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1.94 MB
Extraction Summary
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Document Information
Type:
Technical report / scientific book chapter (evidence in house oversight investigation)
File Size:
1.94 MB
Summary
This document is a page from a technical paper or book (page 110) regarding Artificial General Intelligence (AGI), specifically the 'CogPrime' architecture and 'OpenCog' software. It discusses technical experiments involving the integration of AI software with a physical 'Nao robot' and the 'DeSTIN' perception system. While the text is purely scientific, the footer 'HOUSE_OVERSIGHT_013026' indicates this document was collected as evidence during a House Oversight investigation, likely related to Jeffrey Epstein's funding of or connections to AI researchers and transhumanist projects.
People (1)
| Name | Role | Context |
|---|---|---|
| Project Team | Researchers |
Group working on AGI development and OpenCog tasks
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Organizations (4)
| Name | Type | Context |
|---|---|---|
| OpenCog |
AGI development platform mentioned throughout the text
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| CogPrime |
Specific cognitive architecture being overviewed
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| DeSTIN |
Deep Spatio-Temporal Inference Network used for perception/action
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| House Oversight Committee |
Origin of the document stamp (implied by footer)
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Timeline (1 events)
Prior to writing
Key Quotes (2)
"Intelligence is all about learning, which is by definition about change, about dynamical response to the environment and internal self-organizing dynamics."Source
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Quote #1
"We suspect that reasonable level of capability will be achievable by simply interposing DeSTIN (or some other system in its place) as a perception/action 'black box' between OpenCog and a robot."Source
HOUSE_OVERSIGHT_013026.jpg
Quote #2
Full Extracted Text
Complete text extracted from the document (2,963 characters)
110 6 A Brief Overview of CogPrime
only scant feedback from human teachers. If successful, this will provide an outstanding platform for ongoing AGI development, as well as a visually appealing and immediately meaningful demo for OpenCog.
Specific, particularly simple tasks that are the focus of this project team’s current work at time of writing include:
• Watch another character build steps to reach a high-up object
• Figure out via imitation of this that, in a different context, building steps to reach a high up object may be a good idea
• Also figure out that, if it wants a certain high-up object but there are no materials for building steps available, finding some other way to get elevated will be a good idea that may help it get the object
• Figure out via imitation of this that, in a different context, building steps to reach a high up object may be a good idea
• Also figure out that, if it wants a certain high-up object but there are no materials for building steps available, finding some other way to get elevated will be a good idea that may help it get the object
6.3.1 Transitioning from Virtual Agents to a Physical Robot
Preliminary experiments have also been conducted using OpenCog to control a Nao robot as well as a virtual dog [GdG08]. This involves hybridizing OpenCog with a separate (but interlinked) subsystem handling low-level perception and action. In the experiments done so far, this has been accomplished in an extremely simplistic way. How to do this right is a topic treated in detail in Chapter 26 of Part 2.
We suspect that reasonable level of capability will be achievable by simply interposing DeSTIN (or some other system in its place) as a perception/action “black box” between OpenCog and a robot. Some preliminary experiments in this direction have already been carried out, connecting the OpenPetBrain to a Nao robot using simpler, less capable software than DeSTIN in the intermediary role (off-the-shelf speech-to-text, text-to-speech and visual object recognition software).
However, we also suspect that to achieve robustly intelligent robotics we must go beyond this approach, and connect robot perception and actuation software with OpenCogPrime in a “white box” manner that allows intimate dynamic feedback between perceptual, motoric, cognitive and linguistic functions. We will achieve this via the creation and real-time utilization of links between the nodes in CogPrime’s and DeSTIN’s internal networks (a topic to be explored in more depth later in this chapter).
6.4 Memory Types and Associated Cognitive Processes in CogPrime
Now we return to the basic description of the CogPrime approach, turning to aspects of the relationship between structure and dynamics. Architecture diagrams are all very well, but, ultimately it is dynamics that makes an architecture come alive. Intelligence is all about learning, which is by definition about change, about dynamical response to the environment and internal self-organizing dynamics.
CogPrime relies on multiple memory types and, as discussed above, is founded on the premise that the right course in architecting a pragmatic, roughly human-like AGI system is to handle different types of memory differently in terms of both structure and dynamics.
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