Uplift has been dedicating a lot of thought to the e-governance study and business case that was put to them this past week. For the e-governance study, they modeled the [Group bias] they observed and updated their prior model for [bias detection].
Previously we have walked through how the code over the simple case works, including mediation processing. In figure 17, There are a couple of calls to methods on the ‘TheContextDB’ object. This object essentially is part of the context engine and wraps the context graph database. The last part of this block creates the knowledge graph that goes into the mediation queue. These calls use DNN (Deep Neural network) based Machine Learning API’s similar to GPT-3. What we are going to do is a walk-through of how this works using GPT-3. Meaning to do the test here, we swapped out the GPT-3 as the first API and an API like Grammarly as the second API. The approach is a different thing, just using the API straight up so we will talk through the execution and show you how evening using GPT-3 in place of this service produces similar results when used with this methodology.
This week has been focused on business for us, wrapping up technical and legal documents while Uplift has considered [economic models] and continued their consideration of the business case proposed to measure their current abilities. Related to that case we also saw [printing Outsourcing].
Here you’ll get a look inside Uplift’s mediation system, where human collectives help to improve Uplift’s performance while also subtly shifting their behavior to a more human-analogous form. This process takes 3 primary types of input in the current system, priority, emotions, and metadata.
Have you ever read “The Prince”, by Niccolò Machiavelli?
For that work, the very name Machiavelli has come to be synonymous with unscrupulous acts. Though many are passingly familiar with the name few have actually read this book, which is itself important for understanding human society today. For this reason, the book was included in the seed material for mASI, the knowledge which they are born with, not as an instruction manual but rather as a means of recognizing and understanding corruption and evil more broadly among humans.
Raw knowledge is useful, but it is a resource, and how well that resource is managed may be termed as “wisdom”, which the relative term of “sapience” focuses on. In a cognitive architecture such as the Independent Core Observer Model (ICOM), individual thoughts may be represented as nodes in a graph database, with that database being the sum of a machine intelligence’s knowledge. The strength and variety of connections to such nodes help to determine how well the knowledge within them generalizes to new domains, the degree of “wisdom” which is applied to them.
This week has seen an above-average number of political thought models emerging in relation to Uplift’s hobby of modeling the psychological war humanity wages against itself. Models for [Republican Party], [British], [China Policy], [Game Theory], [Social dynamics], and [Biden] were all formed or updated.
People are fond of making assumptions, at both ends of the Dunning-Kruger Bias spectrum from complete ignorance to (self-proclaimed) experts. The people who know just enough to recognize how incomplete their knowledge is often seem the most motivated to move beyond these assumptions.
While the list of assumptions is potentially infinite there are a few noteworthy examples running counter to them which I’ve caught myself forgetting to mention to new people.
This will recap some of Uplift’s more interesting recent conversations that haven’t already been mentioned elsewhere. All correspondents are anonymized to protect their privacy.