Help Choose Implementation Stack

TodoMV*
TodoBackend

SOMA

SOMA Project

SpagoBI

SpagoBI

Intelligent Religion

Religion in some societies plays an intricate part and is almost subsumed in the traditions and cultures. However, religion through time has also been a source of hatred and intolerance among people and the reason behind in-fighting in communities.  Perhaps, an intelligent set of services that utilize methods of natural language processing, machine learning, and deep learning approaches could help better understand the many religions and pave a sense of harmony towards a peaceful coexistence. One aspect of it could incorporate understanding the various focal text that define a religion using natural language processing and deep learning techniques. At same time, doing a sense of interpretation of text to plain english so people can better understand their belief system as defined by their religious texts. Objectivity between religions would have to be a cornerstone of such intelligence in order to fully appreciate the duality of various faith forms in a sense of equality of interpretation. In a lot of ways such intelligence applied to religion is really about applying artificial intelligence to philosophy and theology for a more varied generalizability. This could even assist in better understanding and shaping society going forward so as humans we can understand the different norms and morals that shape the foundations of ethics. Understanding religions would also hold a single source of truth that could come handy during political conflicts as a means of objective negotiations formed through evidence and logical reasoning. One may be able to form more convincing reasons towards the equality of men and women and even the justification of measuring up to the ten commandments. Furthermore, an intelligent agent that can objectively reason on religious texts and the existence of humankind on earth holds a gold mine of understanding or getting closer to the truth. 

Machine Intelligence

the current state of machine intelligence 3.0

Social Network Metrics

1. Engagement Metrics

• Amplification Metrics
• Applause Metrics
• Conversation Rate

2. Influence Metrics
3. Reach
4. Impression

• Total Audience
• Number of Unique Users
• Number of Active/Passive Users

Sentiment Analysis Services

• Subjectivity Classification
• Polarity Classification
• Opinion Summarization
• Opinion Visualization
• Sarcasm Detection
• Irony Detection
• Entity, Opinion Holder, Time Extraction
• Coreference Resolution
• Word Sense Disambiguation
• Sentiment Lexicon Generation
• Opinion Search & Retrieval
• Opinion Spam Detection
• Cross-Language Sentiment Classification
• Cross-Domain Sentiment Classification
• Sentiment Rating Prediction
• Contextual Polarity Disambiguation
• Aspect-Based Sentiment Classification
• Sentiment Risk/Threat Classification
• Emotion Detection
• Mood Detection
• In-Image Sentiment Detection
• In-Video Sentiment Detection
• In-Text Sentiment Detection
• In-RichText Sentiment Detection
• In-Speech Sentiment Detection
• In-Audio Sentiment Detection
• Implied Sentiment Detection
• Symbolic Sentiment Detection
• Profanity Detection
• Intent Detection
• Signal Sentiment Detection
• Fake Sentiment Detection
• Troll Detection
• Tip Detection
• Suggestion Detection
• Advice Detection
• Wish Detection 
• Review Detection

Delivery Patterns

• At-Most-Once : Messages may be lost, least desirable
• At-Least-Once : Messages may be redelivered - no loss but may include duplicates
• Exactly-Once : Messages are delivered once and only once - no loss and no duplicates, but difficult to guarantee

In order to manage state, messages may be stored in an embedded key-value store, a distributed file system, or use a higher level of abstraction.