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;
  
• Epistemological/Ethical Relativism - 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;
  

Synthesis.
Responding to these questions calls for some baseline information and insights about the issues that this new technology entails. We propose to suggest we look

• Impressions that has been expressed by knowledgeable observers; a short sampling of impressions and perceptions include voices that are
  • in favor of rapid and forceful development
  • expressing caution
  • are urging extreme caution going forward;
  • pointing to extreme external risks and urging rapid further development;
• Caveats.
  After assessing the range of sentiment from very well informed observers and experts our conclusion is that a summary of caveats and qualifications might help in constraining and focusing the discussion going forward.
• An inflection point either has or momentarily will be or has been reached;
• mitigating surprise the concerned community would be well advised to regularly monitor the archive for papers so as to mitigate the gain early warning;
• Imaginative expectation is advised because novel combinations are emerging that will herald entirely new forms of CP capabilities; most of which can not be guessed at as of this writing (Fall 2023);
• Quantum Computing will upend all expectations. As of this report the major actors in this space have already reported advances. In the case of the IBM corporation their Quantum System Two may very well shatter all expectations. Current indications are that deep learning tools can be hosted on these new environments. At that point in time the ability of a deep learning CP based tool will take on qualities that can not now be estimated. Current training times have been reported to have required weeks to months to train as was the case of CG4. These times may now collapse to seconds or less.
• Pseudo-AGI (PAGI) might be a way to envision what to expect. This terminology is intended to suggest that the term AGI might be too simplistic and may require further analysis and refinement. This situation could obtain because the ability of a Quantum Based CP (QBCP) could show improvements in the instant; i.e. a new training data set or sets could be provided to such a QBCP, the resultant training time might be measured in seconds or less; meaning that it could acquire new knowledge instantly and respond accordingly. There have been no such capabilities reported as of yet that suggest this pathway forward. Though such a capability might not represent an actual realization of AGI in that moment the appearance might suggest that it has in fact arrived; subsequent usage might spotlight perceived shortcomings, many of which might be quickly remedied; so again, we could see an accelerated Moore's Law of evolution taking place over the course of days and weeks.
• High ground stake out.Prudence suggests siding with the camp favoring a rapid all-of-national-resources-type effort toward achieving the high. To not do so risks seeing a peer or near-peer rival pre-emptively demonstrate capabilities that even informed observers might find mysterious and unfathomable.
• Terms, Theory of Operation of technology paradigm used to produce its results;
  • concepts are presented that contrast large language models with generative and generative adversarial models;
  • several other deep learning models are summarized as well for the purpose of further context;
  • LLM operation is sketched out in this section; several helpful video segments are presented that are intended to further facilitate why the LLM approach has become so successful; **novel and unexpected forms of emergent behavior are mentioned;
    
• Risks our approach has been to present a few commonly occurring risks,
  • that are inherent, organic and systemic with any new revolutionary science or technology
  • those that arise spontaneously as a result of malicious actors and
  • those that are theoretical;
• Intermediate Considerations. If we step back for a moment and summarize what some observers have had to say about this new capability then we might tentatively start with that:
  • is based upon and is a refinement of its predecessor, the Chat GPT 3.5 system;
  • has been developed using the generative predictive transformer (GPT) model;
  • has been trained on a very large data set including textual material that can be found on the internet; unconfirmed rumors suggest that it has been trained on 1 trillion parameters;
  • is capable of sustaining conversational interaction using text based input provided by a user;
  • can provide contextually relevant and consistent responses;
  • can link topics in a chronologically consistent manner and refer back to them in current prompt requests;
  • is a Large Language Models that uses prediction as the basis of its actions;
  • uses deep learning neural networks and very large training data sets;
  • uses a SAAS model; like Google Search, Youtube or Morningstar Financial;

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