | Essential Cheat Sheets for Machine Learning and Deep Learning Engineers | Scikit-Learn Cheat Sheet: Python Machine Learning |

Artificial Intelligence

| AI Cheat Sheet | Penn Treebank POS Tags | Brown Corpus |

Statistics and Mathematics

| MIT Statistics Cheat Sheet | ALL IN ONE MATHEMATICS CHEAT SHEET |

Software, Packages, and Libraries

| The Data Stack | bigdata-2016 | Big Data’s Leadership & Development

Databases and querying languages

| MongoDB - Cheat Sheet |

Shortcuts

| Jupyter Notebook Keyboard Shortcuts|

Markup and Syntax

| GitHub markdown cheatsheet | GitHub markdown guide |

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Python for Bigdata

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nxDTMqJ

R for Bigdata

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eVZHWtc

Open Datasets

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Dataset By Category

Specific Data Sets

Data Portals and Meta portals

Data Marketplaces and Monetization

AI MTDB

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Movies

|| 2001: A Space Odyssey | A.I. Artificial Intelligence | Automata | Blade Runner | Chappie | Ex Machina | Her | I, Robot | Prometheus | The Terminator series | Transcendence | The Matrix Trilogy | WarGames | Chappie (2015) | Metropolis (1927) | Star Wars series | The Hitchhiker's Guide to the Galaxy (2005) | Avengers series | Automata (2014) | Stealth (2005) | Enthiran (2010) | TRON: Legacy (2010) | The Creation of the Humanoids (1962) ||

TV Shows

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|| Black Mirror | Intelligence | Minority Report | Almost Human | Battlestar Galactica | Caprica | Numb3rs | Humans | Person of Interest | Small Wonder | Mr. Robot | Total Recall 2070 | Westworld | Terminator: The Sarah Connor Chronicles ||

Documentaries

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|| The Rise of AI Deep Learning - Documentary 2018 HD | Ray Kurzweil - The Singularity Is Near | Lo and Behold, Reveries of the Connected World (2016) | AlphaGo | Revolutionaries: Artificial Intelligence | Road to AI | Artificial Intelligence and Robotics | Singularity Or Bust | The Smartest Machine on Earth | Humans Need not Apply | Technocalyps | Hans Rosling's 200 Countries, 200 Years, 4 Minutes - The Joy of Stats - BBC Four | Future Intelligence | Hans Rosling: Let my dataset change your mindset | BBC Documentaries 2016: The Joy of Data | Science Documentary 2016 | Big Data | Dangers of artificial intelligence documentary (2018) | BBC Documentary 2018 Artificial Intelligence | Great Debate - Artificial Intelligence: Who is in control? (OFFICIAL) | Future World 2030: Dr Michio Kaku's predictions. Documentary 2018 | The World In 2050 | Deep Learning: Intelligence from Big Data | How to Become a Data Scientist | The Future of AI: from Deep Learning to Deep Understanding, Ben Goertzel | Ted- Artificial intelligence ||

Books

Principles and Rules

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  • Principle of least priviledge - Requires that in a particular abstraction layer of a computing environment, every module (such as a process, a user, or a program, depending on the subject) must be able to access only the information and resources that are necessary for its legitimate purpose
  • Peter principle - The selection of a candidate for a position is based on the candidate's performance in their current role, rather than on abilities relevant to the intended role
    • Employees only stop being promoted once they can no longer perform effectively, and "managers rise to the level of their incompetence."
  • Pareto principle
    • 20% of the features will account for 80% of the value
    • 20% of the work will produce 80% of the value
  • Ninety/Ninety Rule
    • The first 90 percent of the code accounts for the first 90 percent of the development time. The remaining 10 percent of the code accounts for the other 90 percent of the development time. (180% of time)
  • SUCCESs - Made to stick principles
    • Simplicity
    • Unexpectedness
    • Concreteness
    • Credibility
    • Emotions
    • Stories
  • Build, measure, learn
    • Maximize learning through incremental and iterative engineering
    • Build relates to MVP, ie, the simplest product to show customers to get most learning at that time
    • Goal is always to maximize learning and not build fully featured beta/prototype
    • Lean startup version: hypothesis, design experiments, test, insight

Laws

  • Parkinson's law of triviality - Members of an organisation give disproportionate weight to trivial issues
  • Parkinson's law - Work expands so as to fill the time available for its completion
  • Brooks's law - Adding manpower to a late software project makes it later
  • Hofstadter's law - It always takes longer than you expect, even when you take into account Hofstadter's Law
  • Little's law - The long-term average number of customers in a stable system L is equal to the long-term average effective arrival rate, λ, multiplied by the (Palm‑)average time a customer spends in the system, W; or expressed algebraically: L = λW.

Quotes, Idioms, and Sayings

  • Nothing is more permanent than a temporary solution
  • The world doesn't need wrong answers in record time
  • Work on the business, not in the business
  • Fail to plan is to plan to fail
  • Silence equals agreement
  • Deliver results rather than excuses
  • Don't prove own importance by vetoing good ideas and road-blocking productive work
  • Accept total accountability and make it happen
  • When making decisions, slower ultimate success is better than rapid permanent failure
  • YAGNI (from XP) - You aren’t gonna need it
    • "Always implement things when you actually need them, never when you just foresee that you need them."
  • DTSTTCPW (from XP) - "do the simplest thing that could possibly work"
  • Skip blame and complain game and get things done despite major obstacles. Victim mentality is the kiss of death.
  • KISS - Keep it simple, stupid
  • A good plan implemented today is better than a perfect plan implemented tomorrow
  • MLP - minimum lovable product
  • A problem without a solution is a complaint
  • Working on the right thing is as—if not more important—than how hard you are working
  • You can’t have five North Stars, you can’t have five most important goals…
  • Prioritize goals that are ‘critical’ ahead of goals that are ‘beneficial

General terms, models, acronymns, and concepts

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  • Priorities for business (in order)
    • People
    • Products
    • Profits
  • Iron triangle success
    • Build MVP based on perceived value and market research with associated scope and let that choose the time and resources required
  • GRIT
    • Generosity
    • Respect
    • Integrity
      • Accountability
    • Truth
  • Give credit, don't take it
  • The most effective way to solve any problem is to put together all of the people with the skills required to solve it, i.e., a cross-functional or multi-disciplinary team
  • A startup is a temporary organization designed to search for a repeatable and scalable business model
  • When people don’t take responsibility
    • Reputations decline
    • Timelines are extended
    • Performance falls short
    • Frustration shoots up
    • Goals are adapted
    • Accusation and blame escalates
    • Gossip soars. “What’s up with Fred? He never get’s things done on time.
    • Excuses abound. Irresponsible people give “good” reason for irresponsibility.
    • Higher ups complain
    • Stress increase
  • Curse of knowledge
    • The curse of knowledge is a cognitive bias that leads better-informed parties to find it extremely difficult to think about problems from the perspective of lesser-informed parties
    • Aka the ‘tapper effect’ (2.5% guessed right), charades, etc.
  • Maslow's Hierarchy of Needs
    • Self-actualization
    • Esteem
    • Love/belonging
    • Safety
    • Physiological
  • Analysis paralysis
    • The state of over- analyzing (or over-thinking) a situation so that a decision or action is never taken, in effect paralyzing the outcome
  • The paradox of choice
  • SMART model for goals
    • S – Specific
    • M – Measurable
    • A – Achievable
    • R – Relevant
    • T – Time-boxed
  • FURPS - Model for classifying software quality attributes
    • Functionality
    • Usability
    • Reliability
    • Performance
    • Supportability
  • AAARR metrics for pirates
    • Acquisition
    • Activation
    • Retention
    • Referrals
    • Revenue
  • MTMM - Metric that matters most
  • Problem space vs solution space
    • Problem space - user benefit
      • A customer problem, need, or benefit that the product should address
      • A product requirement
    • Solution space - product
      • A specific implementation to address the need or product requirement
  • INVEST - user story model
    • Independent: The user story should be self-contained, in a way that there is no inherent dependency on another user story.
    • Negotiable: User stories, up until they are part of an iteration, can always be changed and rewritten.
    • Valuable: A user story must deliver value to the end user.
    • Estimatable: You must always be able to estimate the size of a user story.
    • Small: User stories should not be so big as to become impossible to plan/task/prioritize with a certain level of certainty.
    • Testable: The user story or its related description must provide the necessary information to make test development possible.

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AI Industrial Applications

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uc

Healthcare

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media and blogs

Manufacturing

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media and blogs

Legal or Law

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media and blogs

related paper

Games

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+ Examples
    * AlphaGo (Monte-Carlo Tree Search)
    * DeepBlue
    * Watson Jeapordy

media and blogs

Telecommunications

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+ 5G
+ IoT
+ Autonomous vehicles
+ Smart cities
+ Engagement insights whose info is sold back to advertisers

media and blogs

Music

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media and blogs

HR and Recruitment

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media and blogs

Agriculture

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media and blogs

Education

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media and blogs

Media and Entertainment

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media and blogs

Advertising and Marketing

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+ Segmentation
+ Ranking/scoring
+ Market basket analysis > location and promotions of items
+ Cohort analysis and segmentation > targeted marketing
+ Customer churn prediction > churn prevention
+ Customer lifetime value forecasting > future business value and predicting growth
+ Targeted and personalized advertising
+ Companies
    * [Appier](https://www.appier.com/)
    * [Voyager Labs](http://voyagerlabs.co/)

media and blogs

Retail and Fashion

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+ Recommendation engines
+ Virtual reality fitting systems
+ Shopping experience

media and blogs

Cybersecurity

Security intelligence (security, fraud, and risk analysis)

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wesome-ml-for-cybersecurity

media and blogs

Aviation

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media and blogs

Automotive and Trasportation

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+ Companies
    * Google
    * Apple
    * Uber
    * Lyft
    * Tesla
    * Waymo

|| awesome-autonomous-vehicles | self-driving-cars-medium | awesome-self-driving-cars | av-resources ||

media and blogs

Finance and Banking

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augmented-finance-machine-intelligence

Top Media

related papers

  • An Ontology-Based Dialogue Management System for Banking and Finance Dialogue Systems.arxiv - 2018
  • Deep Learning in Finance. arxiv - 2018
  • Forecasting Economics and Financial Time Series: ARIMA vs. LSTM. arxiv - 2018
  • Geometric Learning and Filtering in Finance. arxiv - 2017
  • On Feature Reduction using Deep Learning for Trend Prediction in Finance. arxiv - 2017
  • Forecasting Volatility in Indian Stock Market using Artificial Neural Network with Multiple Inputs and Outputs. arxiv - 2016
  • Financial Market Modeling with Quantum Neural Networks. arxiv - 2015
  • Identifying Metaphoric Antonyms in a Corpus Analysis of Finance Articles. arxiv - 2013
  • Good Debt or Bad Debt: Detecting Semantic Orientations in Economic Texts. arxiv - 2013
  • Identifying Metaphor Hierarchies in a Corpus Analysis of Finance Articles. arxiv - 2012

Logistics

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media and blogs

Customer Service experience and engagement

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media and blogs

Oil and gas

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IT industry

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AI Safety Surveys

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Quotes on AI

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  • "AI is one of the most important things humanity is working on. It is more profound than, I dunno, electricity or fire," - Sundar Pichai

  • “What we really need to do is make sure that life continues into the future. […] It’s best to try to prevent a negative circumstance from occurring than to wait for it to occur and then be reactive.” -Elon Musk on keeping AI safe and beneficial

  • “The development of full artificial intelligence could spell the end of the human race….It would take off on its own, and re-design itself at an ever increasing rate. Humans, who are limited by slow biological evolution, couldn't compete, and would be superseded.”— Stephen Hawking told the BBC

  • “I visualize a time when we will be to robots what dogs are to humans, and I’m rooting for the machines.” —Claude Shannon

  • “Artificial intelligence would be the ultimate version of Google. The ultimate search engine that would understand everything on the web. It would understand exactly what you wanted, and it would give you the right thing. We're nowhere near doing that now. However, we can get incrementally closer to that, and that is basically what we work on.” —Larry Page

  • “The pace of progress in artificial intelligence (I’m not referring to narrow AI) is incredibly fast. Unless you have direct exposure to groups like Deepmind, you have no idea how fast—it is growing at a pace close to exponential. The risk of something seriously dangerous happening is in the five-year timeframe. 10 years at most.” —Elon Musk wrote in a comment on Edge.org

  • “The upheavals [of artificial intelligence] can escalate quickly and become scarier and even cataclysmic. Imagine how a medical robot, originally programmed to rid cancer, could conclude that the best way to obliterate cancer is to exterminate humans who are genetically prone to the disease.” — Nick Bilton, tech columnist wrote in the New York Times

  • “I don’t want to really scare you, but it was alarming how many people I talked to who are highly placed people in AI who have retreats that are sort of 'bug out' houses, to which they could flee if it all hits the fan.”—James Barrat, author of Our Final Invention: Artificial Intelligence and the End of the Human Era, told the Washington Post

  • “I’m increasingly inclined to think that there should be some regulatory oversight, maybe at the national and international level, just to make sure that we don’t do something very foolish. I mean with artificial intelligence we’re summoning the demon.” —Elon Musk warned at MIT’s AeroAstro Centennial Symposium

  • “The real question is, when will we draft an artificial intelligence bill of rights? What will that consist of? And who will get to decide that?” —Gray Scott

  • “We must address, individually and collectively, moral and ethical issues raised by cutting-edge research in artificial intelligence and biotechnology, which will enable significant life extension, designer babies, and memory extraction.” —Klaus Schwab

  • “Some people call this artificial intelligence, but the reality is this technology will enhance us. So instead of artificial intelligence, I think we'll augment our intelligence.” —Ginni Rometty

  • “I'm more frightened than interested by artificial intelligence - in fact, perhaps fright and interest are not far away from one another. Things can become real in your mind, you can be tricked, and you believe things you wouldn't ordinarily. A world run by automatons doesn't seem completely unrealistic anymore. It's a bit chilling.” —Gemma Whelan

  • “You have to talk about 'The Terminator' if you're talking about artificial intelligence. I actually think that that's way off. I don't think that an artificially intelligent system that has superhuman intelligence will be violent. I do think that it will disrupt our culture.” —Gray Scott

  • “You have to talk about 'The Terminator' if you're talking about artificial intelligence. I actually think that that's way off. I don't think that an artificially intelligent system that has superhuman intelligence will be violent. I do think that it will disrupt our culture.” —Gray Scott

  • “If the government regulates against use of drones or stem cells or artificial intelligence, all that means is that the work and the research leave the borders of that country and go someplace else.” —Peter Diamandis

  • “The key to artificial intelligence has always been the representation.” —Jeff Hawkins

  • “It's going to be interesting to see how society deals with artificial intelligence, but it will definitely be cool.” —Colin Angle

  • “Anything that could give rise to smarter-than-human intelligence—in the form of Artificial Intelligence, brain-computer interfaces, or neuroscience-based human intelligence enhancement - wins hands down beyond contest as doing the most to change the world. Nothing else is even in the same league.” —Eliezer Yudkowsky

  • “Artificial intelligence is growing up fast, as are robots whose facial expressions can elicit empathy and make your mirror neurons quiver.” —Diane Ackerman

  • “Someone on TV has only to say, ‘Alexa,’ and she lights up. She’s always ready for action, the perfect woman, never says, ‘Not tonight, dear.’” —Sybil Sage, as quoted in a New York Times article

  • “Some people worry that artificial intelligence will make us feel inferior, but then, anybody in his right mind should have an inferiority complex every time he looks at a flower.” —Alan Kay

  • “Artificial intelligence will reach human levels by around 2029. Follow that out further to, say, 2045, we will have multiplied the intelligence, the human biological machine intelligence of our civilization a billion-fold.” —Ray Kurzweil

  • “Nobody phrases it this way, but I think that artificial intelligence is almost a humanities discipline. It's really an attempt to understand human intelligence and human cognition.” —Sebastian Thrun

  • “A year spent in artificial intelligence is enough to make one believe in God.” —Alan Perlis

  • “There is no reason and no way that a human mind can keep up with an artificial intelligence machine by 2035.” —Gray Scott

  • “Is artificial intelligence less than our intelligence?” —Spike Jonze

  • “By far, the greatest danger of Artificial Intelligence is that people conclude too early that they understand it.” —Eliezer Yudkowsky

  • “The sad thing about artificial intelligence is that it lacks artifice and therefore intelligence.” —Jean Baudrillard

  • “Forget artificial intelligence - in the brave new world of big data, it's artificial idiocy we should be looking out for.” —Tom Chatfield

  • “Before we work on artificial intelligence why don’t we do something about natural stupidity?” —Steve Polyak

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Gopala KR / @gopala-kr

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