VOSAI, which translates to “Your AI” in Latin, is a democratically controlled artificial general intelligence (AGI) for image classification and natural language processing backed by distributed compute and blockchain. Leveraging the power of GPU-intensive mining rigs on the World Computer, VOSAI has the potential to be the most powerful computational network ever created. The intent, purposes, direction, features and enhancements of the AGI will be determined by the VOSAI community which includes learners (miners), developers, data providers, data validators and customers. In essence, VOSAI is an AGI for the people, by the people.
VOSAI will be a democratically controlled artificial general intelligence (AGI) for image classification and contextualization as well as a natural language processing (NLP) (for starters!)running on a decentralized network of computers (World Computer).
VOSAI will leverage the “World Computer” as follows:
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The goal of the VOSAI is to build protocols, systems, and tools to provide an artificial general intelligence applicable to any domain. Founded in 2018 by Daniel Rodriquez and Jorge Puignau, we are overseeing the R&D of VOSAI. We intend to implement VOSAI based on open-source technologies and systems. VOSAI will be established as the long-term governance body for the VOSAI technology.
VOSAI will seek to grow an open ecosystem for decentralized processing power, while giving developers an open and sustainable platform to build, enhance and monetize.
Prior to founding VOSAI, we founded Animusoft Corporation in 2012 with the intent of offering quality software products and services. Animusoft is a group of seasoned computer scientists, artists and engineers, with whom we will contract for development and maintenance services related to VOSAI technology.
The applications of VOSAI revolve around understanding the real world. VOSAI is applicable to multiple industries including Construction, Agriculture, Law Enforcement and Infrastructure.
In a world that’s ever growing in population, the demands on our food production are strained. Diseases, illnesses, infections and wasteful methods are all problems farmers face. VOSAI aids them in detecting these issues ahead of time by consuming real world data (e.g. images) of farms and distilling them down to actionable results (e.g. trees infected with disease).
With this growth comes more problems. Where are these people living? Where do they work? How much strain are we putting on our environment? This is a very real problem. There is not much that can be done about the growth in population. However, we can improve construction by minimizing waste as much as possible. To minimize waste, we focus on the rework performed per project which is 5-10% of a project size. VOSAI abates this by distilling terabytes of imagery down to actionable results. Meaning, it can inform field engineers of errors as they occur (i.e. “pipe 123 is off by 2.5 cm”), or prevent them from happening in the first place by providing distilled reports to project teams with instructions on how to proceed with the build based on the previous day’s progress.
VOSAI has a list of clients in queue,with Animusoft first in line to leverage the technology for its customers in the construction and farming industries, which has developed and validated a market for this technology over the last few years.
Initial Network LaunchNovember, 2019
VOSAI Presale StartsApril, 2018
Mining Pool owner CollaborationMarch, 2018
Legal PreparationFebruary, 2018
Final Release of WhitepapersJanuary, 2018
Release Candidate Whitepapers released for a Decentralized AGIJanuary, 2017
Initial AGI Whitepapers released for Scientic Community Peer ReviewJanuary, 2016
Draft Architecture and Technology Design for AGIJanuary, 2015
Product Market Fit valudidation with real world customersJanuary, 2014
Cognitive & Neural Science Applications for AI ResearchJanuary, 2011
Neural Networks and its Applications to Natural Language Processing ResearchJanuary, 2008
Neural Network based Anti-Virus and Network Intrusion ResearchJanuary, 2006
Highly Distributed AI Computing ResearchJanuary, 2004