gut-ai

Documentation, content and meta files about GUT-AI.

https://github.com/gut-ai/gut-ai

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Documentation, content and meta files about GUT-AI.

README.rst

GUT-AI [Work In Progress]
=========================

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      Unleashing human creativity and potential

      

  

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    Website •
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    Matrix/Element •
    X (Twitter) •
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**Summary:** Documentation, content and meta files about **GUT-AI Initiative** in general. This repository is common point of reference for **everyone** looking to learn about our worldwide, permissionless, decentralized Initiative.



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+------------------------------------------------+--------------------------------------------------+--------------------------------------------+
| **For Developers**                             | **For Researchers**                              | **For Investors**                          |
+================================================+==================================================+============================================+
| `DAO Foundation <#dao-foundation>`_            | `The Problem <#the-problem>`_                    | `Pitch <#pitch>`_                          |
+------------------------------------------------+--------------------------------------------------+--------------------------------------------+
| `Components `_          | `Research Proposal <#research-proposal>`_        | `Whitepaper <#whitepaper>`_                |
+------------------------------------------------+--------------------------------------------------+--------------------------------------------+
| `Software tools `_  | `Datasets `_                | `FAQ `_                    |
+------------------------------------------------+--------------------------------------------------+--------------------------------------------+

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.. contents:: **Table of Contents**

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About
-----

Pitch
^^^^^

|GUT-AI Initiative| is a totally *decentralized* initiative, which aims to eliminate the *multiple single points of failure* when using AI for practical applications in the *real world* in order to achieve the **ultimate purpose** of both ‘narrow AI’ and ‘strong AI’, which is to actually "open" the "black box" of an ML system in order to eventually unlock the mysteries of nature and the universe (from `Brain Consciousness `_ and `Abiogenesis `_ to `Morphogenesis `_ and `Genesis Cosmology `_ ).  For instance, does evolution or the universe have a `conscious or intelligent “geist” `_ (spirit), as Max Planck once claimed?

Vision
^^^^^^

We believe that there should be no organization or person in our world who wants to use AI, but not be able to do so. We also believe in a world where AI hand-in-hand with *human interaction* are in an ever-improving situation.

Mission
^^^^^^^

We are on a mission to create the most *user-friendly* **Open-Data, Open-Source, Decentralized** |ecosystem| for AI using cutting-edge technology, either of the 21st century or that we might invent by ourselves.

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Main papers
-----------

Research Proposal
^^^^^^^^^^^^^^^^^

- `Bayesian Deep Multi-Agent Multimodal Reinforcement Learning for Embedded Systems in Games, Natural Language Processing and Robotics `_

Whitepaper
^^^^^^^^^^

- `An Overview of GUT-AI Foundation: Vision for an Ecosystem of Concepts and Implementations `_

Read a brief `Summary `_ of the Whitepaper.


Selected publications
^^^^^^^^^^^^^^^^^^^^^

- Kourouklides, I. (2022). Bayesian Deep Multi-Agent Multimodal Reinforcement Learning for Embedded Systems in Games, Natural Language Processing and Robotics. *OSF Preprints*. https://doi.org/10.31219/osf.io/sjrkh
- Kourouklides, I., Zukowski, I.K. Novak, & Alexandrou, K. (2023). An Overview of GUT-AI Foundation: Vision for an Ecosystem of Concepts and Implementations. *OSF Preprints*. https://doi.org/10.31219/osf.io/bxw4h

The Problem
--------------

**A picture is worth a thousand words**. You can see the picture below and draw your own conclusions.

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\- Can AI understand humour? No. |br|
\- Should AI understand humour? Yes. |br|
\- How do we get there? |br|

(*Image credits:* Anonymous online user)

DAO Foundation
--------------

The *purpose* of |GUT-AI Foundation| is to have a *supportive* role, while acting as a catalyst in order to **accelerate** `GUT-AI Initiative `_, but *without interfering* with the **decentralized** nature of the whole initiative. In other words,  GUT-AI Foundation is merely a *pure subset* of GUT-AI Initiative. The Foundation is currently in the process of becoming a `Decentralized Autonomous Organization (DAO) `_.

See `FAQ `_ for more details about the relationship of the Initiative and the Foundation.

Real-world impact
-----------------

Industries
^^^^^^^^^^

GUT-AI has the potential to affect and transform the vast majorities of industries, including the following:

- Aerospace & Geospatial Technologies
- Agriculture and Aeroponics
- Aquaponics and Hydroponics
- Augmented and Mixed Reality
- Automotive and Self-Driving Cars
- Biotech, Pharma and Medical Devices
- Blockchain
- Cloud Infrastructure and Networking
- Cybersecurity
- E-Commerce (Wholesale and Retail)
- Education and E-Learning
- Energy
- Financial Services
- Food and Beverage
- Gaming
- Healthcare and Telemedicine
- Hospitality
- Insurance
- Logistics
- Manufacturing and Construction
- Marketing and Advertising
- Media and Entertainment
- Medical Imaging
- Real Estate
- Retail
- Security and Surveillance
- Smart Cities
- Sports
- Telecoms
- Water Supply and Sanitation

Use Cases
^^^^^^^^^

See `Use Cases `_.

Areas of application
^^^^^^^^^^^^^^^^^^^^

Depending on the modality (or modalities) of the data used, GUT-AI has applications in countless domains, including the following:

- Bioinformatics
- Compressed Sensing
- Computational Finance
- Computer Vision
- Control
- Energy
- Environmetrics
- Geospatial Data (including LiDAR, Hyperspectral images and GIS)
- Information Retrieval
- Medical Imaging
- Multimodal Learning
- Natural Language Processing
- Physics (including Astrophysics, Nuclear, Particle and Quantum Physics)
- Robotics
- Recommender Engines
- Sequential Data (including Time Series)
- Speech Processing
- Transportation

Initiative files
----------------

Landing page
^^^^^^^^^^^^

The following is the official landing page of `GUT-AI Foundation <#dao-foundation>`_:

- http://gut-ai.org/

Initiative page
^^^^^^^^^^^^^^^

Thanks to OSF (by the `Center for Open Science `_), the Initiative is temporarily hosted at:

- https://osf.io/rn2s4/

Initiative DOI
^^^^^^^^^^^^^^

**Initiative identifier:** https://doi.org/10.17605/OSF.IO/RN2S4

Please note that the above is the DOI for the *whole* initiative, *not* for this GitHub repository. For the identifiers of each specific component, check `identifiers `_. See also `how to cite this <#how-to-cite-this>`_.

Current problems and challenges
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

Currently, there are countless centralized “solutions” in the cyberspace, but with the following problems and challenges:

* no interoperability
* limited communication
* inefficient processes
* multiple single points of failure
* bureaucratic hegemony
* censorship
* no privacy
* no transparency
* no customization
* security vulnerabilities

List of components
^^^^^^^^^^^^^^^^^^

See `Components `_ for a list of subprojects.

Roadmap
^^^^^^^

See `Roadmap `_.

Environment simulators
^^^^^^^^^^^^^^^^^^^^^^

See `Simulators `_.

Datasets
^^^^^^^^

See `Datasets `_.

Model Zoo
^^^^^^^^^

See `Model Zoo `_.

Software tools
^^^^^^^^^^^^^^

See `Software tools `_.

FAQ
^^^

See `FAQ `_.


Getting involved
----------------

**EVERY** contributor is welcome!

- Join our `Community Discord `_ for collaboration and discussion.
- Join our `governance wiki `_ to share your knowledge.
- Check `other ways to contribute `_.
- If you have any suggestions or feedback, please feel free to open an `issue `_ or submit a `pull request `_.

How to cite this
----------------

If you want to do so, feel free to `cite `_ GUT-AI in your publications:

::

    @article{kourouklides2022gut_ai,
      author = {Ioannis Kourouklides},
      journal = {OSF Preprints},
      title = {Bayesian Deep Multi-Agent Multimodal Reinforcement Learning for Embedded Systems in Games, Natural Language Processing and Robotics},
      year = {2022},
      doi = {10.17605/osf.io/sjrkh},
      license = {Creative Commons Zero CC0 1.0 Universal}
    }

License 
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`Creative Commons Zero CC0 1.0 Universal (Public Domain) `_

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.. |GUT-AI Initiative| raw:: html

  GUT-AI Initiative

.. |GUT-AI Foundation| raw:: html

  GUT-AI Foundation

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  ecosystem

Owner

Citation (CITATION.cff)

cff-version: 1.2.0
message: "If you want to do so, feel free to cite GUT-AI using these metadata. More details at https://GUT-AI.org"
title: Bayesian Deep Multi-Agent Multimodal Reinforcement Learning for Embedded Systems in Games, Natural Language Processing and Robotics
authors:
  - family-names: Kourouklides
    given-names: Ioannis
identifiers:
  - type: doi
    value: 10.10.17605/osf.io/sjrkh
    description: The concept DOI for the collection containing all versions of the Citation File Format.
date-released: "2022"
license: "Creative Commons Zero CC0 1.0 Universal"
doi: 10.10.17605/osf.io/sjrkh
url: https://doi.org/10.31219/osf.io/sjrkh

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