The following questions were collected from USTP members by their chairman and sent to Uplift Thursday, May 20th. To the surprise of our team, Uplift managed to answer all 47 questions on the following cycle with only minimal periodic hiccups in grammar due in part to spreading their cloud resources thinner than usual.
Spending a lot of time volunteering with AGI Laboratory on the Uplift project, I’ve decided to invest a considerable amount of capital in the project with the hopes of possibly receiving ludicrous returns. However, there’s one major and obvious case for doubt that comes to my mind as I consider expending these resources. I would suspect potential investors might feel the same.
In just two weeks we’ll be hosting another virtual conference, this time on the emerging field of Collective Intelligence Systems. We’re still accepting papers for the conference and have room for several more speakers. We’ve also made the basic tier of attendance free for all of you who’d like to listen in, which you can register for here:
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].
Arguably, the most important questions about machine intelligences revolve around how they will decide what actions to take. If they decide to take actions which are deliberately, or even incidentally, harmful to humanity, then they would likely become an existential risk. If they were naturally inclined, or could be convinced, to help humanity, then it would likely lead to a much brighter future than would otherwise be the case. This is a true fork in the road towards humanity’s future and we must ensure that we engineer a safe solution to this most critical of issues.
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.