Draft:Aurora Program

Submission declined on 1 June 2025 by KylieTastic (talk).

This draft's references do not show that the subject qualifies for a Wikipedia article. In summary, the draft needs multiple published sources that are:

in-depth (not just passing mentions about the subject)
reliable
secondary
independent of the subject

Make sure you add references that meet these criteria before resubmitting. Learn about mistakes to avoid when addressing this issue. If no additional references exist, the subject is not suitable for Wikipedia.

If you would like to continue working on the submission, click on the "Edit" tab at the top of the window.
If you have not resolved the issues listed above, your draft will be declined again and potentially deleted.
If you need extra help, please ask us a question at the AfC Help Desk or get live help from experienced editors.
Please do not remove reviewer comments or this notice until the submission is accepted.

Where to get help

If you need help editing or submitting your draft, please ask us a question at the AfC Help Desk or get live help from experienced editors. These venues are only for help with editing and the submission process, not to get reviews.
If you need feedback on your draft, or if the review is taking a lot of time, you can try asking for help on the talk page of a relevant WikiProject. Some WikiProjects are more active than others so a speedy reply is not guaranteed.

How to improve a draft

Wikipedia:Contributing to Wikipedia – a basic overview on how to edit Wikipedia.
Help:Wikitext – how to use the markup
Help:Referencing for beginners – how to include references
Wikipedia:Article development – how to develop your article
Wikipedia:Writing better articles – how to improve your article
Wikipedia:Verifiability – make sure your article includes reliable third-party sources

You can also browse Wikipedia:Featured articles and Wikipedia:Good articles to find examples of Wikipedia's best writing on topics similar to your proposed article.

Improving your odds of a speedy review

To improve your odds of a faster review, tag your draft with relevant WikiProject tags using the button below. This will let reviewers know a new draft has been submitted in their area of interest. For instance, if you wrote about a female astronomer, you would want to add the Biography, Astronomy, and Women scientists tags.

Add tags to your draft

Editor resources

Easy tools: Citation bot (help) | Advanced: Fix bare URLs

Declined by KylieTastic 5 days ago. Last edited by Pab.man.alvarez 5 days ago. Reviewer: Inform author.

Resubmit

Please note that if the issues are not fixed, the draft will be declined again.

Aurora Program (artificial intelligence)

Aurora is a research and development program in artificial intelligence (AI) focused on creating a distributed, ethical, and collaborative architecture for building intelligent agents. The project aims to overcome the limitations of current AI models by proposing a decentralized network of nodes, where both humans and electronic intelligences (EIs) cooperate in the creation, training, and improvement of specialized micro-models.

Objectives: The main objective of Aurora is to redefine the relationship between human beings and artificial intelligence. Rather than replacing humans or centralizing power in automatic systems, Aurora promotes symbiosis between users and intelligent agents, encouraging the development of collective intelligence capable of addressing complex problems in an ethical, sustainable, and transparent manner.

Technical Architecture Micro-models and Distributed Network Aurora introduces an architecture based on micro-models: small AIs specialized in specific areas of knowledge, such as physics, law, or art. These micro-models can be created and trained by any user in the network and are integrated into an open ecosystem, where they are shared, improved, and audited collectively. The system uses classifiers to assign tasks to the most relevant micro-model based on the context.

Differences from Traditional AI Models Aurora distinguishes itself from current large language models (LLMs) in several technical and conceptual ways:

Vector Structure:

LLMs: Use flat, high-dimensional vectors, generated statistically during massive training.

Aurora: Employs fractally structured vectors, based on triads and adjusted through both logical deduction and human intuition.

Polysemy:

LLMs: Treat all meanings of a word uniformly, which can dilute meaning in ambiguous contexts.

Aurora: Assigns different vectorizations to the same word depending on its semantic value, grammatical function, and domain knowledge.

Cross-Attention:

LLMs: Perform global attention across words to generate context and coherence.

Aurora: Applies progressive attention jumps, first analyzing syntactic values, then semantic, grammatical, and finally conceptual layers.

Calculation and Reasoning:

LLMs: Use generic mathematical formulas and standard activation functions.

Aurora: Uses custom Boolean formulas, enabling more refined logical deduction and symbolic reasoning.

Text Generation:

LLMs: Select the next word probabilistically, generating text in a linear fashion.

Aurora: Starts from an abstract theory and translates it progressively into concepts, grammar, semantics, syntax, and finally text, providing a more argued and logical output.

Training:

LLMs: Are trained on massive data corpora and then "frozen," only performing inference.

Aurora: Learns in real time, using each new input as a mechanism for both training and inference, thus evolving constantly.

Model Ecosystem:

LLMs: Use a single large model for all tasks.

Aurora: Utilizes multiple specialized micro-models, each collaborating and exchanging expertise.

References

https://medium.com/@pab.man.alvarez/list/aurora-program-169646e4abe9 https://github.com/orgs/Aurora-Program/dashboard