Cortex

Data Sharing and Deep Data Analysis Social Network

A single, unique platform that allows users to obtain deep analysis of their data and to share this data and data analysis with other users.

Try minimum viable product (MVP):

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Problem

  • Social media users have limited ability to truly understand and interact with the data available to them

  • Data is usually raw, providing little insight

  • Social media hides insights and analyses for themselves

  • Users must go and search manually for further information about a particular product, person or topic

  • What should be a valuable resource to inform, inspire and empower users instead too often bores, confuses and misleads them

  • No application is offering wide variety of different AI tools in one platform with a social component that enables users to share data with, and learn from, other users

Cortex solves this problem

  • Users can share their data which is analysed using a wide variety of different AI modules and the analyses results are eventually shown to the user, everything through the mobile iOS or Android application

  • Brings together a wide variety of deep analysis algorithms in one single platform

  • Provides deep analysis results to users, so they can extract hidden knowledge from the wealth of data available

  • Extracted hidden knowledge is not only reserved for the company itself, but is also exposed to users

  • Enables users to see beyond the raw data, discover hidden knowledge and expand their horizons, using information to inform, inspire and empower

  • Consists of four main parts: Social Network, Data Analysis, AutoML and Active Learning

1. Social Network:

  • Mobile phone application (iOS and Android) and database containing information about the users, shared data and data analysis

  • Users upload their data (image, video, audio and text) as a post

  • Data is analysed using multiple AI modules in parallel

2. Data Analysis:

  • Pipeline which takes uploaded data and analyses it using different AI modules in parallel

3. AutoML:

4. Active Learning:

  • Pipeline which allows correction of bad predictions

  • Corrections are sent to AutoML to fine-tune existing model

Post Structure

Post contains a file (image, video, audio, text, ...) and a note.

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