ChatGPT4-Questions/User:Darwin2049/Overview

2023.11.09 - PAGE IS NOW ALL SCREWED UP....
FIRST ORDER OF BUSINESS FOR TOMORROW IS TO PUT IT BACK IN ORDER
AND FIX UP ANY OTHER DISCREPANCIES; SHOULD BE: (AND EXPLAIN WHY)
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.

Responses. The following links connect to responses to their respective questions:

• 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. The links above point to this observer's guesses as to what to make of the CG4 and related technologies;
The links below point to the analytic actions that led to what the reader finds
The way forward to answer these questions meant taking each of the following factors into consideration and attempting to connect them to the issues described above; this has meant that going forward required a steps that would:

• reify "which is to say...": generating the responses provided above meant suggested that this be the opening action;
• sentiment "so... people are saying...": this involved making a quick superficial glance at the rapidly growing body of reviews and opinions; the resulting observations showed that there is a broad range of sentiment to enthusiasm to panic;
• how does it work "so... open the hood": this meant making a superficial probing of how an LLM works; early uses of the most popular system CG4 showed intrinsic shortcomings that obliged attention; other Deep Learning systems were examined superficially but coverage of their relative features is deferred for another round of analysis;
• risks "this is great/dreadful news!": almost all new technologies involve risk of some sort; these might inherent or organic simply because of their existence or intentionally malicious; past experience provides a framework to speculate about what might come knowing what has been done before; some examples are provided to offer context for further discussion;
• caveats "however..." : given what is now known about these new capabilities it appeared to be useful to offer some qualifying caveats; these can be expanded upon, dismissed or modified as needed;
• intermediate observations "so therefore": have been put forward as a way to approach the more speculative and theoretical prospects of where events may go;
• conclusions - "what if": up to this point in time we attempt to summarize what is now known; this is made more difficult because events are moving forward apace; therefore due to the moving-target nature of this development what has been observed thus far may be dated by the time of this writing;

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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