GraphQL

GraphQL

AWS Lambda Use Cases

• Application Backends - scaling out various serverless components into various modular functions to meet uneven demands
• Data Processing - ETL pipelines for manipulation of files in form of XML, CSV, JSON in order to collate, aggregate, resize, dedup, and convert
• Real-Time Analytics - AWS Kinesis Streams to Lambda functions spawning across number of shards
• Legacy API Proxy - AWS API Gateway to Lambda as an API layer over legacy APIs for transposing request/responses as well as marshalling data formats
• Scheduling Services - Setting up notifications, reminders, and alerts for such things as daily backups
• Bots - Building task specific bots like for Slack or with Facebook Messenger

ReWork Events 2017/2018

ReWork Events 2017/2018

Record Linkage & Matching

Record Linkage and Matching
Comparative Study on Record Matching
Probabilistic Matching
Record Linkage
Entity Resolution with Markov Logic

Consensus Algorithms

RAFT
Paxos
ZAB
ARK
Viewstamped Replication
Chandra-Toueg
State Machine
Two-Phase Commit
Three-Phase Commit

Using Paxos to Build a Scalable Consistent Data Store
Replication: Theory and Practice
consensus algorithms

Apache Arrow

Apache Arrow

Artificial Intelligence Conferences

Artificial Intelligence Conference June
Artificial Intelligence Conference September

Strata Conferences

Strata Conferences

Sequelize

Sequelize

Social Media Evolution

From Moments to Memories
From Images & Videos to Dynamic Stories
From Static Content to Reactions & Live In-Moment Feeds
From Messages to Communities
From Social Networks to Knowledge & Sentiment Search
...

VR, AR, MR, 360, & Holographics

Immersive entertainment is where the future is heading towards. Virtual Reality is already appearing in games. Augmented Reality is already appearing in retail. Investment Banking are already experimenting in Virtual Reality. Aeronauts already use sophisticated virtual simulations. Home entertainment is really the natural way forward to adapt from 3D to virtual reality and augment the reality of immersion. We should be able to virtually feel part of what is happening on our TV screens in a form of mixed reality. Being able to feel the premier league game as if one would while watching it live at the stadium. Walking into a TV screen and virtually feeling the experience of being part of the Game of Thrones or Lord of the Rings. A documentary about an animal hunt in nature which could incorporate the viewing audience. Or, even a documentary into a historical war episode that one could teleport into to feel the entire experience. Perhaps, similar to a deep sleep experience of awakening into a new world dimension or emulation like in Avatar. There are just endless possibilities for advertising in entertainment, when the audience experience is enhanced, with limitless possibilities for personalization, simulation, and immersion. People want to feel part of every thing that is happening around them and that is true entertainment, all from the comfort and enhancement of the home cinema experience, where the lines are blurred between what is real and what isn't. In the end, immersion implies a focus on audience engagement and a higher return of investment in content quality, enrichment, and delivery.

Vs Of Big Data

Volume - amount of data
Velocity - speed of data flowing into a system
Variety - different types of data from multiple sources
Veracity - accuracy of inbound and outbound data

Many tools exist that address the above characteristics of Big Data which can be split into three different purposeful strands:

• Data Transfer - e.g Kafka, Flume, Scribe, Scoop
• Data Storage - e.g Hadoop, GlusterFS, Cassandra
• Data Processing - e.g Storm, Flink, Spark, Samza

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.