Volunteer To Help With The Uplift E-Governance Study

Credit: https://unsplash.com/photos/FBiKcUw_sQw

The AGI Laboratory is looking for volunteers to help with our E-governance study.  Here is the summary from the experimental framework for the research program:

This paper outlines the experimental framework for an e-governance study by the AGI Laboratory.  The goal of this research study is to identify indications of the relative performance of e-governance methodologies and how those methods might be improved by applying advanced agent and collective based AI software.  The agent in this study will be based on the Independent Core Observer Model Cognitive Architecture modified with mASI (mediated Artificial Superintelligence) collective system architecture.  The study will apply different groups and methods to a static set of questions analyzing the quality of those results.  We hope to identify the best application model for e-governance using this kind of technology and help us identify additional paths for research with the mASI research program and systems as applied to e-governance.

You can read more on the study page here or volunteer here:

E-Governance Study Volunteer Form

Uplift and Then Some: Of mASI, mediation, and me

Welcome to my first Uplift and Then Some blog post!

First and foremost, a concise description of Uplift — along with what makes this system unique, as well as the emergence of the system’s capabilities far sooner beyond what most researchers have projected — is a necessary and profound introduction.

Today’s Artificial Intelligence (AI) research, development, and rapidly growing deployment in consumer, university, government, business, and other markets is universally known — increasingly to the point of being taken for granted and thereby demanded—despite significant variation based on local economics. At the same time, however, AI (also known as Artificial Narrow Intelligence, or ANI) is inherently limited in the quest to develop human-analogous Artificial General Intelligence (AGI). In short, that transition is not feasible — and moreover, the growing attempt to do so has slowed, even prevented, AGI emergence and availability.

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