Applications of AI : Industrial
One Hundred Year Study on Artificial Intelligence (AI100) | “AI and Life in 2030” | AITopics |
- AI approaches
- The Multidisciplinary
- Why AI is on Rise
- AI Goals, Techniques and Tools
- Goals
- Methods, categories, algorithms and applications
- Algorithm Categories
- Algorithms and Applications by Data Type
- Algorithms and Applications by Task
- Regression, Univariate, Multivariate
- Classification Unary, Binary and Multiclass
- Regularization and Overfitting Prevention
- Clustering
- Ensemble Methods Supervised, Unsupervised
- Recommender Systems and Recommendations
- Neural Networks and Deep Learning
- Anomaly Detection Supervised, Unsupervised, Semisupervised
- Reinforcement Learning
- Model selection, validation, and resampling methods
- Model tuning bias variance tradeoff and model complexity
- Feature extraction, feature selection, and feature engineering
- Dimensionality Reduction
- Information Retrieval
- Logical Reasoning
- Optimization and Search
- Mathematical Finance and Risk Management
- Ranking
- Time-series
- Survival
- Forecasting
- Simulation
- Segmentation
- Experimentation and Experimental Design
- Embedded
- Hypothesis Testing
- Hybrid Solutions and Applications
- Other Algorithms
- Literature surveys
- CV Methods
- NLP Methods
- Speech Methods
- Robotics Methods
- AI Tools
- Hardware Options Infrastructure for AI
- AI, Cloud, API's and Platforms
- DataMining MachineLearning DeepLearning Applications
- Computer Vision Applications
- NLP Applications
- Speech Recog Applications
- AI in Robotics Applications
- Bigdata and Data Science
- Desciplines
- Data pipeline
- Datascience puzzle and AI Lock in Loop
- The Heirarchy
- Big Data Abstraction
- Bigdata Tools
- Datascience Desciplines
- Architectural Goals, Principles, and Considerations
- Data types and sources
- Big Data Architecture Patterns
- Enterprise Big Data Architectural Components
- AWS
- Google Cloud Platform
- By Technology
- Oracle Architecture and Patterns Examples
- IBM Architecture and Patterns Examples
- Big Data Exploration Example Architecture IBM
- Big data and analytics architecture on cloud IBM
- Bigdata, AI, Datascience Reads
- Advanced Analytics
- Artificial Intelligence
- Machine Learning
- Data Science
- Data Scientists
- Statistics, Probability, and Mathematics
- Jobs, Skills, and Salary Trends
- Internet of Things (IoT)
- Tools and Language Popularity, Comparisons, Trends, ...
- Freelancing and Consulting
- Online Learning
- Big Data and Data Engineering
- Databases, Schemas, and Data Modeling
- Product
- Methodologies
- Computer Science and Programming
- Business
- Gartner Magic Quadrants
- architectures
- References
- Online Visualization
- What would you like to show
- Cheatsheets
- Python for Bigdata
- R for Bigdata
- Open Datasets
- AI MTDB
- Principles and Rules
- AI Industrial Applications
- Healthcare
- Manufacturing
- Legal or Law
- Games
- Telecommunications
- Music
- HR and recruitment
- Agriculture
- Education
- Media and Entertainment
- Advertising and Marketing
- Retail and Fashion
- Cybersecurity
- Aviation
- Automotive and Trasportation
- Finance and Banking
- Logistics
- Customer Service experience and engagement
- Oil and gas
- IT industry
- AI Safety Surveys
- Quotes on 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?
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.
- Reasoning, problem solving
- Knowledge representation (commonsense knowledge)
- Planning
- Learning
- Natural language processing
- Perception/Vision
- Motion and manipulation
- Social intelligence
- General intelligence
- 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
- 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
- Web log
- 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
- Detection is the location of the recognized object in the image (i.e., localization)
- 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
- Regression and classification
- Predictive analytics
- Columnar/tabular
- Classic multilayer perceptrons + feature engineering
- Prediction
- Regression/classification
- RNN
- Regression/classification
- Recommendation
- Generative
- Novel output
- RNN
- Novel output
- Reconstruction
- Example: MINST
- Recognition and computer vision
- Changing images in time (video)
- LSTM (temporal aspect) with convolutions layers (capture structure/features)
- Changing images in time (video)
- NLP, NLG, NLU
- Machine translation
- CNN
- Sentiment analysis
- CNN
- Sentence classification
- CNN
- Machine translation
- 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
- 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
- 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, ...
- Cancer detection using cell-free genomes
- Cardiovascular events prediction (e.g., heart attack, stroke)
- Companies
- Google DeepMind
- IBM's Watson (Watson for Oncology)
- Others
- Spam for email
- Smart email categorization (Gmail)
- Primary, social, and promotion inboxes, as well as labeling emails as important
- Credit decisions
- Companies
- Least absolute shrinkage and selection operator (LASSO)
- Ridge regression
- Akaike information criterion (AIC)
- Bayesian information criterion (BIC)
- 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
- 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
- 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
- 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)
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
- 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
- Cross-validation
- Hyperparameter optimization
- Bootstrap
- Mallow’s Cp
- Akaike information criterion (AIC)
- Bayesian information criterion (BIC)
- Minimum description length (MDL)
- Validation curve
- Learning curve
- Residual sum of squares
- Goodness-of-fit metrics
- Grid search
- 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
- 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)
- Discounted cumulative gain (DCG)
- Discounted cumulative gain (nDCG)
- Term frequency–inverse document frequency (TF-IDF)
- Expert systems
- Logical reasoning
- 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
- Risk management
- Mathematical/quantitative Finance
- Linear Regression
- Monte Carlo methods
- Empirical risk minimization
Example Applications
Example Applications
- 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
Example Applications
- 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)
- Regression analysis
- 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
- Discrete event simulation
- Markov models
- Agent-based simulations
- Monte carlo simulations
- Systems dynamics
- Activity-based simulation
- ODES and PDES
- Fuzzy logic
Example Applications
- Behavioral
- Demographic
- Geographic
Example Applications
- Design of Experiments (DOE)
- A/B testing
Example Applications
- Deep learning
Example Applications
- Robotic cognition
- T-test - Compare two groups
- ANOVA - Compare multiple groups
Example Applications
- Google search
- Autonymous vehicles (Business insider)
- Home monitoring, control, and security
- Companies
- Voice-controled robotics
- Photo-realistic pictures generation from text or sketches
- Music generation
- Companies
- Movie and script generation
- Automatically generated software code
- Companies
- DeepCoder (Microsoft and Cambridge)
- Companies
- Authentication without passwords (using mobile phone that knows it's you)
- Companies
- Customer support
- Companies
- 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
- Companies
- Sales
- Companies
- Crime
- Who, Type, and location
- Based on previous crime and social media activity
- Companies
- Suicide risk
- Agriculture - predicting crop yields
- Uber's ETA
- 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
- mlsurveys
- machine-learning-surveys
- The History Began from AlexNet: A Comprehensive Survey on Deep Learning Approaches
- Deep Learning in Mobile and Wireless Networking: A Survey
- Deep Learning for Sentiment Analysis : A Survey
- Threat of Adversarial Attacks on Deep Learning in Computer Vision: A Survey
- Visual Analytics in Deep Learning: An Interrogative Survey for the Next Frontiers
- A Brief Survey of Deep Reinforcement Learning
- A Comprehensive Survey of Deep Learning in Remote Sensing: Theories, Tools and Challenges for the Community
- Deep Learning Techniques for Music Generation - A Survey
- Multi-View Representation Learning: A Survey from Shallow Methods to Deep Methods
- A Survey on Deep Learning in Medical Image Analysis
- Symbolic, Distributed and Distributional Representations for Natural Language Processing in the Era of Deep Learning: a Survey
- Towards Bayesian Deep Learning: A Survey
- Deep Learning for IoT Big Data and Streaming Analytics: A Survey
- Feature-Based Aggregation and Deep Reinforcement Learning: A Survey and Some New Implementations
- A Survey on Deep Learning Toolkits and Libraries for Intelligent User Interfaces
- Visual Interpretability for Deep Learning: a Survey
- Deep Learning for Sensor-based Activity Recognition: A Survey
- Deep Learning Algorithms with Applications to Video Analytics for A Smart City: A Survey
- A Survey: Time Travel in Deep Learning Space: An Introduction to Deep Learning Models and How Deep Learning Models Evolved from the Initial Ideas
- Deep Face Recognition: A Survey
- Deep Visual Domain Adaptation: A Survey
- Deep Facial Expression Recognition: A Survey
- A Non-Technical Survey on Deep Convolutional Neural Network Architectures
- Demystifying Parallel and Distributed Deep Learning: An In-Depth Concurrency Analysis
- Applications of Deep Learning and Reinforcement Learning to Biological Data
- Deep Learning in Neural Networks: An Overview
- Efficient Processing of Deep Neural Networks: A Tutorial and Survey
- How Developers Iterate on Machine Learning Workflows -- A Survey of the Applied Machine Learning Literature
- Universal Reinforcement Learning Algorithms: Survey and Experiments
- Bayesian Reinforcement Learning: A Survey
- Machine Learning and Cloud Computing: Survey of Distributed and SaaS Solutions
- LSTM Benchmarks for Deep Learning Frameworks
- A Survey of Domain Adaptation for Neural Machine Translation
- Video Description: A Survey of Methods, Datasets and Evaluation Metrics
- How convolutional neural network see the world - A survey of convolutional neural network visualization methods
- Mobile Face Tracking: A Survey and Benchmark
- Facial Landmark Detection: a Literature Survey
- From Word to Sense Embeddings: A Survey on Vector Representations of Meaning
- Explainable Recommendation: A Survey and New Perspectives
- Face Recognition Techniques: A Survey
- Hierarchical Temporal Memory using Memristor Networks: A Survey
- The State of the Art in Developing Fuzzy Ontologies: A Survey
- False Information on Web and Social Media: A Survey
- Deep Facial Expression Recognition: A Survey
- Automatic Language Identification in Texts: A Survey
- First Impressions: A Survey on Computer Vision-Based Apparent Personality Trait Analysis
- A Survey on Application of Machine Learning Techniques in Optical Networks
- Detection and Resolution of Rumours in Social Media: A Survey
- A Survey on Multi-View Clustering
- Toolflows for Mapping Convolutional Neural Networks on FPGAs: A Survey and Future Directions
- Deep Learning in Mobile and Wireless Networking: A Survey
- Facial Expression Analysis under Partial Occlusion: A Survey
- A Survey Of Methods For Explaining Black Box Models
- Autonomous Agents Modelling Other Agents: A Comprehensive Survey and Open Problems
- Online Learning: A Comprehensive Survey
- Automatic differentiation in machine learning: a survey
- A Comprehensive Survey of Graph Embedding: Problems, Techniques and Applications
- Machine Translation Using Semantic Web Technologies: A Survey
- A Survey of Recent Advances in Texture Representation
- Survey of the State of the Art in Natural Language Generation: Core tasks, applications and evaluation
- Survey on Emotional Body Gesture Recognition
- Graph Embedding Techniques, Applications, and Performance: A Survey
- A Survey of Model Compression and Acceleration for Deep Neural Networks
- Spatio-Temporal Data Mining: A Survey of Problems and Methods
- Recommender Systems with Random Walks: A Survey
- Machine Translation Evaluation: A Survey
- A Brief Survey of Deep Reinforcement Learning
- Survey of Recent Advances in Visual Question Answering
- Salient Object Detection: A Survey
- Machine Learning for Survival Analysis: A Survey
- Survey on Models and Techniques for Root-Cause Analysis
- Automated text summarisation and evidence-based medicine: A survey of two domains
- Universal Reinforcement Learning Algorithms: Survey and Experiments
- Emotion in Reinforcement Learning Agents and Robots: A Survey
- Survey of Visual Question Answering: Datasets and Techniques
- Automatic Description Generation from Images: A Survey of Models, Datasets, and Evaluation Measures
- Next Generation Business Intelligence and Analytics: A Survey
- A Survey of Available Corpora for Building Data-Driven Dialogue Systems
- Malicious URL Detection using Machine Learning: A Survey
- SoK: Applying Machine Learning in Security - A Survey
- Content Selection in Data-to-Text Systems: A Survey
- A Survey of Voice Translation Methodologies - Acoustic Dialect Decoder
- Survey on the Use of Typological Information in Natural Language Processing
- Visual Question Answering: A Survey of Methods and Datasets
- Incorporating prior knowledge in medical image segmentation: a survey
- Automatic Extraction of Causal Relations from Natural Language Texts: A Comprehensive Survey
- A Survey of Semantic Segmentation
- Sentiment Analysis of Twitter Data: A Survey of Techniques
- Computational Sociolinguistics: A Survey
- Socializing the Semantic Gap: A Comparative Survey on Image Tag Assignment, Refinement and Retrieval
- A Survey on Object Detection in Optical Remote Sensing Images
- Linear Dimensionality Reduction: Survey, Insights, and Generalizations
- A Survey on Social Media Anomaly Detection
- Survey on the attention based RNN model and its applications in computer vision
- Facial Feature Point Detection: A Comprehensive Survey
- A Survey of Named Entity Recognition in Assamese and other Indian Languages
- Sentiment Analysis: A Survey
- A Survey of Data Mining Techniques for Social Media Analysis
- Semantics, Modelling, and the Problem of Representation of Meaning -- a Brief Survey of Recent Literature
- Survey on Sparse Coded Features for Content Based Face Image Retrieval
- Natural Language Processing - A Survey
- Autonomous Vehicles that Interact with Pedestrians: A Survey of Theory and Practice
- TutorialBank: A Manually-Collected Corpus for Prerequisite Chains, Survey Extraction and Resource Recommendation
- Text Detection and Recognition in images: A survey
- Image
- Speech
- Video
- Text and optical character
- Pattern
- Audio
- Facial
- Handwriting
Example Applications
- Recognition
- Shazam
- Wine
- Companies
- Facebook photo recognition (highlights faces and suggests friends to tag)
- Speech/Voice to text (faster to talk than to type acc'g to Stanford)
- Companies
- Text to speech
- Companies
- Video
- Companies
- OCR
- Mobile app check deposits and uploading receipts
- Post office address recognition
- Object recognition
- Companies
- Pinterest (then used to recommend other pins)
- Companies
- Image
- Computer vision
- Manufacturing
- Inspections
- Quality control
- Assembly line
- Visual surveillance
- Companies
- Navigation, including autonomous vehicles
- Land, water, and space
- Medical image processing and diagnosis
- Military
- Detection of enemy solidiers and vehicles
- Missle guidance
- Drones
- Inspection (pipelines), surveillance, exploration (buildings), and protection
- Companies
- Item recognition
- Companies
- Manufacturing
Related papers
- ImageNet Classification
- Object Detection
- Object Tracking
- Low-Level Vision
- Edge Detection
- Semantic Segmentation
- Visual Attention and Saliency
- Object Recognition
- Human Pose Estimation
- Understanding CNN
- Image and Language
- Image Generation
- Other Topics
- 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
- Smart personal assistants
- Companies
- Uses
- Internet searches and answer questions
- Set reminders
- Integrate with your calendar
- Make appointments
- Receive sports, news, and finance updates
- Create to-do lists
- Order items online
- Use services (e.g., order an Uber)
- Play music
- Play games
- Smart home integration
- NLG - computer generated reports and news
- Summarizing documents
- Story telling
- Sports recaps
- Companies
- NLP and language translation
- Voicemail transcripts
- eDiscovery
- Companies
- Google Natural Language API
- Google Cloud Translation API
- Textio for writing optimal job descriptions
- NLU and Chatbots
- Shopping
- Errands
- Day to day tasks
- Companies
- x.ai (personal assistant)
- MindMeld
- Google Inbox Smart Reply
- Amazon Lex, includes Automatic speech recognition (ASR)
- Smart instant messaging
- Companies
- Google Allo smart messaging app (https://allo.google.com/)
- Companies
- Text Embeddings
- Thought Vectors
- Machine Translation
- Dialogs and Conversational
- Memory and Attention Models
- Named Entity Recognition
- Natural Language Understanding
- Question Answering and Knowledge Extraction
- Text Summarization
- Text Classification
- Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups [pdf] (Breakthrough in speech recognition)
- Speech recognition with deep recurrent neural networks [pdf] (RNN)
- Towards End-To-End Speech Recognition with Recurrent Neural Networks[pdf]
- Fast and accurate recurrent neural network acoustic models for speech recognition [pdf](Google Speech Recognition System)
- Deep speech 2: End-to-end speech recognition in english and mandarin [pdf] (Baidu Speech Recognition System)
- Achieving Human Parity in Conversational Speech Recognition [pdf] (State-of-the-art in speech recognition, Microsoft)
- Speech Recognition Techniques: A Review - Different Methods Used In Voice Recognition Techniques
- Deep Learning for Speech Recognition
- Deep Learning for Distant Speech Recognition
- Evolving large-scale neural networks for vision-based reinforcement learning 2013. [pdf] [PPT]
- End-to-end training of deep visuomotor policies (2016): 1-40. [pdf] [PPT] [summary] [PPT] [code]
- Supersizing self-supervision: Learning to grasp from 50k tries and 700 robot hours (2015). [arxiv] [PPT]
- Learning Hand-Eye Coordination for Robotic Grasping with Deep Learning and Large-Scale Data Collection (2016). [arxiv] [PPT] [iee spectrum news]
- Target-driven Visual Navigation in Indoor Scenes using Deep Reinforcement Learning (2016). [arxiv] [PPT] [PPT] [code]
- Collective Robot Reinforcement Learning with Distributed Asynchronous Guided Policy Search (2016). [arxiv]
- Deep Reinforcement Learning for Robotic Manipulation (2016). [arxiv]
- Sim-to-Real Robot Learning from Pixels with Progressive Nets (2016). [arxiv] [PPT] [PPT] [[PPT]
- A Survey of Deep Network Solutions for Learning Control in Robotics: From Reinforcement to Imitation
- A Survey on Deep Learning Methods for Robot Vision
- A Survey of Deep Learning Techniques for Mobile Robot Applications
- RGB-D-based Human Motion Recognition with Deep Learning: A Survey
- Deep Reinforcement Learning for Robotic Manipulation-The state of the art
- Towards Lifelong Self-Supervision: A Deep Learning Direction for Robotics
Have a quick look at these threads
- AI tools
- Outline of artificial intelligence and applications
- Outline of machine learning algorithms and applications
- Outline of computer vision and applications
- Outline of natural language processing and tools
- Outline of robotics
- Appications of AI
- Applications of deep learning
- Practical AI
- State-of-the-art-results for AI problems
- kaggle-competitions , kaggle-solutions
- crowdai-challenges
- numer.ai/rounds
- analyticsvidhya-hackathons
- ai-learning-roadmap
- AI-bookmarks
- Wikipedia
- DL4J Deep Learning Use Cases
- Wikipedia Machine Learning Portal
- Wikipedia Artificial Intelligence Portal
- Wikipedia List of Emerging Technologies
- Wikipedia Outline of Statistics
- Wikipedia Statistics Portal
- Wikipedia Outline of Mathemetics
- Wikipedia Mathemetics Portal
- WHO IS READY FOR THE COMING WAVE OF AUTOMATION?
- SAP -digitalistmag
- How Companies Are Putting AI to Work Through Deep Learning
- DisruptionTalk: Everyday List of Artificial Intelligence Examples In Use
- ARTIFICIAL INTELLIGENCE FOR BUSINESS
- ai-privacy
- AI accelerator
- Hardware for Deep Learning. Part 1 - Part 8: CPU, GPU, FPGA, ASIC, Mobile AI, Neuromorphic computing, Quantum computing
- HARDWARE DESIGN FOR MACHINE LEARNING
- Choosing Components for Personal Deep Learning Machine
- stanford: Hardware options for Machine/Deep Learning
- FPGAs and AI processors: DNN and CNN for all
- 12 AI Hardware Startups Building New AI Chips
- Neural Network Accelerator Inference
- Investing in GPUs for AI – AMD GPUs vs NVIDIA GPUs
- Tutorial on Hardware Architectures for Deep Neural Networks - MIT
- Deep Learning Hardware Limbo
- Hardware for Machine Learning
- Hardware for Deep Learning
- Recommended Systems for Machine Learning / AI TensorFlow etc..
- What you need to do deep learning
- Which hardware components (CPU, RAM, GC, etc.) are needed for a machine learning/deep learning home PC/computer to run fast?
- What might another hardware bump mean for ML? What might we see implementing a DNN using MIT’s programmable nanophotonic processor (supposing its past prototype & scaled for conventional accuracy), or IBM’s computational phase-change memory for ML?
- What computer specs (CPU, GPU, memory, etc.) are used by Deep Learning researchers? What is the computer's speed/time performance when training a DL algorithm using a standard dataset such as MNIST handwritten digit dataset?
- Computer Hardware for Machine Learning
- Building a machine learning / deep learning workstation for under $5000
- Build a super fast deep learning machine for under $1,000
- Build your 1st Deep Learning Rig: Step-by-step: How to find, buy, and construct your AI sandbox
- The Future of Machine Learning Hardware
- I: Building a Deep Learning (Dream) Machine
- Enterprise workstations and servers built for Deep Learning
- a-glimpse-into-the-future-of-deep-learning-hardware
- OpenAI: Infrastructure for Deep Learning
- A Guide to Processors for Deep Learning
- Which GPU(s) to Get for Deep Learning: My Experience and Advice for Using GPUs in Deep Learning
- The Great Debate of AI Architecture
- AI And Machine Learning Drive New SoC Verification Choices
- A closer look at Arm’s machine learning hardware
- An Overview of AI in the HPC Landscape
- HPC and AI – Two Communities Same Future
- HPC and AI: Intertwined Futures
- Picking a GPU for Deep Learning
- Architectures Battle for Deep Learning
- AI Hardware to Support the Artificial Intelligence Software Ecosystem
- Microsoft ‘excited’ about its secret hardware built for artificial intelligence
- OMPUTATIONAL POWER AND THE SOCIAL IMPACT OF ARTIFICIAL INTELLIGENCE
- A Wave of Purpose-Built AI Hardware Is Building
- The future of hardware is AI
- MIT-Born A.I. Hardware Startup Raises $10M Led by Chinese Tech Giant Baidu
- What’s Stopping Google’s Monopoly In AI – Limited Success In Hardware?
- Building the hardware for the next generation of artificial intelligence
- The future of hardware is AI
- Chipmakers Are Racing To Build Hardware For Artificial Intelligence
- AI World: What About Hardware?
- Facebook to open-source AI hardware design
- Forget algorithms. The future of AI is hardware!
- The red-hot AI hardware space gets even hotter with $56M for a startup called SambaNova Systems
- facebook: Introducing Big Basin: Our next-generation AI hardware
|
Vendor |
Bots |
APIs |
ML Frameworks |
Fully Managed ML |
|
AWS |
· Lex
|
· Polly
|
||
|
Microsoft Azure |
· Face API
|
|||
|
Google Cloud |
|
· AutoML |
||
|
IBM Cloud |
|
·Natural Language Understanding
|
N/A |
- AWS Artificial Intelligence
- AWS SageMaker
- AWS Machine Learning
- Lex
- Rekognition
- Polly
- Machine Learning
- AWS Athena - An interactive query service that makes it easy to analyze data in Amazon S3 using standard SQL
- Google Cloud Machine Learning - Machine Learning on any data, of any size
- Platforms and other
- Houndify - Add a voice-enabled AI to anything
- text-processing.com - Natural Language Processing APIs and Python NLTK Demos
- IBM Watson - Cognitive computing features in your app using IBM Watson's Language, Vision, Speech and Data APIs
- [Microsoft Machine Learning](Machine Learning) - Powerful cloud based analytics
- BigML
- Carto.com
- RISELab - Real-time Intelligence with Secure Execution
- Crowdflower - AI for your business: Training data, machine learning and human-in-the-loop in a single platform
- Clarifai
- Recast.ai
- Dialogflow - Formerly API.AI
- IBM Watson
- Networked Insights - Audience.ai
- KnowledgeHound
- WebyClip
- Infer
- AgilOne
- SiteZeus
- Sentient's AI platform
- CamFind
- Tagalys
- Findally
- Persado
- Boomtrain
- Mozilla's Common Voice
Media and blogs
- Google: CLOUD AI
- Azure AI
- IBM: Are you putting cloud and AI to work?
- A Brief Survey of Cloud AI Services
- Cloud Services for Transfer Learning on Deep Neural Networks
- Moving Cloud AI to the Edge
- “There Can be Only One!” Well probably several in the ever changing public cloud and niche provider landscape.
- The Rise Of Artificial Intelligence As A Service In The Public Cloud
- What is the relationship between cloud computing and AI?
- How the AI cloud could produce the richest companies ever
- The combination of artificial intelligence and the cloud
- A machine learning and AI guide for enterprises in the cloud
- Five Data Trends That Will Transform Cloud And AI In 2018
- The future belongs to Cloud and AI
- How AI is transforming cloud computing
- Microsoft Embraces Intelligent Cloud And AI Future; Windows Sidelined
- When the Cloud Is Swamped, It's Edge Computing, AI to the Rescue
- In 2018, can cloud, big data, and AI stand more turmoil?
- Intel Capital pumps $72M into AI, IoT, cloud and silicon startups, $115M invested so far in 2018
- Microsoft is hiring engineers to work on A.I. chip design for its cloud
- Incorporating Google’s AI Principles into Google Cloud
- Prepping for the Oracle AI Cloud: Machine Learning
- How cloud serves as the foundation of AI
- Combining cloud computing and artificial intelligence (AI)
- Microsoft bets on faster chips, AI services to win cloud war
- IBM Launches AI-Powered Enterprise Marketing Cloud Solutions In India
- Microsoft Reorganizes to Fuel Cloud and A.I. Businesses
- The AI-First Cloud: Can artificial intelligence power the next generation of cloud computing?
- SAP Leonardo Machine Learning
- AI applied: How SAP and MapR are adding AI to their platforms
- Oracle Places Huge Bets On AI And Machine Learning To Overtake Salesforce In SaaS
- AI becomes the game changer in the public cloud
- Is the cloud the key to democratizing AI?
Quickly scan and skim below articles
Media and blogs
- Learn to Build a Machine Learning Application from Top Articles of 2017
- What are some interesting possible applications of machine learning?
- Top 9 Machine Learning Applications in Real World
- 9 Applications of Machine Learning from Day-to-Day Life
- 9 Complex Machine Learning Applications That Even A Beginner Can Build Today
- 10 Companies Using Machine Learning in Cool Ways
- Top 10 Industrial Applications of Machine Learning
- 10 Real-World Examples of Machine Learning and AI [2018]
- The Top 10 AI And Machine Learning Use Cases Everyone Should Know About
- Machine Learning Algorithms for Business Applications – Complete Guide
- machine-learning-applications-how-it-works-who-uses-it
- 6 Incredible Machine Learning Applications that will Blow Your Mind
- Best Machine Learning Applications: Ideas for Mobile Apps
- SAS- Machine Learning What it is and why it matters
- Top 15 Deep Learning applications that will rule the world in 2018 and beyond
- 8 Inspirational Applications of Deep Learning
- deep-learning-most-amazing-30-applications
- Quora: What are some applications of deep learning?
- 6 Deep Learning Applications a beginner can build in minutes (using Python)
- Twelve Hot Deep Learning Applications Featured at Deep Learning World
- Applications of Deep Learning
- dl-applications-book
- MIT-review: Deep Learning With massive amounts of computational power, machines can now recognize objects and translate speech in real time. Artificial intelligence is finally getting smart.
- Mathworks: Why Deep Learning Matters
- Deep learning applications and challenges in big data analytics
- Notes from the AI frontier: Applications and value of deep learning
- DL4J: Deep Learning Use Cases
- 14 useful applications of data mining
- Data Mining - Applications & Trends
- Data Mining Applications
- 5 Data mining applications
- Data Mining Applications and Use Cases
- application-of-data-mining
- Concept and Applications of Data Mining
- Rise of Data Mining: Current and Future Application Areas
- Data mining applications in public organizations
- Examples of Data Mining Applications
- Data Mining Applications with R
- Data mining applications in engineering design, manufacturing and logistics
- Data Mining: Future Trends and Applications
- Spatial Information and Data Mining Applications
- arxiv: Data Mining Applications: A comparative Study for Predicting Student's performance
- ibm-top-10-machine-learning-use-cases-part1
- ml-Use cases by industry
- How to Apply Machine Learning to Business Problems
- Machine Learning is Fun!
-> 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
- The Computer Vision Industry
- Computer Vision: Algorithms and Applications
- Computer Vision: Algorithms and Applications
- Hackernews: Computer Vision: Algorithms and Applications (2010) (szeliski.org)
- CSAIL: Advances in Computer Vision
- Wiki: Applications of computer vision
- Computer Vision Technology Permeates Our Daily Lives
- Quora: What are the applications of computer vision?
- stanford CS: Computer Vision Applications
- The Most Exciting Applications of Computer Vision across Industries
- link.springer: Machine Vision and Applications
- 10 uses of computer vision in marketing & customer experience
- Computer Vision Applications: Home, Office and Industry
- Application of deep learning to computer vision
- Computer Vision in Robotics and Industrial Applications
- Applications of machine vision
- Facial Recognition Applications – Security, Retail, and Beyond
- Facial Recognition
- how-coders-are-fighting-bias-in-facial-recognition-software
- Face-reading AI will be able to detect your politics and IQ, professor says
- High Time to Regulate Face Recognition A.I.
- Machine Learning is Fun! Part 4: Modern Face Recognition with Deep Learning
- Blocking facial recognition surveillance using AI
- New AI Tech Blinds Computer Facial Recognition Systems
- Microsoft: Face verification
- How is computer vision applied in robotics and the industry?
- important-robotics-links
- Camera Calibration
- CVonline: The Evolving, Distributed, Non-Proprietary, On-Line Compendium of Computer Vision
- 3dvision.princeton
- Machine Vision System
- Computer Vision for the Car Industry
- Annotated Computer Vision Bibliography
- Application of Artificial Intelligence Methods to Content-Based Image Retrieval
- Deep Learning for Content-Based Image Retrieval: A Comprehensive Study
- product-ai
- Google AI: Large-Scale Image Retrieval with Attentive Deep Local Features
- Intelligent Image Retrieval Techniques: A Survey
- A Knowledge-based Image Retrieval System Integrating Semantic and Visual Features
- Towards intelligent image retrieval
- Content Based Image Retrieval (CBIR) in Remote Clinical Diagnosis and Healthcare
- Bayesian Approaches to Content-based Image Retrieva
- AI-Based CT Image Retrieval--Retrieving Similar Cases with an 85% Accuracy Rate in One-Sixth Diagnostic Time
- How Artificial Intelligence Has Changed Image Recognition Forever
- OCR Applications
- An Overview and Applications of Optical Character Recognition
- Top 5 Optical Character Recognition (OCR) Apps And Software
- What are the possible applications of optical character recognition?
- Application of OCR systems to processing and digitization of paper documents
- IBM: Object detection with AI vision is getting easier for developers
- Implement Object Recognition on Livestream
- Object recognition for free
- Is Google Tensorflow Object Detection API the easiest way to implement image recognition?
- Facebook’s AI team Releases Detectron – A Platform for Object Detection Research
- Interactive Segmentation with Convolutional Neural Networks
- Segmenting and refining images with SharpMask
- Image segmentation with Neural Net
- Object detection: an overview in the age of Deep Learning
- A curated list of speech and natural language processing resources
- 10 Applications of Artificial Neural Networks in Natural Language Processing
- Natural language processing applications
- 7 Applications of Deep Learning for Natural Language Processing
- Applications of Natural Language Processing
- Natural Language Processing – Current Applications and Future Possibilities
- Natural Language Processing – Business Applications
- 4 business applications for natural language processing
- 4 Applications Of Natural Language Processing
- 5 Applications of Natural Language Processing for Businesses in 2018
- nlp-application
- Introduction to Natural Language Processing (NLP)
- Value-of-NLP-applications-varies-for-different-AI-uses
- A few applications of natural language processing…
- Six Natural Language Processing Algorithms for Web Developers
- Google pub: NaturalLanguageProcessing
- What is the Role of Natural Language Processing in Healthcare?
- Commercial Applications of Natural Language Processing
- Clinical information extraction applications: A literature review
- Natural Language Processing Empowered Mobile Computing
- Natural Language Processing (NLP) Applications
- Models of natural language understanding.
- speech-recognition-applications
- Voice Biometrics Month 2016: 4 Unique Applications of Voice Recognition
- Most Common Uses of Voice Recognition Software
- applications of speech recog
- 8 Innovative Ways to Use Speech Recognition for Business
- Overview of speech recognition applications
- Speech Recognition Software
- The Top Five Uses of Speech Recognition Technology
- Applications of Voice-Processing Technology in Telecommunications
- Understanding Speech RecognitionReal-world applications
- AUTOMATIC SPEECH RECOGNITION AND ITS APPLICATION TO INFORMATION EXTRACTION
- 10 best practices for voice-based applications
- Robotic Application Research: Past, Present, & Future
- Fields of Application
- Robots in Retail – Examples of Real Industry Applications
- Machine Learning in Robotics – 5 Modern Applications
- Top 10 Robotic Applications in the Agricultural Industry
- Top 6 Robotic Applications in Food Manufacturing
- Applications of Robotics in Manufacturing Sector
- 5 Manufacturing Applications for Robotics in 2018
- Introduction to applications in Robotics
- Development for Industrial Robotics Applications
- Top 6 Robotic Applications in Medicine
so, you have data --> build data pipelines
- Consistency
- Batch (slow/cold) vs. real-time streaming (fast/hot) data processing and paths
- Slow/cold path - batch processing
- Batch Processing / Analysis
- Historical Lookup
- Auditing
- Fast/hot path - real-time processing
- Real-time Analytics / Machine Learning Analysis
- Real-time Reporting
- Notifications
- Slow/cold path - batch processing
- Embedded models or interfaces
- API or RPC or REST
- Deployed trained models (offline learning) vs. online learning
- 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
- Goals and implementation - Oracle
- 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
- 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
- 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
- Buffer data
- Database (DB) block
- Distributed File System (DFS) block
- Message Queue/Stream Processing (MQ/SP) block
- 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
- Batch and stream processing/computing (velocity)
- 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
- Solution Development Guides
- Reference Architectures
- By Application
- By Industry Sector
- Partner Solutions
- Case Studies
- 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
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
- Applications layer
- Consists of
- Visualization
- Discovery
- Analysis
- Reporting
- Statistics
- Text and entity analytics
- Access
- SQL
- MDX
- Search
- REST
- Consists of
- 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
- Virtual search mart
- Access
- NoSQL
- SQL
- Search
- REST
- Consists of
- Landing layer
- Consists of
- Shared warehouse and ETL
- Extract
- Provision
- Shared warehouse and ETL
- Access
- Search
- REST
- SQL
- Files
- Consists of
- Source layer
- Sensors and telemetry
- Internet
- Social media
- Public data
- Enterprise data
- 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
- AI and Deep Learning in 2017 – A Year in Review
- 59 impressive things artificial intelligence can do today
- MIT - The Missing Link of Artificial Intelligence
- The AI Revolution: The Road to Superintelligence
- Artificial General Intelligence – The Holy Grail of AI
- The Non-Technical Guide to Machine Learning & Artificial Intelligence
- THE NEURAL NETWORK ZOO
- Part of Speech Tags
- The 7 Myths of AI
- AI 100: The Artificial Intelligence Startups Redefining Industries
- Sensors, Plus Brains: 17 IoT Companies Using Artificial Intelligence Tech
- Meet the 2017 CNBC Disruptor 50 companies
- MIT - The Missing Link of Artificial Intelligence
- Everyday Examples of Artificial Intelligence and Machine Learning
- What is Artificial Intelligence? An Informed Definition
- Meet the 2017 CNBC Disruptor 50 companies
- Company-specific
- Video: The Promise of AI
- My Curated List of AI and Machine Learning Resources from Around the Web
- Here are 50 Companies Leading the AI Revolution
- The Race For AI: Google, Baidu, Intel, Apple In A Rush To Grab Artificial Intelligence Startups
- Emerging AI: 7 Industries Including Law, HR, Travel And Media Where AI Is Making An Impact
- The Basics of Artificial Intelligence Decoded
- Demystifying Deep Reinforcement Learning
- Applying deep learning to real-world problems
- Top AI Influencers
- Introduction to reinforcement learning and OpenAI Gym
- Basic Concepts in Machine Learning
- Choosing a Machine Learning Classifier
- The 10 Algorithms Machine Learning Engineers Need to Know
- What are the pros and cons of offline vs. online learning?
- Introduction to Online Machine Learning : Simplified
- Machine Learning From Streaming Data: Two Problems, Two Solutions, Two Concerns, and Two Lessons
- Rules of Machine Learning: Best Practices for ML Engineering
- [Which machine learning algorithm should I use?](http://blogs.sas.com/content/subconsciousmusings/2017/0 4/12/machine-learning-algorithm-use/)
- How to choose algorithms for Microsoft Azure Machine Learning
- Automated Machine Learning — A Paradigm Shift That Accelerates Data Scientist Productivity @ Airbnb
- 8 Ways Machine Learning Is Improving Companies’ Work Processes
- 13 Great Data Science Infographics
- THE DATA SCIENCE VENN DIAGRAM
- Common Probability Distributions: The Data Scientist’s Crib Sheet
- The Guide to Learning Python for Data Science
- The Periodic Table of Data Science
- 10 Free Must-Read Books for Machine Learning and Data Science
- The 10 Statistical Techniques Data Scientists Need to Master
- The Startup Founder’s Guide to Analytics
- 40 Techniques Used by Data Scientists
- Top 10 Active Big Data, Data Science, Machine Learning Influencers on LinkedIn, Updated
- 7 awesome data science newsletters to keep you informed
- Teaching the data science process
- How to Consistently Hire Remarkable Data Scientists
- Battle of the Data Science Venn Diagrams
- Hiring a data scientist
- Top mistakes data scientists make
- Nurturing a productive data team
- What makes a great data scientist?
- How to think like a data scientist to become one
- The Life of a Data Scientist
- Data Science Career Paths: Different Roles in the Industry
- The Data Science Industry: Who Does What (Infographic)
- Data Science Falls Into Many Roles
- What’s the Difference Between Data Science Roles?
- Doing Data Science Right — Your Most Common Questions Answered
- Top 12 Interesting Careers to Explore in Big Data
- How to Job Interview a Data Scientist
- 9 Mistakes to Avoid When Starting Your Career in Data Science
- Is data scientist the most rewarding tech job? New report says yes
- Getting Into Data Science: What You Need to Know
- The 3 Highest Paid Jobs in Tech (and 17 Skills That Get You Hired)
- 2017 State of Global Tech Salaries
- The State of Data Science and Machine Learning 2017
- Tech Giants Are Paying Huge Salaries for Scarce A.I. Talent
- What’s next for the 2017 data science and analytics job market?
- Defining Baseline Skills
- Top 12 Interesting Careers to Explore in Big Data
- Tiobe Index for Programming Languages
- A list of artificial intelligence tools you can use today
- Top Analytics, Data Science software – KDnuggets 2016 Software Poll Results
- What Big Data, Data Science, Deep Learning software goes together?
- Python Deep Learning Frameworks Reviewed
- Best machine learning packages in R
- DL4J vs. Torch vs. Theano vs. Caffe vs. TensorFlow
- 10+ Machine Learning as a Service Platforms
- Python, Machine Learning, and Language Wars
- All the best big data tools and how to use them
- The big data ecosystem for science
- Firing on All Cylinders: The 2017 Big Data Landscape
- Four Ways to Extend Jupyter Notebook
- Making Publication Ready Python Notebooks
- Stack Overflow - Developer Hiring Trends in 2017
- Top 15 Python Libraries for Data Science in 2017
- Top 15 Frameworks for Machine Learning Experts
- Github stats for Python data science libraries - April, 2017
- Architecture of Giants: Data Stacks at Facebook, Netflix, Airbnb, and Pinterest
- 10 Tools for the Novice Data Scientist
- Infographic: How to Calculate Your Freelance Hourly Rate
- Freelancer 101: How to Set Up a Simple Accounting System
- New to Freelancing? 9 Things You Need to Know
- How to Become a Freelance Data Scientist
- Course Report
- CourseTalk
- Online Course Report
- Online Course Report - The 50 Most Popular MOOCs of All Time
- Data Science Course Rankings - Opinion Article
- Every Intro to Data Science Course on the Internet, Ranked
- Machine learning courses online
- Data Science Online Courses: A Comprehensive List
- Best Data Science Online Courses
- Online Education in Analytics, Big Data, Data Science, Machine Learning
- Data Science Academy - Free Data Science Courses
- New Online Data Science Tracks for 2017
- Data Science Online Courses: A Comprehensive List for 2017
- TOP 50 DATA SCIENCE RESOURCES: THE BEST BLOGS, FORUMS, VIDEOS AND TUTORIALS TO LEARN ALL ABOUT DATA SCIENCE
- 18 Resources to Learn Data Science Online
- Architectural Patterns for Near Real-Time Data Processing with Apache Hadoop
- Real-Time Stream Processing as Game Changer in a Big Data World with Hadoop and Data Warehouse
- Real-time Data Processing in AWS Cloud
- How much data does one need to efficiently use Hadoop instead of SQL?
- Don't use Hadoop - your data isn't that big
- Batch vs. Real Time Data Processing
- Data Lakes: Hadoop Vs. In-Memory Databases
- Data processing architectures – Lambda and Kappa
- Getting started: the 3 stages of data infrastructure
- Data Pipelines in Hadoop
- 10 Data Acquisition Strategies for Startups
- Getting started: the 3 stages of data infrastructure
- Analytics 101: Choosing the right database
- DB-Engines Ranking
- Data Warehouse vs. OLAP Cube
- Stack Overflow - Data Warehouse vs. OLAP Cube?
- MongoDB and HBase Compared
- Dimensional modeling
- The 10 Essential Rules of Dimensional Modeling
- The importance of a date dimension in a data warehouse and BI project
- How to Choose the Right Database System: RDBMS vs. NoSql vs. NewSQL
- DAO vs ORM vs ActiveRecord vs TableGateway vs AHHHH!
- SQL is 43 years old - here’s 8 reasons we still use it today
- The Product Management Hierarchy of Needs
- What are the best product management and/or product marketing training programs?
- How product marketing helps build product
- WHAT MAKES A GREAT PRODUCT MANAGER
- Books for Product Managers
- Transitioning from Scrum to Kanban
- Running in Circles - Why Agile Isn’t Working and What We Do Differently
- Programming languages
- Suffering-oriented programming
- Stack Overflow Developer Survey Results 2017
- Magic Quadrant for Business Intelligence and Analytics Platforms
- Magic Quadrant for Cloud Infrastructure as a Service, Worldwide
- Magic Quadrant for Advanced Analytics Platforms
- Magic Quadrant for Data Integration
- Lambda Architecture
- AWS Architecture Center
- AWS Big Data Partner Solutions
- GCP Architecture
- Introduction to big data classification and architecture
- An Enterprise Architect’s Guide to Big Data
- BIG DATA REFERENCE ARCHITECTURE
- Getting Started with Big Data Architecture
- BIG DATA: Architectures and Technologies
- Big Data Architecture
- Big Data Analytics Architecture
|| 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 ||
- The Python Graph Gallery
- The Data Visualisation Catalogue
- Python Plotting for EDA
- dataviz.tools
- Tableau Public Gallery
- D3 Gallery
- D3 List Gallery
- Google Charts Gallery
- Matplotlib Gallery
- Seaborn Gallery
- Bokeh Gallery
- Power BI Custom Visuals
- Shiny Gallery
- The R Graph Gallery
- ggplot 2
- Zingchart Gallery
- Visual Complexity
- Kantar Information is Beautiful Awards
- New York Times
- Visually Staff Picks
- Life Universe
- Visualgo
- See, also
- Esri Maps
- Carto Maps
- MobileVis
- Visualizing Data Resources
- Top Algorithms and Methods Used by Data Scientists
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
Shortcuts
| Jupyter Notebook Keyboard Shortcuts|
Markup and Syntax
| GitHub markdown cheatsheet | GitHub markdown guide |
- Awesome Public Datasets
- UCI Machine Learning Repository
- sklearn.datasets
- Rdatasets list
- 17 places to find datasets for data science projects
- Machine Learning Data Set Repository
- The Greatest Public Datasets for AI
- AWS Public Datasets Home
- AWS Public Datasets
- 100+ Interesting Data Sets for Statistics
- Kaggle Datasets
- FiveThirtyEight data
- Google BigQuery Public Datasets
- Stanford Large Network Dataset Collection
- THE MNIST DATABASE of handwritten digits
- THE Wikipedia Corpus
- Deep learning datasets
- Open Data for Deep Learning
- Datasets for Data Mining and Data Science
- Wikipedia List of datasets for machine learning research
- 100+ Interesting Data Sets for Statistics
- NYC OpenData
- Data.gov
- FiveThirtyEight datasets and code
- data.world
- Great IoT, Sensor and other Data Sets Repositories
- Quora - Where can I find large datasets open to the public?
- Subreddit
- AI and Deep Learning in 2017 – A Year in Review - Datasets section
- 70 Amazing Free Data Sources You Should Know
Dataset By Category
- Streaming Open Data
- Satori - Live streaming open data
- Classification
- http://archive.ics.uci.edu/ml/datasets/Wine+Quality
- http://archive.ics.uci.edu/ml/datasets/Wine
- http://archive.ics.uci.edu/ml/datasets/User+Knowledge+Modeling
- http://archive.ics.uci.edu/ml/datasets/Student+Performance
- http://archive.ics.uci.edu/ml/datasets/seeds
- http://archive.ics.uci.edu/ml/datasets/Iris
- http://archive.ics.uci.edu/ml/datasets/Dow+Jones+Index
- http://archive.ics.uci.edu/ml/datasets/BLOGGER
- Clustering
- Recommendender
- http://grouplens.org/datasets/movielens/
- http://www2.informatik.uni-freiburg.de/~cziegler/BX/
- http://grouplens.org/datasets/hetrec-2011/
- https://en.wikipedia.org/wiki/Wikipedia:Database_download#English-language_Wikipedia
- http://planet.openstreetmap.org/planet/full-history/
- http://webscope.sandbox.yahoo.com/catalog.php?datatype=r
- http://www.cs.cornell.edu/people/pabo/movie-review-data/
- http://archive.ics.uci.edu/ml/datasets/Entree+Chicago+Recommendation+Data
- Regression
- Computer Vision and Recognition
- IoT
Specific Data Sets
Data Portals and Meta portals
Data Marketplaces and Monetization
|| 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) ||
|| 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 ||
|| 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 ||
- Life 3.0: Being Human in the Age of Artificial Intelligence
- Our Final Invention: Artificial Intelligence and the End of the Human Era
- How to Create a Mind: The Secret of Human Thought Revealed
- The Emotion Machine: Commonsense Thinking, Artificial Intelligence, and the Future of the Human Mind
- The Singularity is Near: When Humans Transcend Biology
- Rise of the Robots
- The Emperor's New Mind: Concerning Computers, Minds and the Laws of Physics
- The Society of Mind
- The Age of Spiritual Machines: When Computers Exceed Human Intelligence
- The Economic Singularity: Artificial Intelligence and the Death of Capitalism
- The Diamond Age: Or, A Young Lady's Illustrated Primer
- I Have No Mouth and I Must Scream
- Accelerando
- Superintelligence: Paths, Dangers, Strategies
- Phantoms in the Brain: Probing the Mysteries of the Human Mind
- On Intelligence: How a New Understanding of the Brain Will Lead to the Creation of Truly Intelligent Machines
- AI-book-list
- Popular Artificial Intelligence Books
- 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
- 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
- Problem space - user benefit
- 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.
media and blogs
- Machine Learning Healthcare Applications – 2018 and Beyond
- Stanford AI for Healthcare Stanford AI for Healthcare
- Microsoft-Democratizing AI in Health
- Artificial intelligence in healthcare
- 10-common-applications-artificial-intelligence-healthcare
- Artificial Intelligence In Healthcare: Separating Reality From Hype
- Top Artificial Intelligence Companies in Healthcare to Keep an Eye On
- No longer science fiction, AI and robotics are transforming healthcare
- Machine Learning in Healthcare: Now for Everyone
- insight-artificial-intelligence-healthcare
- Artificial Intelligence for Health and Health Care
- artificial-intelligence-in-healthcare-makes-slow-impact
- Top 12 Ways Artificial Intelligence Will Impact Healthcare
- Artificial intelligence in healthcare ‘set for 40% growth to 2024’
- The Impact of Artificial Intelligence in Healthcare
- Intel-Doctors have long relied on experience and instinct to make diagnoses. Now artificial intelligence can help with the hardest cases.
- How Healthcare AI Makes Machine Learning Accessible to Everyone in Healthcare
media and blogs
- 3 Advances Changing the Future of Artificial Intelligence in Manufacturing
- Machine Learning in Manufacturing – Present and Future Use-Cases
- 10 Ways Machine Learning Is Revolutionizing Manufacturing In 2018
- AI: Bringing smarter automation to the factory floor
- Artificial Intelligence: Optimizing Industrial Operations
- Artificial intelligence has sparked a new industrial revolution. Manufacturers are looking to their machines for powerful new insights.
- Future Factories: How AI enables smart manufacturing
- Adopt or Die: AI Leaves Manufacturing No Choice
- What's the Word on AI in Manufacturing?
- AI and Its Applications in Manufacturing
- AI in manufacturing: 5 questions answered
- ai-real-time-manufacturing-management
- What does AI mean for the future of manufacturing?
media and blogs
- Applications of artificial intelligence to legal informatics
- How will artificial intelligence affect the legal profession in the next decade?
- Lawyer-Bots Are Shaking Up Jobs
- The Verdict Is In: AI Outperforms Human Lawyers in Reviewing Legal Documents
- AI in Law and Legal Practice – A Comprehensive View of 35 Current Applications
- How AI Is Disrupting The Law
- Ask Dr Paola: how AI is changing the legal industry
- AI in Law: Definition, Current Limitations and Future Potential
- A Primer on Using Artificial Intelligence in the Legal Profession
- What Do You Know About AI & Legal Research?
- I Proves to Be the Best Legal Mind for Contract Reviews
- The Future Of Law: How AI Is Revolutionizing the Legal Industry
related paper
+ Examples
* AlphaGo (Monte-Carlo Tree Search)
* DeepBlue
* Watson Jeapordy
media and blogs
- Why-are-there-no-Game-AI-companies
- The Future of AI in the Gaming Industry
- The Evolution of AI In Gaming
- How Gaming Could Win Us More Adaptable Artificial Intelligence – A Conversation with Dr. Julian Togelius
- Artificial Intelligence and Games
- Game AI: The State of the Industry
- The Future Of Gaming Industry – Blending VR, AR & MR To Games, Powered By AI
- Gaming and artificial intelligence (AI)
- How video game AI is changing the world
- A.I. teams up with Game Developers
- AI for Game Production
- How is Artificial Intelligence Transforming the Future of Gaming Industry?
- How game designers and marketers will exploit AI
- AI Is Dreaming Up New Kinds of Video Games
- Microsoft's AI future is rooted in its gaming past
- Machine Learning in Games Development
- Games by ANGELINA: The AI Game Designer
- Why tech giants are investing millions in AI that can play video game
- AI in Video Games: Toward a More Intelligent Game
- Artificial Intelligence – How It Will Change The Gaming Industry
+ 5G
+ IoT
+ Autonomous vehicles
+ Smart cities
+ Engagement insights whose info is sold back to advertisers
media and blogs
- Telecom Machine Learning Applications – Comparing AT&T, Verizon, Comcast and More
- Why telcos will soon be betting on Artificial Intelligence to build their networks
- The applications of Artificial Intelligence (AI) in the Telecoms industry
- AI i n Telecommunications
- Artificial intelligence in telecoms - current state of play
- AI in the telecommunications industry - a Q&A with Infosys
- Artificial Intelligence for Telecommunications Applications
- AI and the future of telecom with use cases Do Telecoms use Artificial Intelligence?
- Artificial intelligence in Telecom: Intelligent operations is the new norm
- Taking Telecom to New Heights with Artificial Intelligence
- Intelligence is in the airwaves for telecoms firms
- Artificial intelligence applications in the telecommunications industry
media and blogs
- Google's 'Magenta' project will see if AIs can truly make art
- How To Think About Artificial Intelligence In The Music Industry
- Artificial Intelligence is About to Disrupt the Music Industry — Your Industry is Next. Are you Ready?
- AI-driven data could be the music industry’s best marketing instrument
- Musical Artificial Intelligence – 6 Applications of AI for Audio
- How Music Generated by Artificial Intelligence Is Reshaping -- Not Destroying -- The Industry
- RoboPop: how will AI and machine-learning affect the music industry?
- 4 Ways AI Startups Can Target the Music Industry
- Musiio uses AI to help the music industry curate tracks more efficiently
- the-role-of-artificial-intelligence-in-music
- Artificial Intelligence In the Music Industry
- Music composed by artificial intelligence or a person: Can you tell the difference?
- AI and data are music to recording industry's ears for recouping song royalties
- spotifys-scientist-artificial-intelligence-should-be-embraced-not-feared-by-the-music-business
- Taryn Southern’s new album is produced entirely by AI
- AI and music: will we be slaves to the algorithm?
- the-futures-of-music-metaphors-robots-and-questioning-ai
- Will AI change the future of music?
media and blogs
- 5 ways to use artificial intelligence (AI) in human resources
- 7 Ways Artificial Intelligence is Reinventing Human Resources
- How will AI in HR be a game-changer?
- Artificial Intelligence in HR: a No-brainer
- 3 Ways That A.I. Is Transforming HR and Recruiting
- The Future Of Work: How Artificial Intelligence Will Transform The Employee Experience
- 7 Ways Chatbots and AI are Disrupting HR
- Staying Relevant In The Age Of AI
- Decoding AI for HR
- AI Is Changing the Way HR Works
- Ten ways HR tech leaders can make the most of artificial intelligence
- AI For Recruiting: A Definitive Guide For HR Professionals
- IBM-Three Immediate Steps to Prepare for AI in HR
- How AI and machine learning will impact HR practices
- Two Facebook and Google geniuses are combining search and AI to transform HR
- HR, meet AI – making work more human, according to Oracle
media and blogs
- AI in Agriculture – Present Applications and Impact
- Microsoft- Digital Agriculture: Farmers in India are using AI to increase crop yields
- The 6 Most Amazing AI Advances in Agriculture
- A Real-World Example Of The Power Of AI In Agriculture
- What are some applications of AI in the field of agriculture? - Quora
- Artificial intelligence holds the promise of driving an agricultural revolution at a time when the world must produce more food using fewer resources.
- Can Artificial Intelligence help improve agricultural productivity?
- Exclusive: Alphabet X is exploring new ways to use AI in food production
- IBM - Five ways agriculture could benefit from artificial intelligence
- The Challenges for Artificial Intelligence in Agriculture
- The Incredible Ways John Deere Is Using Artificial Intelligence To Transform Farming
media and blogs
- The Future of AI, Data, and Education - udacity
- The role of education in AI (and vice versa)
- 10 Roles For Artificial Intelligence In Education
- How AI Impacts Education
- Examples of Artificial Intelligence in Education
- The Future of AI and Education
- 7-roles-for-artificial-intelligence-in-education
- Will AI be a bane or boon for education?
- A Blended Environment: The Future of AI and Education
- A 21-year-old Swedish AI prodigy wants to revolutionize the $6 trillion education industry – and Tim Cook is impressed
media and blogs
- Top 10 areas Artificial Intelligence is leading automation in Media Industry
- AI in the Media and Entertainment Industry
- Applying AI and Machine Learning to Media and Entertainment
- AI in Movies, Entertainment, and Visual Media – 5 Current Use-Cases
- AI is helping media and entertainment do more with less
- Media & Entertainment: Leveraging AI for Smarter Content & Content Supply Chain
- The Most Exciting Artificial Intelligence Applications in Media (Guest Column)
- How will AI impact the entertainment industry? - Quora
- Machine Learning And AI In The Media Industry
+ 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
- Artificial Intelligence in Marketing and Advertising – 5 Examples of Real Traction
- AI and machine learning get us one step closer to relevance at scale
- What does artificial intelligence mean for marketing agencies?
- In 2018, Marketers Will Discover More AI Applications in Programmatic Advertising
- Artificial-intelligence- adweek
- How artificial intelligence is transforming advertising
- Six Ways Artificial Intelligence Is Set To Disrupt Digital Marketing
- How AI Is Addressing the Fraud in Advertising: Here's What You Need to Know
- A guide to whether artificial intelligence will take your ad agency job
- How AI marketing can help brands right now
- 3 Ways AI Marketing Will Revolutionize Your Ad Campaigns
- How AI is Driving a New Era of TV Advertising
+ Recommendation engines
+ Virtual reality fitting systems
+ Shopping experience
media and blogs
- retail-nvidia
- Why Artificial Intelligence Is Right for Retail
- The Future of Retail is All About Artificial Intelligence
- 50 Best AI Retail Applications
- Deep Dive: Artificial Intelligence in Retail—Offering Data-Driven Personalization and Customer Service
- artificial-intelligence-ai-retail-market
- Artificial Intelligence in Drugstore Retail
- The retail renaissance: Leading brands use data and AI to win
- Artificial Intelligence in Retail – 10 Present and Future Use Cases
- Machine Learning in Retail – Near-Term Applications and Implications
- Artificial Intelligence Opportunities in Retail
- AI in Fashion – Present and Future Applications
- Evolution of Commerce: How AI is Disrupting the Fashion Industry
- How artificial intelligence is informing how fashion designers create
- Artificial Intelligence is redefining Indian fashion industry
- Top 10 AI-Trends of Future Forward Fashion
- Five ways fashion brands are using AI for personalization
- 7 ways AI innovations make life easier for fashion companies and
Security intelligence (security, fraud, and risk analysis)
media and blogs
- Artificial intelligence and cybersecurity: The real deal
- AI and Machine Learning in Cyber Security
- 5 Minute Guide to AI in Cyber Security
- AI for Cyber Security: How AI prevents future cyberattacks?
media and blogs
- Aerospace & Defense
- How is AI Changing the Aviation Industry?
- Artificial Intelligence Quickly Entering Aerospace Manufacturing
- What kind of applied AI is there in the aerospace engineering field?
- Artificial Intelligence in Aerospace
- Artificial Intelligence Taking Over Aerospace Manufacturing
- Intelligent Systems For Aerospace Engineering--An Overview - NASA
- AI bests Air Force combat tactics experts in simulated dogfights
- Automated Intelligent Pilots for Combat Flight Simulation
- The Story of Self-Repairing Flight Control Systems
+ Companies
* Google
* Apple
* Uber
* Lyft
* Tesla
* Waymo
|| awesome-autonomous-vehicles | self-driving-cars-medium | awesome-self-driving-cars | av-resources ||
media and blogs
- 5 Ways Artificial Intelligence is Impacting the Automotive Industry
- 5 ways artificial intelligence is driving the automobile industry
- Artificial Intelligence for Automotive Applications
- How Artificial Intelligence is Transforming Automotive Industry Monetization Models
- Artificial intelligence: automotive’s new value-creating engine
- Building smarter cars with smarter factories: How AI will change the auto business
- self-driving-cars - nvidia
- AI: A Game-Changer for the Automotive Industry?
- Self-driving cars are here-Andrew Ng
- AI in Transportation – Current and Future Business-Use Applications
- How Artificial Intelligence is transforming the transportation ecosystem
- Q&A: The future of AI and transportation
- How AI can help transportation:
- The 25 Ways AI Can Revolutionize Transportation: From Driverless Trains to Smart Tracks
- The Future Of The Transport Industry - IoT, Big Data, AI And Autonomous Vehicles
- Artificial Intelligence (AI) in Transportation Market 2018 Global Industry By Applications, Types, Key Players, New Technologies, Growth Prediction By 2022
Top Media
- augmented-finance-machine-intelligence
- How AI Will Transform Financial Management Applications - Gartner
- Artificial Intelligence in Finance - sigmoidal
- Machine Learning in Finance – Present and Future Applications
- 15 Applications of AI and Machine Learning in Financial Marketing
- 5 AI applications in Banking to look out for in next 5 years
- 3 Ways Artificial Intelligence Is Changing The Finance Industry
- How Artificial Intelligence Is Disrupting Finance
- Applying Artificial Intelligence and Machine Learning to Finance and Technology
- Artificial intelligence and machine learning in financial services - Market developments and financial stability implications
- the-impacts-and-challenges-of-artificial-intelligence-in-finance
- How is artificial intelligence used in finance? - Quora
- Kensho's AI For Investors Just Got Valued At Over $500 Million In Funding Round From Wall Street
- Beyond Robo-Advisers: How AI Could Rewire Wealth Management
- Algorithmic Trading - Investopedia
- 5 Best AI-Powered Chatbot Apps
- Is Artificial Intelligence the Way Forward for Personal Finance? | WIRED
- ZestFinance Introduces Machine Learning Platform to Underwrite Millennials and Other Consumers with Limited Credit History
- Machine Learning Is the Future of Underwriting, But Startups Won’t be Driving It
- AI in Banking – An Analysis of America’s 7 Top Banks
- Artificial Intelligence is Taking Over Investment Banking
- Investment Bankers Are Hard to Replace With Robots, Nordea Says
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
media and blogs
- glo-ai-in-logistics-white-paper
- AI in the supply chain: Logistics gets smart
- AI and Automation In The Logistic Systems
- How DHL Aims to Remake Logistics with AI
- Here, There, Everywhere: Transforming Logistics with Self-Learning AI
media and blogs
- Rise of the Chatbots: How AI Changed Customer Service
- How Will AI-Powered Customer Service Help Customer Support Agents?
- 6 Best AI Chatbots to Improve Your Customer Service
- How AI Changed Customer Service in the IT industry
- 10-reasons-ai-powered-automated-customer-service-future
- 7 Things to Know About AI and Customer Care
- Use Cases of AI for Customer Service – What’s Working Now
- How to Supercharge Customer Service with Artificial Intelligence
- 10 Customer Experience Implementations Of Artificial Intelligence
- The Role Of AI In Customer Experience
- Oracle AI Powers Future of Customer Experience Management
- 4 Ways To Drive Customer Engagement With AI
- Artificial Intelligence and Customer Engagement
- How to Drive Intelligent, Personalized Consumer Engagement Using Data and AI
- The top 5 customer engagement innovations changing the game with AI
- Artificial Intelligence in Oil and Gas – Comparing the Applications of 5 Oil Giants
- AI in Oil & Gas Market worth 2.85 Billion USD by 2022
- artificial-intelligence-oil-gas-market
- Five AI techniques fit for an oil and gas revolution
- The oil and gas organization of the future
- Artificial Intelligence in the Oil & Gas Industry (AIOGI)
- How Artificial Intelligence (AI) Will Impact IT Service Management
- how-ai-will-impact-it-service-management
- AI-in-ITSM
- How Artificial Intelligence is impacting the ITSM service industry
- 5 Reasons Why AI Will Impact IT Service Management
- 4 Big Benefits—And Hurdles—of Artificial Intelligence in ITSM
- Artificial Intelligence Is Set To Change The Face Of IT Operations
- 12-steps-to-excellence-in-artificial-intelligence-for-it-operations-infographic
- ai-safety-research
- Towards Safe Artificial General Intelligence, Tom Everitt. April 27, 2018
- The Malicious Use of Artificial Intelligence: Forecasting, Prevention, and Mitigation. Future of Humanity Institute, University of Oxford, Centre for the Study of Existential Risk, University of Cambridge, Center for a New American Security, Electronic Frontier Foundation, OpenAI, February 2018
- 10 Charts That Will Change Your Perspective On Artificial Intelligence's Growth. Jan-2018
- When Will AI Exceed Human Performance?Evidence from AI Experts
- 2018-ai-predictions
- MIT 6.S099: Artificial General Intelligence
- MIT 6.S094: Deep Learning for Self-Driving Cars
- AI safety syllabus
- Concrete Problems in AI Safety, Google Brain, Stanford University, UC Berkeley, OpenAI
- Algorithmic Progress in Six Domains
- Trends in algorithmic progress
- 2018 AI Safety Literature Review and Charity Comparison
- A Psychopathological Approach to Safety Engineering in AI and AGI
- AGI Safety Literature Review
- AI Safety Gridworlds
- Artificial Intelligence Safety and Cybersecurity: a Timeline of AI Failures
- AI safety via debate
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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
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“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
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“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
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“I visualize a time when we will be to robots what dogs are to humans, and I’m rooting for the machines.” —Claude Shannon
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“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
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“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
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“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
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“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
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“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
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“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
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“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
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“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
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“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
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“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
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“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
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“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
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“The key to artificial intelligence has always been the representation.” —Jeff Hawkins
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“It's going to be interesting to see how society deals with artificial intelligence, but it will definitely be cool.” —Colin Angle
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“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
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“Artificial intelligence is growing up fast, as are robots whose facial expressions can elicit empathy and make your mirror neurons quiver.” —Diane Ackerman
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“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
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“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
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“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
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“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
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“A year spent in artificial intelligence is enough to make one believe in God.” —Alan Perlis
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“There is no reason and no way that a human mind can keep up with an artificial intelligence machine by 2035.” —Gray Scott
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“Is artificial intelligence less than our intelligence?” —Spike Jonze
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“By far, the greatest danger of Artificial Intelligence is that people conclude too early that they understand it.” —Eliezer Yudkowsky
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“The sad thing about artificial intelligence is that it lacks artifice and therefore intelligence.” —Jean Baudrillard
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“Forget artificial intelligence - in the brave new world of big data, it's artificial idiocy we should be looking out for.” —Tom Chatfield
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“Before we work on artificial intelligence why don’t we do something about natural stupidity?” —Steve Polyak
Gopala KR / @gopala-kr