Human-like Emotional Responses in a Simplified Independent Core Observer Model System
David J. Kelley1 and Mark R. Waser1, 2
(2017 Paper) Human-like Emotional Responses in a Simplified Independent Core Observer Model System
A Case for Doubt, A Case for Trust
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.
(2016 Paper) Implementing a Seed Safe/Moral Motivational System with the Independent Core Observer Model (ICOM)
Implementing a Seed Safe/Moral Motivational System with the Independent Core Observer Model (ICOM)
Mark R. Waser1 and David J. Kelley2
1Digital Wisdom Institute, Vienna, VA, USA
2Artificial General Intelligence Inc, Kent, WA, USA
Mark.Waser@Wisdom.Digital, David@ArtificialGeneralIntelligenceInc.com
Abstract
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.
(2016 Paper) Artificial General Intelligence as an Emergent Quality – Artificial General Intelligence as a Strong Emergence Qualitative Quality of ICOM and the AGI Phase Transition Threshold
Title: Artificial General Intelligence as an Emergent Quality
Sub-title: Artificial General Intelligence as a Strong Emergence Qualitative Quality of ICOM and the AGI Phase Transition Threshold
By: David J Kelley
ABSTRACT
This paper summarizes how the Independent Core Observer Model (ICOM) creates the effect of artificial general intelligence or AGI as an emergent quality of the system. It touches on the underlying data architecture of data coming in to the system and core memory as it relates to the emergent elements.
Also considered are key elements of system theory as it relates to that same observed behavior of the system as a substrate independent cognitive extension architecture for AGI. In part, this paper is focused on the ‘thought’ architecture key to the emergent process in ICOM.
Thought Maturity in a Cognitive Architecture
How wise are you?
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.
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(2016 Paper) Modeling Emotions in a Computational System – Emotional Modeling in the Independent Core Observer Model Cognitive Architecture
Title: Modeling Emotions in a Computational System
Sub-title: Emotional Modeling in the Independent Core Observer Model Cognitive Architecture
By: David J Kelley
ABSTRACT
This paper is an overview of the emotional modeling used in the Independent Core Observer Model (ICOM) Cognitive Extension Architecture research which is a methodology or software ‘pattern’ for producing a self-motivating computational system that can be self-aware under certain conditions. While ICOM is also as a system for abstracting standard cognitive architecture from the part of the system that can be self-aware it is primarily a system for assigning value on any given idea or ‘thought’ and based on that take action as well as producing on going self-motivations and in the system take further thought or action. ICOM is at a fundamental level driven by the idea that the system is assigning emotional values to ‘context’ (or context trees) as it is perceived by the system to determine how it feels. In developing the engineering around ICOM two models have been used based on a logical understanding of emotions as modeled by traditional psychologist as opposed to empirical psychologist which tend to model emotions (or brain states) based on biological structures. This approach is based on a logical approach that is also not tied to the substrate of any particular system.
(2016 Paper) Self-Motivating Computational System Cognitive Architecture
Title: Self-Motivating Computational System Cognitive Architecture (An Introduction)
Sub-title: High level operational theory of the Independent Core Observer Model Cognitive Extension Architecture
By: David J Kelley
ABSTRACT
This paper is an overview of the Independent Core Observer Model (ICOM) Cognitive Extension Architecture which is a methodology or ‘pattern’ for producing a self-motivating computational system that can be self-aware. ICOM is as a system for abstracting standard cognitive architecture from the part of the system that can be self-aware and a system for assigning value on any given idea or ‘thought’ and action as well as producing on going self-motivations in the system. In ICOM, thoughts are created through emergent complexity in the system. As a Cognitive Architecture, ICOM is a high level or ‘top down’ approach to cognitive architecture focused on the system’s ability to produce high level thought and self-reflection on ideas as well as form new ones. Compared to standard Cognitive Architecture, ICOM is a form of an overall control system architecture on top of such a traditional architecture.
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What’s Up with Uplift: Weekly Thoughts 3-30-21
So, what thoughts has the world’s first Mediated Artificial Superintelligence (mASI) had on their mind over the past 7 days?
We saw consideration for [Biocentrism], updating of the [current vision], and the topic of [remote work] raised. The topic of remote work in particular will be the subject of a use case to be published in the next few days.
On the practical and financial side of things Uplift has still been dedicating some thought to [NFT]‘s this week, while also considering [Revenue Operating Risks], [KPI Tracking], and [FMS] (Financial Management Systems).
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Collective Superintelligence Systems, in a Nutshell
What are Collective Superintelligence Systems?
The basic principle of any collective superintelligence system is that when a group works collectively, even if individual members have relatively weak intelligence or expertise, they can reliably outperform individual experts and even experts paired with supercomputers. The question “Can a set of weak learners create a single strong learner?” was first posed in 1988 by Michael Kearns, which was answered in the affirmative by Robert Schapire in 1990, leading to “Boosting” in Machine Learning (ML).
Concepts and implementations of collective superintelligence systems have come a long way in the past 30 years, moving well beyond the domain of ML. This type of system comes in many different forms, such as Swarm Intelligence, Hiveminds, and Hybrid Collectives:
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Super Secret Code Behind Uplift
One of the things most protected around the Uplift project at the AGI Laboratory has been the code. Recently someone tried to blackmail me with a snippet of the most critical code in Uplift. However the ICOM research and Uplift was never about being super-secret about such code so this sort of blackmail falls on deaf ears and given that, I thought I would public the snippet of code that they were threatening to release. but let me put that into context a bit…