ChatGPT4-Questions/User:Darwin2049/Overview

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.

Specifically, risks that involve or are:

• 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;
  

Synthesis. Four question groups (indicated above interface, political, evolution and epistemological) are the foundation upon which the analysis as presented follows. Addressing these questions meant acknowledging that they entail both explicit and implicit questions. What do these questions mean. These questions each point at the question of what the shift in power dynamics will change between existent groups. Put simply, which group(s) gain vs. those groups that lose benefit i.e.:

• zero-sum game – who gains advantage, and who loses advantage;
• non-zero-sum game -. what group(s) is/are a primary benefactor, what associated groups might also gain as a result; conversely, what group(s) loses benefit, and what associated groups might also lose as a result;
• mechanism - what is CG4 about and in summary sketch, how does it work; how do similar Deep Learning systems work and what do they do;
  

In order to address question meant making a short survey of reporting with the intent to gauge the sentiment of the reporting. The results suggested four major categories. These are the:

• positives: they favor continued advances
• worried: some were cautious or actually worried about it;
• alarmed: this group is alarmed and petitioned for government intervention;
• hair on fire: rapid advances and incorporation; being overtaken by a rival is unacceptable;

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. Framing the observable sentiment into categories made describing CG4 into risk categories became considerably easier. Three primary categories emerged: systemic, malicious and theoretical. The category of theoretical obliged the recognition that CG4 is inherently dual-use. The result was the category of risks that included risks that are:

• systemic: risks arise that are simply because the new technology exists; no one has intended for it to cause problems, but it did or does;
• malicious: people with mal intent will recognize this development to further an already malicious agenda; they will replace what they have been using with this more sophisticated capability;
• theoretical: this is a somewhat less straightforward delineation because CG4 technology is inherently dual use; it can be used for beneficial purposes as well as dangerous or deadly purposes; in this regard it is like fire;

Now What?: Identifying and describing the various risk categories indicated the question of "so now what?"; this is presented in a speculative step into a near future when new tools and processes have become accepted. Black Swan Events. These are events of low to extremely low probability. However the upcoming IBM release of its IBM Quantum System Two can probably be categorized as a before/after event. Something comparable to a black swan event. When combined with LLM’s and related deep learning systems the results will be nothing short of ground breaking.

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