ChatGPT4-Questions/User:Darwin2049/Overview

2023.11.09 - PAGE IS NOW ALL SCREWED UP....
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IMPRESSIONS
OPERATIONS
CAVEATS
RISKS
INTERMEDIATE OBSERVATIONS/CONCLUSIONS
PHASE SHIFT
SPECULATION
CONCLUSION

OpenAI - ChatGPT4.
In what follows we attempt to address several basic questions about the onrushing progress with the current focus of artificial intelligence. There are several competing actors in this space. These include OpenAI, DeepMind, Anthropic, and Cohere. A number of other competitors are active in the artificial intelligence market place. But for purposes of brevity and because of the overlap we will limit focus on ChatGPT4 (CG4). Further, we focus on several salient questions that that raise questions of safety, risk and prospects.

Synthesis. Following are some proposed responses to the questions as posed:

• Interfacing/Accessibility-Conformability - Synthesis. how will different groups interact with, respond to and be affected by it; might access modalities available to one group have positive or negative implications for other groups;
• Political/Competitive - Synthesis. how might different groups or actors gain or lose relative advantage; also, how might it be used as a tool of control;
• Evolutionary/Stratification - Synthesis. might new classifications of social categories emerge; were phenotypical bifurcations to emerge would or how would the manifest themselves;
  
• Epistemological - Synthesis how to reconcile ethical issues within a society, between societies; more specifically, might it provide solutions or results that are acceptable to the one group but unacceptable to the other group;
  

Summary. Attempting to address these issues suggested that a sound basis for forward motion meant to reify them in terms of the implications that they present. The way that they are presented suggest that this new LLM technology entails risks to various constituencies. In order to characterize these risk meant that they needed to be identified. The nature of risk involve the potential for payoff. Which then suggested answering them in terms of game theory. Therefore how to view these various constituencies in terms of zero/non-zero sum outcomes. In order to gain leverage on that question meant getting an overview of sentiment. The sentiment used in this discourse involved a snap review of what some of the most informed observers were saying. What followed from this assessment was an evaluation of just what an LLM is and how it compares to other forms of Deep Learning systems.

Impressions

The reification process suggested a review of these questions that needed some form of underpinning. That underpinning was derived from a cursory review of sentiment made by a small set of informed observers. Their positions made clear that they perceive risk and reward. The questions posed above are clearly grounded in terms of who wins and who loses.
Putting this another way results in the recognition that a traditional game theory proposition wherein there are or will be zero and non-zero outcomes.

In the following we note that there were sentiments that fell into the categories of those who were: positive, cautious (or cautiously worried) or alarmed.

The way that these sentiments were categorized in terms of risk involved describing the kind of risk that each seemed to be either stating explicitly or implying.
An analysis of sentiment and impressions could easily become a research effort in and of itself. That did not appear to be the primary objective of the examination. Therefore only a small sampling of well publicized reports have been included. Possibly a more detailed approach might be indicated at a later time.

Operations, Understanding the CG4 internal mechanisms might offer some insight into how it does what it does. And therefore by extension how one group might gain or lose advantage. The result is that several variants of the Deep Learning approach came to light but the Large Language Model (LLM) seemed to be the preferred point of entre'. This was because it has shown itself to be remarkably versatile in its range of applicability.

Risks. Three categories emerged: systemic, malicious and theoretical. In each case our observation is that this new technology is inherently dual use.
That this technology does show itself to be dual use led to the intimation that a pause for some considerations was in order before proceeding. They led to the intermediate synthesis that can be found next.

Intermediate Synthesis Based upon what we have observed our deliberations suggested that we make more explicit what we think and feel as well as offering some caveats for further consideration.

Caveats Our analysis to this point has suggested that several crucial factors be acknowledged. These include such observations that Deep Learning technology results are dual use. They can be used to further facilitate social, economic and political well being. But they can also be used for malicious purposes that can not yet be imagined.

Phase Shift. The IBM Quantum System Two (EOY 2023) has served notice that it will release 432 Q-Bit Osprey. Migrating Deep Learning systems to a quantum computing environment will result in a before/after event.

Theoretical These theoretical thoughts are more speculative. However they attempt to avoid going beyond the bounds of what is actually possible.

Conclusions Synthesis Finally we try to arrive at what supports our position regarding how the question groups were answered. These were the questions on interfaces, political, evolutionary and epistemology.

Note & References The notes and references that follow are intended to provide further support to the theses promoted in this effort.

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